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Title: LyaCoLoRe: synthetic datasets for current and future Lyman-α forest BAO surveys

Abstract

© 2020 IOP Publishing Ltd and Sissa Medialab. The statistical power of Lyman-α forest Baryon Acoustic Oscillation (BAO) measurements is set to increase significantly in the coming years as new instruments such as the Dark Energy Spectroscopic Instrument deliver progressively more constraining data. Generating mock datasets for such measurements will be important for validating analysis pipelines and evaluating the effects of systematics. With such studies in mind, we present LyaCoLoRe: A package for producing synthetic Lyman-α forest survey datasets for BAO analyses. LyaCoLoRe transforms initial Gaussian random field skewers into skewers of transmitted flux fraction via a number of fast approximations. In this work we explain the methods of producing mock datasets used in LyaCoLoRe, and then measure correlation functions on a suite of realisations of such data. We demonstrate that we are able to recover the correct BAO signal, as well as large-scale bias parameters similar to literature values. Finally, we briefly describe methods to add further astrophysical effects to our skewers-high column density systems and metal absorbers-which act as potential complications for BAO analyses.

Authors:
 [1];  [1];  [2];  [3];  [4];  [1];  [5];  [6];  [1];  [1];  [7];  [8];  [7];  [3];  [7];  [9];  [7];  [10];  [11];  [12] more »;  [8] « less
+ Show Author Affiliations
  1. Univ. College London (United Kingdom)
  2. Univ. of Utah, Salt Lake City, UT (United States)
  3. Univ. Nacional Autonoma de Mexico (UNAM), Mexico City (Mexico)
  4. Univ. of California, Irvine, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  5. Univ. of Guanajuato (Mexico); Consejo Nacional de Ciencia y Tecnologia, Mexico City (Mexico)
  6. Univ. of Oxford (United Kingdom)
  7. Univ. Paris-Saclay, Gif-sur-Yvette (France)
  8. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  9. Sorbonne Univ., Paris (France)
  10. Brookhaven National Lab. (BNL), Upton, NY (United States)
  11. Univ. of Michigan, Ann Arbor, MI (United States)
  12. Boston Univ., MA (United States)
Publication Date:
Research Org.:
Univ. of Michigan, Ann Arbor, MI (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP); National Science Foundation (NSF)
OSTI Identifier:
1682234
Alternate Identifier(s):
OSTI ID: 1737605
Grant/Contract Number:  
SC0019193; AC02-05CH1123; AST-0950945; ST/N003853/1; ST/R000476/1
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Cosmology and Astroparticle Physics
Additional Journal Information:
Journal Volume: 2020; Journal Issue: 03; Journal ID: ISSN 1475-7516
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; cosmology; Lyman alpha forest; baryon acoustic oscillations

Citation Formats

Farr, James, Font-Ribera, Andreu, Bourboux, Hélion des, Muñoz-Gutiérrez, Andrea, Sánchez, F. Javier, Pontzen, Andrew, González-Morales, Alma Xochitl, Alonso, David, Brooks, David, Doel, Peter, Etourneau, Thomas, Guy, Julien, Goff, Jean-Marc Le, Macorra, Axel la, Palanque-Delabrouille, Nathalie, Pérez-Ràfols, Ignasi, Rich, James, Slosar, Anže, Tarle, Gregory, Yutong, Duan, and Zhang, Kai. LyaCoLoRe: synthetic datasets for current and future Lyman-α forest BAO surveys. United States: N. p., 2020. Web. doi:10.1088/1475-7516/2020/03/068.
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Farr, James, Font-Ribera, Andreu, Bourboux, Hélion des, Muñoz-Gutiérrez, Andrea, Sánchez, F. Javier, Pontzen, Andrew, González-Morales, Alma Xochitl, Alonso, David, Brooks, David, Doel, Peter, Etourneau, Thomas, Guy, Julien, Goff, Jean-Marc Le, Macorra, Axel la, Palanque-Delabrouille, Nathalie, Pérez-Ràfols, Ignasi, Rich, James, Slosar, Anže, Tarle, Gregory, Yutong, Duan, & Zhang, Kai. LyaCoLoRe: synthetic datasets for current and future Lyman-α forest BAO surveys. United States. https://doi.org/10.1088/1475-7516/2020/03/068
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Farr, James, Font-Ribera, Andreu, Bourboux, Hélion des, Muñoz-Gutiérrez, Andrea, Sánchez, F. Javier, Pontzen, Andrew, González-Morales, Alma Xochitl, Alonso, David, Brooks, David, Doel, Peter, Etourneau, Thomas, Guy, Julien, Goff, Jean-Marc Le, Macorra, Axel la, Palanque-Delabrouille, Nathalie, Pérez-Ràfols, Ignasi, Rich, James, Slosar, Anže, Tarle, Gregory, Yutong, Duan, and Zhang, Kai. Tue . "LyaCoLoRe: synthetic datasets for current and future Lyman-α forest BAO surveys". United States. https://doi.org/10.1088/1475-7516/2020/03/068. https://www.osti.gov/servlets/purl/1682234.
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@article{osti_1682234,
title = {LyaCoLoRe: synthetic datasets for current and future Lyman-α forest BAO surveys},
author = {Farr, James and Font-Ribera, Andreu and Bourboux, Hélion des and Muñoz-Gutiérrez, Andrea and Sánchez, F. Javier and Pontzen, Andrew and González-Morales, Alma Xochitl and Alonso, David and Brooks, David and Doel, Peter and Etourneau, Thomas and Guy, Julien and Goff, Jean-Marc Le and Macorra, Axel la and Palanque-Delabrouille, Nathalie and Pérez-Ràfols, Ignasi and Rich, James and Slosar, Anže and Tarle, Gregory and Yutong, Duan and Zhang, Kai},
abstractNote = {© 2020 IOP Publishing Ltd and Sissa Medialab. The statistical power of Lyman-α forest Baryon Acoustic Oscillation (BAO) measurements is set to increase significantly in the coming years as new instruments such as the Dark Energy Spectroscopic Instrument deliver progressively more constraining data. Generating mock datasets for such measurements will be important for validating analysis pipelines and evaluating the effects of systematics. With such studies in mind, we present LyaCoLoRe: A package for producing synthetic Lyman-α forest survey datasets for BAO analyses. LyaCoLoRe transforms initial Gaussian random field skewers into skewers of transmitted flux fraction via a number of fast approximations. In this work we explain the methods of producing mock datasets used in LyaCoLoRe, and then measure correlation functions on a suite of realisations of such data. We demonstrate that we are able to recover the correct BAO signal, as well as large-scale bias parameters similar to literature values. Finally, we briefly describe methods to add further astrophysical effects to our skewers-high column density systems and metal absorbers-which act as potential complications for BAO analyses.},
doi = {10.1088/1475-7516/2020/03/068},
journal = {Journal of Cosmology and Astroparticle Physics},
number = 03,
volume = 2020,
place = {United States},
year = {2020},
month = {3}
}
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