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Five Decades of Chromospheric Activity in 59 Sun-like Stars and New Maunder Minimum Candidate HD 166620

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We present five decades of chromospheric activity measurements in 59 Sun-like stars as time series. These include and extend the 35 yr of stellar chromospheric activity observations by the Mount Wilson Survey (1966--2001), and continued observations at Keck by the California Planet Search (1996--). The Mount Wilson Survey was studied closely in 1995, and revealed periodic activity cycles similar to the Sun's 11 yr cycle. The California Planet Search provides more than five decades of measurements, significantly improving our understanding of these stars' activity behavior. We have curated the activity measurements in order to create contiguous time series, and have classified the stellar sample according to a predetermined system. We have analyzed 29 stars with periodic cycles using the Lomb-Scargle periodogram, and present best-fit sinusoids to their activity time series. We report the best-fit periods for each cycling star, along with stellar parameters (T$_{eff}$, log(g), v$sin(i)$, etc.) for the entire sample. As a first application of these data, we offer a possible Maunder minimum candidate, HD 166620.
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Five Decades of Chromospheric Activity in 59 Sun-like Stars and New Maunder Minimum Candidate
HD 166620
Anna C. Baum ,1, 2 Jason T. Wright ,1, 3, 4 Jacob K. Luhn ,1, 3, 5 and Howard Isaacson 6, 7
1Department of Astronomy & Astrophysics, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA, 16802, USA
2Department of Physics, Lehigh University, Bethlehem, PA, 18015, USA
3Center for Exoplanets and Habitable Worlds, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA, 16802,
USA
4Penn State Extraterrestrial Intelligence Center, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA, 16802,
USA
5Department of Physics & Astronomy, UC Irvine, Irvine, CA, 92697, USA
6Department of Physics and Astronomy, San Francisco State University, San Francisco, CA, 94132, USA
7Department of Astronomy, UC Berkeley, Berkeley, CA, 94720, USA
ABSTRACT
We present five decades of chromospheric activity measurements in 59 Sun-like stars as time series.
These include and extend the 35 yr of stellar chromospheric activity observations by the Mount Wilson
Survey (1966–2001), and continued observations at Keck by the California Planet Search (1996–). The
Mount Wilson Survey was studied closely in 1995, and revealed periodic activity cycles similar to the
Sun’s 11 yr cycle. The California Planet Search provides more than five decades of measurements,
significantly improving our understanding of these stars’ activity behavior. We have curated the
activity measurements in order to create contiguous time series, and have classified the stellar sample
according to a predetermined system. We have analyzed 29 stars with periodic cycles using the Lomb-
Scargle periodogram, and present best-fit sinusoids to their activity time series. We report the best-fit
periods for each cycling star, along with stellar parameters (Tef f , log(g), vsin(i), etc.) for the entire
sample. As a first application of these data, we offer a possible Maunder minimum candidate, HD
166620.
Keywords:
1. INTRODUCTION
1.1. Long-term studies of stellar magnetic activity
The study of stellar activity has evolved from the ini-
tial discovery of the 11 yr sunspot cycle to the continued
observation of hundreds of stars and their chromospheric
activity, both variable and invariable. Baliunas et al.
(1995, hereafter B95) suggested that long-term observa-
tions of activity would reveal a plethora of cycling stars.
The broad Ca II H (396.8 nm) and K (393.4 nm) spec-
tral lines that are prominent in Sun-like stars, or, stars
with Sun-like mass, radius, and temperature, and show
emission peaks in their line cores, which trace stellar ac-
tivity (B95;Wright et al. 2004). The strength of the
emission core is correlated with the level of heating in
the chromosphere by magnetic fields. A common metric
of the strength of these emission cores is the S-value,
which was originally developed by the Mount Wilson
Observatory HK Project (Duncan et al. 1991) and is
roughly proportional to the equivalent width of the emis-
sion peak. The S-value can be calculated by summing
counts of the Ca II H and K passbands and normalizing
by the counts in the violet and red continuum bands. S
is given by
S=αH+K
V+R,(1)
where αis a calibration factor used to align the S-value
with the established Mount Wilson scale (B95;Wright
et al. 2004). Very active stars will have S0.5 (Duncan
et al. 1991).
Another metric for studying activity is the fraction
of the observed stars’ total luminosity emitted by the
chromosphere in the Ca II H and K lines. This value of
R0
HK is useful when comparing the activity level of stars
of varying spectral type (Noyes et al. 1984).
There are many reasons for the continued study of
chromospheric activity, considering both its causes and
observable effects on stars. For example, the study of ac-
tivity is important for the detection of exoplanets. Ra-
dial velocity (RV) detection has proven to be a valuable
arXiv:2203.13376v1 [astro-ph.SR] 24 Mar 2022
2Baum et al.
tool in the discovery of planets beyond our solar system
(Mayor & Queloz 1995), and has benefited from a better
understanding of stellar activity.
There are a number of sources of systematic and star-
induced error in measuring RVs. A significant contrib-
utor of error due to the star is “jitter,” caused by in-
homogeneities on the surface of a star, pulsations, and
variations in the amount of convective blueshift. Jitter
causes variations in measured radial velocity, and can
even falsely indicate the presence of an orbiting planet
(Wright 2005;Luhn et al. 2020, and references therein).
A primary source of jitter is stellar magnetic activity,
the subject of our study.
Long-term records of chromospheric activity allow for
characterization of the multiyear timescales of magnetic
fields and therefore a better understanding of their ori-
gin. Stellar magnetic fields are induced by a magne-
tohydrodynamic dynamo mechanism, involving interac-
tions between plasma flows and magnetic fields. At the
surface, these fields cause sunspots, flares, faculae, and
other observable features (Brun et al. 2015). Studies of
the Sun allow us to place constraints on the dynamo
origin of stellar magnetic fields, and observations of ac-
tivity in Sun-like stars can be a prime resource for addi-
tional information about stellar dynamos. Establishing
a relationship between activity and rotation (activity
cycles) plays a key role in developing knowledge on dy-
namos, and by extending our observations of Sun-like
stars, we enhance our ability to develop a stellar dynamo
model. In conjunction with asteroseismology, observa-
tions of stellar activity proxies are key to forming a more
cohesive understanding of the origin of stellar magnetic
fields and improving models of stellar dynamos.
Our most well-understood star, the Sun, underwent a
period known as the Maunder minimum, during which
activity was very low, and perhaps constant rather than
periodic (Eddy 1976). The search for other stars that
exhibit a halt in cyclic activity is ongoing, though it is
difficult with the lack of long-term records of S-value in
other stars. While our own Sun has been studied and ob-
served for centuries (Egeland et al. 2017), our records for
other stars are span, at most, multiple decades. Ongo-
ing observations provide extended time series, allowing
for the continued search for stellar Maunder minimum
candidates.
Activity monitoring was pioneered by the Mount Wil-
son Observatory HK Project (described in Section 1.2).
The success of the program led to similar surveys to
study the effects and implications of chromospheric ac-
tivity.
The Solar-Stellar Spectrograph (SSS) at Lowell Ob-
servatory was directly inspired by the Mount Wilson
survey. It began monitoring the Sun and many Sun-like
stars in 1994 (Hall et al. 2007). Henry et al. (1995) uti-
lized the Vanderbilt/Tennessee State robotic telescope
to conduct a photometric survey of 66 potentially active
late-type stars. This work discovered 41 stars exhibit-
ing flux variability and conducted spectroscopic obser-
vations at Kitt Peak National Observatory, compiling
stellar parameters as well stellar activity metrics.
Saar & Donahue (1997) investigated the effect of chro-
mospheric activity on low-amplitude radial velocity vari-
ations. This work provided early stellar activity insight
into the now-buzzing field studying exoplanet detection.
With the study of activity came the analysis of relation-
ships between the rotational and activity cycle periods,
as well as other stellar parameters. Saar & Branden-
burg (1999) utilized the Mount Wilson survey and an ex-
tended simple dynamo model (Brandenburg et al. 1998)
to predict magnetic dynamo cycle periods in stars. Their
high-quality photometric data were later used to inves-
tigate how Ca II emission line fluxes depend on rotation
and effective temperature (ohm-Vitense 2007).
Based at Lowell and Fairbord Observatories, Lock-
wood et al. (2007) produced 13-20 yr time series for 32
Sun-like stars, examining the relationship between pho-
tospheric and chromospheric variability. Using the SSS
and the Tennessee State University Automatic Photo-
metric Telescope, Hall et al. (2009) examined the corre-
lations between activity and variability for 28 stars over
a time span of 14 years. They also identified several
Maunder minimum analog candidates. We discuss the
SSS more thoroughly in Section 5.2.
The Mount Wilson S-values have been used for several
analyses in the last 10 years. Ol´ah et al. (2009) analyzed
10 yr of photometric and Ca II H&K observations of
20 active stars and concluded that stellar cycles are typ-
ically multiple and changing. We see similar complex
cycles in our own work. More recently, they were ana-
lyzed and compared with measurements from the SSS in
order to confirm the S-index scale placement of the Sun
and lead to the accurate evaluation of S-values in Sun-
like stars (Egeland et al. 2017). Egeland et al. (2017)
also closely examined the offsets between HKP-1 and
HKP-2 data for the Sun, a subject we discuss in Sec-
tion 2. Mount Wilson and its follow-up surveys provide
a wealth of information because of the sample size and
long time baseline. It is crucial for identifying long-term
trends, periodic and otherwise.
We have combined the records of two activity sur-
veys to obtain an extensive record of stellar activity over
time. The time periods previously studied were short
term and do not offer the same results as long-term stud-
ies. As with the solar cycle, many stars have identifiable
3
activity cycles of varying periods, typically about 10 yr.
Our sample of Sun-like stars consists of primarily spec-
tral type G, with some F and K stars. Their masses
range between a minimum of 0.7 Mand an outlying
maximum of about 2.3 M, with the majority of the
sample lying between 0.7 and 1.5 M. Effective temper-
atures are contained between 4900 K and 6000 K. Two
stars with masses around 5.0 and 6.0 Mare discussed
in Section 2.3 as having unphysical parameters and are
not considered in this description. As expected given
our baseline, several stars exhibit decade-long activity
cycles for which we have observed multiple cycles. As
surveys continue, activity cycles with much longer pe-
riods will be more easily identified. We have combined
several surveys of stellar activity, and present curated
long-term time series for 59 well-observed stars.
1.2. Mount Wilson Program
The Mount Wilson program was started in 1966 by
Olin Wilson to study stellar chromospheric activity, and
has since become one of the most extensive records of
stellar chromospheric activity. From 1966 to 1977, the
project utilized the “HKP-1” photometer: a photoelec-
tric scanner on the coud´e focus of the 100 inch telescope.
Measurements continued in 1977 on the “HKP-2” pho-
tometer: a new photomultiplier on the Cassegrain focus
of the 60 inch telescope (B95). Measurements on HKP-2
allowed the increase in both sample size and frequency of
observations. B95 estimated the long-term precision of
the measurements to be 1.2%, later verified by Richard
Radick(Radick & Pevtsov 2018a) to be 1-2%. The sam-
ple size of stars continuously increased to the current
record of almost 2300 stars, 35 of which were observed
through 2001 (Radick & Pevtsov 2018b,c).
In search of periodic activity cycles, B95 examined
chromospheric activity of 111 stars from 1966 to 1995,
utilizing the data from the Mount Wilson survey (Wil-
son 1978;Vaughan et al. 1978;Duncan et al. 1991).
They analyzed the comprehensive time series of mea-
sured S-values for each of these stars, identifying pe-
riodic activity variations in a significant portion of the
sample. Stars with irregular or no variation in activity
level were also identified. Some stellar activity candi-
date cycles could not be verified because an observation
time equivalent to two full periods is necessary for con-
firmation. Periodograms were used to approximate the
periods of the “cycling” stars, and the time series of
each star was presented, along with each star’s annually
averaged mean S-value, color index, and spectral type.
B95 concluded that chromospheric activity depended
heavily on stellar mass and age, and follows an evolu-
tionary time scale. Activity was also noted to increase
with B-V, primarily because S-value is sensitive to pho-
tospheric temperature. Longer intervals of observation
can reveal cycles of stars with longer periods, as well as
possible Maunder minima or inconsistent periodicity.
1.3. California Planet Search
The California Planet Search (CPS) and its prede-
cessor surveys compose a modern program based at the
Keck and Lick Observatories that primarily targets stars
thought to be good planet-search targets. It captures
some of the brightest, oldest F, G, and K dwarfs visi-
ble from Keck, and notably omits very active stars. CPS
uses the High Resolution Echelle Spectrometer (HIRES)
at Keck Observatory (Vogt et al. 1994) and the Hamilton
spectrograph (Vogt 1987) with the Shane 3 m telescope
and the 0.6 m Coude Auxilary Telescope at Lick Obser-
vatory, both echelle spectrometers with high resolution
(Wright et al. 2004). The Keck Observatory uses an im-
age rotor to align the slit axis with the elevation axis
to keep the efficiency of measurements high. Our work
includes only the measurements taken at Keck because
of its lower measurement uncertainties.
Wright et al. (2004) and Isaacson & Fischer (2010)
published CPS S-value activity catalogs, representing
distinct data-analysis efforts on data taken with two dif-
ferent HIRES detectors. After 2004, the HIRES spec-
trometer was upgraded with a new CCD to a achieve
an RV precision of 1 ms1. We label the pre-2004 de-
tector “HIRES-1” and the post-2004 upgraded detec-
tor “HIRES-2.” Wright et al. (2004) recorded the S-
values of about 700 stars, and Isaacson & Fischer (2010)
recorded the S-values of more than 2600 stars. In addi-
tion to these two sets of published data, we also present
new S-value measurements taken at HIRES since the
publication of Isaacson & Fischer (2010), using the same
methods described in that work, creating an even more
complete record of activity measurements.
Wright (2004) proposed several conclusions on ex-
amination of long-term activity observations. Wright
suggested that the low-activity and flat stars in the
sample analyzed were primarily composed of subgiants;
we examine this conclusion in the new, extended data.
Wright (2004) was particularly concerned with the
(mis)identification by Baliunas & Jastrow (1990) of low-
activity stars as Maunder minimum candidates on the
basis of their low activity levels. We use the extended
time series to explore Maunder minimum-like events.
2. DATA
2.1. S-Values
We have collected all observations of S-values from
four sources in order to assemble the most complete
4Baum et al.
time series possible from 1966-present. This includes
the compilation and cross-referencing of each data set
from various resources. Once combined, we addressed
and corrected offsets between data sets.
2.1.1. Mount Wilson
The data from the Mount Wilson program for 2300
stars were provided by Radick & Pevtsov (2018b) and
Radick & Pevtsov (2018c) as found on the Harvard
Dataverse. We collected the data for all stars observed
between 1966 and 1995 along with the observations of
35 stars through 2001 appended. We note the change
from HKP-1 to HKP-2 is noted in the plots and our
data tables. The observations made pre-1977, in some
cases, showed clear, large offsets with the more frequent
post-1977 observations, most notably for HD 22072, HD
23249, and HD 217014. We remedied these offsets by
shifting the median of the pre-1977 measurements to
align with the median of the more extensive post-1977
measurements.
2.1.2. Wright et al. 2004
We take HIRES-1 data from 1996 to 2004 from Wright
et al. (2004). For three stars (HD 3795, 10145, and
65583), we find significantly and consistently offset S-
values between Wright et al. (2004) and Mount Wilson
data. Where we felt we could confidently correct these
offsets, we adjusted the Wright et al. (2004) data with a
constant shift to have a consistent median S-value with
the Mount Wilson data set, the most reliable resource we
have on the thorough and consistent observation of S-
values. These offsets can be explained by the calibration
uncertainty between the very different instruments.
A couple of stars (HD 34411, 142267) show possi-
ble large offset due to calibration, but we chose not to
correct them because the more recent data are sparse
enough that the difference could be a real change in the
star’s activity level.
2.1.3. Isaacson & Fischer 2010
We take HIRES-2 data from 2004 to 2010 from Isaac-
son & Fischer (2010). These data are the more precise of
the CPS data sets. Fourteen stars in the post-2010 CPS
data showed a clear offset, much like those seen in the
Wright et al. (2004) data, and were shifted accordingly,
in the same manner.
In the individual stars’ plots, we display two dashed
lines when offsets are applied: one red, showing the orig-
inal median of the data, and one green, showing the
corrected median. HD 10145 is shown in Figure 1as an
example of this formatting.
2.1.4. Post-2010 CPS Data
Figure 1. Initial plots of HD 10145 showed large, consistent
offsets in S-value between pre- and post-2004 CPS data. We
present data with adjusted median S-value.
CPS data dating from 2010 to 2020, serving as an
extension of the Isaacson & Fischer (2010) data, are
included in these measurements. CPS data from 2010 to
present are calculated in the same manner as data prior
to 2010, allowing for continuity in each star’s values from
2005 to present. Note that the use of the C2 decker
to observe bright stars in twilight and also faint stars
through the night causes occasional issues with the S-
values when seeing is poor and the observation is taken
at high air mass. This is addressed in Section 2.5.
2.2. Stellar Parameters
Brewer et al. (2016) provides a source for precise stel-
lar parameters for F, G, and K stars that were unavail-
able to B95. These parameters allowed us to retrieve
effective temperature, rather than color index, for the
future examination of trends in chromospheric activity,
as well as surface gravity, rotational velocity, metallic-
ity, mass, and age. All spectral properties were re-
trieved from the Keck HIRES spectrometer as part of
the CPS. We use [Fe/H] to generalize metallicity; other
abundances can be found in Brewer et al. (2016).
Most spectra from Brewer et al. (2016) had a high
signal-to-noise ratio (S/N), and therefore made obser-
vational uncertainties a negligible contribution to error
in these parameters. Brewer et al. (2016) quantified
and minimized other sources of error, such as model-
ing inconsistencies or limitations, to their best ability.
Because spectral type was not included in the Brewer
et al. (2016) parameters, the spectral types of all stars
in our sample were retrieved from the SIMBAD database
(Wenger et al. 2000).
2.3. Target Selection
We cross-checked the sample of 2326 stars from the
Mount Wilson data with the Brewer et al. (2016) data
set to ensure each star had precise measurements of stel-
lar parameters recorded, which limited the sample to 189
stars.
5
Four stars, including HD 178911B, HD 145958A and
145958B, and HD 219834B were noted to have unphysi-
cal parameters relating to either mass (recorded masses
greater than 3 M) or age (stellar ages greater than the
age of the universe). These results were likely due to
errors in spectroscopic analysis. We have decided to in-
clude these stars regardless, in order to present a more
complete catalog of chromospheric activity in stars.
We combine CPS observations with the Mount Wilson
records, providing a far more extensive record of activity
up to 2020. The time series spans 1966-present, with
some gaps but overall enough data to properly observe
trends in activity over a long time period.
From our sample of 189 stars with Mount Wilson
observations and measured stellar parameters, 59 stars
have significant and classifiable records of S-value from
both Mount Wilson and CPS. Of the 59 stars included
in our sample, 26 were also evaluated in B95.
2.4. Outliers
For the entire length of combined data, single data
point outliers were removed in order to improve anal-
ysis of long-term trends. Any data points points more
than three standard deviations from the mean were re-
moved. We checked to confirm that this process did not
truncate natural variations of stars due to long-term ac-
tivity trends.
In addition to the occasional instrumental and data re-
duction errors, our sigma-clipping outlier rejection may
have removed possible activity peaks or stellar flares.
However, we are interested in the curated time series to
identify low-frequency long-term changes in activity, so
the rejection of flares is not of concern to us. We include
all points rejected due to sigma-clipping in the supple-
mental data with flags labeled ‘sigma’ to indicate their
removal.
Other S-values that were possibly unphysical or due
to instrumental or data reduction error left behind af-
ter the sigma-clipping procedure were removed by hand.
This was limited to stars HD 140144, HD 1461, and HD
182572, each of which had single instance observations of
S-values measurements two or more standard deviations
from the median S-value. For the case of HD 182572, we
also note a high density of points at an S-value around
3σfrom the mean. This particular instance could be fol-
lowed up in future work as a possible activity burst. For
the purposes of long-term trend analysis, these points
are left out of our analysis and can be found in supple-
mental data.
Several stars have instances of recorded S-values of
0.00, which is unphysical, and is indicative of bad data,
most likely resulting from instrumental or calibration er-
rors. These were also removed from the data set. All
points removed due to certain instrumental or calibra-
tion error, such as those with 0.00 S-values or measure-
ments taken with the C2 decker under poor seeing con-
ditions, discussed in Section 2.5, are excluded from the
final data sample entirely, including supplemental ma-
terial, because they are of no use for future analysis.
2.5. Seeing
In the time series, ‘dips’ in S-value were found to con-
sistently occur in post-2004 CPS data, with implemen-
tation of the C2 decker after 2009. Similar to the offsets,
these dips were initially noted in our initial examination
of the time series. We referenced the original obser-
vation log sheets in search of possible causes for these
dips. Using the decker and seeing from CPS observa-
tion records, we determined that the combination of the
C2 decker and poor seeing conditions was correlated to
dipping measurements of S-values. We analyzed the re-
lationship between seeing and S-values for observations
made using the C2 decker, and found the drop off into
dipping S-values occurred at a seeing of 1.5”, and pro-
gressively declined as seeing increased.
In the blue CCD, which contains the Ca II H&K lines,
the spectral orders are closer together than in the mid-
dle and red CCDs. This results in overlapping orders
when the C2 decker is used and the seeing is >1.5”.
Contamination from adjacent orders makes the S-value
measurement unreliable. Therefore, CPS data observa-
tions made using the C2 decker with seeing greater than
1.5” were removed. These adjustments made possible
the curated, contiguous time series presented here.
2.6. Final Sample
Stars without considerable added value were also re-
moved from our final sample. This included any stars
with few observations from either the Mount Wilson or
CPS program. Of the 189 stars, we deem 59 stars to
include significantly improved data from the results pre-
sented in B95. The final sample of stars is fully repre-
sented in Table 2.
Table 1shows the first five lines of the file contain-
ing HD number, S-value observation, Gregorian date of
observation, and instrument used. The complete table
contains the curated observations of all 59 stars in the
sample. We report many significant figures in our table;
however the actual times of observation are all only given
to precision one day for HKP-1 and HKP-2 1977-1979
configuration, up to 0.0001 days for subsequent HKP-2
configurations, 0.0001 days for HIRES-1, and 0.01 days
for HIRES-2. Observations taken with HKP-1 and early
HKP-2 are given arbitrary or inaccurate times of obser-
vation, which correlate to times the star was at very low
6Baum et al.
altitude. We include flags on entries for which precision
is only within one day. For data entries including two
flags–one day precision and sigma-clipped, we separate
them by a forward slash, i.e., ‘1day/sigma’.
Table 1. Curated S-values
Star S-value Time Instrument Flags
1388 0.1533 1992.67864290 HKP-2 none
1388 0.1463 1992.67864822 HKP-2 none
1388 0.1504 1992.67865224 HKP-2 none
1388 0.1552 1992.69221205 HKP-2 none
1388 0.1552 1992.69221738 HKP-2 none
Note—This paper shows only a sample of the full table.
The complete table is available electronically.
3. ANALYSIS
Prior to analyzing each time series, we calculated the
cadence and baseline of observations for each star. The
cadence is denoted by the average number of observa-
tions per year, and baseline is the number of years the
star was observed. For stars with gaps in the records,
we determined the baseline using the earliest and latest
observations recorded. We then calculated and recorded
the median S-value over the total baseline in Table 2.
We separated main-sequence stars and subgiants by sur-
face gravity, defining main-sequence stars as those with
log(g)>4.2 and subgiants as stars with log(g)4.2.
Initial assessment of the S-value over time and was
done by eye using specific criteria, loosely based on those
used in B95. To identify a star as cycling, it must have:
1. clear periodic variation
2. a significant number of observations
3. at least two full periods (preferably)
4. similar and consistent amplitude peaks and
troughs (otherwise labeled as insuf or var)
Other time series classifications included “flat” (no
observed variation in activity), “long” (possible long-
term cycle), “var” (clear, nonperiodic variations),
and “insuf” (insufficient data to make a classifica-
tion). “Insuf” was a heavily used classification, in or-
der to factor out any stars where classification was
unclear. The criteria for these classifications were
based on looser foundations than those from B95, be-
cause our primary goal was more focused on data
presentation, and presenting curated data for pub-
lic use. Fig. Set 2. Activity Time Series
Figure 2. Activity vs. time in 59 Sun-like stars: the com-
plete figure set (59 images) is available in the online journal.
3.1. Periodograms
Similar to B95, we next calculated a periodogram
(Lomb 1976;Scargle 1982) for each time series to as-
sist in classification. We found a period estimate for
each cycling star as that which had the most power in
the periodogram. The period was used to model a sinu-
soid with the same period and approximate amplitude
and phase to best fit and illustrate the activity cycle. A
good example of a well-fit cycle is HD 10476 in Figure 2
We chose this method to generate a simple description
of the apparent activity cycle, but activity cycles are
not strictly periodic (see Figure 3), and certainly not
sinusoidal.
Some stars, such as HD 4628, 101501, 146233, 166620,
and 190406, have variations in their cycle or large gaps in
data collection that result in an unreliable periodogram
analysis. Two of these stars, HD 101501 and 166620,
are discussed in greater detail in section 4.1. For these
stars, which appear to be cycling, as seen in Figure 4,
a period estimate was made by eye in order to produce
a curve of best fit. HD 190406 appears to have a sec-
ondary periodic trend, to which we did not fit a curve.
Additionally, stars with very low-amplitude variability
were not closely analyzed for periodic trends, and are
classified as either ‘flat?’ or variable. All period esti-
mates for stars with cycling activity are listed in Table
2. Stars for which estimates were made by eye rather
than periodogram include a ‘?’ with the value. In accor-
dance with B95, periods are estimated only to one-10th
of a year and are not proposed as precise.
We verify the estimated periods of each star using a
sinusoidal model. This could not always produce a good
fit because stellar activity variation is not strictly peri-
odic; stars cycle with a far more complicated variance
7
(a) Cycling in HD 4628 has a phase inconsistent with our sinu-
soidal model.
(b) Cycling in HD 146233 follows a highly variable trend.
Figure 3.
and are not consistent in period over many cycles. Some
stars could be entering or exiting a Maunder minimum,
discussed in section 4.1, or could have a variance that
appears to be cyclic but is not. Additionally, many of
the stars appear to have a cycle that changes over time.
Ol´ah et al. (2009) examined this behavior in detail. HD
4628 and 146233 are two good examples of this phe-
nomenon.
4. RESULTS
We present a table of the 59 stars in our sample along
with their classification and several other parameters.
We also present the 50 yr time series for these stars,
doubling the length of time series previously presented
by B95.
4.1. HD 166620: A Maunder Minimum Candidate
HD 166620 was one of the 111 stars from B95 that
was classified as cycling, and estimated to have a period
of about 16 yr. With the addition of new observations,
the star now appears to have entered a phase of low,
flat activity. We propose that HD 166620 is a Maunder
minimum candidate.
Unfortunately, it appears that the star’s activity
“turned off” while there was a gap in data collection
and a switch in our data stream from one instrument
to another, between the years of 1995 and 2004. This
transition into what appears to be a Maunder minimum
phenomenon after change in instrumentation is very sus-
picious, so we undertook many checks to confirm that
the change is real.
Inspection of the observation logs at HIRES revealed
no apparent errors or clues in records of this star’s ac-
tivity measurements. Inspection of the data for HD
166620 from HIRES show no reason that the measure-
ments should be inaccurate. The data at face value
argue that the grand minimum is not at a lower level
than the local minima, which would have strong impli-
cations for the nature of magnetic grand minima (they
are merely the end of cycling, not a shutdown of the
dynamo). However, some other stars required offsets
between instruments, so this conclusion is not robust.
The question is then whether Mount Wilson somehow
observed some star other than HD 166620.
There are not a large number of stars bright enough
and of the correct spectral type for such measurements
at Mount Wilson, so a simple transcription error in the
star name or routine pointing error is exceedingly un-
likely. We have no ability to inspect the Mount Wilson
data beyond what appears in the published tables, so we
attempted to deduce the star’s rough position on the sky
from the median date of observations during a season.
We use tau Ceti, HD 10700, to determine typical error
in HKP measurements. The median observation date
corresponds to a sidereal time at midnight of 0h52m for
HKP-1, and 2h 08m for HKP-2. The real R.A. of tau
Ceti is 1h44m, indicating that HKP-1 is off by 52m, and
HKP-2 is off only by 23m.
The median observation date for HD 166620 corre-
sponds to a sidereal time at midnight of 18h39m for
HKP-1, and 19h23m for HKP-2. The R.A. of HD 166620
is 18h10m. This result is very similar to that of tau
Ceti, with a difference of 29m and 1h13m for HKP-1
and HKP-2, respectively. Given the vagaries of seasonal
weather patterns, observing strategies, and the inherent
imprecision of this method, we consider that these val-
ues are all consistent with one another. We are left with
the conclusion that HD 166620 suddenly switched from
cycling behavior to flat in the time between the Mount
Wilson and Keck surveys.
Based on the our visual analysis and curve-fitting, we
estimate the cycle period for this main-sequence star
prior to entering a minimum state to be 17 yr.
With continued monitoring, we hope to capture this
star through its period of minimum activity, and into its
return to an activity cycle. We see evidence of a simi-
lar behavior star exiting a potential Maunder minimum
period in the star HD 101501. This star, while possibly
still exhibiting a low-amplitude cycle, is a good example
8Baum et al.
(a) HD 166620 appears to have entered a Maunder minimum be-
tween its final observations with HIRES-1 and first observations
with HIRES-2.
(b) HD 101501 experienced 10 yr of lower activity, a much lower
amplitude cycle than the rest of its cycle.
Figure 4.
of capturing both the drop from- and return to- cyclic
behavior. There are little recent data from CPS, but
what data we have are consistent with continued cyclic
variation today.
This star appears to have entered a time period of
about 10 yr from 1980 to 1990 where its previously iden-
tified periodic behavior dropped to a low S-value with
little variation. It then returned to its strong, periodic
activity variation.
When attempting to analyze this star using a peri-
odogram, its phase of low-variation activity caused dif-
ficulty. When attempting to fit a curve with our period
estimate made by eye, it became clear that the star,
upon returning to its cyclic behavior, it was out of phase
with its original cycle by 180. For this reason, the time
series was left without a fit.
In the same manner as Shah et al. (2018) with HD
4915, HIRES CPS data can be used to continue monitor-
ing of Maunder minimum candidates as well as identify
more candidates. HD 166620 shows promise for captur-
ing the return from low, constant activity to a cycle.
4.2. Classifications
We have reclassified all stars that B95 previously clas-
sified, and assigned new classifications to 33 stars. Some
of the stars maintained their original period and classifi-
cation, and some were assigned a new classification. Of
the 26 stars that were included in both the B95 cata-
log and this one, 14 stars were given new classifications
based on our criteria.
For example, HD 10700 was originally classified ‘flat?’
and was noted to have possible increasing activity after
1988, but in the CPS data has returned to definitively
flat behavior.
Of the three stars in our sample originally classified
as long, only HD 141004 maintained this classification.
HD 9562 and HD 143761 were reclassified as flat.
5. FUTURE WORK
While our work shows a significant step forward from
B95, there is still a great deal of work to be done in order
to reach a better understanding of stellar activity. As
we continue to lengthen the time series and increase the
sample size of observed stars, activity cycles will become
significantly more observable, and perhaps trends will
become clearer.
5.1. Maunder Minimum Stars
Shah et al. (2018) studied the activity cycle of HD
4915 using CPS data from 2006 to 2018 and discov-
ered a pattern suggesting the beginning of a magnetic
grand minimum. Continued observations are necessary
to confirm this phenomenon and to develop a better un-
derstanding of the Sun’s Maunder minimum phase and
magnetic fields. Our new extended time series of stel-
lar activity will be a great resource for the continued
monitoring and discovery of new Maunder minimum
candidates. Stars that appear to be in Maunder min-
imum should be studied for coronal X-ray and chromo-
spheric emission for comparison in the future when they
return to their cycling state. This will inform studies of
the dynamo and reveal whether Maunder-minimum-like
events are simply extended periods of low-cycle ampli-
tude, or periods of extraordinarily low surface magnetic
field strength. A much more thorough search for these
stars will aid in our understanding of stellar activity and
the patterns, or lack thereof, in chromospheric activity.
5.2. SSS and Other Surveys
Since the Mount Wilson HK Project came to an end,
several other surveys have sparked opportunities for
new research, extensions, and updates from older re-
search, and have continued a path toward understand-
ing of chromospheric activity. SSS at Lowell Observa-
tory was directly inspired by the Mount Wilson Observa-
tory. This spectrograph incorporates both an HK spec-
trograph and echelle, and is an excellent resource for the
observation of chromospheric activity and photospheric
9
variability (Hall et al. 2007). After the termination of
the Mount Wilson survey, SSS was, and continues to
be, an excellent resource to continue research on flux
and chromospheric emissions. There are likely archives
of unpublished data from Lowell Observatory that could
provide additional data for the stars in our sample, and
be added to these time series in the future.
The High Accuracy Radial velocity Planet Searcher
(HARPS) spectrograph is capable of measuring high-
precision Ca II H&K indices in the same manner as the
Mount Wilson survey (Lovis et al. 2011). HARPS will
be integral for understanding the effects of activity and
jitter on radial velocity measurements and the detection
of exoplanets.
In general, more extensive and long-term observations
should be done in the future. A cadence of about 10
observations per year is ideal.
5.3. CPS Continued
The 594 stars that were included in CPS but not the
Mount Wilson surveys were not examined. Luhn et al.
(2020) noted individual stars from this sample with ap-
parent cycles as part of their analysis of RV jitter. An
in-depth analysis of the activity time series of the CPS
sample is forthcoming, and will likely be the next legacy
survey for activity in addition to (and in some cases in
tandem with) the Mount Wilson survey. The contin-
ued observation of these stars is important to increase
the sample size allowing for concrete conclusions about
what affects stellar activity. As an active collaboration
studying nearby stars and planet host stars beyond the
solar neighborhood, CPS will continue to observe these
stars as often as feasible in an effort to search for planets
and further understand stellar activity cycles.
5.4. RV Follow-up Allocation
Stars with high activity have been shown to have in-
creased radial velocity jitter (see Section 1), which ham-
pers the ability to detect small, Earth-like planets e.g.
(e.g., Saar et al. 1998;Santos et al. 2000;Wright 2005;
Isaacson & Fischer 2010). Therefore, the best targets
for RV surveys are the less active stars. It is crucial
that we understand the trends between stellar proper-
ties and stellar chromospheric activity in order to build
expectations for finding low-activity stars a priori. It is
also worth noting that some stars show strong positive
correlations between activity and RVs, some do not, and
some show negative correlations. Trends between activ-
ity and their translation to RVs are not clear. It has
been noted that, like activity, stars experience different
stages of RV jitter as they evolve (Luhn et al. 2020), but
that appears to vary on a star-by-star basis.
5.5. Identification of Trends
The availability of stellar parameters from Brewer
et al. (2016) allows for an examination of how stellar ac-
tivity varies due to particular characteristics, e.g., tem-
perature, surface gravity, mass, etc. Longer-term obser-
vations could shed light on trends that were not clear
based on shorter time series. It is of particular inter-
est to understand what non-cycling main-sequence stars
have in common, as well as what kind of stellar parame-
ters cycling subgiants have. This could provide us with
information deeper than how activity relates to stellar
evolution.
The relationship between effective temperature and
star classification is apparent, as established by B95.
Vaughan & Preston (1980) also identified the tendency
of hSito increase with BV. Effective temperature is
now available to be used in place of BVto investigate
the relationship between activity and stellar evolution.
Wright et al. (2004) showed that the low-activity flat
stars were all subgiants, and we see hints of this in
our own analysis as well, but our sample of subgiants
is small. The subgiants we did study exhibit the low-
est activity levels, with consistently lower loghSithan
main-sequence stars. Many of them are also classified
as flat, though some show evidence of an activity cycle
or variability. Analyzing the activity levels of a larger
sample of subgiants could be more illuminating for this
trend. The role of stellar evolution in affecting activity
cycles of stars still remains unclear.
6. CONCLUSION
We have compiled five decades of curated chromo-
spheric activity measurements, the corresponding time
series and stellar parameters for 59 Sun-like stars, as
well as the period estimates for all cycling stars in our
sample. We have identified one Maunder minimum can-
didate, HD 166620, and access to stellar activity time
series data that will push forward the study of stellar
activity and its related topics.
10 Baum et al.
Table 2. Parameters and Classifications of Program Stars
HD Number S1
med T2
eff log(g) vsin(i) [Fe/H] Mass Age Spectral Baseline4Cadence5Classification
(K) (cm/s2) (km/s) (-) (M) (Gyr) Type3(yrs) hobs/yriBaliunas et al.19956This Paper 7
1388 0.154 5924 4.32 2.30 0.0300 1.05 4.3 G0V 27 10.34 flat
1461 0.158 5739 4.34 1.80 0.160 0.970 4.1 G3V 27 29.44 var
3795 0.155 5379 4.11 1.90 0.540 1.67 11.0 K0V 51 18.11 var flat
4307 0.145 5795 4.05 2.50 0.180 1.12 7.7 G0V 32 8.33 flat
4628 0.223 4937 4.54 0.700 0.250 0.690 11.4 K2.5V 53 35.43 excl-8.4 10.0?
7924 0.210 5136 4.55 0.200 0.160 0.770 8.7 K0.5V 40 19.49 7.2
9562 0.137 5837 4.02 4.20 0.220 1.30 5.0 G1V 52 59.92 long flat
10145 0.150 5650 4.43 1.30 0.0400 1.06 7.3 G5V 40 2.93 flat
10476 0.186 5190 4.51 0.100 0.0300 0.780 8.1 K1V 52 44.86 excl-9.6 10.3
10697 0.149 5600 3.96 1.50 0.130 1.07 7.2 G3Va 41 4.64 flat
10700 0.169 5333 4.60 0.100 0.530 0.990 12.4 G8V 52 48.24 flat? flat
10780 0.273 5344 4.54 0.800 0.0400 0.890 4.4 K0V 37 24.38 ¡7 var
13043 0.149 5859 4.19 2.00 0.0900 1.07 5.0 G2V 26 12.39 flat
20165 0.205 5098 4.51 1.60 0.0100 0.730 7.5 K1V 41 4.21 7.8
22072 0.131 4941 3.46 0.100 0.250 1.14 10.7 K0.5IV 52 18.96 var flat
23249 0.136 5037 3.75 0.100 0.160 1.11 6.7 K0+IV 52 25.58 flat? flat
26965 0.196 5092 4.51 0.500 0.300 0.800 12.8 K0V 52 30.82 excl-10.1 9.9
34411 0.146 5873 4.26 0.100 0.100 1.08 4.8 G1.5IV-V 41 6.01 flat
37124 0.181 5604 4.60 0.300 0.450 1.38 11.6 G4IV-V 41 2.49 var
37394 0.451 5249 4.50 3.00 0.140 0.780 4.1 K1V 37 16.83 poor-4 var
45067 0.141 5940 3.92 5.30 0.0200 1.10 5.0 F9V 53 60.63 flat flat
50692 0.156 5913 4.39 0.100 0.140 1.03 4.7 G0V 41 7.96 long
52711 0.158 5886 4.39 0.100 0.0900 1.07 5.1 G0V 41 9.80 11.0
65583 0.167 5238 4.61 0.100 0.730 0.930 13.3 K0V 41 4.38 flat
72905 0.360 5866 4.50 9.60 0.0200 1.04 1.3 G1.5Vb 41 20.59 var var
75732 0.176 5250 4.36 1.70 0.350 0.710 7.9 G8V 36 19.33 10.9
86728 0.147 5742 4.31 2.40 0.200 1.04 4.9 G3Va 41 6.58 flat
100180 0.163 6002 4.38 1.70 0.0500 1.02 2.0 F9.5V 51 26.38 fair-3.6+12.9 var
101501 0.297 5502 4.52 2.20 0.0400 0.900 3.5 G8V 49 45.67 var 4.2?
115617 0.164 5562 4.44 0.800 0.0400 0.930 7.1 G6.5V 41 44.98 var flat
126053 0.166 5714 4.54 0.100 0.350 1.05 6.5 G1.5V 54 23.47 22? flat
132142 0.169 5145 4.55 0.400 0.410 0.850 12.5 K1V 42 2.75 flat
141004 0.156 5901 4.22 2.00 0.0500 1.15 4.8 G0-V 53 39.35 long long
142267 0.174 5829 4.53 0.100 0.410 1.15 7.5 G1V 35 3.69 flat
143761 0.149 5833 4.29 0.100 0.210 1.19 8.4 G0+Va 54 86.60 long flat
145958A 0.177 5414 4.48 1.60 0.050 1.51 13.8 G9V 25 3.39 7.5
145958B 0.180 5343 4.46 1.70 0.050 1.55 14.7 G9V 25 3.07 var
Table 2 continued
11
Table 2 (continued)
HD Number S1
med T2
eff log(g) vsin(i) [Fe/H] Mass Age Spectral Baseline4Cadence5Classification
(K) (cm/s2) (km/s) (-) (M) (Gyr) Type3(yrs) hobs/yriBaliunas et al.19956This Paper 7
146233 0.170 5785 4.41 1.50 0.0400 0.970 3.3 G2Va 39 25.27 var
152391 0.386 5425 4.51 3.80 0.0100 0.870 4.6 G8.5Vk 53 72.53 excl-10.9 9.1
157214 0.155 5817 4.61 0.100 0.350 1.79 8.0 G0V 41 6.67 flat
159222 0.174 5870 4.41 1.00 0.170 1.01 1.6 G1V 41 7.17 var
166620 0.185 4970 4.51 0.100 0.160 0.760 12.4 K2V 54 32.95 excl-15.8 17.0?
173701 0.193 5337 4.36 2.20 0.290 0.720 5.9 G8V 36 6.33 var
176377 0.179 5877 4.52 0.900 0.210 1.02 2.3 G1V 41 10.29 var
178911B 0.179 5564 4.40 2.20 0.210 5.24 4.3 G5D 24 1.77 var
179957 0.149 5741 4.42 0.100 0.00 2.27 7.9 G3V 41 7.64 flat
182572 0.148 5587 4.15 1.30 0.330 0.980 6.6 G7IV 52 41.26 flat var
185144 0.214 5242 4.56 0.500 0.210 0.810 8.8 K0V 42 49.21 6.2
186408 0.150 5778 4.28 2.30 0.0900 1.03 5.9 G1.5Vb 37 10.37 long
186427 0.152 5747 4.37 1.60 0.0600 1.03 5.6 G3V 38 11.40 long
188512 0.136 5081 3.55 0.100 0.100 1.30 4.4 G8IV 51 25.90 poor var
190360 0.146 5549 4.29 2.10 0.180 0.920 8.2 G7IV-V 53 31.48 flat flat
190406 0.192 5940 4.40 2.30 0.0700 1.02 1.8 G0V 53 48.26 fair-2.6+good-16.9 17.2?
197076 0.179 5810 4.42 0.100 0.0900 0.920 3.4 G5V 42 8.40 var
199960 0.145 5885 4.22 2.60 0.270 1.12 4.0 G1V 34 6.00 var
210277 0.153 5484 4.32 0.100 0.160 0.930 9.6 G8V 38 6.71 flat
215704 0.248 5374 4.48 1.50 0.150 0.840 3.8 K0 37 3.39 var
217014 0.149 5758 4.32 2.00 0.190 1.03 4.7 G2IV 53 31.97 var flat
219834B 0.201 5135 4.48 1.20 0.210 6.37 5.2 K2D 52 25.86 excl-10.0 9.4
1Median S-value of star: calculated in this paper. Excludes sigma-clipped values.
2Brewer et al. (2016) values. Uncertainties: Teff ±25K; log(g)±0.028; vsin(i)±0.7 km/s; [Fe/H] ±0.010. Mass and age were modeled using
these spectroscopic parameters. See Brewer et al. (2016) for additional details.
3Sourced from SIMBAD online database.
4Number of years star was observed from first to final observation: calculated in this paper.
5Average number of observations per year: calculated in this paper.
6For the purposes of our work, we have included the FAP (false alarm probability) grade in the classifications from 1995. The error in period
estimate was not included, but for the stars given a secondary classification, this is noted after the plus sign, e.g., HD 100180 had a primary
classification of period 3.6 yr and fair FAP grade, and a secondary classification of a 12.9 yr period, also given a fair FAP grade.
7All periods listed are rough estimates, and are estimated only to the precision of one-tenth of a year. This follows the same format as B95 period
estimates.
Note—The final two columns compare the classifications from B95, if included in their sample, and the classifications from this work. Following
a similar format as B95, our classifications for cycling stars provide the estimated period. All stars for which a perio dogram analysis was not
sufficient due to variable periodicity or significant gaps in data collection (see Section 3.1), include a “?” following their period estimate. These
estimates were made by eye.
12 Baum et al.
ACKNOWLEDGMENTS
We would like to thank Dr. Fabienne Bastien for her
many contributions toward this work. We would like to
acknowledge Olin Wilson and Sallie Baliunas for their
work on stellar activity and paving the path toward this
work. We would like to thank Arvind Gupta for his work
on twilight contamination. We also thank the anony-
mous referee for their helpful feedback.
The Center for Exoplanets and Habitable Worlds
and the Penn State Extraterrestrial Intelligence Cen-
ter are supported by the Pennsylvania State Univer-
sity and its Eberly College of Science. Jacob Luhn
was supported by the National Science Foundation
Graduate Research Fellowship Program under Grant
No. DGE1255832. The HK Project v1995 NSO and
HK Project v2001 NSO data derive from the Mount
Wilson Observatory HK Project, which was supported
by both public and private funds through the Carnegie
Observatories, the Mount Wilson Institute, and the
Harvard-Smithsonian Center for Astrophysics starting
in 1966 and continuing for over 36 years. These data
are the result of the dedicated work of O. Wilson, A.
Vaughan, G. Preston, D. Duncan, S. Baliunas, and
many others.
Some of the data presented herin were obtained at the
W. M. Keck Observatory, which is operated as a scien-
tific partnership among the California Institute of Tech-
nology, the University of California and the National
Aeronautics and Space Administration. The Observa-
tory was made possible by the generous financial sup-
port of the W. M. Keck Foundation. The authors wish
to recognize and acknowledge the very significant cul-
tural role and reverence that the summit of Maunakea
has always had within the indigenous Hawaiian commu-
nity. We are most fortunate to have the opportunity to
conduct observations from this mountain.
This research has made use of the SIMBAD database,
operated at CDS, Strasbourg, France, and of NASA’s
Astrophysics Data System Bibliographic Services. This
research made use of Astropy,8a community-developed
core Python package for Astronomy (Astropy Collabo-
ration et al. 2013;Price-Whelan et al. 2018).
Facilities:
Software: astropy (Astropy Collaboration et al.
2013;Price-Whelan et al. 2018)
13
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