Calibration-induced Systematics in SALT3 Training and Their Impact on Dark Energy Constraints from Stage IV Supernova Surveys
13 affiliations
- 1Duke University
- 2University of Hawai’i
- 3University of Chicago
- 4Stockholm University
- 5Stanford University
- 6SLAC National Accelerator Laboratory
- 7University of Oxford
- 8University of Michigan
- 9Baylor University
- 10Boston University
- 11University of Pennsylvania
- 12UMBC
- 13NASA Goddard Space Flight Center
Abstract
In the coming years, the Vera Rubin Observatory’s Legacy Survey of Space and Time (Rubin-LSST) and the Nancy Grace Roman Space Telescope’s (Roman) High Latitude Time Domain Survey (HLTDS) are expected to discover more than a million Type Ia supernovae (SNe Ia), several orders of magnitude more than current samples and with a tighter control on systematic uncertainties. One of the largest systematic uncertainties in cosmological analyses with SNe Ia is the accuracy of the spectrophotometric model for SNe Ia time series data, which depends on the photometric calibration of the surveys. To quantify the impact of this uncertainty, we analyze simulated Rubin-LSST and HLTDS data, perturb the photometric zero-points and filter mean wavelengths, and propagate these systematics to spectral model recovery, estimated distances, and dark energy Figure of Merit (FoM) based on the w ₀ w a CDM model. Zero-point shifts of 5 mmag and filter mean wavelength shifts of 5 Å lead to a ∼50% decrease in the FoM relative to a statistical-only case when calibration uncertainties are propagated only through light-curve fitting. The same calibration shifts applied only during model training produce a smaller ∼13% degradation. Contrary to previous analyses, calibration uncertainties in light-curve fitting dominate over those from model training. Their effect during light-curve fitting varies smoothly with redshift and is nearly degenerate with cosmology, preventing mitigation through self-calibration. Finally, we show that the FoM dependence on the size of the calibration uncertainties (in the range of expected sizes) is roughly linear.
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BibTeX
@article{anumba2026calibrationinduced,
title = {{Calibration-induced Systematics in SALT3 Training and Their Impact on Dark Energy Constraints from Stage IV Supernova Surveys}},
author = {Kene Anumba and David O. Jones and Richard Kessler and Daniel Scolnic and W. D’Arcy Kenworthy and Rebecca C. Chen and Bastien Carreres and Maria Vincenzi and Erik R. Peterson and Maria Acevedo and Ben Rose and Dillon Brout and Jillian Paulin and Rujuta A. Purohit and Rebekah Hounsell and The Roman Supernova Cosmology Project Infrastructure Team},
journal = {The Astrophysical Journal},
year = {2026},
month = oct,
doi = {10.3847/1538-4357/aea56b},
url = {https://iopscience.iop.org/article/10.3847/1538-4357/aea56b},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Calibration-induced Systematics in SALT3 Training and Their Impact on Dark Energy Constraints from Stage IV Supernova Surveys
AU - Kene Anumba
AU - David O. Jones
AU - Richard Kessler
AU - Daniel Scolnic
AU - W. D’Arcy Kenworthy
AU - Rebecca C. Chen
AU - Bastien Carreres
AU - Maria Vincenzi
AU - Erik R. Peterson
AU - Maria Acevedo
AU - Ben Rose
AU - Dillon Brout
AU - Jillian Paulin
AU - Rujuta A. Purohit
AU - Rebekah Hounsell
AU - The Roman Supernova Cosmology Project Infrastructure Team
PY - 2026
DA - 2026/10/07
JO - The Astrophysical Journal
DO - 10.3847/1538-4357/aea56b
UR - https://iopscience.iop.org/article/10.3847/1538-4357/aea56b
AB - In the coming years, the Vera Rubin Observatory’s Legacy Survey of Space and Time (Rubin-LSST) and the Nancy Grace Roman Space Telescope’s (Roman) High Latitude Time Domain Survey (HLTDS) are expected to discover more than a million Type Ia supernovae (SNe Ia), several orders of magnitude more than current samples and with a tighter control on systematic uncertainties. One of the largest systematic uncertainties in cosmological analyses with SNe Ia is the accuracy of the spectrophotometric model for SNe Ia time series data, which depends on the photometric calibration of the surveys. To quantify the impact of this uncertainty, we analyze simulated Rubin-LSST and HLTDS data, perturb the photometric zero-points and filter mean wavelengths, and propagate these systematics to spectral model recovery, estimated distances, and dark energy Figure of Merit (FoM) based on the w ₀ w a CDM model. Zero-point shifts of 5 mmag and filter mean wavelength shifts of 5 Å lead to a ∼50% decrease in the FoM relative to a statistical-only case when calibration uncertainties are propagated only through light-curve fitting. The same calibration shifts applied only during model training produce a smaller ∼13% degradation. Contrary to previous analyses, calibration uncertainties in light-curve fitting dominate over those from model training. Their effect during light-curve fitting varies smoothly with redshift and is nearly degenerate with cosmology, preventing mitigation through self-calibration. Finally, we show that the FoM dependence on the size of the calibration uncertainties (in the range of expected sizes) is roughly linear.
SN - 0004-637X
SN - 1538-4357
ER -