{"id":"W4414870706","doi":"10.1051/0004-6361/202554065","title":"Interpreting deep learning-based stellar mass estimation via causal analysis and mutual information decomposition","year":2025,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Astronomical Observations and Instrumentation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; University of California, Los Angeles; National Natural Science Foundation of China; York University; Carnegie Mellon University; Office of Science; Johns Hopkins University; College of Engineering, Michigan State University; Harvard University; Ohio State University; New Mexico State University; University of Portsmouth; Yale University; Vanderbilt University; National Science Foundation; University of Washington; Alfred P. Sloan Foundation; Brookhaven National Laboratory; U.S. Department of Energy; California Institute of Technology; National Aeronautics and Space Administration; Jet Propulsion Laboratory; Princeton University","keywords":"Interpretability; Photometry (optics); Galaxy; Stellar mass; Sky; Mutual information; Concatenation (mathematics); Astrometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003884583,0.0009272653,0.0005820309,0.00184512,0.0003172846,0.001633082,0.001089232,0.0007841453,0.001253615],"category_scores_gemma":[0.01653795,0.0005237178,0.0007514664,0.0009281553,0.001344696,0.00177212,0.001681852,0.001492017,0.0001616879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001358184,"about_ca_system_score_gemma":0.0009600885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004091742,"about_ca_topic_score_gemma":0.003217646,"domain_scores_codex":[0.9988196,0.0005394641,0.00007924828,0.0002538919,0.0002328432,0.00007477032],"domain_scores_gemma":[0.9914275,0.005707433,0.001293126,0.0007241953,0.0006588606,0.0001888906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002429491,0.000132641,0.02602554,0.0002068204,0.0002587818,0.0003683107,0.0003014795,0.7714561,0.004144724,0.117665,0.002043584,0.07715413],"study_design_scores_gemma":[0.000005603131,0.000009794248,0.001245501,0.00001493174,0.00001385267,0.00001658874,0.00001415751,0.9409473,0.0006886171,0.05676758,0.000267768,0.000008261895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08108533,0.0004140825,0.9147766,0.0009906932,0.00003284422,0.00003955329,0.0004723224,0.0006135426,0.001575145],"genre_scores_gemma":[0.9193388,0.00028512,0.07834335,0.0001836992,0.00008369085,0.0000835618,0.0007285058,0.00009180419,0.0008613927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004091742,"threshold_uncertainty_score":0.02054387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001972305892795454,"score_gpt":0.1923322088434321,"score_spread":0.1903599029506366,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}