Estimating Flight Characteristics of Anomalous
Unidentiﬁed Aerial Vehicles in the 2004
Nimitz Encounter †
Kevin H. Knuth 1,2,∗, Robert M. Powell 2, and Peter A. Reali 2
1Department of Physics, University at Albany (SUNY), Albany, NY 12206, USA
2Scientiﬁc Coalition for UAP Studies (SCU), Fort Myers, FL 33913, USA;
email@example.com (R.M.P.); firstname.lastname@example.org (P.A.R.)
† Presented at the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in
Science and Engineering, Garching, Germany, 30 June–5 July 2019.
Published: 16 December 2019
A number of Unidentiﬁed Aerial Phenomena (UAP) encountered by military, commercial,
and civilian aircraft have been reported to be structured craft that exhibit ‘impossible’ ﬂight
characteristics. We consider the 2004 UAP encounters with the Nimitz Carrier Group off the coast
of California, and estimate lower bounds on the accelerations exhibited by the craft during the
observed maneuvers. Estimated accelerations range from 75
to more than 5000
with no observed
air disturbance, no sonic booms, and no evidence of excessive heat commensurate with even the
minimal estimated energies. In accordance with observations, the estimated parameters describing the
behavior of these craft are both anomalous and surprising. The extreme estimated ﬂight characteristics
reveal that these observations are either fabricated or seriously in error, or that these craft exhibit
technology far more advanced than any known craft on Earth. In the case of the Nimitz encounters
the number and quality of witnesses, the variety of roles they played in the encounters, and the
equipment used to track and record the craft favor the latter hypothesis that these are technologically
Keywords: UAP; UAV; UFO
Unidentiﬁed Aerial Phenomena (UAPs) partially identiﬁed as being unknown anomalous aircraft,
referred to as Unidentiﬁed Anomalous Vehicles (UAVs) or Unidentiﬁed Flying Objects (UFOs), have
been observed globally for some time [
]. Such phenomena were studied ofﬁcially by the United
States Air Force in a series of projects: Project Sign (1947), Project Grudge (1949) and Project Blue
Book (1952–1969) [
]. Other nations, such as Australia, Brazil, Canada, Chile [
], Denmark, France,
New Zealand, Russia (the former Soviet Union), Spain, Sweden, the United Kingdom, Uruguay, and
the Vatican have also conducted studies, or are currently studying, UAPs [
]. In December of 2017 it
was revealed that the United States government had been studying UAPs through at least one secret
program called the Anomalous Aerospace Threat Identiﬁcation Program (AATIP) [
], and that there
have been times at which United States Naval pilots have had to deal with nearly daily encounters
with UAVs [
]. These unidentiﬁed craft typically exhibit anomalous ﬂight characteristics, such as
traveling at extremely high speeds, changing direction or accelerating at extremely high rates, and
hovering motionless for long periods of time. Furthermore, these craft appear to violate the laws of
physics in that they do not have ﬂight or control surfaces, any visible means of propulsion apparently
Proceedings 2019,33, 26; doi:10.3390/proceedings2019033026 www.mdpi.com/journal/proceedings
Proceedings 2019,33, 26 2 of 11
violating Newton’s Third Law, and can operate in multiple media, such as space (low Earth orbit), air,
and water without apparent hindrance, sonic booms, or heat dumps .
The nature, origin, and purpose of these UAVs are unknown. It is also not known if they are
piloted, controlled remotely, or autonomous. If some of these UAVs are of extraterrestrial origin, then it
would be important to assess the potential threat they pose [
]. More interestingly, these UAVs have the
potential to provide new insights into aerospace engineering and other technologies [
]. The potential
of a serious threat as well as the promise of advancements in science and engineering, along with our
evolving expectations about extraterrestrial life are important reasons for scientists to seriously study
and understand these objects [
]. We carefully examine a series of encounters in 2004 by pilots
and radar operators of the Nimitz carrier group, and estimate lower bounds on their accelerations. We
demonstrate that the estimated accelerations are indeed extraordinary and surprising.
2. Nimitz Encounters (2004)
For a two week period in November of 2004, the U.S. Navy’s Carrier Strike Group Eleven (CSG-11),
which includes the USS Nimitz nuclear aircraft carrier and the Ticonderoga-class guided missile cruiser
USS Princeton, encountered as many as 100 UAVs. We estimated the accelerations of UAVs relying
on (1) radar information from USS Princeton former Senior Chief Operations Specialist Kevin Day;
(2) eyewitness information from CDR David Fravor, commanding ofﬁcer of Strike Fighter Squadron 41
and the other jet’s weapons system operator, LCDR Jim Slaight; and (3) analyses of a segment of the
Defense Intelligence Agency-released Advanced Targeting Forward Looking Infrared (ATFLIR) video.
The following descriptions of the Nimitz encounters were summarized from the more detailed study
published by the Scientiﬁc Coalition for UAP Studies (SCU) .
2.1. Senior Chief Operations Specialist Kevin Day (RADAR)
An important role of the USS Princeton is to act as air defense protection for the strike group.
The Princeton was equipped with the SPY-1 radar system which provided situational awareness of
the surrounding airspace. The main incident occurred on 14 November 2004, but several days earlier,
radar operators on the USS Princeton were detecting UAVs appearing on radar at about 80,000+ feet
altitude to the north of CSG-11 in the vicinity of Santa Catalina and San Clemente Islands. Senior
Chief Kevin Day informed us that the Ballistic Missile Defense (BMD) radar systems had detected
the UAVs in low Earth orbit before they dropped down to 80,000 feet [
]. The UAVs would arrive
in groups of 10 to 20, subsequently drop down to 28,000 feet with a several hundred foot variation,
and track south at a speed of about 100 knots [
]. Periodically, the UAVs would drop from 28,000 feet
to sea level (approx. 50 feet), or under the surface, in 0.78 seconds. Without detailed radar data, it is
not possible to know the acceleration of the UAVs as a function of time as they descended to the sea
surface. However, one can estimate a lower bound on the acceleration by assuming that the UAVs
accelerated at a constant rate halfway and then decelerated at the same rate for the remaining distance
The data consisted of the change in altitude
) and the
, where the goal was to estimate the acceleration,
. The dominant
source of uncertainty in altitude was due to the observed variation in altitude among the observed
UAVs, which was on the order of 200 to 300 ft .
In the ﬁrst analysis, we assigned a joint Gaussian likelihood,
for the measured altitude
change and the duration of the maneuver. Since the altitude change and the duration are independently
measured, the joint likelihood is factored into the product of two likelihoods, and one can marginalize
over the duration of the maneuver to obtain a likelihood for the altitude y
Proceedings 2019,33, 26 3 of 11
P(y|a,I) = Z∞
dt P(y|a,t,σy,I)P(t|t0,σt,I), (3)
where the symbol
represents the fact that these probabilities are conditional on all prior information.
Assigning Gaussian likelihoods we have that
P(y|a,I) = Z∞
dt exp "−1
The integrand is the exponential of a quartic polynomial in
, which was solved numerically. Assigning
a uniform prior probability for the acceleration over a wide range of possible accelerations results in a
posterior that is proportional to the likelihood (5) above resulting in a maximum likelihood analysis,
which gave an estimate of a=5600 +2270
−1190 g, as illustrated in Figure 1A.
As a second analysis, we employed sampling for which the change in altitude and the elapsed
time were described by Gaussian distributions with
respectively. The most probable acceleration was 5370
while the mean acceleration was 5950
Minimum Acceleration (g)
Minimum Acceleration (g)
4000 6000 8000
2000 4000 80006000 10000
An analysis of Senior Chief Day’s radar observations. (
) The posterior probability indicates
the maximum likelihood estimate of the acceleration to be 5600
) The accelerations obtained
by sampling resulted in the most probable acceleration of 5370
(red lines) while the mean
acceleration is 5950 g (black dotted line).
With acceleration estimates in hand, we obtained a ballpark estimate of the power involved to
accelerate the UAV. Of course, this required an estimate of the mass of the UAV, which we did not have.
The UAV was estimated to be approximately the same size as an F/A-18 Super Hornet, which has a
weight of about 32, 000
, corresponding to 14, 550
. Since we want a minimal power estimate, we
took the acceleration as 5370
and assumed that the UAV had a mass of 1000
. The UAV would
have then reached a maximum speed of about 46, 000
during the descent, or 60 times the speed
of sound, at which point the required power peaked at a shocking 1100
, which exceeds the total
nuclear power production of the United States by more than a factor of ten. For comparison, the
largest nuclear power plant in the United States, the Palo Verde Nuclear Generating Station in Arizona,
provides about 3.3 GW of power for about four million people .
Proceedings 2019,33, 26 4 of 11
2.2. Commander David Fravor (PILOT)
On Nov. 14, 2004, CSG-11 was preparing for training exercises. Two F/A-18F Super Hornets were
launched from the Nimitz for the air defense exercise to be conducted in an area 80–150 miles SSW of
San Diego. Both planes, with call signs “FastEagle01” and “FastEagle02”, had a pilot and a weapons
system operator (WSO) onboard. VFA-41 Squadron Commanding Ofﬁcer David Fravor was piloting
FastEagle01 and LCDR Jim Slaight was the WSO of FastEagle02. CDR Fravor and his wingman were
headed for the Combat Air Patrol (CAP) point, which is given by predeﬁned latitude, longitude and
altitude coordinates, where they would conduct the training exercises.
About a half-hour after take-off, Senior Chief Day operating the SPY-1 radar system on the
Princeton detected UAVs entering the training area. The training exercise was delayed and FastEagle01
and FastEagle02 were directed to intercept a UAV at a distance of 60 miles and an altitude of 20,000 feet.
As the F-18s approached merge plot, which is the point at which the radar could not differentiate the
positions of the F-18s and the UAV, Fravor and Slaight noticed a disturbed patch of water, where it
appeared as if there was a large object, possibly a downed aircraft, submerged 10 to 15 feet below the
surface. As they observed the disturbance from 20,000 ft, all four pilots spotted a white UAV, shaped
like a large cylindrical butane tank, or a Tic-Tac candy, moving erratically back and forth, almost like a
bouncing ping-pong ball making instantaneous changes in direction without changing speed. The
Tic-Tac UAV was estimated to be about the size of an F-18, about 40–50 feet in length and 10–15 feet
wide, but had no apparent ﬂight surfaces or means of propulsion, and its movement had no apparent
effect on the ocean surface as one would expect from something like rotor wash from a helicopter.
34 5 6M inimum Acceleration (g)
200 600 1000 1400 1800
An analysis of CDR Fravor’s encounter based on a Truncated Gaussian distribution
) of Fravor’s visual acuity and a Truncated Gaussian distribution (1
) of elapsed
time. A. Gaussian distribution of distances based on the visual acuity distribution. B. The distribution
of accelerations has a maximum at 150 +140
−80 g (red lines) and a mean of 550 g (black dotted line).
Fravor started a descent to investigate while his wingman kept high cover. As Fravor circled
and descended, the UAV appeared to take notice of him and rose to meet him. The F-18 and the UAV
circled one another. When Fravor reached the nine o’clock position, he performed a maneuver to close
the distance by cutting across the circle to the three o’clock position. As he did so, the Tic-Tac UAV
], p.12) across Fravor’s nose heading south. Fravor said that the UAV was gone within
a second. As a comparison, Fravor noted that even a jet at Mach 3 takes 10 to 15 seconds to disappear
from sight ([
], p.11). LCDR Slaight described the UAV as accelerating as if it was “shot out of a riﬂe”
and that it was out of sight in a split second. (, p. 12).
The engagement lasted ﬁve minutes. With the Tic-Tac gone, the pilots turned their attention
toward the large object in the water, but the disturbance has disappeared. The two FastEagles returned
to the Nimitz, without sufﬁcient fuel to attempt to pursue the Tic-Tac. On their way back, they received
a call from the Princeton that the Tic-Tac UAV was waiting precisely at their CAP point. Senior Chief
Day noted that this was surprising because those coordinates were predetermined and secret. Given
that the CAP point was approximately
away, the probability of selecting the CAP point out
Proceedings 2019,33, 26 5 of 11
of all the locations within the 60 mile radius, to within a one mile resolution (slightly more than the
resolution of the radar system), is
P(x|I) = 1
11310 =0.0088%, (6)
discounting the altitude. It appears that the Tic-Tac UAV intentionally went to that location, although
it is not clear how this would be possible.
To obtain a lower bound on the acceleration, we assume that the UAV exhibited constant
acceleration so that the distance traveled was given by
during the elapsed time. The length of the Tic-Tac UAV was estimated to be about 40
with a cross
sectional width of about
. Given that the acuity of human vision is about
at its narrowest, would be out of sight at a maximum distance of
. It is difﬁcult to know what Fravor’s acuity was given the viewing conditions. For
this reason, we model the acuity conservatively as a truncated Gaussian distribution with a peak at
. The truncation at
resulted in a discontinuity in the distribution of the
distances (Figure 2A), which peaks around 2.25 mi.
The elapsed time is modeled as a Gaussian distribution with a mean of 1
for positive values of time. The resulting acceleration distribution was a skewed distribution of
accelerations (Figure 2B) with a most probable acceleration of 150
, indicated in the ﬁgure by
the red vertical lines and a mean acceleration of about 550
indicated by the black vertical dotted line.
Note that this is a lower bound, probably far below the observed acceleration if the UAV accelerated
brieﬂy as if “shot out of a riﬂe” and then traveled at a constant speed.
2.3. ATFLIR Video
Upon returning to the Nimitz, CDR Fravor requested that a crew equipped with the ATFLIR pod
obtain videos of the Tic-Tac UAV. Two F/A-18Fs were launched under the guidance of an E-2 Hawkeye
airborne radar plane. The two planes separated in search of the UAV, with one plane heading south
toward the CAP point where the UAV was last seen on radar. That plane picked up a contact 33 miles
to the south on the Range While Search (RWS) scan. This Tic-Tac UAV was ﬁlmed using the ATFLIR
system, and the video was released to the public as the “Nimitz video” (Figure 3A).
We examined the last 32 frames of the Nimitz video in which the Tic-Tac UAV accelerated to
the left and the targeting system lost lock. The video frame rate was 29.97
. As the UAV
accelerates the image of the UAV becomes elongated and blurred. If the shutter speed was known, then
this information could be used to better estimate the speed of the craft. This could be accomplished
by treating the shutter speed as a model parameter, but such analysis is beyond the scope of this
project. Instead, we concentrated on tracking the position of the right edge of the UAV and using those
positions to estimate the kinematics. The left edge of the UAV was also estimated in the ﬁrst frame to
provide some information about the range,
, to the UAV given that that UAV was estimated to be
about 40 feet in length. However, since the orientation was not known, this is modeled as a uniformly
distributed unknown angular parameter
, which allowed one to at least put an upper
bound on the range zo.
To estimate the position of the right edge of the craft in each frame (Figure 3A), the row of pixels
for which the UAV has a maximum intensity was examined. The pixel intensities along that row at the
Proceedings 2019,33, 26 6 of 11
right edge of the UAP were ﬁt (maximum likelihood method with a Student-t likelihood) to half of
a Gaussian curve. The center position of the Gaussian plus the standard deviation was used as the
position of the right side of the UAV for that frame (Figure 3B).
Horizontal positions of the UAV are related to the pixel coordinates by noting that the entire ﬁeld
of view (approximately
) corresponds to an angular ﬁeld of view of 0.7
narrow (NAR) mode, which is indicated in the upper left hand corner of the video. At the range,
the UAV this results in the proportion
Xscal e =FOVpix /2
is in units of
. The ATFLIR has a zoom feature that can change
the ﬁeld of view. In the Nimitz video frames analyzed, the zoom is ﬁrst set to unity in the NAR mode
so that the angular ﬁeld of view is 0.7
. However, at frame 16, the zoom changes to two, so that the
angular ﬁeld of view in the NAR mode changes to 0.35
. This appears as a discontinuity in the data
(‘+’ symbols) illustrated in Figure 3C.
Relative Pixel Position
0 20 40 60
0 0.2 0.4 0.6 0.8 1 1.2
) Frame 19 of the last 32 frames of the Nimitz ATFLIR video. The narrow horizontal and
vertical lines intersecting at the right edge of the UAP image indicate the position of the UAP. (
pixel intensities along a row of the frame are plotted along with the best Gaussian curve ﬁt. The
rightmost edge of the craft is deﬁned as the center position of the Gaussian plus one standard deviation
(indicated by the vertical red line). (
) This is an illustration the data (+), the most probable kinematic
ﬁt (solid curves) to the UAV positions in the Nimitz ATFLIR video, and the residuals (model minus
data) for the model described by (11). Details can be found in Table 1.
We analyzed four different kinematic models using nested sampling, and statistically tested
them by comparing the log Bayesian evidence. We used uniform prior probabilities for the kinematic
parameters as well as a Student-t likelihood function, which is robust to outliers, such as those due to
camera (airplane) motion. Model #1 considers constant acceleration to the left (-
#2 considers constant acceleration both to the left (-
direction) and toward or away from the camera
(z-direction). The forward model provides the position of the UAV as a function of time, where
time of the ith video frame:
Models #1 and #2 (x(ti) = 1
z(ti) = 1
const. accel., (10)
, and Model #1
just considers the UAV’s acceleration in the x-direction (to the left) so that az.
Proceedings 2019,33, 26 7 of 11
Models #3 and #4 describe the kinematics as constant acceleration followed by constant velocity
motion after Frame 15:
Models #3 and #4
x(ti) = 1
x(ti) = 1
2axt152+axt15 (ti−t15) + xofor ti≥t16
z(ti) = 1
z(ti) = 1
2azt152+azt15 (ti−t15) + zofor ti≥t16
, and Model #3
just considers the UAV’s acceleration in the x-direction (to the left) so that az.
The models were analyzed using a nested sampling algorithm [
], which allowed for the
estimation of the logarithm of the Bayesian evidence, logZ, as well as the logarithm of the likelihood,
logL, and mean estimates of the model parameters. The analysis was performed for
and was run until the change in logZ from successive iterations was less than 10
, ensuring a
reliable estimate of the log evidence. Tests were performed to ensure that the trial-to-trial variations in
parameter estimates were within the estimated uncertainties.
The results of the nested sampling analysis are listed in Table 1. The uncertainties in the logZ
estimates (not listed) were on the order of one or less. Model 4, which describes the motion of the UAV
as a constant acceleration to the left and away from the observer for the ﬁrst 15 frames (approximately
), is the most probable solution with acceleration components of
for a net acceleration of about 75.9
. The residuals indicate that a more
precise model would consist of multiple episodes of acceleration during the maneuver. This was
observed in SCU’s analysis [
] where the accelerations were estimated to vary from around 40 to 80
Kinematic Models for the Nimitz Video Given the log evidence (logZ), Model 4 (
) is most
probable with a net acceleration of 75.9 ±0.2 g.
Model logZ LogL ax(g)az(g)xo(m)zo(m)
Model 1 −253, 640 −253, 614 −71.1 ±0.7 – −15.40 ±0.04 119, 700 ±1200
Model 2 −236, 950 −236, 287 7.564 ±0.002 99.994 ±0.005 −13.36 ±0.04 12, 193 ±1
Model 3 −53, 282 −53, 261 −40.2 ±3.8 – −4.02 ±0.05 49, 700 ±4800
Model 4 −52,084 −52,031 −35.64 ±0.08 67.04 ±0.18 −3.89 ±0.05 43,870 ±110
A more detailed analysis would involve modeling the motion of the UAV more precisely by
modeling the pixel intensities on the video frames themselves. By considering the shutter speed, the
blurring of the UAV image due to its motion would provide more information about its speed. In
addition, the “change points” at which the accelerations changed could be treated as model parameters
allowing for a more precise description of the UAV’s behavior.
In this paper, we have worked under the assumption that these UAPs were physical craft as
described by the pilots. The fact that these UAPs exhibited astonishing ﬂight characteristics leaves
one searching for other possible explanations. One very clever explanation suggested by one of the
reviewers was that these UAPs could have been generated by the intersection of two or more laser or
maser beams ionizing the air, which could create a visual image, an infrared image, as well as a radar
reﬂective region possibly explaining much of the observations.
While such an explanation could explain the visual, infrared and radar observations, it would not
be able to explain either the suborbital radar returns from the ballistic missile defense (BMD) radar
systems on the Princeton before the UAPs dropped to 80, 000
, or the sonar returns when the TicTac
UAPs went into the ocean [
], both of which are not as well substantiated or documented as the
Proceedings 2019,33, 26 8 of 11
More importantly, the distribution of the UAPs ranged from over 100 miles to the north over
Catalina Island to about 70 miles to the west. This would require an array of widely distributed and
coordinated lasers situated on multiple ships or aircraft. However, it is known that there were no other
ships or airplanes in the area. In addition, the fact that the UAP reacted to CDR Fravor’s maneuvers
would require that radar be used to track the F-18s so that the laser-produced imagery could react to
them. However, any such radar frequencies being used in the area would have been detected by the
Princeton, the E-2 Hawkeye, and the F-18s themselves.
If any such system were being secretly tested against CSG-11, one would expect it to mimic
real-life events, such as an enemy aircraft, drone, or missile launch. But the UAPs and their behavior
were nothing like this. Furthermore, such powerful lasers might endanger the planes or personnel
if anything went wrong in the testing, and the fact that the pilots were forced to take evasive
] reveals that they were being put in harms way. One wouldn’t need to test a system in
this manner, and if such a test did take place it would very likely have been illegal. Furthermore, such
an explanation would have difﬁculty explaining the almost daily encounters experienced by pilots in
the Roosevelt Carrier Group both off the coast of Virginia and during military operations in the Persian
], or earlier encounters, such as that by Lt. Bethune in 1951, two years before the invention of
the maser and nine years before the invention of the laser, which was analyzed in the extended version
of this paper .
We have carefully considered a set of encounters between the Nimitz CSG-11 and UAPs of
unknown nature and origin. Much of the information available consisted of eyewitness descriptions
made by multiple trained witnesses observing in multiple modalities including visual contact from
pilots, radar, and infrared video. While fabrication and exaggeration cannot be ruled out, the fact that
multiple professional trained observers working in different modalities corroborate the reports greatly
minimizes such risks.
The analysis aimed to estimate lower bounds on the acceleration. This was found by assuming
that the UAVs accelerated a constant rate. We worked to obtain conservative estimates by assigning
liberal uncertainties. It was found that the minimum acceleration estimates, ranging from about 70
to well over 5000
, far exceeded those expected for an aircraft (Table 2). For comparison, humans
can endure up to 45
with no injurious or debilitating effects, but this limit decreases with
increasing duration of exposure . For durations more than 0.2 s the limit of tolerance decreases to
25 g and it decreases further still for longer durations .
Summary of Estimated Accelerations ranging from about 75
to over 5300
Modalities refer to Multiple Pilots Visual Contact (Vs), Radar (R), Infrared Video (IR).
Case Detection Modalities Kinematic Model Figure Min. Acceleration
Day R (1) Figure 1B 5370 +1430
Fravor R,Vs (7) Figure 2C 150 +140
ATFLIR R,Vs,IR (11) Figure 3C 75.9 ±0.2 g
These considerations suggest that these UAVs may not have been piloted, but instead may have
been remote controlled or autonomous. However, it should be noted that even equipment can only
handle so much acceleration. For example, the Lockheed Martin F-35 Lightning II has maintained
structural integrity up to 13.5
]. Missiles can handle much higher accelerations. The Crotale NG
VT1 missile has an airframe capable of withstanding 50
and can maintain maneuverability up to
]. However, these accelerations are still only about half of lowest accelerations that we have
estimated for these UAVs. The fact that these UAVs display no ﬂight surfaces or apparent propulsion
mechanisms, and do not produce sonic booms or excessive heat that would be released given the
hundreds of GigaWatts of power that we expect should be involved, strongly suggests that these
Proceedings 2019,33, 26 9 of 11
anomalous craft are taking advantage of technology, engineering, or physics that we are unfamiliar
with. For example, the Tic-Tac UAV dropping from 28,000
to sea level in 0.78
involved at least
of energy (assuming a mass of 1000
), which is equivalent to about 100 tons of TNT, or
the yield of 200 Tomahawk cruise missiles, released in
of a second. One would have expected a
catastrophic effect on the surrounding environment. This does not rule out the possibility that these
UAVs have been developed by governments, organizations, or individuals on Earth, but it suggests
that these UAVs and the technologies they employ may be of extraterrestrial origin. That being said,
it should be strongly emphasized that proving that something is extraterrestrial would be extremely
difﬁcult, even if one had a craft in hand.
The purpose of this paper is to focus on the ﬂight kinematics of these UAVs with the aim of
building up a body of scientiﬁc evidence that will allow for a more precise understanding of their
nature and origin.
As such, it is difﬁcult to draw any useful conclusions at this point. We have characterized the
accelerations of a number of UAVs and have demonstrated that if they are craft then they are indeed
anomalous, displaying technical capabilities far exceeding those of our fastest aircraft and spacecraft.
It is not clear that these objects are extraterrestrial in origin, but it is extremely difﬁcult to imagine
that anyone on Earth with such technology would not put it to use. Moreover, observations of similar
UAPs go back to well before the era of ﬂight [
]. Collectively, these observations strongly suggest that
these UAVs should be carefully studied by scientists [9–13].
Unfortunately, the attitude that the study of UAVs (UFOs) is “unscientiﬁc” pervades the scientiﬁc
community, including SETI (Search for Extraterrestrial Intelligence) [
], which is surprising, especially
since efforts are underway to search for extraterrestrial artifacts in the solar system [
], in particular,
on the Moon, Mars, asteroids [
], and at Earth-associated Lagrange points. Ironically, such attitudes
inhibit scientiﬁc study, perpetuating a state of ignorance about these phenomena that has persisted for
well over 70 years, and is now especially detrimental, since answers are presently needed [31–34].
This work builds on analyses performed independently by K.H.K. and by R.M.P., P.A.R.
and others [
]. For this work, K.H.K. determined the methodology, developed the software, performed the
analysis, and wrote the original draft. R.M.P. and P.A.R. both reviewed and edited the work verifying correctness.
Funding: This research received no external funding.
The authors thank Kevin Day for discussing his experiences during the 2004 Nimitz
encounters and patiently answering our numerous questions. KHK is especially grateful for the comments
and suggestions made by John Skilling, as well as the careful and thoughtful recommendations made by
Udo von Toussaint.
Conﬂicts of Interest:
The authors declare no conﬂict of interest. Editorial decisions, including the decision to
publish this work, were made by the MaxEnt 2019 Organizers.
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