IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae

Abstract

This paper describes the response of the IceCube neutrino telescope located
at the geographic South Pole to outbursts of MeV neutrinos from the core
collapse of nearby massive stars. IceCube was completed in December 2010
forming a lattice of 5160 photomultiplier tubes that monitor a volume of ~ 1
cubic kilometer in the deep Antarctic ice for particle induced photons. The
telescope was designed to detect neutrinos with energies greater than 100 GeV.
Owing to subfreezing ice temperatures, the photomultiplier dark noise rates are
particularly low. Hence IceCube can also detect large numbers of MeV neutrinos
by observing a collective rise in all photomultiplier rates on top of the dark
noise. With 2 ms timing resolution, IceCube can detect subtle features in the
temporal development of the supernova neutrino burst. For a supernova at the
galactic center, its sensitivity matches that of a background-free
megaton-scale supernova search experiment. The sensitivity decreases to 20
standard deviations at the galactic edge (30 kpc) and 6 standard deviations at
the Large Magellanic Cloud (50 kpc). IceCube is sending triggers from potential
supernovae to the Supernova Early Warning System. The sensitivity to neutrino
properties such as the neutrino hierarchy is discussed, as well as the
possibility to detect the neutronization burst, a short outbreak of electron
neutrinos released by electron capture on protons soon after collapse.
Tantalizing signatures, such as the formation of a quark star or a black hole
as well as the characteristics of shock waves, are investigated to illustrate
IceCube's capability for supernova detection.

Full text

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arXiv:1108.0171v1 [astro-ph.HE] 31 Jul 2011
Astronomy & Astrophysics manuscript no. SN1.20
August 2, 2011
c ? ESO 2011
IceCube Sensitivity for Low-Energy Neutrinos
from Nearby Supernovae
IceCube Collaboration: R. Abbasi1, Y. Abdou2, T. Abu-Zayyad3, M. Ackermann10, J. Adams4, J. A. Aguilar1, M. Ahlers5,
M. M. Allen26, D. Altmann18, K. Andeen1,44, J. Auffenberg6, X. Bai7,45, M. Baker1, S. W. Barwick8, V. Baum33, R. Bay9,
J. L. Bazo Alba10, K. Beattie11, J. J. Beatty12,13, S. Bechet14, J. K. Becker15, K.-H. Becker6, M. L. Benabderrahmane10,
S. BenZvi1, J. Berdermann10, P. Berghaus1, D. Berley16, E. Bernardini10, D. Bertrand14, D. Z. Besson17, D. Bindig6,
M. Bissok18, E. Blaufuss16, J. Blumenthal18, D. J. Boersma18, C. Bohm19, D. Bose20, S. B¨ oser21, O. Botner22,
A. M. Brown4, S. Buitink11, K. S. Caballero-Mora26, M. Carson2, D. Chirkin1, B. Christy16, F. Clevermann23, S. Cohen24,
C. Colnard25, D. F. Cowen26,27, A. H. Cruz Silva10, M. V. D’Agostino9, M. Danninger19, J. Daughhetee28, J. C. Davis12,
C. De Clercq20, T. Degner21, L. Demir¨ ors24, F. Descamps2, P. Desiati1, G. de Vries-Uiterweerd2, T. DeYoung26,
J. C. D´ ıaz-V´ elez1, M. Dierckxsens14, J. Dreyer15, J. P. Dumm1, M. Dunkman26, J. Eisch1, R. W. Ellsworth16, O. Engdegård22,
S. Euler18, P. A. Evenson7, O. Fadiran29, A. R. Fazely30, A. Fedynitch15, J. Feintzeig1, T. Feusels2, K. Filimonov9,
C. Finley19, T. Fischer-Wasels6, B. D. Fox26, A. Franckowiak21, R. Franke10, T. K. Gaisser7, J. Gallagher31, L. Gerhardt11,9,
L. Gladstone1, T. Gl¨ usenkamp18, A. Goldschmidt11, J. A. Goodman16, D. G´ ora10, D. Grant32, T. Griesel33, A. Groß4,25,
S. Grullon1, M. Gurtner6, C. Ha26, A. Haj Ismail2, A. Hallgren22, F. Halzen1, K. Han4, K. Hanson14,1, D. Heinen18,
K. Helbing6, R. Hellauer16, S. Hickford4, G. C. Hill1, K. D. Hoffman16, B. Hoffmann18, A. Homeier21, K. Hoshina1,
W. Huelsnitz16,46, J.-P. H¨ ulß18, P. O. Hulth19, K. Hultqvist19, S. Hussain7, A. Ishihara35, E. Jakobi10, J. Jacobsen1,
G. S. Japaridze29, H. Johansson19, K.-H. Kampert6, A. Kappes36, T. Karg6, A. Karle1, P. Kenny17, J. Kiryluk11,9, F. Kislat10,
S. R. Klein11,9, H. K¨ ohne23, G. Kohnen34, H. Kolanoski36, L. K¨ opke33, S. Kopper6, D. J. Koskinen26, M. Kowalski21,
T. Kowarik33, M. Krasberg1, G. Kroll33, N. Kurahashi1, T. Kuwabara7, M. Labare20, K. Laihem18, H. Landsman1,
M. J. Larson26, R. Lauer10, J. L¨ unemann33, J. Madsen3, A. Marotta14, R. Maruyama1, K. Mase35, H. S. Matis11,
K. Meagher16, M. Merck1, P. M´ esz´ aros27,26, T. Meures18, S. Miarecki11,9, E. Middell10, N. Milke23, J. Miller22,
T. Montaruli1,37, R. Morse1, S. M. Movit27, R. Nahnhauer10, J. W. Nam8, U. Naumann6, D. R. Nygren11, S. Odrowski25,
A. Olivas16, M. Olivo22,15, A. O’Murchadha1, S. Panknin21, L. Paul18, C. P´ erez de los Heros22, J. Petrovic14, A. Piegsa33,
D. Pieloth23, R. Porrata9, J. Posselt6, P. B. Price9, G. T. Przybylski11, K. Rawlins38, P. Redl16, E. Resconi25,42, W. Rhode23,
M. Ribordy24, A. S. Richard30, M. Richman16, J. P. Rodrigues1, F. Rothmaier33, C. Rott12, T. Ruhe23, D. Rutledge26,
B. Ruzybayev7, D. Ryckbosch2, H.-G. Sander33, M. Santander1, S. Sarkar5, K. Schatto33, T. Schmidt16, A. Sch¨ onwald10,
A. Schukraft18, L. Schulte33, A. Schultes6, O. Schulz25,43, M. Schunck18, D. Seckel7, B. Semburg6, S. H. Seo19,
Y. Sestayo25, S. Seunarine39, A. Silvestri8, K. Singh20, A. Slipak26, G. M. Spiczak3, C. Spiering10, M. Stamatikos12,40,
T. Stanev7, T. Stezelberger11, R. G. Stokstad11, A. St¨ oßl10, E. A. Strahler20, R. Str¨ om22, M. St¨ uer21, G. W. Sullivan16,
Q. Swillens14, H. Taavola22, I. Taboada28, A. Tamburro3, A. Tepe28, S. Ter-Antonyan30, S. Tilav7, P. A. Toale26, S. Toscano1,
D. Tosi10, N. van Eijndhoven20, J. Vandenbroucke9, A. Van Overloop2, J. van Santen1, M. Vehring18, M. Voge25, C. Walck19,
T. Waldenmaier36, M. Wallraff18, M. Walter10, Ch. Weaver1, C. Wendt1, S. Westerhoff1, N. Whitehorn1, K. Wiebe33,
C. H. Wiebusch18, D. R. Williams41, R. Wischnewski10, H. Wissing16, M. Wolf25, T. R. Wood32, K. Woschnagg9, C. Xu7,
D. L. Xu41, X. W. Xu30, J. P. Yanez10, G. Yodh8, S. Yoshida35, P. Zarzhitsky41, and M. Zoll19
(Affiliations can be found after the references)
Received / Accepted
ABSTRACT
This paper describes the response of the IceCube neutrino telescope located at the geographic South Pole to outbursts of MeV neutrinos from the
core collapse of nearby massive stars. IceCube was completed in December 2010 forming a lattice of 5160 photomultiplier tubes that monitor a
volume of ∼1km3in the deep Antarctic ice for particle induced photons. The telescope was designed to detect neutrinos with energies greater
than 100 GeV. Owing to subfreezing ice temperatures, the photomultiplier dark noise rates are particularly low. Hence IceCube can also detect
large numbers of MeV neutrinos by observing a collective rise in all photomultiplier rates on top of the dark noise. With 2 ms timing resolution,
IceCube can detect subtle features inthe temporal development of thesupernova neutrino burst. For a supernova at the galacticcenter, itssensitivity
matches that of a background-free megaton-scale supernova search experiment. The sensitivity decreases to 20 standard deviations at the galactic
edge (30 kpc) and 6 standard deviations at the Large Magellanic Cloud (50 kpc). IceCube is sending triggers from potential supernovae to the
Supernova Early Warning System. The sensitivity to neutrino properties such as the neutrino hierarchy is discussed, as well as the possibility to
detect the neutronization burst, a short outbreak of νe’s released by electron capture on protons soon after collapse. Tantalizing signatures, such as
the formation of a quark star or a black hole as well as the characteristics of shock waves, are investigated to illustrate IceCube’s capability for
supernova detection.
Key words. neutrinos – supernovae: general – telescope
1

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1. Introduction
On February 23, 1987, a burst of mainly electron anti-
neutrinos with energies of a few tens of MeV emitted by
the Supernova SN1987A was recorded simultaneously by the
Baksan (Alekseev et al., 1987), IMB (Bionta et al., 1987), and
Kamiokande-II(Hirata et al., 1987, 1988) detectors, a few hours
before its optical counterpart was discovered. With just 24 neu-
trinos collected, stringent limits on the mass of the ¯ νe, its life-
time, its magnetic moment and the number of leptonic flavors
could be derived (Kotake et al., 2006). As of now, SN1987A re-
mains theonlysourceof neutrinosthat has beendetectedoutside
of our solar system. Although the optical detection of supernova
explosions has a long history, detailed features of the gravita-
tional collapse can only be studied with neutrinos, which carry
away nearly 99% of the gravitational binding energy soon after
the collapse. The current generation of detectors is capable of
detecting many orders of magnitude more neutrinos and thus it
can study details of the gravitational collapse and neutrino prop-
erties.
The rate of galactic stellar collapses, including those ob-
scured in the optical, is estimated by various methods to be
≈ (1 − 7)/100 years (Diehl et al., 2006; Strom, 1994). A com-
pilation in Giunti & Kim (2007) narrows the expected range to
(1.7 − 2.5)/100 years by taking into account experimental and
theoreticallimits. Thebest experimentalupperlimit is <9.3/100
years (Novoseltseva et al., 2009).
While differences in the onset of the neutrino emission be-
tween various models are small (Kachelriess et al., 2005), the
models have yet to overcome problems with the supernova ex-
plosion mechanisms.The theoretical knowledge about the neu-
trinoemissionattimes longerthanseveral100µs afterthedelep-
tonization (Buras et al., 2003; Kitaura et al., 2006) is limited.
However, three characteristic phases are expected: a rapid lumi-
nosity increase during collapse with the appearance of a shock
breakout burst, an accretion phase ending after O(0.5)s during
which the neutrino flux of all flavors is maximal, and a cool-
ing phase. The O(20)s duration of supernova neutrino emission
is determined by the neutrino diffusion time scale in the dense
matter inside the proto-neutron star. The exact features will de-
pend on the progenitormass with modulationsintroducedby the
dynamics of the collapse.
IceCube is primarily designed to observe TeV neutrino
sources with a wide lattice of light sensors embedded in highly
transparent glacier ice used as Cherenkov medium. However,
it was recognized early by Pryor et al. (1988) and Halzen et al.
(1996) that neutrino telescopes offer the possibility to monitor
our Galaxy for supernovae. In spite of the much lower neutrino
energiesofO(10MeV)involvedinasupernovaburst,Cherenkov
lightinducedbyneutrinointeractionswill increasethecountrate
of all light sensors above their average value. Although the in-
crease in the noise rate in each light sensor is not statistically
significant, the effect will be clearly seen once the rise is consid-
ered collectively over many sensors. Low photomultiplier noise
rates, low photon absorption in the Cherenkov medium and a
large number of sensors are essential.
IceCube is uniquely suited for this measurement due to its
location and 1 km3size. The noise rates in IceCube’s photomul-
tiplier tubes average around 540 Hz since they are surrounded
by inert and cold ice with depth dependent temperatures rang-
ing from −43◦C to −20◦C. At depths between (1450 – 2450)m
they are also largelyshielded from cosmic rays. The noise rate is
further reduced by the use of detector components with reduced
radioactivity. The detected signal rate is essentially independent
of the photon scattering length and depends linearly on the ab-
sorption length of ≈ 100 m in ice.
The expected signal significance in IceCube is somewhat re-
duced due to two types of correlations between pulses that in-
troduce supra-Poissonian fluctuations. The first correlation in-
volves a single photomultiplier tube. It comes about because a
radioactive decay in the pressure sphere can produce a burst of
photons lasting several µs. The second correlation arises from
the cosmic-ray muon background; a single cosmic ray shower
can produce a bundle of muons which is seen by hundreds of
optical modules.
The 5160 photomultipliersare sufficiently far apart such that
the probability to detect light from a single interaction in more
than one DOM is small. Effectively, each DOM independently
monitors several cubic-meters of ice. The detection principle
was demonstrated with the AMANDA experiment, IceCube’s
predecessor (Ahrens et al., 2002).
The inverse beta process ¯ νe+ p → e++ n dominates super-
nova neutrino interactions with O(10MeV) energy in ice or wa-
ter, leading to charged particle tracks of about 0.5cm· Eν/MeV
length. Considering the approximate E2
section, the light yield per neutrino roughly scales with E3
Due to the low rate of galactic supernovae, it is imperative that
the detector operates stably for a long time. IceCube was de-
signed to operate for at least 10 years and is well suited for
such a purpose owing to an automated online data acquisition,
analysis software and alert system. As neutrinos may escape
from an exploding supernova at much higher matter densities
than photons, neutrinos will be observable several hours before
their optical counterpart. The detailed observation of the onset
of a supernova explosion is of much interest to astronomers.
Since2009,IceCubehasbeensendingreal-timedatagramstothe
Supernova Early Warning System (SNEWS) (Antonioli et al.,
2004) when detecting supernova candidate events. SNEWS
has been set up to broadcast a reliable alert to the astron-
omy community when a supernova has been detected by sev-
eral neutrino detectors within seconds of each other. Currently,
Super-Kamiokande (Fukuda et al., 2002; Ikeda et al., 2007),
LVD(Aglietta et al.,2002),Borexino(Alimonti et al.,2009)and
IceCube (Ahrens et al., 2004) contribute to SNEWS, with a
number of other neutrino and gravitational wave detectors plan-
ning to join in the near future.
This paper describes the technical details and expected
physics capability of IceCube as a detector for core collapse su-
pernovae. It also summarizes the performance of the detector
while it was still under construction. The outline of the paper
is as follows: Sect. 2 describes physics processes in supernovae
for selected models and oscillation scenarios that are relevant to
theperformancestudies presentedinthis paper,Sect. 3describes
the aspects of the IceCube detector relevant to the detection of
MeV supernova neutrinos. In Sect. 4, we discuss the processes
that lead to a detectable signal in IceCube as well as the online
analysis that processesandmonitorsthedata, triggersevents and
sends out alerts to SNEWS. Sect. 5 describes the performance
of the detector over two years and the systematic uncertainties
expected when assessing the sensitivity of the detector. Sect. 6
discusses IceCube’s potential in the study of astrophysical and
neutrino properties, and finally, conclusions are given in Sect. 7.
νdependenceof the cross
ν.
2. Supernovae and Neutrinos
After the core of an aging massive star ceases generating en-
ergy and the corresponding radiative pressure from nuclear fu-
sion processes, it undergoes a sudden gravitational collapse as

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
soon as its inactive core grows beyond the Chandrasekhar mass
limit. After several steps to relieve thermal and degeneracypres-
sure from the dense electron gas, the collapse stops once nuclear
densities are reachedandan incompressibleproto-neutronstar is
formed. Matter falling on its surface is promptly stopped and its
momentum is inverted forming an outward moving shock wave.
Neutrinos of different flavors are initially trapped in their
relative neutrino spheres as the mean free path of neutrinos is
smaller than the size of the supernova core at densities larger
than 1013kg/m3. The shock wave following the collapse dis-
sociates nuclei, which suddenly increases the number of pro-
tons, resulting in an increase in electron capture and the produc-
tion of a burst of νe. The timescale of this neutronization burst
(“deleptonization peak”) is on the order of 10ms during which
much of the energy driving the shock wave is carried away. The
shock stalls but is presumably soon revived by interactions of
the large flux of neutrinos generated in the proto-neutron star.
The models describing the prompt neutronization burst appear
to be robust and consistent (Kachelriess et al., 2005). The proto-
neutron star subsequently cools over ∼ 20s. The neutrino flux
decreases until neutrinos are no longer produced in the cooled
down proto-neutronstar (see e.g. Fischer et al. (2010) and refer-
ences therein).
The released gravitational potential is carried away by huge
numbers of neutrinos and to a small extent by heating and ex-
pelling the star’s outer layers. Less than 1% of the gravita-
tional binding energy of a supernova is emitted as kinetic en-
ergy of matter and optically visible radiation. The remaining
99% is released as neutrino energy, of which about 1% will
be carried by electron neutrinos from the initial neutronization
burst. Most neutrinos and antineutrinos, distributed among all
flavors, are created during the subsequent cooling processes. An
estimated Etotal =
?
1059MeV (Burrows et al., 1992) is carried away by the in-
tense neutrino burst produced predominantly through thermal
Kelvin-Helmholtz cooling reactions (Suzuki, 1991). Here Ltotal
is the time dependent all flavor supernova neutrino and anti-
neutrino luminosity. According to Thompson et al. (2003) and
Buras et al. (2003), the mean energy is expected to be about (13
– 14) MeV for νe, (14 – 16) MeV for ¯ νeand (20 – 21) MeV for
all other flavors (νx); the Garching model (Kitaura et al., 2006)
differs in that the mean energies for ¯ νeand νxturn out to be ap-
proximately equal. For a supernova at d = 10 kpc distance and
an average neutrinoenergy Eν= 15MeV, the summed flux of all
neutrino and antineutrino types, Φtotal
ing through the detector is ≈ 1016m−2. More on the theory of
core collapse supernova can e.g. be found in Janka et al. (2006)
and references therein.
In the following paragraphs, we briefly introduce the
Lawrence-Livermore (Totani et al., 1997) and Garching mod-
els (Kitaura et al., 2006) that are used as benchmarks. In ad-
dition, we introduce specific models by Dasgupta et al. (2010)
and Sumiyoshi et al. (2007) that were selected to demonstrate
IceCube’s physics performance in Sect. 6. We also discuss the
effect of neutrino oscillations on the expected signals, and intro-
duce the parametrization of the energy spectra chosen for this
paper.
The spherically symmetric Lawrence-Livermore simulation
was performed from the onset of the collapse to 18s after the
core bounce, encompassing the complete accretion phase and a
large part of the cooling phase. It is modeled after SN 1987A
and assumes a 20 M⊙ progenitor. The total emitted energy is
Ltotal
SN(t′)dt′≈ 3 × 1053erg = 1.87 ×
SN
Earth= Etotal/(4πd2Eν), flow-
2.9 × 1053erg, of which 16% is carried by ¯ νewith 15.3MeV
energy on average.
The newer spherically symmetric Garching simulations in-
clude more detailed informationon neutrino energy spectra amd
use a sophisticated neutrino transport mechanism. They cover
0.80s following the collapse of an O-Ne-Mg 8 – 10 M⊙pro-
genitor star, that is destabilized due to rapid electron capture
on neon and magnesium. This class of stars may represent up
to 30% of all core collapse supernovae. Recent simulations
by (Fischer et al., 2010; H¨ udepohl et al., 2010) extend over
22s from the collapse of a 8.8 M⊙ progenitor to the com-
pleted formation of the deleptonized neutron star. They are the
only examples so far where one-dimensional simulations obtain
neutrino-powered supernova explosions and two-dimensional
simulations yield only minor dynamical and energetic modifca-
tions. In Table 4 we also refer to a two-dimensional axisymmet-
ric simulation by Marek et al. (2009) of a 15 M⊙progenitor star
that covers 0.38s followingthe collapse. Results with full multi-
angle neutrino transport in two dimensions have been reported
by Brandt et al. (2011).
Following original work by Takahara & Sato (1988), re-
cent simulations (Dasgupta et al., 2010) of certain stellar core-
collapse supernovae predict a sharp burst of ¯ νeseveral hundred
milliseconds after the prompt νeneutronization burst associated
with a quark-hadronphase transition at high baryon densities. A
detection of this prominent feature would constitute direct evi-
dence of quark matter.
The gravitational collapse of less than solar metallicity stars
exceeding 25 solar masses will lead to a limited stellar explo-
sion, while stars exceeding 40 solar masses are not expected to
explode at all. In both cases a black hole will develop O(1s) af-
ter bounce. At this point, the neutrino emission quickly comes
to an end, providing a unique signature for black hole forma-
tion (Sumiyoshi et al., 2007) and a model independent time-of-
flight measurement of the neutrino mass (Beacom et al., 2001).
Neutrinos streaming out of the core will encounter matter
densities ranging from 1013kg/m3to zero. Assuming an en-
ergy of 15 MeV, they pass through an MSW-resonance layer
at ≈2 × 106kg/m3associated with ∆m2
on the neutrino mass hierarchy, followed by a second layer at
≈2×104kg/m3associated to the quadratic neutrino mass differ-
ence ∆m2
on the survival probabilities. Although the survival probabilities
depend on the details of the density profiles and generic predic-
tions are impossible, we consider three limiting cases as bench-
marks to discuss the effect of the assumed neutrino hierarchy
on the spectra observed with IceCube. Scenario A describes the
normal neutrino hierarchy case and Scenario B represents the
inverted hierarchy case with a static density profile of the super-
nova,bothpairedwith a relativelylarge mixingangleθ13> 0.9◦.
In Scenario C, the mixing angle θ13is assumed to be very small
(θ13 < 0.09◦) and the hierarchy may be either normal or in-
verted. One should be aware that the predictions are affected by
the unknown density profile of the collapsing star. In addition,
forwards or backwards running single or multiple shock waves
or bubbles can form within the supernova, causing steep density
gradients and - in some cases - changes in the oscillation behav-
ior (Tom` as et al., 2004; Choubey et al., 2006).
As the neutrinos propagate through the Earth, they un-
dergo matter induced oscillations (MSW effect). The neutrino
flux (Giunti & Kim, 2007; Dighe et al., 2004) decreases by up to
8%. The effect depends sensitively on the zenith angle, the su-
pernova model and the assumed neutrino properties. Given the
systematic uncertainties,it will be difficultto establish this effect
23or ∆m2
13, depending
12. Both mix the initial fluxes of νe, ¯ νeand νxdepending
3

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
with IceCube. We will therefore include earth oscillation effects
in the systematic uncertainty.
For this paper, the supernova is considered to be close to
blackbody source of neutrinos while it is cooling down. The
neutrino energies then follow a modified Fermi-Dirac distribu-
tion. Many model predictions discussed in this paper adopt the
followingparametrizationforthe neutrinodifferentialflux Φν
at the position of the Earth at distance d from the supernova:
Earth
dΦν
dEν
Earth
=
LSN(t)
4πd2Eν
f(Eν,Eν,αν)
,
(1)
where
f(Eν,Eν,αν) =
?1 + αν(t)
Eν(t)
?1+αν(t)
×E(αν(t))
ν
e−(1+αν(t))Eν/Eν(t)
Γ(1 + αν(t))
(2)
is the normalized energy distribution depending on a shape pa-
rameter αν (Keil et al., 2003). Theory provides the time de-
pendent supernova luminosity Lν
ν = νe, ¯ νe,νxwith corresponding energies Eν. Other model pre-
dictions are transferred to this framework by fitting the provided
spectra.
SN(t) for the neutrino species
3. The IceCube Detector
IceCube (Ahrens et al., 2004) was installed in the Antarctic ice
sheet at the geographic South Pole between January, 2005 and
December, 2010 by lowering cable assemblies, called strings,
into holes drilled in the ice using hot water. IceCube instru-
ments a volume of about 1km3of clear ice between depths of
1450m and 2450m below the surface with a coarse lattice of
5160Digital Optical Modules(DOMs). Each DOM consists of a
photomultiplier tube housed in a pressure-resistant glass sphere.
Once the water in the holes refreezes, the DOMs are embedded
into the ice sheet with good optical coupling. The DOMs are
installed on 86 cable strings, of which 80 will be separated by
roughly 125m forming a triangular grid, with each string con-
taining 60 DOMs vertically spaced by 17m. More details on the
detectorcan be foundin Achterberg et al. (2006). The remaining
6 strings constitute the denser DeepCore sub-array. DeepCore
strings are separatedby approximately60m and are locatednear
the center of IceCube. Each of these strings contains 60 high
quantum efficiency DOMs, with the bottom 50 DOMs vertically
spaced by 7m and located between depths of (2107 – 2450)m
below a dusty ice layer with reduced transparency. The remain-
ing 10 DOMs, vertically separated by 10m and located above
the dust layer, instrument a volume between (1750 – 1860)m.
IceCube is complemented by a surface array called IceTop,
consisting of a pair of DOMs encased in ice tanks near the top of
eachstring.Duetotheirhighernoiseandsensitivitytothefluctu-
ating solar particle flux (Abbasi et al., 2008), the IceTop DOMs
do not contribute to the IceCube supernova detection system.
The DOM is the fundamental element in the IceCube archi-
tecture. Each DOM is housed in a 13" (33cm) diameter, 0.5"
(1.27cm) thick borosilicate glass pressure sphere. It contains
a Hamamatsu R7081-02 (R7081-MOD in case of DeepCore)
10" (25.4cm) hemisphericalphotomultipliertube (Abbasi et al.,
2010) as well as several electronics boards containing a proces-
sor,memory,flash file system and realtimeoperatingsystem that
allows each DOM to operateas a completeand autonomousdata
acquisition system (Abbasi et al., 2009). It stores the digitized
data internally and transmits the information to a surface data
acquisition system on request.
The supernova detection relies on continuous measurements
of photomultiplier rates. The rate information is stored and
buffered on each DOM in a 4-bit counter in 1.6384ms time bins
(216cycles of the 40 MHz clock).The main data acquisition sys-
tem (Abbasi et al., 2009) transfers the data asynchronously to
the independently operating supernova data acquisition system
(SNDAQ). For the real-time processing, the information is syn-
chronized by a GPS clock and regrouped in 2ms bins.
The South Pole is out of reach for most communication
satellites and high bandwidth connectivity is available only
for about 6 hours per day. Therefore, a dedicated Iridium-
satellite (Pratt et al., 1999) connection is used by the SNDAQ
host system to transmit urgent alerts. In that case, a short data-
gram is sent to the northern hemisphere. The receiving system
parses the message and forwards information on the supernova
candidateevent to the internationalSNEWS group.The time de-
lay between photons hitting the optical module and the arrival of
the datagram at SNEWS stands at about 6min, providing close
to real-time monitoring and triggering. In order to test the signal
path, an internal trigger threshold is adjusted to transmit 1 – 2
background triggers per day.
Due to satellite bandwidthconstraints, the data are re-binned
in 0.5s intervals and then subjected to a statistical online anal-
ysis described in Sect. 4.2; the fine time information in 2ms in-
tervals is transmitted for a period starting 30 s before and ending
60 s after a trigger flagging a candidate supernova explosion.
The system is surveyed by the IceCube experiment control and
monitoring system (“IceCube Live”); supernova alerts are im-
mediately distributed per e-mail to notify experts.
The optical and noise properties of the DOMs are crucial
for the understandingof IceCube’s supernova detection and will
hence be discussed in more detail in the following subsections.
3.1. Optical Properties of the Digital Optical Module
The photomultiplier was chosen on the basis of low dark counts
and good time and charge resolution. Its bialkali photocathode
has a spectral response in the range 300nm to 600nm with a
peak quantum efficiency of (25 ± 1)% at 420nm, well-matched
to the Cherenkov signal spectrum and the optical properties of
the glacial ice. Dark count rates for standard efficiency DOMs
of around 540Hz are typical for DOMs at ice temperatures be-
tween −43◦C at -1450 m depth and −20◦C at -2450 m depth.
The quantumefficiencyof highefficiencyDOMs is roughly1.35
times higher, while the noise increases approximately by a fac-
tor 1.25. The glass of the pressure sphere was selected for high
transmission in the sensitive region of the photomultiplier and
a low rate of background photons from intrinsic radioactivity in
the glass. Optical transparency extends well into the near-UV,
with 50% transmission at T50% ∼ 340nm, but drops to a few
percent at 310nm.
The photomultiplier is mechanically attached to the glass
pressure housing with a silicone elastomer gel (GE6156 RTV).
This gel matches the refractive index of the glass (n=1.48) to
reduce optical losses at the medium interfaces. The spectral
transparency of the gel extends to approximately 250nm with
T50% ∼ 300nm. The combined response of the glass, gel, and
photomultiplier is a critical input to the IceCube Monte Carlo
simulation package. The detection efficiency of the DOM to a
head-on parallel light beam is shown in Fig. 1.
Most of the PMTs are operated at a gain of 107, so single
photoelectronsproducepulsesofapproximately8mVamplitude
and 10ns width across the load impedance. The programmable
front end pulse discriminator is set to 2mV, a factor of ≈ 10
4

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
0.12
0.10
0.08
0.06
0.04
0.02
0
300 350 400 450 500 550 600
Wavelength [nm]
DOM Efficiency Head-On Beam
Fig.1. Overall DOM efficiency versus wavelength for head-on
illumination of the 0.0856 m2DOM cross section. The average
value in the 300 – 600nm range, weighted by the wavelength
dependence of Cherenkov light emission, is ≈ 7.1%.
t [s]∆
Probability density
-3
10
-2
10
-1
10
30
20
10
0
0
0.02
0.010.03
0.04 t [s]
sequential hit #
0 0.001 0.002 0.003 0.004
deadtime
0.00025 s
Fig.2. Probability density distribution of time differences be-
tween pulses for noise (bold line) and the exponential expec-
tation for a Poissonian process fitted in the range 15 ms < ∆T <
50 ms (thin line). The excess is due to bursts of correlated hits,
as can be seen from the 50 ms long snapshot of hit times shown
in the insert.
above the RMS noise level (Abbasi et al., 2010). On average,
85% of all single photo-electron pulses pass the discriminator
threshold.
3.2. Noise Properties of the Digital Optical Module
Severaleffectscontributetotheprevailingaveragerateof540Hz
for standard efficiency DOMs. Atmospheric muons (16Hz),
thermal emission from the photocathode (<10Hz), and photo-
multiplier induced afterpulses (≈ 30Hz) all play a role, but the
majority of hits are due to radioactive decays of which a large
fraction initiates bursts of hits lasting for up to 15ms. These
burstsare presumablyscintillationofresidualceriumintheglass
of the photomultiplier and the pressure sphere energized by β
and α decays. The40K content in the IceCube pressure spheres
is specified to produce less than 100Bq leaving trace elements
from uranium and thorium decay chains as the main source of
radioactivity.
The main characteristics of DOM noise were determined
using a minimum bias data set with pulses recorded by 120
IceCube DOMs. The observed time difference between noise
hits deviates from an exponential distribution expected for a
Poissonian process (see Fig. 2). The inset shows a hit se-
quence from a single DOM. Photomultiplier related afterpulses,
which occur with ≈ 6% probability on time scales of 0.3–
11µs (Abbasi et al., 2010), cannot explain the high occupancy
bursts. We infer that the bursts are caused by an event within a
DOM,butexternaltotheelectronamplificationofthephotomul-
tiplier.
These observations are consistent with a study by Meyer
(2010), who showed that a photomultiplier with bialkali pho-
tocathode produces bursts that increase in rate and size as the
photomultiplier is chilled. Such behavior could result from in-
creased efficiency for radiative decay of excited states in the
glass. In addition, Richardson’s law describes the increase in
thermal emission with photomultipliertemperature.The deploy-
ment of IceCube DOMs on vertical strings places them in en-
vironments ranging from −43◦C to −20◦C (Price et al., 2002),
warmingwith depth.DOM temperaturesaresome 10◦C warmer
due to the energy dissipated in the electronics as monitored by
a sensor mounted on the mainboard, but the photomultiplier and
DOM glass temperature is somewhat uncertain.
To confirm these temperature patterns, we divide the DOM
noise into two contributions. The first is the rate of random ar-
rivals as determined by fitting the slope of the interval distribu-
tion as in Fig. 2. The second is the rate of events contributing
to the excess of short intervals. These contributions are further
divided into six temperature bands, and displayed in Fig. 3. The
fitted excess contributions are then comparedto an empirical ex-
ponential ansatz Meyer (2010),
r(T) = G · AC· e−T
where T is the absolute temperature, AC
the cathode surface of the deployed R7081-02 photomultiplier
(Hamamatsu, 2007) and G = 5 × 104/m2/s is a fixed constant
taken from Meyer (2010). The fit results in Tr ≈ 115 K. The
Poisson component is fitted to the Richardson-type law on ther-
mal emission plus an ad-hoc constant noise term C
Tr
,
(3)
= 0.055 m2is
r(T) = A · T2· e−W
where k denotes Bolzmann’s constant, W ≈ 0.5 eV is the work
function for Bialkali cathodes, and A, C are fit parameters. The
curves match the observed temperature dependence of the noise
rates fairly well, and support the hypothesis that the DOM noise
is primarily due to a temperature dependent spectrum of bursts.
Thedata acquisitionwas designedtoreducethe noise rateby
eliminating the excess hits, while keeping the random arrivals.
Thesignal-to-noiseratioofthemeasurementcanbeimprovedby
enforcing an artificial dead time τ after every count, configured
to 250µs by a field programmable gate array in the DOM. This
reduces the noise rate from 540Hz to 286Hz at the cost of some
13% dead time for signal. The choice of 250µs optimizes sen-
sitivity to the Lawrence-Livermore model (Totani et al., 1997)
for distances up to 75kpc, when neglecting the effect of after-
pulses following the signal. A deadtime τ > 110 µs guarantees
that the 4-bit counters do not overflow. The rate decrease due to
deadtime can be corrected for, however, the corresponding un-
certainty increases once the measured rate approaches 1/τ. An
improveddata acquistion that would avoid distortions of the rate
measurement for supernova distances < O(1kpc) is under dis-
cussion.Variousdeadtime implementations,e.g.schemesmask-
ing hits that arrive within the dead time caused by a previous hit
or schemesallowingeachhit torestart the deadtime, were tested
kT+C
,
(4)
5

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
C]
°
PMT Temperature [
-35-30-25-20 -15 -10-5
Rate [Hz]
200
220
240
260
280
300
320
340
360
380
correlated contribution
Poissonian contribution
Fig.3. Measured average noise rates of 120 DOMs as function
of DOM temperature. The excess of short time intervals due to
bursts (solid) is fitted to the empirical model of Meyer (2010).
The Poissonian contribution (dashed) has not been corrected for
the depth (and thus temperature) dependent contribution of at-
mospheric muons (see Fig. 7). The dotted line is a comparison
to the predictedthermal noise from Richardson’s law plus a con-
stant rate of C = 225 ± 6 Hz.
with only slight differences observed. By eliminating the initial
hits of the bursts, the noise rate can be reduced by up to 100
Hz. Other optimizations may be possible but require a thorough
understandingof the effect on signal hits. Design and implemen-
tation of a new data acquisition system with more efficient noise
rejection will be reported in a subsequent publication.
4. Neutrino Interaction, Detection and Analysis
In this section we first discuss the processes that lead to a de-
tectable signal in IceCube, starting with the relevant neutrino in-
teraction cross sections and continuingwith the Cherenkov light
production,propagationanddetection.Thereal-timeanalysisin-
troduced to monitor collective rate changes in IceCube’s light
sensors is described in the second part of this section.
4.1. Cherenkov Photon Signal in IceCube
Neutrinos of different species will be detected in IceCube via
interactions listed in Table 1. The table also includes the num-
ber of observed photon hits and the corresponding fractions
as expected from the Garching model. The inverse beta reac-
tion dominates in the ice. The small contribution by neutrino-
electron scattering processes poses a challenge to the detection
of the deleptonization peak. Note that the νeand ¯ νecross sec-
tions on16O are strongly energy dependent due to the high reac-
tion thresholds of 15.4 MeV and 11.4 MeV, respectively. While
their contribution to the hit rate in case of the Garching model
is small (≈ 3%), the contribution can be as high as 20% for
a 40 M⊙progenitor (Sumiyoshi et al., 2007) with average neu-
trino energies of 25 MeV and beyond. The νecross sections on
the rare isotopes18O,17O and on2
inant contribution, add a small signal due to their low reaction
thresholds (Haxton, 1987). As the cross sections are only given
for electron energies between (5–13) MeV (Haxton, 1999), they
wereextrapolatedassumingan E2
depositionduetopositronannihilation,neutroncaptureandpho-
1H, with18O giving the dom-
νdependence.Whiletheenergy
ton induced compton electrons arising in the de-excitation of gi-
ant O* resonances in the neutral current interactions νX+16O →
νX+16O∗→ νX+15O/15N + n/p + γ (Langanke et al., 1996)
have been included, we have not yet considered delayed β and
βγ decays fromexcitednuclei.The cross section uncertaintiesof
the reactions on protons and electrons are estimated in the refer-
ences of Table 1 to be smaller than 1%; uncertainties on oxygen
reactions are hard to assess and the cross section may be only
known up to a factor of two.
Reactions producing electrons or positrons in the final state
radiate NγCherenkov photons along their flight path x, as long
as their kinetic energies exceed the Cherenkov threshold of
0.272 MeV. Integrating the Frank-Tamm formula between (300
– 600) nm and accounting for the dispersion in ice, one obtains
Nγ= (316±9) cm−1·x (x in cm). For the inverse beta decay, the
total average positron energy Ee+ is calculated from the average
¯ νeenergy by the relation Ee+ ≈ (E¯ νe−δ)·
withδ = (m2
energy dependenceof the interaction cross section, the observed
positron energies are on average higher than those of the incom-
ing neutrino; the number of Cherenkov photons approximately
rises with E3
?
1 − E¯ νe/(E¯ νe+ mpc2)
?
n−m2
p−m2
e)/2mp. Duetotheapproximatelyquadratic
ν(see Fig. 4).
Particle Energy [MeV]
0 10 2030 4050
Spectral Contribution (0.1MeV binning)
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
> = 12.9 MeV
ν
<E
> = 18.8 MeV
e
<E
<nγ>/178 = 21.9 MeV
Fig.4. Example for an anti-electron neutrino energy spectrum
with α = 2.92 predicted for an 8.8 solar mass O-Ne-Mg core
supernovaecollapse(H¨ udepohl et al., 2010) onesecondafterthe
onset of the burst (solid line). Also shown is the cross section
weighted energy spectrum of produced positrons and electrons
from inverse beta decay (dashed line) as well as a measure of
the detectable energy, which is proportional to the number of
Cherenkovphotons Nγin the (300– 600)nmrange (dottedline).
The relation Nγ= a · Eewith a = 178 MeV−1is used.
The mean travel path for O(10MeV) electrons from νeand
positrons from ¯ νe, including secondary leptons with energies
above the Cherenkov threshold as well as positron annihilation,
was determined with a GEANT-4 Monte Carlo simulation. The
linear relationship ¯ x = (0.560 ± 0.005(stat.) ± 0.030(syst.))cm
·Ee+/MeV was found for positrons. The correspondingrelation-
ship for electrons was determined to be consistant within errors.
The optical scattering and absorption in glacial ice at the
South Pole has been studied extensively (Ackermann et al.,
2006) by the AMANDA and IceCube Collaboration with pulsed
6

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
Table 1. Major neutrino reactions.
Reaction
¯ νe+ p → e++ n
νe+ e−→ νe+ e−
¯ νe+ e−→ ¯ νe+ e−
νµ+τ+ e−→ νµ+τ+ e−
¯ νµ+τ+ e−→ ¯ νν+τ+ e−
νe+16O → e−+ X
¯ νe+16O → e++ X
νall+16O → νall+ X
νe+17/18O/2
# Targets
6 · 1037
3 · 1038
3 · 1038
3 · 1038
3 · 1038
3 · 1037
3 · 1037
3 · 1037
6 · 1034
# Signal Hits
134 k (157 k)
2.35 k (2.25 k)
660 (720)
700 (720)
600 (570)
2.15 k (1.50 k)
1.90 k (2.80 k)
430 (410)
270 (245)
Signal Fraction
93.8% (94.4%)
1.7% (1.4%)
0.5% (0.4%)
0.5% (0.4%)
0.4% (0.4%)
1.5% (0.9%)
1.3% (1.7%)
0.3% (0.3%)
0.2% (0.2%)
Reference
Strumia & Vissani (2003)
Marciano & Parsa (2003)
Marciano & Parsa (2003)
Marciano & Parsa (2003)
Marciano & Parsa (2003)
Kolbe et al. (2002)
Kolbe et al. (2002)
Kolbe et al. (2002)
Haxton (1999)
1H → e−+ X
Notes. The approximate number of targets in a 1 km3ice detector, the detected number of hits at 10 kpc distance and their fraction in stars are given
in the second, third and fourth column, respectively. In order to indicate the effect of neutrino oscillations in the star, signal hits and fractions are
presented both assuming a normal neutrino hierarchy (Scenario A) and - in brackets - assuming an inverted hierarchy (Scenario B). The numbers
are taken from the Garching model using the equation of state by Lattimer & Swesty (1991), integrating over 0.8s and averaging over the neutrino
incidence angle.
and continuous light sources embedded in the ice with the neu-
trino telescope. The detectors span depths ranging from (1300
– 2500)m in the ice where the scattering coefficient varies by a
factor of seven and absorptivity can vary by a factor of three de-
pending on the wavelength. The data (Bramall et al., 2005) are
consistentwiththevariationsindustimpurityconcentrationseen
in ice cores sampled at other Antarctic sites to track climatolog-
ical changes. In the simulation applied for this paper, the ice is
assumed to be homogeneous in the horizontal plane despite an
observed slight tilt.
We use two alternative procedures to calculate the num-
ber of detected signal hits from the number of neutrinos
crossing the detector: the first approach is based on separate
simulations of particle interactions, Cherenkov photon creation,
propagation and detection, the second GEANT-3.21 GCALOR-
based (Zeitnitz et al., 1994) simulation combines all the steps in
one program.
IceCube’s standard simulation of photon propagation within
the ice relies on predetermined tables (Lundberg et al., 2007),
createdtotrackphotonsacross theAntarcticice. Thetables store
the detection probability and the arrival time distribution for
given source and detector locations as well as their orientation.
It includes the source wavelength, angular and intensity infor-
mation, DOM parameters such as the glass and gel transparency
and the quantum efficiency of the photomultiplier tubes. It also
contains information about the ice such as the depth-dependent
absorption and scattering lengths.
The signal hit rate per DOM for a specific reactionand target
is given by:
ntargetLν
4πd2Eν(t)
0
dσ
dEe(Ee,Eν)Nγ(Ee)Veff
wherentargetis thedensityoftargetsinice,d is thedistanceofthe
supernova, Lν
ized neutrinoenergydistributiondefinedin Eq. 2 and Eedenotes
the energy of electrons or positrons emerging from the neutrino
reaction. Veff
γ
denotes the effective volume for a single photon
and Nγ(E) ≈ 178 · Eeis the energy dependent number of radi-
ated Cherenkov photons; their numerical values depend on the
selected wavelength range, chosen as (300 – 600) nm through-
out this paper. The artificial deadtime τ (see Sect. 3.2) reduces
R(t) = ǫdeadtime
SN(t)
?∞
dEe
?∞
0
dEν
×
γ f(Eν,Eν,αν,t),
(5)
SN(t) its luminosity, f(Eν,Eν,αν,t) is the normal-
the total rate of hits. Comparing the observed signal, defined as
the net increase over the nominal noise level, to the full rate of
signal hits defines the deadtime efficiency ǫdeadtime. The approxi-
mate expression ǫdeadtime≈ 0.87/(1+rSN·τ) is foundas function
of signal rate rSNby adding Poissonian signal to the measured
sequence of noise hits and applying a non-paralyzable deadtime
τ = 250 µs.
The single photon effective volume varies strongly with the
photon absorption. As a first approximation, Veff
mated by the product of the Cherenkovspectrum and DOM sen-
sitivity weighted absorption length (≈100m), DOM geometric
cross section (0.0856m2), Cherenkovspectrumweightedoptical
module sensitivity (≈0.071), average angular sensitivity includ-
ing cable shadowing effects (≈0.32), and the fraction of single
photon hits passing the electronic DOM threshold (≈0.85).
Veff
γ
was simulated by randomly placing 107photons with
(300 – 600) nm wavelengths within a sphere of radius 250m
around each DOM. We made the simplifying assumption that
the Cherenkov light arrives at the DOMs isotropically from all
angles. Note that the directions of positrons from the dominant
inversebetadecayreactionareveryweaklycorrelatedwiththose
of the incoming neutrinos.
Veff
γ
was determined as function of depth in the ice (see
Fig. 5). Averaging over all DOMs in one string one obtains
Veff
γ
= 0.163 ± 0.004(stat.) ± 0.020(syst.)m3. The systematic
uncertainty is discussed in Sect. 5.5.
The energy dependent effective volume Veff
an electron or positron is obtained by multiplying Veff
number Nγ(E) of Cherenkovphotons. The mean number of pho-
tons recorded by an optical module averaged over energy is then
given by Ndetect
γ
= ǫdeadtime· ninteract
neutrino density. For positrons with a cross section weighted av-
erage energy of e.g. Ee+=20 MeV (see Fig. 4) one would obtain
the averageeffectivevolumeVeff
(580±80)m3for standard efficiency DOMs. This volume corre-
sponds to an envisioned sphere of ≈ 5.2m radius centered at the
optical moduleposition, with full sensitivity inside and zero out-
side.With 5160opticalmodulesdeployed,IceCubethusroughly
corresponds to a dedicated 3.5 Mton supernova search detector
in terms of geometry. Due to the presence of noise, a fair com-
parisonin terms of statistical accuracyneedsto take intoaccount
thesignaloverbackgroundratioas functionoftimeanddistance.
To give an example, a study of the initial 380 ms of the burst in
γ
can be esti-
e
for detecting
γ with the
ν
· Veff
e, where ninteract
ν
is the
e = (29.0±3.8)m3/MeV · Ee+ ≈
7

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
Depth [m]
]
3
[m
eff
V
γ
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
/ MeV]
3
[m
eff
V
+
e
15
20
25
30
35
40
1400 1600 1800 2000 2200 2400
Fig.5.EffectivevolumeVeff
Cherenkov photons with (300 - 600) nm wavelength plotted as a
function of depth. The effective positron volume can be read off
the right axis. DeepCore strings are not included in these plots.
γ perDOM (left axis) fordetectionof
the Lawrence Livermore model (see Table 4) at distances of 10
kpc (5 kpc) would require a 0.45 (1.6) Mton background free
detector to statistically compete with IceCube.
Thesecondapproachwas toapplya GEANT-3.21GCALOR
based simulation of individual events that includes νe and ¯ νe
on protons, electrons and16/17/18O, positron annihilation and
neutron capture, the photon propagation in the ice including
the effect of dust layers, detector geometry, and the DOM re-
sponse (Richard, 2008). The ¯ νe+ p → e++ n cross section
parametrization of Vogel & Beacom (1999), which is in good
agreement with Strumia & Vissani (2003), was used. Positron
annihilation and hydrogen capture of neutrons produce photons
of 0.51MeV and 2.22MeV energy,respectively.These add, pre-
dominantly by Compton scattering and subsequent Cherenkov
emission, ≈ 1 MeV to the recorded energy. Rates from neutrino
interactions on electrons reveal a 20% dependence on the in-
coming neutrino direction due to the small angle between neu-
trinosandscatteredelectronsandadirectionaldependenceofthe
DOMefficiencies.Fig.6showstheclusteringofdetectedinverse
beta neutrino interactions at the position of the detector strings
to visualize the effective volumes. The use of events with two
or more DOMs detecting photons from the the same positron to
improve upon IceCube’s sensitivity at large supernova distances
and to track relative changes in the average neutrino energy will
be discussed in a future paper. If several photons arrive close
in time at the same DOM they will be counted as one hit; if
one of the photons is delayed by scattering it will be rejected
by the artificial dead time requirement.The two independentap-
proaches for the determination of the detected number of events
agree within 10%.
One may obtain a rate estimate from measured data by
scaling the 11 events Kamiokande-II observed during the
Supernova SN1987A neutrino burst to IceCube’s effective
volume of Antarctic ice. Assuming the ν energy spectrum
of Vissani & Paglioroli (2009), accounting for the Kamiokande-
II energy threshold and positron detection efficiency, and tak-
ing into account the loss due to IceCube’s artificial dead time
we determine a signal expectation of 113 ± 36 detected photons
per IceCube module within the first 15s for a SN1987A like su-
pernova near the galactic center at 10kpc distance. The results
are consistent with earlier simulations (Feser, 2004; Jacobsen,
1996) performedfor AMANDA that assumed homogeneousice,
X-axis [m]
Y-axis [m]
-600
-400
-200
0
200
400
600
-600-400-2000 200400 600
Fig.6. Detected neutrino inverse beta decay interaction vertices
projectedontothehorizontalplanebasedonaGEANT-3.21sim-
ulation with 10 million neutrino interactions.
after correcting for the different photomultiplier sensitive areas,
optical module transparencies and dust layers in the ice.
4.2. Real-Time Analysis Method
The analysis monitors the collective rate increase ∆µ in all
DOMs induced by Cherenkov photons uniformly distributed in
the ice. As discussed in Sect. 4.1, the photons are radiated by e±
produced by reacting supernova neutrinos. Counting Nipulses
during a given time interval ∆t, rates ri= Ni/∆t for DOM i, are
derived. The index i ranges from 1 to the total number of oper-
ational optical modules NDOM. With sufficiently large ∆t’s, the
distributions of the ri’s can be described by lognormal distribu-
tions that, for simplicity, are approximated by Gaussian distri-
butions with rate expectationvalues ?ri? and correspondingstan-
dard deviation expectationvalues ?σi?. These expectationvalues
are computed from moving 300s time intervals before and af-
ter the investigated time interval. Shorter time intervals reduce
the sensitivity of the analysis. At the beginning and the end of a
SNDAQ-run, asymmetric intervals are used. The time windows
exclude 30s before and after the investigated bin in order to re-
duce the impact of a wide signal on the mean rates.
The most likely collective rate deviation ∆µ of all DOM
noise rates rifrom their individual ?ri?’s, assuming the null hy-
pothesis of no signal, is obtained by maximizing the likelihood
L(∆µ) =
NDOM
?
i=1
1
√2π?σi?
exp(−(ri− (?ri? + ǫi∆µ))2
2?σi?2
) .
(6)
Here ǫi denotes a correction for module and depth dependent
detection probabilities.An analytic minimization of −lnL leads
to
∆µ = σ2
∆µ
NDOM
?
i=1
ǫi(ri− ?ri?)
?σi?2
,
(7)
with an approximate uncertainty of
σ2
∆µ=

NDOM
?
i=1
ǫi2
?σi?2

−1
.
(8)
8

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
Note that ∆µ has the structure of a weighted average sum: each
optical module contributes with the deviation of its expected
noise rate weighed by ǫi/?σi?2. Assuming uncorrelated back-
groundnoise and a large numberof contributingDOMs, the sig-
nificance ξ = ∆µ/σ∆µshould approximately follow a Gaussian
distribution with unit width centered at zero. In practice, the
width turns out to be larger (see Sect. 5.3). The likelihood that a
deviation is caused by an isotropic and homogeneous illumina-
tion of the ice can be calculated from the χ2-probability
− 2ln(L) = χ2
∆µ=
NDOM
?
i=1
?ri− (?ri? + ǫi∆µ)
?σi?
?2
.
(9)
In order to suppress high rate deviations due to a temporary
malfunctionof individual detector modules,we reject supernova
candidate events with a χ2
For short time bases ∆t, the Gaussian approximation is no
longer valid and Poissonian probabilities must be used. The col-
lective rate deviation can then be obtained from the equation
∆µ-probability < 0.001.
0 =d ln(L(∆µ))
d∆µ
=
NDOM
?
i=1

niǫi
?ri? + ǫi∆µ− ǫi

,
(10)
which can no longer be solved analytically. The same goes for
the corresponding uncertainty, which is derived by identifying a
drop in lnL by 0.5. The required numerical minimization pre-
vents an online analysis of the raw data in 2 ms time intervals.
However, fine time data in intervals of 30 s before and 60 s after
a trigger are transmitted by satellite to perform a more detailed
analysis offline. For instance, the onset of the neutrino emission
can be determinedwith better than 5ms accuracyfor supernovae
withless than15kpcdistanceconservativelyassuminglowmass
O-Ne-Mg core supernovae (H¨ udepohl et al., 2010). For similar
studiesseePagliaroli et al.(2009a)andHalzen & Raffelt(2009).
This information can then be used to triangulate the supernova
direction with other neutrino experiments.
The optimal time base ∆t to detect faint signals depends on
the expected signal shape. A simple generic description incor-
porates a fast O(10ms) rise of the neutrino flux followed by an
exponentialdecrease as expected during proto neutron star cool-
ing with a time constant of τ ≈ 3s. Maximizing
?∆t
√∆t
∆µ
σ∆µ
∝
0
e−t
τdt
(11)
leads to ∆t ≈ 1.26τ ≈ 3.8s. As the realtime analysis op-
erates on bins of 0.5s length, a time window length of 4s
has therefore been chosen as the best available setting for
this particular model assumption. Assuming the Livermore
model (Totani et al., 1997), with a pronounced flux during the
first seconds due to the high mass progenitor, the optimal time
window is determined to be 1.6s.
To cover these model uncertainties, additional analyses with
time bases of 0.5s and 10s are run in parallel with the one with
∆t = 4s. The 0.5s analysis aims at short neutrino bursts (i.e.
from soft gamma ray repeater sources or from supernovae col-
lapsing into a black hole). The 10s time base accounts for the
observed time window of the detection of the neutrinos from
the supernovaSN1987Aby Kamiokande-II(Hirata et al., 1987).
By removing the cut on χ2for the 0.5s binning, the trigger
has been made sensitive to partial illuminations of the detector.
This gives the possibility to record hypothetical exotic particles
emitting considerable amounts of light and thereby acting as a
slowly moving source (such as ultra-heavy magnetic monopoles
in some theories).
The collective rate deviation ∆µ and its uncertainty σ∆µin
the time bases of 4s and 10s are calculated using sliding win-
dows in 0.5s steps and extractingthe maximalsignificance. This
procedure ensures that the signal detection efficiency is not re-
duced by binning effects.
5. Detector Performance
In this section we will characterize the detector performance
based on two years of data taking experience, discuss detector
qualification criteria and summarize the expectedsystematic un-
certainties. The data were taken with 22 operating strings (211
days between Aug 2, 2007 and April 5, 2008) and with 40 op-
erating strings (345 days between April 9, 2008 and April 15,
2009).
5.1. DOM Stability Requirements
The stability of DOM noise rates is crucial for IceCube’s sen-
sitivity to detect supernovae. Faulty modules are removed from
the analysis using automatic procedures that are applied in real
time. In the 40 string configuration, 41 DOMs out of 2400 de-
ployed DOMs showed no signal (≈ 1.7%); all module rates ful-
filled the requirement ?ri? < 10000Hz. Operational modules are
removed from the analysis if they exhibit a variance ?σi?2much
larger than the Poissonian expectation ?ri? or high skewness |s|.
In the very rare case, where the number of qualified modules
drops below a threshold of 100, the corresponding time periods
are discarded as a safeguard to prevent sending false alarms to
SNEWS.
The filter results in Table 2 show the excellent data quality
of IceCube. Taking the 40 string configuration as an example,
98% of the disqualifications were due to just 11 DOMs. Thus
the module disqualification has a negligible effect on the signal
significance which changes as the square root of active DOMs.
5.2. Long Term Stability
TheDOMratesarecharacterizedbyanexponentialratedecrease
over long time periods and a slight seasonal modulation. For the
purpose of this analysis, the formula
r(t) = r0+ c1e−t/τ+ c2sin(2π(t/year))(12)
represents the effects sufficiently well, as can be seen in Fig. 7.
The decay of the rate is likely due to a decrease of tribolumines-
cence in the ice with time, a byproduct of the freezing process.
For DOMs that have been in the ice for more than 3 years, the
fall time τ exceeds 40 years, except for very deep DOMs where
the freezing process takes longer (τ ≈ 4 years). In any case, the
effect is negligible for the analysis which requires a stable rate
within the analysis time window of 10min. The slightly skewed
rate distribution of a single DOM is better described by a log-
normal distribution than by a Gaussian (see Fig. 8 with 250µs
dead time applied). The average rates for standard efficiency and
high efficiency DOMs are determined to be 286Hz and 359Hz,
respectively. Thanks to the tight quality control, variations be-
tween DOMs of the same type are small showing standard de-
viations of 26Hz and 36Hz, respectively. The seasonal DOM
rate modulation is assumed to arise from a change in the atmo-
spheric muon flux (Tilav et al., 2009). Fitting the time varying
component,the parameterc2can be extracted(see Fig. 7),which
9

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
Table 2. Module disqualification.
Analysis
bin width (s)
0.5
4
10
Broadening cutLoss (%) Skewness cutLoss (%)Fraction of DOMs removed
from analysis (%)
1.73
1.75
1.73
0.64 < ?σi?2/?ri? <4.0
0.64 < ?σi?2/?ri? <4.0
0.36 < ?σi?2/?ri? <6.25
0.02
0.05
0.02
|s| < 0.8
|s| < 1.2
|s| < 1.7
0.01
0.02
0.01
Notes. Disqualification requirements used in the online analysis and corresponding average percentage of DOMs that are rejected. The values
were extracted from successful data taking runs with 40 IceCube strings covering the time from 2008/04/09 to 2009/04/19, which corresponds to
an uptime of about 345 days. Most DOMs removed from the analysis are dysfunctional and provide zero rate.
2008/01/01 2008/07/012008/12/31
Rate [Hz]
226
228
230
232
234
1600 1800 2000 2200 2400
Absolute muon induced rate .[Hz]
Fig.7. Top: Rate of a typical DOM as function of time cover-
ing 556 days of lifetime as measured in 0.5s bins (baseline sup-
pressed). The line corresponds to a rate fit according to Eq. 12.
Bottom: Parameter c2and estimated muon induced rate as func-
tion of depth. The variation with depth is mostly due to the
optical properties of the ice and muons ranging out.
tracks the effect of dust layers similarly to what was observed in
the determination of effective volumes (see Fig. 5). If one inter-
prets the effect as being due to stratospheric temperature vari-
ations measured to modulate the muon flux by ∆r ≈ 8.3% in
2008, the averaged muonic contribution to single DOM rates is
c2/∆r ≈ 12c2 ≈ 16Hz. As will be discussed in the following
section, statistical fluctuations in the atmospheric muon rate, de-
spite the small average muon contribution to the DOM rate of
16Hz, distort the significance spectrum considerably.
Rate [Hz]
160180 200 220240260280 300 320340360
1
10
2
10
3
10
4
10
5
10
Data
Gauss fit
Lognormal fit
Fig.8. Rate distribution of a typical standard efficiency DOM
taken over 29 consecutive days. Each measurement corresponds
to 0.5 s integration time. Gaussian and lognormal fits are shown.
5.3. Background Significance Distribution
For purely uncorrelated background and high statistics, the sig-
nificance ξ is expected to be Gaussian distributed with width
σ = 1 (see Sect. 4.2). As can be seen in Fig. 9, the measured
distribution is broaderthan expected and can be fairly well fitted
by a Gaussian with width σ = 1.27. The broadening increases
with the size of the detector and has reached σ = 1.43 with 79
operatingstrings. This broadeningis due to non-Poissonianfluc-
tuations in the number of hits deposited by atmospheric muons:
highlyenergeticmuonsor muonbundlesclusteringin time leave
correlated hits that will in general pass the χ2cut. In the offline
analysis,onecanpartlyremovethiseffectasthenumberofmuon
induced coincident hits in neighboring DOMs is recorded for
all triggered events. For the 79 (40) string configuruation, the
broadening is thus reduced to σ = 1.06 (1.05). As this section
describes the results of the online analysis, we do not apply this
correction in the discussions below.
An effective significance threshold of ξ = 6.0 provides an
internal trigger for testing the system one to two times per day,
while a threshold at ξ = 7.1 satisfies the SNEWS requirement
of one false backgroundtrigger approximatelyonce per 10 days.
The Gaussian curve shown in Fig. 9 predicts one false back-
ground trigger within ten years at a threshold at ξ = 11. These
thresholds are also depicted in Fig 12. The entries at ξ = 8 and
ξ = 9.5 are due to test runs with artificial light sources.
10

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
ξSignificance
-10-50510
1
10
2
10
3
10
4
10
5
10
6
10
Fig.9. Significance distribution in 0.5s binning for a detector
uptime of 556 days with 22 and 40 strings deployed. The two
outliers at ξ= 8 and 9.5 occured during test runs employing arti-
ficial light. The dashed line shows a Gaussian fit with σ = 1.27.
5.4. Future improvements
Further optimizations may be applied to the data acquisition and
analysis in the future, e.g. by incorporatinga more sophisticated
method to remove correlated noise, by excluding the bin-by-bin
contributionof measured cosmic ray muon hits to the online rate
measurement, by storing time stamps of all hits in case of a sig-
nificant alarm to e.g. improve on the timing resolution, and by
employing temporal templates in likelihood or cross-correlation
studies.
5.5. Systematics
There are three types of systematics relevant to this paper. The
first type has to do with time dependentchanges in the noise rate
in all or a subset of DOMs that can mimic a supernova signal.
Theseincludehighvoltagevariations,longtermtrendssuchpho-
tomultiplieraging,weathereffectsandotherexperimentaleffecs.
For the supernova monitoring, realtime analysis as well as trig-
gering, longterm trends are accounted for by calculating the rate
expectation values ?ri? and their standard deviation ?σi? from a
rolling average of the noise rate for each DOM over 300s on ei-
ther side of the time of interest as was described in Sect. 4.2).
The second type affects our understanding of the overall sensi-
tivity of the detector. Ice properties, the wave length dependent
quantum efficiency and the DOM thresholds all fall under this
category. The third type is due to our current knowledge of rele-
vantcross sections, the distanceto supernovae,the neutrino-type
dependentluminosityand energy,as well as oscillationeffects in
the star and in the Earth.
Detector Stability and Environmental Effects
The detector behaves very stably under normal operation.
Periods during drilling, tests with artificial light sources and pe-
riods with data acquisition problemsas well as a few noisy mod-
ules are excluded (see Sect. 5.1) from the analysis. As discussed
in 5.2, annual variations as well as shorter term modulations
in the atmospheric muon flux change the observed rates which,
however, are tracked by the rolling average. As hits from muons
penetrating the detector are recorded simultaneously by the data
acquisition system, they can be subtracted from the supernova
rate measurements offline. Overall, the uncertainty on the super-
novasensitivityassociatedwiththedetectorstabilityisestimated
to be small (1.6%).
The data were checked for other external sources of rate
changessuchasseismic activityandvaryingmagneticorelectric
fields as tracked by magnetometers and riometers at the South
Pole. Only magnetic field variations show a slight, albeit in-
significant, influence on the rate deviation of −1.3 · 10−6Hz/nT.
The influence is 30 times lower in IceCube than in AMANDA
due to a wire mesh µ metal shielding in IceCube DOMs.
Ice Properties and Sensitivity of the Detector
Dust and air bubbles in the natural ice medium cause photons
to scatter, while dust and the ice itself determine the absorp-
tion length. The range of ice densities ρice = (919.6 ± 1.6)
kg/m3(Price et al., 2002) reflects the 0.4% density decrease
due to the temperature increase between (1.4–2.4) km depth.
Scattering dominates in the shallow ice above 1400m and pos-
sibly in the ice of the hole around the DOMs, which refreezes
soon after deployment. Uncertainties in the optical properties of
the hole ice are estimated to affect the effective volume deter-
mination by < 1%. More important are the uncertainties in the
description of ice properties of the Antarctic glacier.
The distributions of the photon arrival times and number
of photons received at AMANDA modules from artificial light
sources were used to derive the scattering length at different
depths. Pulsed and continuous LED and laser sources give com-
plementary measurements. The measurements of ice properties
are consistent to within 6% of each other including statistical
and systematic uncertainties both for the scattering and absorp-
tion measurements. The information on photon propagations is
stored in tables contributing an estimated 1% uncertainty due
to the finite binning. Our knowledge of ice properties and cor-
responding simulation methods continue to improve. Variation
of DOM optical sensitivites and effects of the photomultiplier
threshold on single photo-electron pulses lead to an ≈10% un-
certainty. We assumed a (7±3)% loss of light due to cables that
shadow the photomultipliersurface.The effects discussed in this
paragraph add up to an overall 12% uncertainty.
The track length of a positron or electron, including that of
secondarieswith kineticenergiesabovetheCherenkovthreshold
of 0.272 MeV, depends linearly on the initial lepton energy. The
statistical uncertainty of the GEANT-4 calculation, including a
systematic difference between electrons and positrons, is 0.3%.
The implementations of low energy electromagnetic processes
have been cross checked between GEANT and NIST ESTAR-
ICRU37 compilations. Good agreement has been found in par-
ticular for electron ranges (Amako et al., 2005). NIST quotes a
(2–5)% systematicuncertaintyontheirimplementationofelec-
tromagnetic cross sections in the energy range relevant to super-
novae. Event to event statistical fluctuations in the track length
and in the number of Cherenkov photons (≈ 2%) are negligible
when investigating the ensemble of all DOMs.
External Sources of Systematics
The estimated uncertainties of the cross sections are listed in
Table 1. Those associated with oxygen scattering processes are
large and difficult to assess. Due to the strong energy depen-
dence of16O neutrino cross sections, the impact of this uncer-
tainty depends on the energy spectra of particular models and
the assumed oscillation scenarios. Processes involving only νe
11

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
Table 3. Summary of statistical and systematic uncertainties.
Source of SystematicsUncertainties
stat. (% )
1.6
-
∼0.9
1.3
-
-
-
-
-
sys. (%)
-
0.2
5
∼12
3
<1
<1
∼[3,20]
∼ [0,-8]
Rate deviation in sliding average within 600 s buffer
Ice density as function of depth
Mean track length of electrons and positrons ¯ x of e±in ice
Uncertainty of the effective volume Veff
Uncertainty on the effect of artifical deadtime on the signal
Uncertainty of the inverse beta-decay cross section
Uncertainty of the electron scattering cross sections
Uncertainty of the oxygen scattering cross section
Angle dependent MSW oscillation in the mantle and core of the Earth
γ for photons
Notes. Estimated uncertainties on the total observed rate of all neutrino and anti-neutrino flavors. The earth effect strongly depends on the model
and the neutrino angle of incidence. The uncertainty due to oxygen scattering depends on the neutrino energies and the neutrino channel. Assuming
oxygen cross sectionsare known withinafactor of two, ratesmay change between 3%(Garching model) and 20%(black hole model). For electron
neutrino scattering alone, oxygen scattering accounts for about 40% of the rate in the Garching model, with correspondingly large uncertainties.
scattering are particularly affected. The total systematic uncer-
tainty from detector effects and cross sections on the total rate of
all neutrinos (electron neutrinos) is ≈ 14% (25%).
The distance to stars in our Galaxy is typically known to
25% accuracy (Scheffler & Elsasser, 1998). However, the dis-
tance of a supernova can in principle be measured by interpret-
ing its light curvewith an accuracyof (5– 10)% (Eastman et al.,
1996). Unfortunately, a considerable fraction of supernovae oc-
curring in our Galaxy may be obscured by dust at optical wave-
lengths.
The uncertainties in the supernova collapse models are large
and difficult to assess. The νerate from the neutronization burst
is largely independentof the progenitormass; the corresponding
uncertaintiesare estimated to be around10%; uncertainties aris-
ing from neutrino oscillations are estimated to be below 5% for
a normal hierarchy (Kachelriess et al., 2005).
Oscillations in the Earth strongly depend on the incoming
neutrino direction and may lead - depending on the neutrino
hierarchy - to a maximal rate decrease of 8% and 3% during
the cooling phases of the Lawrence Livermore and Garching
models, respectively. The differences between various oscilla-
tion scenarios may be as large as 30% or even 50% in the case
of black hole formation.
6. Performance Simulations
In this section we will discuss the capability of IceCube to char-
acterize details of the core collapse of massive stars and of the
supernova remnant, as well as the insights IceCube may pro-
vide into the propertiesof neutrinosand their interactions. There
remain significant uncertainties in our understandingof the neu-
trino emission from supernova explosions, necessitating com-
parisons between several models to map the parameter space. In
order to illustrate IceCube’s performance, we will refer to spe-
cific models chosen to span the possible range of supernovapro-
genitor masses and neutrino energy spectra. We will also refer
to more speculative models in order to demonstrate IceCube’s
high statistical precision in the detection of modulations of the
neutrino light curve from astrophysical effects.
When discussing the complete accretion and cooling phase
extending to 15s, we refer to recent O-Ne-Mg core mod-
els (H¨ udepohl et al., 2010) and the older Lawrence-Livermore
model (Totani et al., 1997) as examples with low and high
progenitor star masses. The calculations consider only the
radial dimension as a parameter. When discussing the first
800 milliseconds of the burst we also refer to the Garching
model (Kitaura et al., 2006) as an example for calculations with
sophisticated transport mechanisms. Because it assumes only
half of the initial star mass, (8 – 10)M⊙ instead of 20M⊙, it
predicts fewer neutrinos than the Lawrence-Livermoremodel.
In order to be compatible with other studies, we will usu-
ally show experimentally predicted neutrino light curves for
distances of 10 kpc, roughly corresponding to the center of
our Galaxy. Depending on the model for the supernova precur-
sor distribution, between 44% (Ahlers et al., 2009) and 53%
(Bahcall & Piran, 1983) of all core collapse candidate stars
in the Milky Way are expected to occur within this distance.
About 90% of all supernovae are predicted to occur within 15.4
kpc (Mirizzi et al., 2006) to 17.5 kpc (Ahlers et al., 2009) dis-
tance from the Earth.
In the study of star matter oscillation effects, we restrict our-
selves to the comparisonsof thethree scenariosA-C for neutrino
hierarchy and θ13mixing angles that were introduced in Sect. 2.
Forsome comparisons,we also show distributionswith star mat-
ter oscillations turned off.
All simulations are performed for the final IceCube array
with 4800 standard and 360 high efficiency DOMs. We assume
that 2% of the DOMs are excluded from the analysis, either be-
cause they are not workingor they giveunstable rates. The back-
groundnoise was accountedfor in two different way. For the de-
termination of the significance and galaxy coverage, the simu-
lated signal was randomized assuming a Poissonian distribution
and added to noise data taken from experimental measurements
and analyzed with the real-time reconstruction programs. For
the simulation and comparision of various models, we added the
calculated and randomized signal rates to the noise floor drawn
froma Gaussian with mean value and standarddeviationderived
from data.
Due to correlated pulses from radioactive decays and atmo-
spheric muons, the measured sample standard deviation in data
taken with 79 strings is ≈ 1.3 and ≈ 1.7 times larger than the
Poissonian expectation for 2 ms and 500 ms bins, respectively.
It is possible to subtract roughly half of the hits introduced by
atmospheric muons from the total noise rate in the offline anal-
ysis, as the number of coincident hits in neighboring DOMs is
recorded for all triggered events. We apply this correction to all
12

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
Monte Carlo analyses describedin this section,as this procedure
lowers the standard deviationto (1.24−1.32)??
Unless noted otherwise, we will use a likelihood ratio
method to determine the range within which models can be
distinguished. From sets of several thousand test experiments,
we will typically determine limits at the 90% confidence level,
while requiring that the tested scenario is detected in at least
50% of the cases. Note that the ranges obtained should be inter-
preted as optimal as we assume that the model shapes are per-
fectly known and only the overall flux is left to vary; we also
disregard the possibility that multiple effects, such as matter in-
duced neutrino oscillations and neutrino self-interactions, could
co-exist and thus may be hard to disentangle.
iri, slightly de-
pendent on the binning.
6.1. Expected Supernova Signal
Evaluating Eq. 5 one obtains the rate spectra of Fig. 10 for a
supernova at 10kpc distance. With a maximal signal-over-noise
ratio of ≈ 55 for the Lawrence-Livermore model, the neutrino
burst can clearly be detected with IceCube. Also, the still hy-
pothetical accretion phase lasting from (0 - 0.5)s can be sep-
arated from the subsequent cooling phase with high statistical
precision. The study of the cooling phase is limited by the pho-
tomultipliernoise in particular for the case of the light O-Ne-Mg
model by H¨ udepohl et al. (2010).
Time Post-Bounce [s]
0123456789
DOM Hits (50ms binning)
70
75
80
85
90
95
100
3
10
×
Lawrence-Livermore
O-Ne-Mg 1D
Fig.10. Expected rate distribution at 10kpc distance for the
Lawrence-Livermore model (dashed line) and O-Ne-Mg model
by H¨ udepohl et al. (2010) with the full set of neutrino opacities
(solid line).The 1σ-band corresponding to measured detector
noise (hatched area) has a width of about ±330counts.
The oscillation scenario B for an inverted neutrino mass hi-
erarchy shows the largest signal for the Lawrence-Livermore
and Garching models because energetic ¯ νxwill oscillate into ¯ νe,
harden their spectrum and thus increase the detection probabil-
ity. The scenario without any oscillation is presented as a ref-
erence and leads to the weakest signal. Scenario A (normal hi-
erarchy) and Scenario C (very small θ13 < 0.09◦) are hard to
distinguish due to their very similar effect on neutrino mixing.
Clear differences between the oscillation scenarios in abso-
lute rate and shape appear in Fig. 11. Assuming that the model
shapes are known but not necessarily the overall normalization,
the invertedhierarchycanbe distinguishedfromthe nullhypoth-
esis of a normal hierarchy up to distances of 16 kpc.
6.2. Significance and Galaxy Coverage
The simulation of an expected signal from a supernova within
the Milky Way has to take into accountthe numberof likely pro-
genitor stars in the Galaxy as a function of the distance from
Earth. The expected significances of supernova signals accord-
ing to the Lawrence-Livermore model for three oscillation sce-
narios are shown in Fig. 12. For this particular model, the sig-
nificances for the 4s and 10s binning turn out to be approx-
imately 20% and 50% lower than for 0.5s, respectively. For
1
10
100
1000
010 203040506070
Significance
Distance [kpc]
Milky Way (center)
Milky Way (edge)
LMC
SMC
0.1y−1false trigger rate
SNEWS trigger threshold
internal trigger threshold
no oscillation 0.5 s
normal hierarchy 0.5 s
inverted hierarchy 0.5 s
Fig.12. Significance versus distance assuming the Lawrence-
Livermore model. The significances are increased by neutrino
oscillations in the star by typically 15% in case of a normal hi-
erarchy (Scenario A) and 40% in case of an inverted hierarchy
(Scenario B). The Magellanic Clouds as well as center and edge
of the Milky Way are marked. The density of the data points
reflect the star distribution.
the graph, the supernova progenitor distribution predicted by
Bahcall & Piran (1983) was used. For the Magellanic Clouds,
which contain roughly 5% of the stars in the Milky Way, a uni-
form star distribution along the diameters of the galaxies was
assumed for simplicity.
IceCube is able to detect supernovae residing in the Large
Magellanic Cloud (LMC) with an average significance of (5.7±
1.5) σ in a 0.5s binning, assuming the Lawrence-Livermore
model. The uncertainty reflects different oscillation scenarios.
Supernovae in the Small Magellanic Cloud (SMC) can be de-
tected with an average significance of (3.2 ± 1.1) σ and will in
general not trigger sending an alarm to SNEWS, as indicated by
a horizontal line in Fig. 12. IceCube will observe supernovae in
the entire Milky Way with at least a significance of 12 at 30kpc
distance.
6.3. Onset of Neutrino Production
The analysis of the deleptonization peak that immediately fol-
lows the collapse is of considerable interest, since its magnitude
and time profile are rather independent of the initial star mass
and of the nuclear equation of state; the variation is estimated
by (Keil et al., 2003) to be around 6%. Thus the electron neu-
trino luminosity may be used as a standard candle to measure
the distance to the supernova.
As the deleptonizationpeak lasts for only 10ms, the data are
evaluatedin the finest available time binningof2ms, as depicted
in Fig. 11. The deleptonization signal is detected by the elastic
νe+e−→ νe+e−reaction with a cross section times the number
13

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
Time Post-Bounce [s]
00.511.522.53
DOM Hits (20ms binning)
28000
30000
32000
34000
36000
38000
40000
42000
No Oscillations
Scenario A (NH)
Scenario B (IH)
Time Post-Bounce [s]
-0.100.10.20.30.40.50.60.7
DOM Hits (20ms binning)
29000
30000
31000
32000
33000
34000
No Oscillations
Scenario A (NH)
Scenario B (IH)
Time Post-Bounce [s]
-0.04-0.0200.02 0.040.06
(2ms binning)
e
ν
Expected DOM Hits aused by
c
0
10
20
30
40
50
60
70
80
90
No Oscillations
Scenario A (NH)
Scenario B (IH)
Time Post-Bounce [s]
-0.04-0.0200.02 0.040.06
DOM Hits (2ms binning)
2700
2800
2900
3000
3100
3200
3300
3400
3500
No Oscillations
Scenario A (NH)
Scenario B (IH)
Fig.11. Top: Expected rate distribution at 10kpc supernova distance for oscillation scenarios A (normal hierarchy) and B (inverted
hierarchy).Fluxes andenergiesin the left plot are takenfromthe Lawrence-Livermoremodelandin the right plot fromthe Garching
model using the equation of state of Lattimer & Swesty (1991). Scenario C (not shown) is almost indistinguishable from Scenario
A. The case of no oscillation is given as a reference. Bottom: Expected average signal rate distribution at 10kpc distance in finest
2ms binning for Scenarios A and B using the Garching model; the unlikely case of no oscillation is given as a reference. The
left plot shows the expected νeinduced signal. As can be seen from the right plot, the signal is no longer apparent, once the large
contribution due to the inverse beta decay and the expected DOM noise are added. The 1σ-bands corresponding to measured
detector noise (hatched area) have a width of about ± 215counts for a 20ms binning and ±70counts for a 2ms binning.
of targets ≈ 50 times smaller than for the ¯ νe+ p → e++ n in-
teraction. As the ¯ νeflux rises rapidly following the collapse, the
deleptonization peak remains almost completely hidden, espe-
cially when neutrinos oscillate in the star. In this case the subtle
structure may be resolved only for distances d ≤ 2kpc.
Largely independently of the model, each oscillation sce-
nario shows a characteristic slope of the rate increase around
the deleptonization peak. Quantifying this by a series of several
thousand simulations for the Garching and Lawrence-Livermore
models and considering oscillation Scenarios A-C and the case
of no oscillation, it is possible to establish the inverse hierar-
chy (Scenario B) w.r.t. the normal hierarchy (Scenario A) with
90% C.L. for distances d ≤ 6kpc (corresponding to 21% of
all progenitor stars, when accounting for the effect of spiral
arms (Ahlers et al., 2009)).
6.4. Shock Waves
For an inverted hierarchy (Scenario B), the rate distribution
should reveal the effects of forwardand backward movingshock
waves traveling through the collapsing star during the cooling
phase, (3 – 10) s after bounce. Assuming the specific model
of Tom` as et al. (2004) (see Fig. 13), scenarios with a static den-
sity profile and one forward shock wave can be distinguished at
90% C.L. up to distances of 13 kpc; the distance reduces to 10
kpc in a scenario with one forward and one reverse shock wave
(not shown).
6.5. Quark Star and Black Hole Formation
IceCube is particularly well suited to study fine details of the
neutrino flux as function of time. As an example, Fig. 14 shows
a simulation based on the prediction of Dasgupta et al. (2010)
for the formation of a quark star. The model predicts a sudden
spike in the ¯ νeflux lasting for a few ms while the neutron star
turns to a quarkstar; the time of the QCD phase transition can be
determinedwith sub-ms accuracy.The likelihood ratio test gives
a deviation larger than 5 σ from the hypothesis of no quark star
formation for distances up to 30 kpc. Height and shape of the
14

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IceCube Collaboration: R. Abbasi et al.: IceCube Sensitivity for Low-Energy Neutrinos from Nearby Supernovae
Time Post-Bounce [s]
345678910 1112
DOM Hits (500ms binning)
715
720
725
730
735
740
745
3
10
×
No Star Matter Effect
Static Density Profile
Forward Shock
Fig.13.Effectof a forwardmovingshockwave appliedto super-
nova at 10 kpc distance, modelled according to the Lawrence-
Livermore model assuming an inverted hierarchy and θ13> 0.9◦
was assumed. A forward shock wave can be distinguished from
a static density profile and the case of no star matter effect. The
1σ-band corresponding to measured detector noise (hatched
area) has a width of about ±1150counts.
Time Post-Bounce [s]
0.05 0.10.15 0.20.25 0.30.350.40.45
DOM Hits (2ms binning)
3000
4000
5000
6000
7000
No Oscillations
Scenario A (NH)
Scenario B (IH)
Fig.14. Comparison of the neutrino light curve with quark-
hadron phase transition for a 10 M⊙progenitor at 10 kpc dis-
tance. Three neutrino oscillation scenarios are shown (see leg-
end). The observation of the sharp ¯ νe induced burst, 257 ms
< t <261 ms after the onset of neutrino emission, would con-
stitute direct evidence of quark matter. The hatched 1σ-band
correspondingtodetectornoisehasa widthofabout±70counts.
peak depend on the neutrino hierarchy. Scenarios A and B can
be distinguished at 90% C.L. up to distances of 30 kpc.
Fig. 15 shows a simulation based on the prediction
of Sumiyoshi et al. (2007) for the formation of a black hole fol-
lowing a collapse of a 40 solar mass progenitor star. Neutrinos
reach energies up to 27 MeV (νeand ¯ νe) and 40 MeV (νµand
ντ), carry a correspondingly large detection probability and thus
produce very clear evidence for the formation of the black hole
after 1.3 s, when the neutrino emission is expected to fade ex-
ponentially (not realized in the simulation). For Fig. 15, a hard
equation of state (Shen, 1998) was chosen, leading to black hole
formation after 1.3 s. This corresponding drop can be identi-
fied at higher than 90% C.L. for all stars in our Galaxy and the
Magellanic Clouds.
Time Post-Bounce [s]
0 0.20.40.6 0.811.2 1.4
DOM Hits (2ms binning)
4000
6000
8000
10000
12000
14000
No Oscillations
Scenario A (NH)
Scenario B (IH)
Fig.15. Expected neutrino signal from the gravitational collapse
of a non rotating massive star of 40 solar masses into a black
hole at 10 kpc distance for a hard equation of state (Shen, 1998)
following Sumiyoshi et al. (2007). The 1σ-band corresponding
to detectornoise(hatchedarea) has a widthof about±70counts.
6.6. Neutrino Hierarchy Sensitivity and Rate Summary
The number of standard deviation with which normal and in-
verted ν hierarchies (Scenarios A and B) can be distinguished
are plotted in Fig. 16 as function of the supernova distance for
selected models. The values represent the optimal cases when
model shapes (but not necessarily the absolute fluxes) are per-
fectly known. Table 4 lists the number of neutrino induced pho-
Distance [kpc]
σ]
Hierarchy Sensitivity [
ν
1
10
100
σ3
σ5
Livermore
Quark Star
ONe-Mg 1d
Garching WH 2d
Black Hole LS
0 5 10 15 20 25 30
Fig.16. Number of standard deviation with which scenarios A
(normal hierarchy) and B (inverted hierarchy) can be distin-
guished in at least 50% of all cases as function of supernova
distance for some of the models listed in Table 4. A likelihood
ratio method was used assuming known model shapes.
ton hits that would be recorded by IceCube on top of the DOM
noise for various supernova models. Note that the number of ex-
pected signal hits scales with 1/distance2; the dependence of the
detection significance as function of distance can be read from
Fig. 12.
15