Machine learning on cosmological simulations achieves 91-94% accuracy classifying over-massive versus under-massive SMBH growth regimes from LSST photometry, with 83-89% cross-simulation transfer accuracy driven primarily by host galaxy colors.
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Five new HI 21-cm absorption detections in LERGs and HERGs at z<0.5 reveal disturbed gas kinematics with velocity offsets over 350 km/s and a 3% detection rate consistent with lower-redshift samples.
The P3 selector achieves 0.9809 purity and 0.8869 completeness for QSO candidates in selected fields, outperforming Gaia's official probabilities.
A Random Forest classifier on Chandra, Gaia, and CatWISE data identifies 1060 new quasar candidates behind the Galactic plane, with two spectroscopically confirmed at z~1.1-1.3.
citing papers explorer
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Classifying Supermassive Black Hole Growth Regimes to Observables Across Cosmological Simulations with Forecasts for LSST
Machine learning on cosmological simulations achieves 91-94% accuracy classifying over-massive versus under-massive SMBH growth regimes from LSST photometry, with 83-89% cross-simulation transfer accuracy driven primarily by host galaxy colors.
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HI 21-cm absorption in low- and high-excitation radio-loud AGNs at $z<0.5$ from MALS
Five new HI 21-cm absorption detections in LERGs and HERGs at z<0.5 reveal disturbed gas kinematics with velocity offsets over 350 km/s and a 3% detection rate consistent with lower-redshift samples.
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A Gaia-linked High-purity QSO Candidate Catalog in Selected Fields with Extinction-binned Calibration and Spectrum-informed Training
The P3 selector achieves 0.9809 purity and 0.8869 completeness for QSO candidates in selected fields, outperforming Gaia's official probabilities.
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Finding Quasars behind the Galactic Plane. IV. Candidate Selection from Chandra with Random Forest
A Random Forest classifier on Chandra, Gaia, and CatWISE data identifies 1060 new quasar candidates behind the Galactic plane, with two spectroscopically confirmed at z~1.1-1.3.