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Published September 20, 2017 | Published + Submitted
Journal Article Open

Trident: a universal tool for generating synthetic absorption spectra from astrophysical simulations

Abstract

Hydrodynamical simulations are increasingly able to accurately model physical systems on stellar, galactic, and cosmological scales; however, the utility of these simulations is often limited by our ability to directly compare them with the data sets produced by observers: spectra, photometry, etc. To address this problem, we have created trident, a Python-based open-source tool for post-processing hydrodynamical simulations to produce synthetic absorption spectra and related data. trident can (i) create absorption-line spectra for any trajectory through a simulated data set mimicking both background quasar and down-the-barrel configurations; (ii) reproduce the spectral characteristics of common instruments like the Cosmic Origins Spectrograph; (iii) operate across the ultraviolet, optical, and infrared using customizable absorption-line lists; (iv) trace simulated physical structures directly to spectral features; (v) approximate the presence of ion species absent from the simulation outputs; (vi) generate column density maps for any ion; and (vii) provide support for all major astrophysical hydrodynamical codes. trident was originally developed to aid in the interpretation of observations of the circumgalactic medium and intergalactic medium, but it remains a general tool applicable in other contexts.

Additional Information

© 2017 The American Astronomical Society. Received 2016 December 12; revised 2017 June 29; accepted 2017 July 4; published 2017 September 20. We are extremely grateful to our co-developers within the yt community for their assistance in coding, reviewing, testing, and timing releases of yt to match the trident development schedule. In particular, we wish to thank Matthew Turk, Nathan Goldbaum, Kacper Kowalik, and John ZuHone for going above and beyond the requirements of a open-source software community. Special thanks go out to Bili Dong, Lauren Corlies, and Andrew Emerick for early code contributions. trident benefited greatly from our discussions with the aforementioned and also Molly Peeples, Brian O'Shea, X Prochaska, Nicolas Tejos, Jess Werk, Jason Tumlinson, Nick Earl, Kate Rubin, Nicholas Lehner, Josh Peek, Chuck Steidel, Gwen Rudie, Sean Johnson, Ben Oppenheimer, Amanda Ford, Romeel Dave, Robert Thompson, David Weinberg, Neal Katz, Joel Primack, Ian McGreer, Greg Bryan, Phil Hopkins, Paul Torrey, Josh Suresh, Simeon Bird, Dus̆an Keres̆ Claude-André Faucher-Giguère, Erika Hamden, and Clayton Strawn. We are especially grateful to Charles Danforth and Ian McGreer for the quasar and Milky Way templates they provided. We thank Jacob Kneibel for his work on the early stages of generating realistic synthetic spectra, particularly in using COS LSFs. Thanks also go to Ji-hoon Kim and members of the agora consortium for providing data sets to test trident across many simulation formats. Support for this work was provided by NASA through Hubble Space Telescope Theory Grants HST-AR-13917, HST-AR-13919, HST-AR-13261.01-A and HST-AR-14315.001-A and ATP grants NNX09AD80G and NNX12AC98G from the Space Telescope Science Institute, operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. Additional support comes from the NSF through AST grants 0908819, 1615848, and the NSF Astronomy and Astrophysics Postdoctoral Fellowship program. Computational resources for this work were provided through NSF XSEDE grant TG-AST140018, NSF BlueWaters grants PRAC-gka and GLCPC_jth. This research also used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. We acknowledge access to NERSC resources made possible by University of California High-Performance AstroComputing Center (UC-HiPACC). Software: hdf,19 h5py,20 scipy,21 numpy22 (van der Walt et al. 2011), matplotlib23 (Hunter 2007), mpi (Forum 1994), mpi4py24 (Dalcin et al. 2005), yt25 (Turk et al. 2011), and astropy26 (Astropy Collaboration et al. 2013).

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Published - Hummels_2017_ApJ_847_59.pdf

Submitted - 1612.03935.pdf

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August 21, 2023
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