UAP গবেষণা প্রোফাইল
Richard Cloete
Computer scientist এবং UAP AI/data-analysis researcher
- পদবিComputer scientist এবং UAP AI/data-analysis researcher
সংক্ষিপ্ত পরিচিতি
Richard Cloete Center for Astrophysics | Harvard & Smithsonian-এর computer scientist এবং Laukien-Oumuamua Postdoctoral Research Fellow। তিনি Galileo Project-এর research member এবং UAP scientific study-এর জন্য computing infrastructure, multimodal observatories, infrared imaging ও automated classification নিয়ে technical papers co-author করেছেন। [S1][S2]
June 2026-এ তিনি UAP Science Advisory Council (UAPSAC)-এ data analysis এবং AI tools role-এ named হন। DefenseScoop independently confirm করে যে UAPSAC ODNI-linked UAP Governance Board-কে support করা external advisory groups-এর একটি। [S3][S4]
Cloete-এর গুরুত্ব extraordinary claim নয়। তাঁর মূল প্রশ্ন: huge ordinary aerial-event stream-এর মধ্যে known classes reliably classify করে genuine unusual residual কীভাবে isolate করা যায়, machine-learning outlier-কে physical anomaly না বানিয়ে?
Outlier এবং anomaly আলাদা category—এটাই profile-এর কেন্দ্র।
Computer-science background
CfA তাঁকে computer scientist with PhD হিসেবে identify করে; Galileo previous Cambridge postdoc উল্লেখ করে। [S1][S5]
Newcastle University তাঁর PhD thesis latency measurement, modelling and management for interactive remote rendering হিসেবে record করে। [S6]
এই work sensor synchronisation, real-time streams, detection/tracking, catalogue comparison, known-object classification, raw-data preservation এবং outlier triage-এর সঙ্গে directly relevant।
Galileo Project
Cloete Galileo Project research member। [S1]
Project ground-based optical, infrared, radar, RF, acoustic এবং environmental sensors দিয়ে anecdotal evidence-এর বদলে prospective measurement collect করতে চায়।
Sky-monitoring system-এর অধিকাংশ detection ordinary হওয়া উচিত।
তাই rare residual meaningful হওয়ার আগে ordinary sky accurately classify করা essential।
Integrated computing platform
Cloete 2023 "Integrated Computing Platform for Detection and Tracking of Unidentified Aerial Phenomena (UAP)" paper-এর lead author। [S2]
Paper multisensor ingest, real-time processing এবং classification/outlier analysis architecture describe করে।
Work explicitly preliminary।
Proposed architecture novel UAP detection proof নয়; এটি engineering contribution।
Outlier বনাম scientific anomaly
Statistical outlier bad calibration, rare known aircraft, weather, corrupt data, saturation, near-camera insect বা incomplete metadata-এর কারণে হতে পারে। [S2]
Scientific anomaly অনেক stronger: corroborated observation যা artefacts এবং known alternatives test করার পরও survive করে।
High AI anomaly score non-human technology evidence নয়; এটি investigation priority signal।
Multimodal observatories ও 3D localisation
Cloete Galileo multimodal-observatory roadmap co-author করেন। [S7]
এছাড়া multiple visible/IR cameras, calibration ও triangulation দিয়ে 3D aerial-object localisation platform co-author করেন। [S8]
Single video rapid angular motion দেখাতে পারে কিন্তু reliable range দেয় না। Multi-camera geometry depth recover করতে পারে।
Acoustic, radar ও IR commissioning
তিনি acoustic monitoring [S9] এবং passive multistatic radar [S10] papers co-author।
2025-এ all-sky IR camera-array commissioning paper-এও co-author। [S11]
Commissioning sensitivity, dead pixels, distortion, thermal behaviour, background noise এবং false detections characterize করে। Sensor behaviour না বুঝে anomaly trust করা যায় না।
Mainstream machine learning
2024-এ Rubin/LSST interstellar-object automated classification paper co-author [S12]; 2025-এ near-Earth-object discovery ML paper co-author। [S13]
এই work known astronomy populations-এ ML performance benchmark করার evidence দেয়।
"Unknown" label-কে strong weight দেওয়ার আগে known classes-এ error rates জানা দরকার।
AI risks
UAP AI training artefacts শিখতে পারে, out-of-distribution objects-এ fail করতে পারে, missing metadata-কে anomaly ভাবতে পারে এবং overconfident হতে পারে।
Serious pipeline needs validation sets, human review, transparent thresholds, reproducible preprocessing, model versions, uncertainty calibration এবং raw-data access।
UAPSAC ও independence
Cloete initial UAPSAC roster-এ AI/data analysis role পান। [S3][S4]
Complete public council pipeline/model বা validated case output এখনও নেই।
Galileo এবং UAPSAC দুটোই Loeb-led হওয়ায় external validation/replication important, যাতে same group design-select-evaluate করলে confirmation bias কমে।
প্রমাণের বিশ্লেষণ
Cloete-এর scientific foundation strong।
Main limitation maturity: papers mostly architecture, commissioning এবং capability নিয়ে, confirmed novel phenomenon নয়।
Current contribution methodological।
যা প্রতিষ্ঠিত
- Harvard-Smithsonian computer scientist/postdoctoral fellow। [S1][S5]
- Newcastle computer-science PhD। [S6]
- Galileo Project member। [S1]
- Integrated computing-platform lead author। [S2]
- Multimodal/localisation/acoustic/radar co-author। [S7][S8][S9][S10]
- 2025 IR commissioning co-author। [S11]
- Mainstream ML astronomy publications। [S12][S13]
- UAPSAC AI/data-analysis member। [S3][S4]
যা প্রতিষ্ঠিত নয়
- AI outlier scientific anomaly নয়।
- Scientific anomaly non-human technology নয়।
সামগ্রিক মূল্যায়ন
Richard Cloete technically strong addition কারণ তাঁর কাজ UAP claim করার আগে ordinary sky classify করার infrastructure তৈরি করে।
সবচেয়ে গুরুত্বপূর্ণ lesson: outlier, unidentified এবং anomalous interchangeable নয়।
AI strange image label করে UAP solve করে না; ordinary environment যথেষ্ট ভালো model করলে genuinely unusual residual চিনতে সাহায্য করে।
প্রস্তাবভিত্তিক আস্থার মাত্রা
| প্রস্তাব | আস্থা | ভিত্তি |
|---|---|---|
| Cloete Harvard-Smithsonian computer scientist with PhD | উচ্চ | CfA/Galileo/Newcastle |
| Peer-reviewed UAP computing work | উচ্চ | Journals |
| Mainstream ML astronomy work | উচ্চ | A&A |
| UAPSAC AI/data-analysis member | উচ্চ | Council/DefenseScoop |
| AI outlier = novel physical phenomenon | নিম্ন | Category error |
| Galileo papers prove extraordinary detections | নিম্ন | Architecture/commissioning |
| Work improves systematic UAP study | উচ্চ | Direct purpose |
| Reliable conclusions without independent validation | নিম্ন | Benchmarking required |
উৎস
[S1] Galileo Project — Richard Cloete. https://galileo.hsites.harvard.edu/people/richard-cloete [S2] Integrated Computing Platform. https://galileo.hsites.harvard.edu/publications/integrated-computing-platform-detection-and-tracking-unidentified-aerial [S3] Disclosure Foundation — UAPSAC. https://disclosure.org/news/uap-science-advisory-council [S4] DefenseScoop. https://defensescoop.com/2026/06/17/new-science-advisory-council-forms-to-help-us-government-resolve-the-uap-mystery/ [S5] CfA — Richard Cloete. https://www.cfa.harvard.edu/people/richard-cloete [S6] Newcastle University. https://theses.ncl.ac.uk/jspui/handle/10443/5058 [S7] Galileo multimodal observatories. https://galileo.hsites.harvard.edu/publications/scientific-investigation-unidentified-aerial-phenomena-uap-using-multimodal [S8] Aerial Object Localization. https://galileo.hsites.harvard.edu/publications/hardware-and-software-platform-aerial-object-localization [S9] Acoustic Monitoring. https://galileo.hsites.harvard.edu/publications/multi-band-acoustic-monitoring-aerial-signatures [S10] SkyWatch radar. https://galileo.hsites.harvard.edu/publications/skywatch-passive-multistatic-radar-network-measurement-object-position-and [S11] All-Sky IR Camera Array. https://pmc.ncbi.nlm.nih.gov/articles/PMC11820869/ [S12] Interstellar object ML. https://www.aanda.org/articles/aa/pdf/2024/11/aa51118-24.pdf [S13] NEO machine learning. https://doi.org/10.1051/0004-6361/202554311