Bulletin of the American Physical Society
2024 APS April Meeting
Wednesday–Saturday, April 3–6, 2024; Sacramento & Virtual
Session E00: Poster Session I & Welcome Reception (5:30PM - 7:30PM PT)
5:30 PM,
Wednesday, April 3, 2024
SAFE Credit Union Convention Center
Room: Exhibit Hall A, Floor 1
Sponsoring
Unit:
APS
Abstract: E00.00032 : Kilonova Light Curve Interpolation with Neural Networks*
Presenter:
Yinglei Peng
(University of Rochester)
Authors:
Yinglei Peng
(University of Rochester)
Marko Ristic
(Rochester Institute of Technology)
Atul Kedia
(Rochester Institute of Technology)
Richard O'Shaughnessy
(Rochester Institute of Technology)
Christopher J Fontes
(Los Alamos National Laboratory)
Chris L Fryer
(Los Alamos National Laboratory)
Oleg Korobkin
(Los Alamos National Laboratory)
Matthew R Mumpower
(LANL)
V. Ashley Villar
(Center for Astrophysics | Harvard & Smithsonian)
Ryan Wollaeger
(Los Alamos National Laboratory)
A kilonova is an astronomical transient that occurs after the merger of two neutron stars or a neutron star and a black hole. The kilonova is powered by the decay of radioactive elements on timescales of hours to weeks post-merger, emitting energy in the form of observable light curves. In modeling kilonova light curves, we use radiative transfer simulations to study the fundamental physics in these complex environments. However, these methods are slow and computationally expensive, prompting the use of surrogate modeling techniques such as neural networks. In this poster, we describe a neural network trained on existing kilonova lightcurve simulations. Our neural network emulator can generate millions of new light curves in minutes when trained on a set of 22248 radiative transfer simulations which took weeks to simulate. We also present our network’s successful recovery of kilonova light curves in our test set, motivating parameter inference application as discussed in an associated talk.
*ROS and MR acknowledge support from NSF AST 1909534 and AST 2206321. AK also acknowledges support from NSF AST 2206321. VAV acknowledges support by the NSF through grant AST-2108676. The work by CLF, CJF, MRM, OK, and RTW was supported by the US Department of Energy through the Los Alamos National Laboratory (LANL). This research used resources provided by LANL through the institutional computing program. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001).
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