TY - JOUR
T1 - Applications of Machine Learning to Predicting Core-collapse Supernova Explosion Outcomes
AU - Tsang, Benny T.H.
AU - Vartanyan, David
AU - Burrows, Adam
N1 - Funding Information:
We are grateful to Daniel Kasen, Tianshu Wang, and Matthew Coleman for valuable insights and discussion. This research was funded by the Gordon and Betty Moore Foundation through grant GBMF5076, and by NASA awards ATP-80NSSC18K0560 and ATP-80NSSC22K0725. This research was supported in part by the National Science Foundation under grant No. NSF PHY-1748958. We acknowledge support from the U.S. Department of Energy Office of Science and the Office of Advanced Scientific Computing Research via the Scientific Discovery through Advanced Computing (SciDAC4) program and grant DE-SC0018297 (subaward 00009650) and support from the U.S. NSF under Grants AST-1714267 and PHY-1804048 (the latter via the Max-Planck/Princeton Center (MPPC) for Plasma Physics). A generous award of computer time was provided by the INCITE program. That research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. We are also grateful for our computational resources through the Texas Advanced Computing Center (TACC) at The University of Texas at Austin via Frontera Large-Scale Community Partnerships under grant SC0018297 as well as the Leadership Resource Allocation under grant No. 1804048. In addition, this overall research project was part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters was a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. This general project was also part of the “Three-Dimensional Simulations of Core-Collapse Supernovae” PRAC allocation support by the National Science Foundation (under award #OAC-1809073). Moreover, we acknowledge access under the local award #TG-AST170045 to the resource Stampede2 in the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant No. ACI-1548562. Finally, the authors employed computational resources provided by the TIGRESS high performance computer center at Princeton University, which is jointly supported by the Princeton Institute for Computational Science and Engineering (PICSciE) and the Princeton University Office of Information Technology, and acknowledge our continuing allocation at the National Energy Research Scientific Computing Center (NERSC), which is supported by the Office of Science of the US Department of Energy (DOE) under contract DE-AC03-76SF00098. Use was made of computational facilities purchased with funds from the National Science Foundation (CNS-1725797) and administered by the Center for Scientific Computing (CSC). The CSC is supported by the California NanoSystems Institute and the Materials Research Science and Engineering Center (MRSEC; NSF DMR 1720256) at UC Santa Barbara.
Funding Information:
We are grateful to Daniel Kasen, Tianshu Wang, and Matthew Coleman for valuable insights and discussion. This research was funded by the Gordon and Betty Moore Foundation through grant GBMF5076, and by NASA awards ATP-80NSSC18K0560 and ATP-80NSSC22K0725. This research was supported in part by the National Science Foundation under grant No. NSF PHY-1748958. We acknowledge support from the U.S. Department of Energy Office of Science and the Office of Advanced Scientific Computing Research via the Scientific Discovery through Advanced Computing (SciDAC4) program and grant DE-SC0018297 (subaward 00009650) and support from the U.S. NSF under Grants AST-1714267 and PHY-1804048 (the latter via the Max-Planck/Princeton Center (MPPC) for Plasma Physics). A generous award of computer time was provided by the INCITE program. That research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. We are also grateful for our computational resources through the Texas Advanced Computing Center (TACC) at The University of Texas at Austin via Frontera Large-Scale Community Partnerships under grant SC0018297 as well as the Leadership Resource Allocation under grant No. 1804048. In addition, this overall research project was part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters was a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. This general project was also part of the “Three-Dimensional Simulations of Core-Collapse Supernovae” PRAC allocation support by the National Science Foundation (under award #OAC-1809073). Moreover, we acknowledge access under the local award #TG-AST170045 to the resource Stampede2 in the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant No. ACI-1548562. Finally, the authors employed computational resources provided by the TIGRESS high performance computer center at Princeton University, which is jointly supported by the Princeton Institute for Computational Science and Engineering (PICSciE) and the Princeton University Office of Information Technology, and acknowledge our continuing allocation at the National Energy Research Scientific Computing Center (NERSC), which is supported by the Office of Science of the US Department of Energy (DOE) under contract DE-AC03-76SF00098. Use was made of computational facilities purchased with funds from the National Science Foundation (CNS-1725797) and administered by the Center for Scientific Computing (CSC). The CSC is supported by the California NanoSystems Institute and the Materials Research Science and Engineering Center (MRSEC; NSF DMR 1720256) at UC Santa Barbara.
Publisher Copyright:
© 2022. The Author(s). Published by the American Astronomical Society.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - Most existing criteria derived from progenitor properties of core-collapse supernovae are not very accurate in predicting explosion outcomes. We present a novel look at identifying the explosion outcome of core-collapse supernovae using a machine-learning approach. Informed by a sample of 100 2D axisymmetric supernova simulations evolved with Fornax, we train and evaluate a random forest classifier as an explosion predictor. Furthermore, we examine physics-based feature sets including the compactness parameter, the Ertl condition, and a newly developed set that characterizes the silicon/oxygen interface. With over 1500 supernovae progenitors from 9−27 M ⊙, we additionally train an autoencoder to extract physics-agnostic features directly from the progenitor density profiles. We find that the density profiles alone contain meaningful information regarding their explodability. Both the silicon/oxygen and autoencoder features predict the explosion outcome with ≈90% accuracy. In anticipation of much larger multidimensional simulation sets, we identify future directions in which machine-learning applications will be useful beyond the explosion outcome prediction.
AB - Most existing criteria derived from progenitor properties of core-collapse supernovae are not very accurate in predicting explosion outcomes. We present a novel look at identifying the explosion outcome of core-collapse supernovae using a machine-learning approach. Informed by a sample of 100 2D axisymmetric supernova simulations evolved with Fornax, we train and evaluate a random forest classifier as an explosion predictor. Furthermore, we examine physics-based feature sets including the compactness parameter, the Ertl condition, and a newly developed set that characterizes the silicon/oxygen interface. With over 1500 supernovae progenitors from 9−27 M ⊙, we additionally train an autoencoder to extract physics-agnostic features directly from the progenitor density profiles. We find that the density profiles alone contain meaningful information regarding their explodability. Both the silicon/oxygen and autoencoder features predict the explosion outcome with ≈90% accuracy. In anticipation of much larger multidimensional simulation sets, we identify future directions in which machine-learning applications will be useful beyond the explosion outcome prediction.
UR - http://www.scopus.com/inward/record.url?scp=85139249833&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139249833&partnerID=8YFLogxK
U2 - 10.3847/2041-8213/ac8f4b
DO - 10.3847/2041-8213/ac8f4b
M3 - Article
AN - SCOPUS:85139249833
SN - 2041-8205
VL - 937
JO - Astrophysical Journal Letters
JF - Astrophysical Journal Letters
IS - 1
M1 - L15
ER -