paper

Cosmological Results from the RAISIN Survey: Using Type Ia Supernovae in the Near Infrared as a Novel Path to Measure the Dark Energy Equation of State

arXiv:2201.07801 · doi:10.3847/1538-4357/ac755b

Abstract

Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012-2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST) to obtain rest-frame NIR light curves for a cosmologically distant sample of 37 SN Ia () discovered by Pan-STARRS and the Dark Energy Survey. By comparing higher- HST data with 42 SN Ia at observed in the NIR by the Carnegie Supernova Project, we construct a Hubble diagram from NIR observations (with only time of maximum light and some selection cuts from optical data) to pursue a unique avenue to constrain the dark energy equation of state parameter, . We analyze the dependence of the full set of Hubble residuals on the SN Ia host galaxy mass and find Hubble residual steps of size 0.06-0.1~mag with 1.5- to 2.5- significance depending on the method and step location. Combining our NIR sample with CMB constraints, we find (statsyst). The largest systematic errors are the redshift-dependent SN selection biases and the properties of the NIR mass step. We also use these data to measure km s Mpc from stars with geometric distance calibration in the hosts of 8 SNe Ia observed in the NIR versus km s Mpc using an inverse distance ladder approach tied to Planck. Using optical data we find and with optical and NIR data combined, we find ; these shifts of up to 0.11 in could point to inconsistency in optical versus NIR SN models. There will be many opportunities to improve this NIR measurement and better understand systematic uncertainties through larger low- samples, new light-curve models, calibration improvements, and by building high- samples from the Roman Space Telescope.

Updated cosmology constraints and text to match published version, and with data release at https://github.com/djones1040/RAISIN_DataRelease