Growth-rate measurement with type-Ia supernovae using ZTF survey simulations

Measurements of the growth rate of structures at \(z < 0.1\) with peculiar velocity surveys have the potential of testing the validity of general relativity on cosmic scales. In this work, we present growth-rate measurements from realistic simulated sets of type-Ia supernovae (SNe Ia) from the Zw...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Bastien Carreres, Bautista, Julian E, Feinstein, Fabrice, Fouchez, Dominique, Racine, Benjamin, Smith, Mathew, Amenouche, Mellissa, Aubert, Marie, Dhawan, Suhail, Ginolin, Madeleine, Goobar, Ariel, Gris, Philippe, Lacroix, Leander, Nuss, Eric, Regnault, Nicolas, Rigault, Mickael, Estelle, Robert, Rosnet, Philippe, Sommer, Kelian, Dekany, Richard, Groom, Steven L, Sravan, Niharika, Masci, Frank J, Purdum, Josiah
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.06.2023
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Measurements of the growth rate of structures at \(z < 0.1\) with peculiar velocity surveys have the potential of testing the validity of general relativity on cosmic scales. In this work, we present growth-rate measurements from realistic simulated sets of type-Ia supernovae (SNe Ia) from the Zwicky Transient Facility (ZTF). We describe our simulation methodology, the light-curve fitting and peculiar velocity estimation. Using the maximum likelihood method, we derive constraints on \(f\sigma_8\) using only ZTF SN Ia peculiar velocities. We carefully tested the method and we quantified biases due to selection effects (photometric detection, spectroscopic follow-up for typing) on several independent realizations. We simulated the equivalent of 6 years of ZTF data, and considering an unbiased spectroscopically typed sample at \(z < 0.06\), we obtained unbiased estimates of \(f\sigma_8\) with an average uncertainty of 19% precision. We also investigated the information gain in applying bias correction methods. Our results validate our framework which can be used on real ZTF data.
ISSN:2331-8422
DOI:10.48550/arxiv.2303.01198