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Determining the systemic redshift of Lyman α emitters with neural networks and improving the measured large-scale clustering

Gurung-López, Siddhartha; Saito, Shun; Baugh, Carlton M; Bonoli, Silvia; Lacey, Cedric G; Orsi, Álvaro A

Determining the systemic redshift of Lyman α emitters with neural networks and improving the measured large-scale clustering Thumbnail


Authors

Siddhartha Gurung-López

Shun Saito

Silvia Bonoli

Álvaro A Orsi



Abstract

We explore how to mitigate the clustering distortions in Lyman α emitter (LAE) samples caused by the misidentification of the Lyman α (⁠Lyα⁠) wavelength in their Lyα line profiles. We use the Lyα line profiles from our previous LAE theoretical model that includes radiative transfer in the interstellar and intergalactic mediums. We introduce a novel approach to measure the systemic redshift of LAEs from their Lyα line using neural networks. In detail, we assume that for a fraction of the whole LAE population their systemic redshift is determined precisely through other spectral features. We then use this subset to train a neural network that predicts the Lyα wavelength given an Lyα line profile. We test two different training sets: (i) the LAEs are selected homogeneously and (ii) only the brightest LAE is selected. In comparison with previous approaches in the literature, our methodology improves significantly the accuracy in determining the Lyα wavelength. In fact, after applying our algorithm in ideal Lyα line profiles, we recover the clustering unperturbed down to 1cMpch−1⁠. Then, we test the performance of our methodology in realistic Lyα line profiles by downgrading their quality. The machine learning technique using the uniform sampling works well even if the Lyα line profile quality is decreased considerably. We conclude that LAE surveys such as HETDEX would benefit from determining with high accuracy the systemic redshift of a subpopulation and applying our methodology to estimate the systemic redshift of the rest of the galaxy sample.

Citation

Gurung-López, S., Saito, S., Baugh, C. M., Bonoli, S., Lacey, C. G., & Orsi, Á. A. (2021). Determining the systemic redshift of Lyman α emitters with neural networks and improving the measured large-scale clustering. Monthly Notices of the Royal Astronomical Society, 500(1), 603-626. https://doi.org/10.1093/mnras/staa3269

Journal Article Type Article
Acceptance Date Oct 11, 2020
Online Publication Date Oct 22, 2020
Publication Date 2021-01
Deposit Date Jun 29, 2021
Publicly Available Date Mar 29, 2024
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Royal Astronomical Society
Peer Reviewed Peer Reviewed
Volume 500
Issue 1
Pages 603-626
DOI https://doi.org/10.1093/mnras/staa3269

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Copyright Statement
This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2020 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.





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