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arXiv · 2507.00157

Forecast for growth-rate measurement using peculiar velocities from LSST supernovae

Damiano Rosselli·Bastien Carreres·Corentin Ravoux·Julian E. Bautista·Dominique Fouchez·Alex G. Kim·Benjamin Racine·Fabrice Feinstein·Bruno Sánchez·Aurelien Valade·The LSST Dark Energy Science Collaboration
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Abstract

In this work, we investigate the feasibility of measuring the cosmic growth-rate parameter, $f\sigma_8$, using peculiar velocities (PVs) derived from Type Ia supernovae (SNe Ia) in the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). We produce simulations of different SN types using a realistic LSST observing strategy, incorporating noise, photometric detection from the Difference Image Analysis (DIA) pipeline, and a PV field modeled from the Uchuu UniverseMachine simulations. We test three observational scenarios, ranging from ideal conditions with spectroscopic host-galaxy redshifts and spectroscopic SN classification, to more realistic settings involving photometric classification and contamination from non-Ia supernovae. Using a maximum-likelihood technique, we show that LSST can measure $f\sigma_8$ with a precision of $10\%$ in the redshift range $ 0.02 < z < 0.14 $ in the most realistic case. Using three tomographic bins, LSST can constrain the growth-rate parameter with errors below $18\%$ up to $z = 0.14$. We also test the impact of contamination on the maximum likelihood method and find that for contamination fractions below $\sim 2\%$, the measurement remains unbiased. These results highlight the potential of the LSST SN Ia sample to complement redshift-space distortion measurements at high redshift, providing a novel avenue for testing general relativity and dark energy models.

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Damiano Rosselli, Bastien Carreres, Corentin Ravoux, Julian E. Bautista, Dominique Fouchez, Alex G. Kim, Benjamin Racine, Fabrice Feinstein, Bruno Sánchez, Aurelien Valade, The LSST Dark Energy Science Collaboration. 2025-06-30. Forecast for growth-rate measurement using peculiar velocities from LSST supernovae. https://doi.org/10.1051/0004-6361%2F202556181

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