Role of future SNIa data from Rubin LSST in reinvestigating cosmological models

Author:

Shah Rahul1ORCID,Mitra Ayan234ORCID,Mukherjee Purba1ORCID,Pal Barun5ORCID,Pal Supratik16ORCID

Affiliation:

1. Physics and Applied Mathematics Unit, Indian Statistical Institute , 203 B.T. Road, Kolkata 700 108 , India

2. Center for AstroPhysical Surveys, National Center for Supercomputing Applications, University of Illinois Urbana-Champaign , Urbana, IL 61801 , USA

3. Department of Astronomy, University of Illinois at Urbana-Champaign , Urbana, IL 61801 , USA

4. School of Material Science, Kazakh-British Technical University , 59 Tole bi street, Almaty 050000 , Kazakhstan

5. Department of Mathematics, Netaji Nagar College for Women , 170/13/1 N.S.C. Bose Road, Regent Estate, Kolkata 700092 , India

6. Technology Innovation Hub on Data Science, Big Data Analytics and Data Curation, Indian Statistical Institute , 203 B.T. Road, Kolkata 700 108 , India

Abstract

ABSTRACT We study how future Type Ia supernovae (SNIa) standard candles detected by the Vera C. Rubin Observatory (LSST) can constrain some cosmological models. We use a realistic 3-yr SNIa simulated data set generated by the LSST Dark Energy Science Collaboration time domain pipeline, which includes a mix of spectroscopic and photometrically identified candidates. We combine these data with cosmic microwave background (CMB) and baryon acoustic oscillation (BAO) measurements to estimate the dark energy model parameters for two models – the baseline Lambda cold dark matter (ΛCDM) and Chevallier–Polarski–Linder (CPL) dark energy parametrization. We compare them with the current constraints obtained from the joint analysis of the latest real data from the Pantheon SNIa compilation, CMB from Planck 2018 and BAO. Our analysis finds tighter constraints on the model parameters along with a significant reduction of correlation between H0 and σ8,0. We find that LSST is expected to significantly improve upon the existing SNIa data in the critical analysis of cosmological models.

Funder

ISI

Department of Science and Technology

Publisher

Oxford University Press (OUP)

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