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 S’anchez, Aurelien Valade, The LSST Dark Energy Science Collaboration
arXiv:2507.00157v1 Announce Type: new
Abstract: In this work, we investigate the feasibility of measuring the cosmic growth-rate parameter, $fsigma_8$, 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 $fsigma_8$ with a precision of $10%$ in the redshift range $ 0.02 arXiv:2507.00157v1 Announce Type: new
Abstract: In this work, we investigate the feasibility of measuring the cosmic growth-rate parameter, $fsigma_8$, 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 $fsigma_8$ with a precision of $10%$ in the redshift range $ 0.02