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The Dark Energy Bedrock All-Sky Supernova Program: Cross Calibration, Simulations, and Cosmology Forecasts

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arxiv 2508.10877 v2 pith:C2CITP2Z submitted 2025-08-14 astro-ph.CO

classification astro-ph.CO
keywords darkenergydebasscalibrationsimulationslow-redshiftresidualsample
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Type Ia supernovae (SNe Ia) have been essential for probing the nature of dark energy; however, most SN analyses rely on the same low-redshift sample, which may lead to shared systematics. In a companion paper (arXiv:2508.10878), we introduce the Dark Energy Bedrock All-Sky Supernova (DEBASS) program, which has already collected more than 500 low-redshift SNe Ia on the Dark Energy Camera (DECam), and present an initial release of 77 SNe Ia within the Dark Energy Survey (DES) footprint observed between 2021 and 2024. Here, we examine the systematics, including photometric calibration and selection effects. We find agreement at the 10 millimagnitude level among the tertiary standard stars of DEBASS, DES, and Pan-STARRS1. Our simulations reproduce the observed distributions of DEBASS SN light-curve properties, and we measure a bias-corrected Hubble residual scatter of $0.08$ mag, which, while small, is found in 10% of our simulations. We compare the DEBASS SN distances to the Foundation sample and find consistency with a median residual offset of $0.016 \pm 0.019$ mag. Selection effects have negligible impacts on distances, but a different photometric calibration solution shifts the median residual $-0.015 \pm 0.019$ mag, highlighting calibration sensitivity. Using conservative simulations, we forecast that replacing historical low-redshift samples with the full DEBASS sample (>400 SNe Ia) will improve the statistical uncertainties on dark energy parameters $w_0$ and $w_a$ by 30% and 24% respectively, enhance the dark energy Figure of Merit by up to 60%, and enable a measurement of $f\sigma_8$ at the 25% level.

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Cited by 2 Pith papers

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    astro-ph.CO 2026-08 conditional novelty 6.0 of 10

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  2. A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    A simulation-based inference pipeline (Stjörnumál) fits SN Ia dust and intrinsic scatter models to DES 5-year data, enabling fast Bayesian model comparison across seven SN Ia population models.

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