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Title: Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

Abstract

We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution, the conditional luminosity function, abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. The package has an optimized toolkit to make mock observations on a synthetic galaxy population—including galaxy clustering, galaxy–galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others—allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation. Halotools has an automated testing suite and is exhaustively documented on http://halotools.readthedocs.io, which includes quickstart guides, source code notes and a large collection of tutorials. Themore » documentation is effectively an online textbook on how to build and study empirical models of galaxy formation with Python.« less

Authors:
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Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Aeronautic and Space Administration (NASA); National Science Foundation (NSF); USDOE Office of Science - Office of High Energy Physics
OSTI Identifier:
1417046
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Astronomical Journal (Online)
Additional Journal Information:
Journal Volume: 154; Journal Issue: 5; Journal ID: ISSN 1538-3881
Publisher:
IOP Publishing - AAAS
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; galaxies: statistics; cosmology: theory; galaxies: halos; large-scale structure of universe

Citation Formats

Hearin, Andrew P., Campbell, Duncan, Tollerud, Erik, Behroozi, Peter, Diemer, Benedikt, Goldbaum, Nathan J., Jennings, Elise, Leauthaud, Alexie, Mao, Yao-Yuan, More, Surhud, Parejko, John, Sinha, Manodeep, Sipöcz, Brigitta, and Zentner, Andrew. Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools. United States: N. p., 2017. Web. doi:10.3847/1538-3881/aa859f.
Hearin, Andrew P., Campbell, Duncan, Tollerud, Erik, Behroozi, Peter, Diemer, Benedikt, Goldbaum, Nathan J., Jennings, Elise, Leauthaud, Alexie, Mao, Yao-Yuan, More, Surhud, Parejko, John, Sinha, Manodeep, Sipöcz, Brigitta, & Zentner, Andrew. Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools. United States. doi:10.3847/1538-3881/aa859f.
Hearin, Andrew P., Campbell, Duncan, Tollerud, Erik, Behroozi, Peter, Diemer, Benedikt, Goldbaum, Nathan J., Jennings, Elise, Leauthaud, Alexie, Mao, Yao-Yuan, More, Surhud, Parejko, John, Sinha, Manodeep, Sipöcz, Brigitta, and Zentner, Andrew. Wed . "Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools". United States. doi:10.3847/1538-3881/aa859f.
@article{osti_1417046,
title = {Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools},
author = {Hearin, Andrew P. and Campbell, Duncan and Tollerud, Erik and Behroozi, Peter and Diemer, Benedikt and Goldbaum, Nathan J. and Jennings, Elise and Leauthaud, Alexie and Mao, Yao-Yuan and More, Surhud and Parejko, John and Sinha, Manodeep and Sipöcz, Brigitta and Zentner, Andrew},
abstractNote = {We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution, the conditional luminosity function, abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. The package has an optimized toolkit to make mock observations on a synthetic galaxy population—including galaxy clustering, galaxy–galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others—allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation. Halotools has an automated testing suite and is exhaustively documented on http://halotools.readthedocs.io, which includes quickstart guides, source code notes and a large collection of tutorials. The documentation is effectively an online textbook on how to build and study empirical models of galaxy formation with Python.},
doi = {10.3847/1538-3881/aa859f},
journal = {Astronomical Journal (Online)},
issn = {1538-3881},
number = 5,
volume = 154,
place = {United States},
year = {2017},
month = {10}
}

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    Works referencing / citing this record:

    Maturing satellite kinematics into a competitive probe of the galaxy–halo connection
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