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SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals

Authors :
Baxter, Amanda L.
BenZvi, Segev
Jaimes, Joahan Castaneda
Coleiro, Alexis
Molla, Marta Colomer
Dornic, Damien
Goldhagen, Tomer
Graf, Anne M.
Griswold, Spencer
Habig, Alec
Hill, Remington
Lang, Shunsaku Horiuchi James P. Kneller Rafael F.
Lincetto, Massimiliano
Migenda, Jost
Nakamura, Ko
O'Connor, Evan
Renshaw, Andrew
Scholberg, Kate
Uberoi, Navya
Worlikar, Arkin
Publication Year :
2021

Abstract

Current neutrino detectors will observe hundreds to thousands of neutrinos from a Galactic supernovae, and future detectors will increase this yield by an order of magnitude or more. With such a data set comes the potential for a huge increase in our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the properties of the neutrino. However, there is currently a large gap between supernova simulations and the corresponding signals in neutrino detectors, which will make any comparison between theory and observation very difficult. SNEWPY is an open-source software package which bridges this gap. The SNEWPY code can interface with supernova simulation data to generate from the model either a time series of neutrino spectral fluences at Earth, or the total time-integrated spectral fluence. Data from several hundred simulations of core-collapse, thermonuclear, and pair-instability supernovae is included in the package. This output may then be used by an event generator such as sntools or an event rate calculator such as SNOwGLoBES. Additional routines in the SNEWPY package automate the processing of the generated data through the SNOwGLoBES software and collate its output into the observable channels of each detector. In this paper we describe the contents of the package, the physics behind SNEWPY, the organization of the code, and provide examples of how to make use of its capabilities.<br />Comment: Software available at https://github.com/SNEWS2/snewpy

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2109.08188
Document Type :
Working Paper