Interpretable machine learning for finding intermediate-mass black holes

• Verfasst von: Pasquato, Mario [VerfasserIn]
• Verfasst von: Trevisan, Piero [VerfasserIn]
• Verfasst von: Askar, Abbas [VerfasserIn]
• Verfasst von: Lemos, Pablo [VerfasserIn]
• Verfasst von: Carenini, Gaia [VerfasserIn]
• Verfasst von: Mapelli, Michela [VerfasserIn]
• Verfasst von: Hezaveh, Yashar [VerfasserIn]
• Titel: Interpretable machine learning for finding intermediate-mass black holes
• Verf.angabe: Mario Pasquato, Piero Trevisan, Abbas Askar, Pablo Lemos, Gaia Carenini, Michela Mapelli, and Yashar Hezaveh
• E-Jahr: 2024
• Jahr: 2024 April 10
• Umfang: 15 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 23.09.2024
• Titel Quelle: Enthalten in: The astrophysical journal
• Ort Quelle: London : Institute of Physics Publ., 1995
• Jahr Quelle: 2024
• Band/Heft Quelle: 965(2024), 1, Artikel-ID 89, Seite 1-15
• ISSN Quelle: 1538-4357
• DOI: doi:10.3847/1538-4357/ad2261
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: ASTROPHYSICAL IMPLICATIONS
• Sach-SW: GLOBULAR-CLUSTERS
• Sach-SW: HIGH-STAKES DECISIONS
• Sach-SW: MILKY-WAY
• Sach-SW: MOCCA CODE
• Sach-SW: MONTE-CARLO SIMULATIONS
• Sach-SW: NO EVIDENCE
• Sach-SW: RUNAWAY COLLISIONS
• Sach-SW: STAR CLUSTER SIMULATIONS
• Sach-SW: SURVEY DATABASE I
• K10plus-PPN: 1903189942

Abstract

Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine-learning (ML) models trained on GC simulations can in principle predict IMBH host candidates based on observable features. This approach has two limitations: first, an accurate ML model is expected to be a black box due to complexity; second, despite our efforts to simulate GCs realistically, the simulation physics or initial conditions may fail to reflect reality fully. Therefore our training data may be biased, leading to a failure in generalization to observational data. Both the first issue-explainability/interpretability-and the second-out of distribution generalization and fairness-are active areas of research in ML. Here we employ techniques from these fields to address them: we use the anchors method to explain an Extreme Gradient Boosting (XGBoost) classifier; we also independently train a natively interpretable model using Certifiably Optimal RulE ListS (CORELS). The resulting model has a clear physical meaning, but loses some performance with respect to XGBoost. We evaluate potential candidates in real data based not only on classifier predictions but also on their similarity to the training data, measured by the likelihood of a kernel density estimation model. This measures the realism of our simulated data and mitigates the risk that our models may produce biased predictions by working in extrapolation. We apply our classifiers to real GCs, obtaining a predicted classification, a measure of the confidence of the prediction, an out-of-distribution flag, a local rule explaining the prediction of XGBoost, and a global rule from CORELS.