{"paper":{"title":"Machine Learning Methods for Stellar Collisions. I. Predicting Outcomes of SPH Simulations","arxiv_id":"2602.10191","primary_cat":"astro-ph.HE","submitted_at":"2026-02-10T19:00:01+00:00","version_compiled":"v2"},"notice":"Every quoted sentence is the authors' text from the arXiv listing abstract. Labels and probabilities are the outputs of classifier-1.13.0 on the question bank abstract-p1-20260923, exactly as returned. No sentence in this record was written by a language model.","schema":"pith.compiled-record/v0","status":"compiled","abstract":{"source":"arXiv listing abstract v2","sentences":[{"i":0,"p":1.0,"id":"s0","own":false,"role":"background","text":"Stellar collisions can occur frequently in dense cluster environments, and play a crucial role in producing exotic phenomena from blue stragglers in globular clusters to high-energy transients in galactic nuclei.","hedge":{"p":0.95,"label":"asserted"},"own_p":0.05,"main_claim_p":0.02,"quantitative":false,"quantitative_p":0.07,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.0,"background":1.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":1,"p":1.0,"id":"s1","own":false,"role":"background","text":"Successive collisions and mergers of massive stars could also lead to the formation of massive black holes, serving as seeds for supermassive black hole in the early universe.","hedge":{"p":1.0,"label":"hedged_possibility"},"own_p":0.05,"main_claim_p":0.02,"quantitative":false,"quantitative_p":0.04,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.0,"background":1.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":2,"p":0.96,"id":"s2","own":false,"role":"gap","text":"While analytic fitting formulae exist for predicting collision outcomes, they do not generalize across different energy scales or stellar evolutionary phases.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.06,"main_claim_p":0.03,"quantitative":false,"quantitative_p":0.12,"role_probabilities":{"gap":0.96,"method":0.0,"result":0.0,"background":0.04,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":3,"p":0.9,"id":"s3","own":false,"role":"background","text":"Smoothed particle hydrodynamics (SPH) simulations are often used to compute the outcomes of stellar collisions, but, even at low resolution, their computational cost makes running on-the-fly calculations during an $N$-body simulation quite challenging.","hedge":{"p":0.98,"label":"asserted"},"own_p":0.09,"main_claim_p":0.03,"quantitative":false,"quantitative_p":0.14,"role_probabilities":{"gap":0.08,"method":0.0,"result":0.0,"background":0.9,"limitation":0.02,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":4,"p":0.52,"id":"s4","own":true,"role":"method","text":"Here we present a new grid of $27,720$ SPH calculations of main-sequence star collisions, spanning a wide range of masses, ages, relative velocities, and impact parameters.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.97,"main_claim_p":0.07,"quantitative":true,"quantitative_p":0.98,"role_probabilities":{"gap":0.0,"method":0.52,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.48,"interpretation":0.0}},{"i":5,"p":0.82,"id":"s5","own":true,"role":"method","text":"Using this grid, we train machine learning models to predict both collision outcomes (merger vs disruption, or flyby) and final remnant masses.","hedge":{"p":0.99,"label":"asserted"},"own_p":0.97,"main_claim_p":0.15,"quantitative":false,"quantitative_p":0.05,"role_probabilities":{"gap":0.0,"method":0.82,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.18,"interpretation":0.0}},{"i":6,"p":1.0,"id":"s6","own":true,"role":"result","text":"We compare the performance of nearest neighbors, support vector machines, and neural networks, achieving classification balanced accuracy of $98.4\\%$, and regression relative errors as low as $0.11\\%$ and $0.15\\%$ for the final stars $1$ and $2$, respectively.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.98,"main_claim_p":0.41,"quantitative":true,"quantitative_p":0.99,"role_probabilities":{"gap":0.0,"method":0.0,"result":1.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":7,"p":0.98,"id":"s7","own":true,"role":"contribution","text":"We make our trained models publicly available as part of the package collAIder, enabling rapid predictions of stellar collision outcomes in $N$-body models of dense star cluster dynamics.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.95,"main_claim_p":0.27,"quantitative":false,"quantitative_p":0.04,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.02,"contribution":0.98,"interpretation":0.0}}]},"compiled":{"main_claim":{"p":0.69,"text":"We compare the performance of nearest neighbors, support vector machines, and neural networks, achieving classification balanced accuracy of $98.4\\%$, and regression relative errors as low as $0.11\\%$ and $0.15\\%$ for the final stars $1$ and $2$, respectively.","location":"abstract s6","hedge_strength":{"p":1.0,"label":"asserted"}},"provenance":{"route":{"via":"TypeSafe API","model":"classifier-1.13.0","endpoint":"https://api.typesafe.ai/v1/systemone","provider":"TypeSafe"},"packing":"one request 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