XClass is an automated multiwavelength machine-learning pipeline for classifying extragalactic X-ray point sources detected by the Chandra X-ray Observatory into seven astrophysical classes (AGN, LMXB, HMXB, CV, LM-STAR, HM-STAR, SNR), using SED-translated HST photometry and a two-stage Random Forest classifier. This release accompanies Paper I (the pipeline description paper, submitted to The Astrophysical Journal) and contains the complete source code: training-data assembly, SED translation of ground-based photometry into synthetic HST magnitudes, feature engineering, and the two-stage Random Forest classifier, along with the diagnostic and validation scripts and the manuscript figures. What's in this repository The xclass Python package (installable via pip install .; entry point xclass-run) Diagnostic, ablation, and validation scripts under scripts/ Test suite under tests/ Publication figures under figures/ What's deposited separately on Zenodo The trained classifier models (_rf_optical_v2) and the machine-readable training catalog (11,374 sources) are archived in the linked Zenodo record rather than in this repository, and are required to reproduce the published results. Requirements Python ≥ 3.12. Runtime dependencies are declared in pyproject.toml and install automatically.
This paper presents an independent external assessment of OpenAI — the OpenAI Group PBC, the controlling OpenAI Foundation, and its publicly released GPT-series and o-series models — benchmarked against four governance frameworks: the EU AI Act (including its general-purpose AI a…
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