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UAP / UFO Honeypot Sensor Project

June 4, 2025
Updated: August 13, 2025
UAP UFO honeypot citizen science multi-sensor Americium-241 open data 📁 Xaxis/uap-ufo-honeypot-sensor-project

Table of Contents

Abstract

Unidentified Anomalous Phenomena (UAP) continue to challenge aerospace safety, national security, and scientific curiosity. Robust, high-fidelity data remain scarce because historical sightings are largely anecdotal, sensor-poor, or classified. This paper proposes an open, globally distributed “honeypot” network that purposefully emits modest, safely encapsulated physical and electromagnetic signatures—most notably the 59.5 keV γ-ray line of ^241Am sourced from recycled ionization-type smoke detectors—in order to invite instrumented interaction. Each honeypot node fuses consumer-grade electro-optical, radio-frequency, thermal-IR, radar, and radiation sensors with edge AI on affordable drones or ground masts. A layered data-integrity pipeline preserves provenance and privacy while enabling large-scale collaborative analysis. We detail scientific rationale, engineering design, deployment logistics, safety and regulatory considerations, adversarial-scenario risk mitigation, expected outcomes, and future expansion paths. The goal is to accelerate the transition from anecdote to evidence by harnessing citizen ingenuity at planetary scale.

1 Introduction

Public interest in UAP surged after declassified U.S. Navy videos (2004 – 2015) and the 2022 NASA Independent Study announcement, which highlighted the absence of systematically collected, open data sets for rigorous scrutiny [1]. Parallel civic initiatives such as Sky360/SkyHub and Harvard’s Galileo Project are already demonstrating the power of inexpensive sensors, edge AI, and open science [3, 4]. Yet most efforts remain passive: they wait for phenomena. Inspired by wildlife-ecology “camera-trap” practices that use baits or lures, we explore an active approach—deploying spatially distributed “honeypot” signatures that might attract or at least stimulate technologically advanced probes while simultaneously maximizing the quantity and quality of multi-modality measurements that a local node can record.

2 Scientific Background

2.1 Observational Challenges

Typical UAP sightings suffer from (a) incomplete spectral coverage, (b) ambiguous geospatial metadata, (c) proprietary or classified sensors, and (d) inadequate synchronization between instruments. NASA’s 2024 report concluded that an expanded sensor-rich, open data effort is essential [2]. A honeypot architecture directly addresses points (a) through (c) by colocating diverse instruments and exposing raw streams to the public domain.

2.2 Rationale for Radioisotope Lures

Americium-241 offers unique properties:

  • Availability & Cost – Each discarded ionization smoke detector contains ~0.9 μCi encapsulated ^241Am discs [5].
  • Signature – Decay emits a dominant 59.5 keV γ-ray and accompanying α-particles; the γ-line is unmistakable in portable spectroscopy [6].
  • Safety & Regulation – Activity per detector stays below U.S. NRC general-license thresholds; encapsulation plus acrylic shielding reduces α exposure to negligible levels.
  • Detection Feasibility – While 59 keV photons attenuate strongly in air, advanced wide-area sensors (e.g., large-aperture HPGe arrays or hypothetically superior extraterrestrial instrumentation) could register a localized anomaly.

Alternative lures—pulsed infrared lasers, microwave bursts, or broadband RF noise—may complement the γ source, providing a “signature stack” that probes different hypothetical detection channels.

2.3 Historical Precedents

Cold-War–era “radioactive beacons” aided satellite calibration, and modern search-and-rescue ELTs emit unique RF tones; both show that distinctive emissions attract remote-sensing assets. The honeypot extends this principle to UAP research.

3 Project Objectives

  1. Generate High-Fidelity, Time-Synchronized, Multispectral Data for any aerial incursions within a 1 km radius of a node.
  2. Establish a Global, Modular, Open-Source Architecture that hobbyists can replicate for < USD 2 500 per site.
  3. Evaluate the Attractiveness of Specific Signatures (e.g., ^241Am γ-flux, pulsed-IR, broadband RF) via controlled A/B experiments.
  4. Preserve Data Integrity & Privacy with cryptographic hashing, selective redaction, and opt-in participation.
  5. Foster International Collaboration among academia, citizen scientists, and industry to refine sensors, analytics, and theory.

4 Methodology

4.1 Node Typology

Node Type Platform Typical Altitude Core Use-Case
Ground Mast Fixed tripod, 3 – 8 m 0 m AGL Long-duration, 360° sky surveillance
Tethered Drone Weather-balloon or power-tethered quad-rotor 50 – 120 m AGL Elevated field-of-view; persistent
Free-Flight UAS COTS quad/hex-rotor with BVLOS waiver 0 – 400 m AGL Dynamic tracking, pursuit, triangulation

4.2 Signature Emission Module (SEM)

  • Radioisotope Cartridge: Aggregates up to ten smoke-detector discs (~9 μCi total), epoxied to a 2 mm aluminum heat sink, encased in acrylic. Gamma transparency is ~98 % while α + β are fully absorbed. Self-attenuation and inverse-square predict ~40 nGy h⁻¹ at 1 m—well below occupational limits.
  • Pulsed-IR Beacon: 850 nm, 5 W peak, 10 kHz PRF, randomized duty cycle.
  • UHF Broadband Noise Source: 400 – 600 MHz spread-spectrum chirp, < 100 mW EIRP, FCC Part 15 compliant (spread reduces spectral power density).
  • Magnetic-Field Perturbator (optional): Coil driven at 10 – 100 Hz to oscillate ±10 µT.

4.3 Sensor Stack

Modality Example Consumer-Grade Hardware Data Rate
Visible RGB Sony IMX678 4 K global-shutter (120 fps) 900 Mbps raw
Near-IR & UV Raspberry Pi HQ with band-pass; UV-A lens 300 Mbps
Thermal LWIR FLIR Boson 640 (60 Hz) 70 Mbps
SWIR InGaAs 640 × 512 (DJI enterprise payload) [10] 80 Mbps
Millimeter-Wave Radar Texas Instruments IWR6843 (60 GHz) 15 Mbps
Passive RF SDR HackRF One, 1 MHz – 6 GHz 20 Mbps avg
Radiation CsI(Tl) scintillator + SiPM + Amptek MCA 1 Mbps
Environmental MEMS IMU, GNSS, barometer, magnetometer < 1 Mbps

Processing occurs on an NVIDIA Jetson Orin Nano running YOLOv8-custom (object detection), OpenCV Kalman tracking, and real-time spectral FFT for RF data. All streams are hardware-time-stamped (GPS PPS) to ±50 ns.

4.4 Data Pipeline

  1. Edge Pre-Filter: Motion & anomaly triggers reduce storage by 85 %.
  2. Local Redundant Storage: Dual 2 TB NVMe.
  3. Hash-Chain Integrity: Each 1-minute chunk hashed (SHA-256); Merkle root broadcast to IPFS & Arweave.
  4. Selective Cloud Upload: Starlink or LTE with Onion-routed tunnels; personally identifiable imagery (e.g., low-altitude houses) blurred before transmission.
  5. Open-Access API: Streams available under CC-BY license after 24-h embargo for quality vetting.

5 Deployment Strategy

5.1 Site Selection

Nodes concentrate on historically active regions—e.g., Hessdalen Valley (Norway), San Luis Valley (Colorado, USA), and coastal Baja California. Volunteer clusters enable ∆-time-of-arrival multilateration.

5.2 Phased Roll-Out

  1. Alpha (3 sites) – Validate SEM safety, sensor synchronization, pipeline.
  2. Beta (10 sites) – Conduct A/B signature trials (radioisotope vs. inert dummy).
  3. Release (100 sites) – Global crowdsourcing, open-hardware bill-of-materials, online dashboard with live anomaly map.

5.3 Community Engagement

  • Build documentation wikis, 3-D-printable mounts, and auto-flashing SD-card images.
  • Monthly hackathons to improve AI models (move-to-earn bug-bounty style).
  • Data challenges with Kaggle-style leaderboards for anomaly classification.

6 Experimental Design

Hypothesis Metric Control Experimental
H₀: SEM does not alter incident-UAP probability Rate of anomalous tracks per 100 h Node w/out ^241Am Node with ^241Am
H₁: SEM increases track count or quality SNR, multi-modal coherence Randomized schedule Same

Randomized cross-over (every 72 h) balances local weather variations. A “track” requires ≥ 2 concurrent modalities (e.g., LWIR + radar) and kinematic non-conformance to known aircraft.

7 Safety, Regulatory, and Ethical Analysis

7.1 Radiation Safety

  • Compliance with 10 CFR 30.15 (< 1 μCi per individual device; general license).
  • Public-dose estimate: < 0.01 mSv yr⁻¹—< 1 % of typical background.
  • Mandatory training videos and ANSI Z353.1 handling instructions.

7.2 Aviation, RF & Privacy

  • UAV operations follow Part 107 or local equivalents; BVLOS missions require waivers.
  • RF emitters remain within Part 15 unlicensed limits; IR lasers use Class 3R or lower to avoid ocular hazards.
  • Camera FOVs tilt ≥ 15° above horizon in populated zones; on-device face blurring protects individual privacy.

7.3 Adversarial Scenarios

  • Sensor spoofing detected via redundant modalities.
  • Tamper-evident seals on SEM cartridges; any break invalidates data segment.
  • Legal counsel monitors proliferation concerns; explicit ban on higher-activity isotopes.

8 Expected Outcomes

  1. Comprehensive, Public, Multi-TB Archive spanning optical to RF regimes, with cryptographic provenance.
  2. Statistical Evaluation of signature efficacy; Bayesian models will quantify any uptick in anomalous tracks attributable to SEM active periods.
  3. Refined Detection Algorithms—edge-trained models retrained with community-labeled data outperform generalized YOLO by ≥ 30 % precision.
  4. Policy Impact—dataset informs academic, governmental, and aerospace stakeholders, reducing stigma and improving hazard assessments.

9 Future Work

  • Higher-Energy Signatures: Investigate safe neutron emitters (e.g., ^252Cf proxies using photoneutron tubes) with proper shielding.
  • Space-Based Nodes: CubeSat-class platforms expand coverage and triangulation.
  • Quantum-Optical Sensors: Single-photon-counting SWIR imagers may reveal non-classical EM signatures.
  • Federated Learning: Privacy-preserving model updates across nodes without raw video sharing.

10 Conclusion

The proposed UAP Honeypot Sensor Project merges citizen science, open hardware, and thoughtful stimulus design to transform sporadic eyewitness testimony into a continuous, sharable, instrument-grade record of the skies. By coupling low-activity ^241Am γ-emissions and auxiliary cues with tightly synchronized, AI-assisted sensor suites, the network maximizes the probability of capturing high-value events while remaining financially and operationally accessible. Success—or even a null result—will meaningfully shrink the uncertainty space around UAPs, aligning with NASA’s call for transparent, data-driven inquiry and inviting constructive participation from every corner of the planet.

References

  1. NASA Science. “Unidentified Anomalous Phenomena (UAP).” 2022.
  2. NASA. “Independent Study Report on UAP.” 2024.
  3. Harvard Galileo Project. “Mission & Activities.” 2023.
  4. SkyHub / Sky360. “Building a Sky Hub UAP Tracker.” Medium, 2021; Instructables update 2023.
  5. First Alert. “Facts About Smoke Detector Radiation.” 2022.
  6. GammaSpectacular. “Americium-241 Gamma Spectrum.” 2023.
  7. ScienceDirect Topics. “Americium-241 – Radiological Characteristics.” 2024.
  8. Wikipedia. “Americium-241.” Accessed 2025-06-04.
  9. Advexure Drones. “Multispectral Cameras & Sensors.” 2024.
  10. Drone-Payload.com. “InGaAs SWIR/LWIR Multispectral Camera.” 2025.
  11. ScienceDirect. “Determining Am-241 Activity for Large Area Contamination.” 2017.