Optimizing BAO measurements with nonlinear transformations of the Lymanα forest
Abstract
We explore the effect of applying a nonlinear transformation to the Lymanα forest transmitted flux F=e{sup −τ} and the ability of analytic models to predict the resulting clustering amplitude. Both the largescale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signaltonoise ratio on large scales using Taylor expansion up to the third order. In particular, however, we achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore an analytic model for the largescale biasing of the Lyα forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to largescale density fluctuations, especially for very nonlinear transformations.
 Authors:
 Department of Physics, University of California, South Hall Rd, Berkeley (United States)
 Publication Date:
 OSTI Identifier:
 22525907
 Resource Type:
 Journal Article
 Resource Relation:
 Journal Name: Journal of Cosmology and Astroparticle Physics; Journal Volume: 2015; Journal Issue: 04; Other Information: Country of input: International Atomic Energy Agency (IAEA)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; BARYONS; DENSITY; FLUCTUATIONS; HYDRODYNAMICS; LYMAN LINES; NONLINEAR PROBLEMS; OPTIMIZATION; OSCILLATIONS; SIGNALTONOISE RATIO; TRANSFORMATIONS; VELOCITY
Citation Formats
Wang, Xinkang, FontRibera, Andreu, and Seljak, Uroš, Email: xinkang.wang@berkeley.edu, Email: afont@lbl.gov, Email: useljak@berkeley.edu. Optimizing BAO measurements with nonlinear transformations of the Lymanα forest. United States: N. p., 2015.
Web. doi:10.1088/14757516/2015/04/009.
Wang, Xinkang, FontRibera, Andreu, & Seljak, Uroš, Email: xinkang.wang@berkeley.edu, Email: afont@lbl.gov, Email: useljak@berkeley.edu. Optimizing BAO measurements with nonlinear transformations of the Lymanα forest. United States. doi:10.1088/14757516/2015/04/009.
Wang, Xinkang, FontRibera, Andreu, and Seljak, Uroš, Email: xinkang.wang@berkeley.edu, Email: afont@lbl.gov, Email: useljak@berkeley.edu. 2015.
"Optimizing BAO measurements with nonlinear transformations of the Lymanα forest". United States.
doi:10.1088/14757516/2015/04/009.
@article{osti_22525907,
title = {Optimizing BAO measurements with nonlinear transformations of the Lymanα forest},
author = {Wang, Xinkang and FontRibera, Andreu and Seljak, Uroš, Email: xinkang.wang@berkeley.edu, Email: afont@lbl.gov, Email: useljak@berkeley.edu},
abstractNote = {We explore the effect of applying a nonlinear transformation to the Lymanα forest transmitted flux F=e{sup −τ} and the ability of analytic models to predict the resulting clustering amplitude. Both the largescale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signaltonoise ratio on large scales using Taylor expansion up to the third order. In particular, however, we achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore an analytic model for the largescale biasing of the Lyα forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to largescale density fluctuations, especially for very nonlinear transformations.},
doi = {10.1088/14757516/2015/04/009},
journal = {Journal of Cosmology and Astroparticle Physics},
number = 04,
volume = 2015,
place = {United States},
year = 2015,
month = 4
}

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