Published May 2023 | Version Published
Journal Article Open

A new versatile code for gamma-ray Monte-Carlo radiative transfer

  • 1. ROR icon SUNY Polytechnic Institute
  • 2. ROR icon California Institute of Technology

Abstract

Ongoing MeV telescopes such as INTEGRAL/SPI and Fermi/GBM, and proposed telescopes including the recently accepted COSI and the e-ASTROGAM and AMEGO missions, provide another window in understanding transients. Their signals contain information about the stellar explosion mechanisms and their corresponding nucleosynthesis of short-lived radioactive isotopes. This raises the need of a radiative transfer code which may efficiently explore different types of astrophysical γ-ray sources and their dependence on model parameters and input physics. In view of this, we present our new Monte-Carlo Radiative Transfer code in python. The code synthesizes the γ-ray spectra and light curves suitable for modelling supernova ejecta, including C+O novae, O+Ne novae, Type Ia and core-collapse supernovae. We test the code extensively for reproducing results consistent with analytic models. We also compare our results with similar models in the literature and discuss how our code depends on selected input physics and setting.

Additional Information

© 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). S.C.L. acknowledges support by NASA grants HST-AR-15021.001-A and 80NSSC18K1017. S.C.L. thank Thomas Siegert for the encouragement and many ideas during the development of this code. Data Availability: The data underlying this article will be shared on reasonable request to the corresponding author. The source code is available on Zenodo (10.5281/zenodo.6578600). This project is done with the use of Python libraries: Matplotlib (Hunter 2007), Pandas (pandas development team 2020), Numpy (Harris et al. 2020), Scikit-Learn (Pedregosa et al. 2011).

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Additional details

Identifiers

Eprint ID
121783
Resolver ID
CaltechAUTHORS:20230613-730222500.3

Related works

Describes
10.5281/zenodo.6578600 (DOI)

Funding

NASA Hubble Fellowship
HST-AR-15021.001-A
NASA
80NSSC18K1017

Dates

Created
2023-06-15
Created from EPrint's datestamp field
Updated
2023-06-15
Created from EPrint's last_modified field

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