Within NASA Gap
Normal Skies Are the Control Group
Routine recordings of aircraft, birds, satellites, insects and glitches make unusual detections easier to judge later.
On this page
- Why anomaly only archives are weak evidence
- What ordinary sky traffic teaches a detector
- How baseline records improve later classification
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Introduction
A sky-monitoring system that records only unusual events cannot reliably distinguish genuine anomalies from ordinary objects that merely look unusual under specific conditions. That is why NASA’s 2023 independent study of unidentified anomalous phenomena (UAP) identified the lack of baseline data as one of the major weaknesses in existing evidence, alongside poor calibration, missing metadata and the absence of multiple independent measurements. A detector that continuously records normal skies builds a control group: it shows what aircraft, satellites, birds, insects, clouds, atmospheric effects and sensor artefacts routinely look like from the same location with the same instruments. When an unusual detection occurs later, investigators can compare it against thousands of ordinary observations rather than treating it as an isolated mystery. [NASA Science]science.nasa.govNASA ScienceIndependent Study Team ReportUnidentified Anomalous Phenomena (UAP) are one of our planet's greatest mysteries. Observations…
Why anomaly-only archives are weak evidence
Many UAP collections consist almost entirely of clips that someone judged interesting at the time. This creates a selection problem. Analysts see only the apparent exceptions and never the far larger population of routine events that were ignored or deleted.
Without continuous background recording, important questions cannot be answered:
- Does the same light pattern appear every clear evening when certain satellites pass overhead?
- Does the camera frequently produce identical streaks during humid weather?
- Do nearby insects regularly create large, apparently fast-moving blurred objects?
- Does a particular lens flare occur whenever the Moon reaches a specific position?
If those ordinary events were never recorded, later investigators cannot estimate how common they are. A dramatic-looking video therefore lacks statistical context. NASA explicitly argues that this absence of baseline observations makes it difficult to determine whether a reported event is truly unusual or simply an example of normal sensor behaviour. [NASA Science]science.nasa.govNASA ScienceIndependent Study Team ReportUnidentified Anomalous Phenomena (UAP) are one of our planet's greatest mysteries. Observations…
The problem resembles studying rare diseases without knowing the health of the surrounding population. A single case may appear extraordinary until compared with a large reference dataset.
What ordinary sky traffic teaches a detector
Continuous monitoring produces a catalogue of the environment in which future anomalies will be judged. That catalogue improves both human interpretation and automated classification.
Aircraft become predictable rather than mysterious
Commercial aircraft, helicopters and general aviation traffic generate recurring patterns in optical and infrared imagery. Their brightness changes with altitude, atmospheric conditions, viewing angle and engine temperature.
When detectors archive months of normal observations alongside Automatic Dependent Surveillance–Broadcast (ADS-B) data, they learn how genuine aircraft appear under different conditions. This makes later aircraft misidentifications less likely and reveals the limitations of the sensor itself. The Galileo Project’s commissioning work, for example, used hundreds of thousands of reconstructed aircraft trajectories to establish operational baselines before searching for statistical outliers. Most apparent anomalies proved consistent with ordinary aerial traffic or limitations in the available measurements rather than evidence of unknown technology. [arXiv]arxiv.orgCommissioning An All-Sky Infrared Camera Array for Detection Of Airborne ObjectsNovember 12, 2024…
Birds, insects and biological activity reveal recurring signatures
Small nearby objects frequently create deceptive images.
An insect flying close to a lens can appear much larger than a distant aircraft. Birds can produce irregular flight paths, changing wing shapes and fluctuating brightness that confuse automated detectors.
Computer vision researchers working on flying-object detection repeatedly identify birds as an important source of false positives. Meteor-monitoring projects similarly report that aircraft, insects and other mundane objects dominate initial candidate detections, requiring extensive filtering and classification before genuine meteors remain. [ResearchGate+3Tethys+3Publica]tethys.pnnl.gov3.Read moreA Temporal Boosted YOLO-Based Model for Birds…December 1, 2021 — by H Alqaysi · 2021 · Cited by 37 — This work presents a robust…
Recording these events routinely allows the system to learn what “normal biological clutter” looks like instead of treating every unexpected shape as potentially extraordinary.
Satellites and predictable astronomical events establish timing patterns
Satellite passes, bright planets, meteor showers and changing lunar illumination all recur according to known schedules.
A baseline archive links visual appearance with date, time and observing conditions. If an object with nearly identical brightness and motion appears every few nights at similar times, it becomes part of the site’s normal observational environment rather than an unexplained event.
How baseline records improve later classification
Baseline datasets are valuable because they support comparison rather than speculation.
When a new event is detected, investigators can ask:
- Has this appearance occurred before?
- Under what weather conditions?
- With which camera settings? [arxiv.org]arxiv.orgarXiv Cloud Identification from All-sky Camera Data with Machine LearningarXiv Cloud Identification from All-sky Camera Data with Machine Learning
- During which seasons?
- Was similar air traffic present?
- Did identical image artefacts appear previously?
Instead of evaluating one clip in isolation, analysts compare it with thousands of archived examples collected using identical equipment.
Modern machine-learning systems rely on precisely this principle. Object detectors improve because they are trained on large numbers of correctly labelled examples containing both targets and non-targets. Research on sky monitoring, meteor detection and airborne object recognition consistently treats false-positive examples as essential training material rather than unwanted noise. [arXiv+3PMC+3ResearchGate]pmc.ncbi.nlm.nih.govSky Monitoring System for Flying Object Detection Using 4K…by T Kashiyama · 2020 · Cited by 19 — This study developed a monitoring…
Baselines expose sensor artefacts as well as natural objects
Not every false anomaly originates in the sky.
Long-term archives reveal recurring instrument behaviour, including:
- hot pixels appearing at fixed detector locations;
- compression artefacts during low-light recording;
- autofocus errors;
- lens reflections from bright sources;
- thermal sensor noise;
- cloud-induced contrast changes.
Because these effects recur under similar operating conditions, they become recognisable only after many hours of routine observation.
Observatories that operate all-sky cameras routinely build extensive image archives precisely because weather, cloud cover and instrument behaviour must be characterised before automated decisions can be trusted. Machine-learning systems for cloud identification and observatory safety similarly depend on labelled archives representing ordinary operating conditions rather than isolated unusual frames. [arXiv]arxiv.orgarXiv Cloud Identification from All-sky Camera Data with Machine LearningarXiv Cloud Identification from All-sky Camera Data with Machine Learning
A practical example of the control-group principle
Imagine two observatories recording the same unexplained light.
The first stores only the thirty-second clip containing the light.
The second has six months of uninterrupted recordings from the same calibrated cameras, together with weather logs, aircraft tracking, astronomical conditions and sensor metadata.
The second observatory can immediately determine whether:
- identical events occur weekly;
- nearby insects create matching images;
- aircraft followed comparable paths;
- clouds produced similar reflections;
- camera artefacts repeated under identical settings.
Even if the object remains unidentified, investigators know much more about what it is not. The baseline narrows the range of plausible explanations instead of relying on subjective impressions.
Why this matters for automated instrumented UFO detectors
Within NASA’s broader call for better-calibrated UAP observations, baseline recording serves as the control group against which every future detection is measured. It shifts a detector away from chasing spectacular clips and towards building an evidence-rich observational record.
The most scientifically valuable detector may spend most of its operating life recording completely ordinary skies. Those seemingly uneventful observations establish the frequency and appearance of everyday aerial phenomena, quantify the detector’s own error patterns and provide the reference dataset needed to recognise genuine outliers. Without that ordinary archive, even a striking event cannot be evaluated with confidence because there is no reliable measure of how unusual it actually is. [NASA Science+2NASA Science]science.nasa.govNASA ScienceIndependent Study Team ReportUnidentified Anomalous Phenomena (UAP) are one of our planet's greatest mysteries. Observations…
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Endnotes
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Source: science.nasa.gov
Link: https://science.nasa.gov/wp-content/uploads/2023/09/uap-independent-study-team-final-report.pdfSource snippet
NASA ScienceIndependent Study Team ReportUnidentified Anomalous Phenomena (UAP) are one of our planet's greatest mysteries. Observations...
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Source: arxiv.org
Link: https://arxiv.org/abs/2411.07956Source snippet
Commissioning An All-Sky Infrared Camera Array for Detection Of Airborne ObjectsNovember 12, 2024...
Published: November 12, 2024
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Source: researchgate.net
Link: https://www.researchgate.net/publication/372401700_Automation_of_Meteor_Reduction_Using_Convolutional_Neural_NetworksSource snippet
Automation of Meteor Reduction Using Convolutional...28 Aug 2024 — Airplanes, insects, and other types of noise get mistaken...
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Source: researchgate.net
Link: https://www.researchgate.net/publication/345396957_Deep_Learning_Algorithms_Applied_to_the_Classification_of_Video_Meteor_DetectionsSource snippet
Concerning meteor detection, distinguishing false positives between meteor and non-...Read more...
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Source: pmc.ncbi.nlm.nih.gov
Link: https://pmc.ncbi.nlm.nih.gov/articles/PMC7763826/Source snippet
Sky Monitoring System for Flying Object Detection Using 4K...by T Kashiyama · 2020 · Cited by 19 — This study developed a monitoring...
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Source: arxiv.org
Title: arXiv Cloud Identification from All-sky Camera Data with Machine Learning
Link: https://arxiv.org/abs/2003.11109 -
Source: arxiv.org
Link: https://arxiv.org/abs/2503.18670 -
Source: science.nasa.gov
Link: https://science.nasa.gov/uap/Source snippet
NASA ScienceUAP9 Jun 2022 — The UAP Independent Study shall report on the following questions: What types of scientific data currently co...
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Source: researchgate.net
Title: To accurately locate the meteor
Link: https://www.researchgate.net/publication/375212342_Deep_machine_learning_for_meteor_monitoring_Advances_with_transfer_learning_and_gradient-weighted_class_activation_mappingSource snippet
(PDF) Deep machine learning for meteor monitoringOur new method is able to detect meteors even in images that contain static elements suc...
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Source: researchgate.net
Link: https://www.researchgate.net/publication/395858774_Toward_a_Reliability_Scale_for_Assessing_Reports_of_Unidentified_Anomalous_Phenomena_UAPSource snippet
(PDF) Toward a Reliability Scale for Assessing Reports of...18 Sept 2025 — Unidentified Anomalous Phenomena (UAP) observations have been...
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Title: nasa to release discuss unidentified anomalous phenomena report
Link: https://www.nasa.gov/news-release/nasa-to-release-discuss-unidentified-anomalous-phenomena-report/Source snippet
NASA to Release, Discuss Unidentified Anomalous...NASA commissioned the study to examine UAP from a scientific perspective and create a...
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BirdRecorder's AI on Sky: Safeguarding birds of prey by...25 Aug 2025 — 1.1% false positive: non-bird object recognised as a bird, and...
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Nighttime Cloud Detection, Tracking and Prediction with All...by S Buntin · 2025 · Cited by 1 — This paper presents a novel method for r...
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Source: tethys.pnnl.gov
Title: 3.Read more
Link: https://tethys.pnnl.gov/sites/default/files/publications/YOLO_Based_Model_birds_wind_farms.pdfSource snippet
A Temporal Boosted YOLO-Based Model for Birds...December 1, 2021 — by H Alqaysi · 2021 · Cited by 37 — This work presents a robust...
Published: December 1, 2021
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Source: publica.fraunhofer.de
Link: https://publica.fraunhofer.de/bitstreams/dd478bb1-c147-4f60-8fe4-de4b4dccdd89/downloadSource snippet
Flying Object Detection for Automatic UAV Recognitionby L Sommer · Cited by 59 — Finally, we train a convolutional neural network...
Additional References
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NASA Shares Unidentified Anomalous Phenomena...NASA commissioned the independent study to better understand how the agency can contribut...
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Link: https://www.academia.edu/107167561/UFOs_and_NASA_There_is_No_Reliable_Data_But_We_Still_Want_to_Investigate_ThemSource snippet
UFOs and NASA: There is No Reliable Data, But We Still...UFOs and NASA: There is No Reliable Data, But We Still Want to Investigate Them...
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Unidentified Anomalous Phenomena Independent Study ReportNASA commissioned an independent study team to examine unidentified anomalous ph...
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Link: https://avi-loeb.medium.com/a-new-calculation-on-the-fly-to-the-nasa-uap-study-2dacaf860cacSource snippet
New Calculation on the Fly to the NASA UAP Study - Avi LoebThe NASA Study will examine unclassified data on UAP in an attempt to separate...
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Title: nasa unidentified anomalous phenomena independent study team report
Link: https://ralphbuncheinstitute.org/nasa-unidentified-anomalous-phenomena-independent-study-team-report/Source snippet
Figuring out the truth behind Unidentified Anomalous Phenomena (UAPs) takes more than speculation, it requires hard science.Read more...
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Flying Object Detection and Tracking in Digital...by P Thai · Cited by 2 — Moreover, birds are another challenge which can cause many fa...
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NASA Unidentified Anomalous Phenomena Independent...UAPs are defined as phenomena or observations of events in the air, sea, space, a...
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Title: nasas unidentified anomalous phenomena report key takeaways
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NASA's Unidentified Anomalous Phenomena Report14 Sept 2023 — “At present, analysis of UAP data is hampered by poor sensor calibration, th...
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Unidentified Anomalous Phenomena: Independent...14 Oct 2023 — NASA Unidentified Anomalous Phenomena: Independent Study Team Report...
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