Show simple item record

dc.contributor.authorAlmualla, Mouza
dc.contributor.authorNing, Yuhong
dc.contributor.authorBulla, Mattia
dc.contributor.authorDietrich, Tim
dc.contributor.authorCoughlin, Michael W.
dc.contributor.authorGuessoum, Nidhal
dc.date.accessioned2022-01-12T12:16:48Z
dc.date.available2022-01-12T12:16:48Z
dc.date.issued2021-12-31
dc.identifier.urihttp://hdl.handle.net/10754/674924
dc.description.abstractOn the 17th of August, 2017 came the simultaneous detections of GW170817, a gravitational wave that originated from the coalescence of two neutron stars, along with the gamma-ray burst GRB170817A, and the kilonova counterpart AT2017gfo. Since then, there has been much excitement surrounding the study of neutron star mergers, both observationally, using a variety of tools, and theoretically, with the development of complex models describing the gravitational-wave and electromagnetic signals. In this work, we improve upon our pipeline to infer kilonova properties from observed light-curves by employing a Neural-Network framework that reduces execution time and handles much larger simulation sets than previously possible. In particular, we use the radiative transfer code POSSIS to construct 5-dimensional kilonova grids where we employ different functional forms for the angular dependence of the dynamical ejecta component. We find that incorporating an angular dependence improves the fit to the AT2017gfo light-curves by up to ~50% when quantified in terms of the weighted Mean Square Error.
dc.description.sponsorshipWe thank Stephan Rosswog, Oleg Korobkin, and Masaomi Tanaka for sharing the heating rate libraries and opacities used in this work. We are grateful for computational resources provided by the Leonard E Parker Center for Gravitation, Cosmology and Astrophysics at the University of Wisconsin-Milwaukee, as well as by the Supercomputing Laboratory at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2112.15470.pdf
dc.titleUsing Neural Networks to Perform Rapid High-Dimensional Kilonova Parameter Inference
dc.typePreprint
dc.contributor.institutionAmerican University of Sharjah, Physics Department, PO Box 26666, Sharjah, UAE
dc.contributor.institutionZhiyuan College (School of Mathematical Sciences), Shanghai Jiao Tong University, Shanghai, 200240, China
dc.contributor.institutionThe Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-106 91 Stockholm, Sweden
dc.contributor.institutionInstitute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476, Potsdam, Germany
dc.contributor.institutionMax Planck Institute for Gravitational Physics (Albert Einstein Institute), Am M¨uhlenberg 1, D-14476 Potsdam, Germany
dc.contributor.institutionSchool of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, USA
dc.identifier.arxivid2112.15470
kaust.acknowledged.supportUnitSupercomputing Laboratory


This item appears in the following Collection(s)

Show simple item record