Exploring the Impact of Systematic Bias in Type Ia Supernova Cosmology Across Diverse Dark Energy Parametrizations

TL;DR

This study assesses how instrumental and astrophysical systematics influence dark energy constraints from Type Ia supernova data, highlighting calibration and progenitor evolution as key uncertainties affecting different dark energy parametrizations.

Contribution

It introduces the Generalised Scale Factor (GEN) parametrization as more stable against systematics compared to traditional models like CPL, JBP, and LOG.

Findings

Calibration biases are the dominant systematic source.

Progenitor evolution significantly affects dark energy constraints.

GEN parametrization remains stable under systematic uncertainties.

Abstract

We investigate the impact of instrumental and astrophysical systematics on dark energy constraints derived from Type~Ia supernova (SN-Ia) observations. Using simulated datasets consistent with current SN-Ia measurements, we explore how uncertainties in photometric calibration, intergalactic dust, progenitor evolution in luminosity and light-curve stretch, and intrinsic color scatter affect the inferred dark energy equation of state parameters (w0, wa). We test the Generalised Scale Factor (GEN) evolution and benchmark it against three time-evolving dark energy models; namely Chevallier Polarski Linder (CPL), Jassal Bagla Padmanabhan (JBP) and Logarithmic (LOG) parametrizations; comparing their sensitivity to these systematic effects. Calibration biases and progenitor evolution emerge as the dominant sources of uncertainty, while simpler parametrisations, viz. GEN, which directly describes the expansion rate, remains relatively stable under all systematic injections, unlike CPL, JBP and LOG that rely on the dark energy equation of state. These findings underscore the need for sub-per cent calibration precision and enhanced astrophysical modelling to ensure the robustness of dark energy inferences from current and future SN-Ia cosmology experiments.