머신러닝, 역사적 천문 사진 속 미지의 일시적 현상 발견

--> Astrophysics > Instrumentation and Methods for Astrophysics arXiv:2604.18799 (astro-ph) [Submitted on 20 Apr 2026 ( v1 ), last revised 22 Apr 2026 (this version, v2)] Title: Machine Learning Supports Existence of Previously Unrecognized Transient Astronomical Phenomena in Historical Observatory Images Authors: Stephen Bruehl , Brian Doherty , Alina Streblyanska , Beatriz Villarroel View a PDF of the paper titled Machine Learning Supports Existence of Previously Unrecognized Transient Astronomical Phenomena in Historical Observatory Images, by Stephen Bruehl and 3 other authors View PDF Abstract: Transient, star-like point sources that appear and vanish over short timescales are described in astronomical images prior to launch of Sputnik. We have reported that transient numbers diminish significantly in Earth's shadow (shadow deficit) and are more likely within (plus/minus) one day of nuclear testing (nuclear window). These findings remain debated with some arguing that transients identified via existing automated pipelines are simply plate defects. Therefore, we use machine learning (ML) to enhance transient identification accuracy and validate the phenomenon. The model was trained against 250 transient image pairs taken 30 minutes apart that were classified as real versus plate defect by expert visual review; the model demonstrated good discrimination (out-of-fold AUC$=$0.81; sensitivity$=$0.71, specificity$=$0.71). After deployment in a dataset of 107,875 previously-identified transients, the model assigned each a probability of being real. After controlling for ML-identified artifacts, transient counts were significantly elevated for dates within a nuclear window (p$=$.024); transients with the highest probability of being real were more likely to occur within a nuclear window (p$<$.0001). The shadow deficit was significant (p$<$.0001) and largest in the highest probability transients relative to lower probability transients (p$=$.003). Results strongly support existence of an unrecognized population of transient objects in historical astronomical plates warranting further study. Comments: 34 pages, 4 figures Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM) Cite as: arXiv:2604.18799 [astro-ph.IM] (or arXiv:2604.18799v2 [astro-ph.IM] for this version) https://doi.org/10.48550/arXiv.2604.18799 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alina Streblyanska [ view email ] [v1] Mon, 20 Apr 2026 20:06:47 UTC (600 KB) [v2] Wed, 22 Apr 2026 15:59:06 UTC (682 KB) Full-text links: Access Paper: View a PDF of the paper titled Machine Learning Supports Existence of Previously Unrecognized Transient Astronomical Phenomena in Historical Observatory Images, by Stephen Bruehl and 3 other authors View PDF view license Current browse context: astro-ph.IM < prev | next > new | recent | 2026-04 Change to browse by: astro-ph References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )