Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations

Abstract

Binary stars undergo a variety of interactions and evolutionary phases, critical for predicting and explaining observed properties. Binary population synthesis with full stellar-structure and evolution simulations are computationally expensive requiring a large number of mass-transfer sequences. The recently developed binary population synthesis code POSYDON incorporates grids of MESA binary star simulations which are then interpolated to model large-scale populations of massive binaries. The traditional method of computing a high-density rectilinear grid of simulations is not scalable for higher-dimension grids, accounting for a range of metallicities, rotation, and eccentricity. We present a new active learning algorithm, psy-cris, which uses machine learning in the data-gathering process to adaptively and iteratively select targeted simulations to run, resulting in a custom, high-performance training set. We test psy-cris on a toy problem and find the resulting training sets require fewer simulations for accurate classification and regression than either regular or randomly sampled grids. We further apply psy-cris to the target problem of building a dynamic grid of MESA simulations, and we demonstrate that, even without fine tuning, a simulation set of only $\sim 1/4$ the size of a rectilinear grid is sufficient to achieve the same classification accuracy. We anticipate further gains when algorithmic parameters are optimized for the targeted application. We find that optimizing for classification only may lead to performance losses in regression, and vice versa. Lowering the computational cost of producing grids will enable future versions of POSYDON to cover more input parameters while preserving interpolation accuracies.

21 pages, 10 figures, ApJ in press

References in corpus (18)

• The NumPy array: a structure for efficient numerical computation
• Modules for Experiments in Stellar Astrophysics (MESA)
• Modules for Experiments in Stellar Astrophysics (MESA): Pulsating Variable Stars, Rotation, Convective Boundaries, and Energy Conservation
• Binary Population and Spectral Synthesis Version 2.1: construction, observational verification and new results
• Merging black hole binaries: the effects of progenitor's metallicity, mass-loss rate and Eddington factor
• Self-consistent 3D Supernova Models From -7 Minutes to +7 Seconds: a 1-bethe Explosion of a ~19 Solar-mass Progenitor
• Efficiency of mass transfer in massive close binaries, Tests from double-lined eclipsing binaries in the SMC
• The role of mass transfer and common envelope evolution in the formation of merging binary black holes
• Binary Population Synthesis
• Binary Black Hole Formation with Detailed Modeling: Stable Mass Transfer Leads to Lower Merger Rates
• The Ecological Impact of High-performance Computing in Astrophysics
• On Carbon Burning in Super Asymptotic Giant Branch Stars
• Statistical Gravitational Waveform Models: What to Simulate Next?
• On the Formation of Ultraluminous X-ray Sources with Neutron Star Accretors: the Case of M82 X-2
• The role of core-collapse physics in the observability of black-hole neutron-star mergers as multi-messenger sources
• Progenitors of low-mass binary black-hole mergers in the isolated binary evolution scenario
• Interpolating Detailed Simulations of Kilonovae: Adaptive Learning and Parameter Inference Applications
• Estimation of the Galaxy Quenching Rate in the Illustris Simulation

Cited by in corpus (3)

• POSYDON: A General-Purpose Population Synthesis Code with Detailed Binary-Evolution Simulations
• Scalable stellar evolution forecasting: Deep learning emulation vs. hierarchical nearest neighbor interpolation
• Emulators for stellar profiles in binary population modeling