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arXiv · 2511.08580

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

Drishti Sharma·Purba Mukherjee·Anjan A Sen·Suhail Dhawan
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Abstract

We investigate the impact of instrumental and astrophysical systematics on dark energy (DE) constraints from Type Ia supernova (SN-Ia) observations. Using simulated datasets consistent with current SN-Ia measurements, we examine how photometric calibration, intergalactic dust, progenitor evolution in luminosity and light-curve stretch, intrinsic color scatter, and matter density mismatch affect the inferred DE equation of state (EoS) parameters $(w_0,w_a)$. We test the Generalised Scale Factor (GEN) parametrization against three time-evolving DE models: Chevallier-Polarski-Linder (CPL), Jassal-Bagla-Padmanabhan (JBP), and Logarithmic (LOG). Calibration and progenitor-related effects emerge as the dominant sources of bias. {In particular, a calibration offset of $\Delta M_B=0.02$ can shift the inferred parameters by up to $\Delta w_0 \simeq -0.12$ and $\Delta w_a \simeq +0.60$ in JBP, while the corresponding shift in GEN is much smaller, with $\Delta w_0 \simeq -0.02$ and $\Delta w_a \simeq -0.04$. Progenitor-stretch evolution also induces substantial shifts, whereas intergalactic dust and color-scatter systematics produce only minor deviations for the fiducial amplitudes adopted here. Overall, JBP is the most sensitive to injected systematics, CPL and LOG show intermediate sensitivity, and GEN remains the most stable. We also quantify the deviation from the fiducial $\Lambda$CDM ($w_0=-1,\, w_a=0$) for the injected-systematic cases, and find that the second set of systematic injections supports the same qualitative hierarchy. These results highlight the need for sub-percent calibration precision and improved astrophysical modelling for robust DE inference from present and future SN-Ia cosmology experiments. More broadly, our results motivate model-independent tests of late-time physics, with phenomenological $(w_0,w_a)$ parametrizations used as summary statistics.

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Drishti Sharma, Purba Mukherjee, Anjan A Sen, Suhail Dhawan. 2025-11-11. Exploring the Impact of Systematic Bias in Type Ia Supernova Cosmology Across Diverse Dark Energy Parametrizations. https://doi.org/10.1103/3dt9-qqnd

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