Is there a “UAP phenomenon” worth studying?

[Andreas]

This discussion came up in another thread, and I want to continue it here. Personally, I see no reason to study the so-called "UAP phenomenon" scientifically, since I see no reason to believe such a phenomenon exists. To me, UAPs only exist in the LIZ, and not even better equipment will help us, since it will only move the "phenomenon" further into the distance. When people claim we should conduct more scientific studies on UAPs, I'm personally confused about what that even means.

It might sound like a reasonable demand. We want to know what those blurry videos actually show and what the "whistleblowers" are actually talking about, don't we? But when people make such demands, it's often implied that the "UAP phenomenon" is something anomalous—something strange that has been kept secret from the public. The sad truth, however, is that after seeing piles of videos, we still have no real reason to believe that's the case.

To me, the role of a skeptic should be quite passive. It is important to react to the videos that are released and to comment on the claims being made. But I will never demand more material showing "strange stuff." I will never stand on the barricades demanding more data and more information. If it looks like a pig, walks like a pig, and smells like a pig, then it's probably a pig. I don't need additional data confirming that it's actually a pig. Someone claiming it's a cow will need to provide the evidence themselves.

Not wanting to spend government dollars and scientists' time studying a phenomenon that has not been confirmed to be real seems to be an extreme opinion nowadays. But that's my humble opinion, and I'm happy to be challenged on it.

 

[JMartJr]

Any scientist who studied UFOs and found a new phenomenon would make their career... if they discovered aliens or the like they'd achieve immortal fame.

Few scientists choose to study them. None of the ones that have done so made any important discoveries. Correct me if I am missing something, everybody, but as far as I can tell none of them have discovered any moderately important discoveries. Or any minor discoveries.

It does not seem that they think there is anything there worth their time and effort. Ask scientists if they'd support UFOs being studied, many will say "Yes," as scientists are seldom opposed to the quest for knowledge. Ask if they'll switch from their current work to UFOlogy, I doubt they'll be interested, if they were they'd have been studying them already.

Me, if any scientist wants to study UFOs, I'm all for it. Knock yourselves out, folks, and good luck to you. If they'd rather study something else, that they feel is more likely to produce something of value, I'm all for it.

If they don't want to do work on the topic, what are we going to do, force them?

I guess anybody is free to raise some money and try to hire some of them. I think you'll need some deep pockets, if there is little interest among researchers.

 

[Ann K]

I'm willing to dump it in the laps of the True Believers for now. I disbelieve there to be any inter-stellar component or non-human entity behind the sightings which get the UFOlogists all a-twitter, and that s true for the "anomalous" (unidentified) things as well as the ones we can identify. But, like any good scientist, I'm willing to revisit my beliefs IF AND WHEN some credible evidence surfaces. It's just not my job to dig it out, and that's the thing I'll cheerfully leave to those who make the claims.

 

[MonkeeSage]

I think often the call for further scientific study of UAP means something like more data being collected and analyzed to see if there are any anomalies. I think this can be a good thing if done well and privately funded rather than government subsidized.

For example, the Galileo Project's array of FLIR cameras and data processing pipeline, can be useful for skeptics in that it shows that even with better sensors, data and analysis, almost everything detected is attributable to mundane causes, which establishes a concrete base rate. It can also provide examples of what mundane objects can look like (see image from paper below). And if there ever are actual anomalies in the outliers (none so far), like apparent instantaneous acceleration, there will be more robust sensor data to analyze rather than just grainy videos.

Papers about the Galileo Project cameras and data processing pipeline:

Domine et al, Commissioning An All-Sky Infrared Camera Array for Detection of Airborne Objects, Feb 2025.
https://arxiv.org/pdf/2411.07956

Abstract: To date, there is little publicly available scientific data on unidentified aerial phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal, multi-spectral ground-based observatory to continuously monitor the sky and collect data for UAP studies via a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave-infrared FLIR Boson 640 cameras. In addition to performing intrinsic and thermal calibrations, we implement a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance–Broadcast (ADS-B) data that we collect synchronously on site. Using a You Only Look Once (YOLO) machine learning model for object detection and the Simple Online and Realtime Tracking (SORT) algorithm for trajectory reconstruction, we establish a first baseline for the performance of the system over five months of field operation. Using an automatically generated real-world dataset derived from ADS-B data, a dataset of synthetic 3D trajectories, and a hand-labeled real-world dataset, we find an acceptance rate (fraction of in-range airplanes passing through the effective field of view of at least one camera that are recorded) of 41% for ADS-B-equipped aircraft, and a mean frame-by-frame aircraft detection efficiency (fraction of recorded airplanes in individual frames which are successfully detected) of 36%. The detection efficiency is heavily dependent on weather conditions, range, and aircraft size. Approximately 500,000 trajectories of various aerial objects are reconstructed from this five-month commissioning period. These trajectories are analyzed with a toy outlier search focused on the large sinuosity of apparent 2D reconstructed object trajectories. About 16% of the trajectories are flagged as outliers and manually examined in the IR images. From these ∼80,000 outliers and 144 trajectories remain ambiguous, which are likely mundane objects but cannot be further elucidated at this stage of development without information about distance and kinematics or other sensor modalities. We demonstrate the application of a likelihood-based statistical test to evaluate the significance of this toy outlier analysis. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers for the five-month interval at a 95% confidence level. This test is applicable to all of our future outlier searches.

Figure 32. After manual classification of reconstructed trajectories, we sample typical objects (in pairs) from each category. These images are crops of the objects for illustration purposes. First row: flocks of birds and the Moon; second row: planes and single birds; third row: clouds

Bridgham et al, Galileo Project's Observatory Class System Architecture, May 2025.
https://arxiv.org/pdf/2506.00125v1

Abstract: Scientific investigation of Unidentified Anomalous Phenomena (UAP) is limited by poor data quality and incomplete data sets. Existing data are often fragmented, uncalibrated, and missing critical metadata. To address these limitations, the authors present the Observatory Class Integrated Computing Platform (OCICP), a system designed for the comprehensive scientific study of aerial phenomena which integrates multiple sensors to collect and analyze data on UAP. The OCICP system consists of two subsystems. The first is the Edge Computing Subsystem which directly interfaces with the sensors and is located within the observatory site. This subsystem performs real-time data acquisition, sensor optimization, and data provenance management. The second is the Post-Processing Subsystem which resides outside the observatory. This subsystem supports data analysis workflows, including commissioning, census operations, science operations, and system effectiveness monitoring. This design and implementation paper describes the system lifecycle, associated processes, design, implementation, and preliminary results of OCICP, emphasizing the system's ability to collect comprehensive, calibrated, and scientifically robust data.