We present constraints on cosmological parameters from the Pantheon+ analysis of 1701 light curves of 1550 distinct Type Ia supernovae (SNe Ia) ranging in redshift from z = 0.001 to 2.26. This work features an increased sample size from the addition of multiple cross-calibrated photometric systems of SNe covering an increased redshift span, and improved treatments of systematic uncertainties in comparison to the original Pantheon analysis, which together result in a factor of 2 improvement in cosmological constraining power. For a flat ΛCDM model, we find Ω M = 0.334 ± 0.018 from SNe Ia alone. For a flat w 0CDM model, we measure w 0 = −0.90 ± 0.14 from SNe Ia alone, H 0 = 73.5 ± 1.1 km s−1 Mpc−1 when including the Cepheid host distances and covariance (SH0ES), and w 0 = − 0.978 − 0.031 + 0.024 when combining the SN likelihood with Planck constraints from the cosmic microwave background (CMB) and baryon acoustic oscillations (BAO); both w 0 values are consistent with a cosmological constant. We also present the most precise measurements to date on the evolution of dark energy in a flat w 0 w a CDM universe, and measure w a = − 0.1 − 2.0 + 0.9 from Pantheon+ SNe Ia alone, H 0 = 73.3 ± 1.1 km s−1 Mpc−1 when including SH0ES Cepheid distances, and w a = − 0.65 − 0.32 + 0.28 when combining Pantheon+ SNe Ia with CMB and BAO data. Finally, we find that systematic uncertainties in the use of SNe Ia along the distance ladder comprise less than one-third of the total uncertainty in the measurement of H 0 and cannot explain the present “Hubble tension” between local measurements and early universe predictions from the cosmological model.
We describe the simulated data sample for the "Photometric LSST Astronomical Time Series Classification Challenge" (PLAsTiCC), a publicly available challenge to classify transient and variable events that will be observed by the Large Synoptic Survey Telescope (LSST), a new facility expected to start in the early 2020s. The challenge was hosted by Kaggle, ran from 2018 September 28 to 2018 December 17, and included 1,094 teams competing for prizes. Here we provide details of the 18 transient and variable source models, which were not revealed until after the challenge, and release the model libraries at https://doi.org/10.5281/zenodo.2612896. We describe the LSST Operations Simulator used to predict realistic observing conditions, and we describe the publicly available SNANA simulation code used to transform the models into observed fluxes and uncertainties in the LSST passbands (ugrizy). Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of type Ia supernova used to measure properties of dark energy. Our simulation framework will continue serving as a platform to improve the PLAsTiCC models, and to develop new models.
We present constraints on cosmological parameters from the Pantheon+ analysis of 1701 light curves of 1550 distinct Type Ia supernovae (SNe Ia) ranging in redshift from z = 0.001 to 2.26. This work features an increased sample size, increased redshift span, and improved treatment of systematic uncertainties in comparison to the original Pantheon analysis and results in a factor of 2 improvement in cosmological constraining power. For a FlatΛCDM model, we find Ω M = 0.338 ± 0.018 from SNe Ia alone. For a Flatw 0 CDM model, we measure w 0 = −0.89 ± 0.13 from SNe Ia alone, H 0 = 72.86 +0.94 −1.06 km s −1 Mpc −1 when including the Cepheid host distances and covariance (SH0ES), and w 0 = −0.978 +0.024 −0.031 when combining the SN likelihood with constraints from the cosmic microwave background (CMB) and baryon acoustic oscillations (BAO); both w 0 values are consistent with a cosmological constant. We also present the most precise measurements to date on the evolution of dark energy in a Flatw 0 w a CDM universe, and measure w a = −0.4 +1.0 −1.8 from Pantheon+ alone, H 0 = 73.40 +0.99 −1.22 km s −1 Mpc −1 when including SH0ES, and w a = −0.65 +0.28 −0.32 when combining Pan-theon+ with CMB and BAO data. Finally, we find that systematic uncertainties in the use of SNe Ia along the distance ladder comprise less than one third of the total uncertainty in the measurement of H 0 and cannot explain the present "Hubble tension" between local measurements and early-Universe predictions from the cosmological model.
A spectral-energy distribution (SED) model for Type Ia supernovae (SNe Ia) is a critical tool for measuring precise and accurate distances across a large redshift range and constraining cosmological parameters. We present an improved model framework, SALT3, which has several advantages over current models—including the leading SALT2 model (SALT2.4). While SALT3 has a similar philosophy, it differs from SALT2 by having improved estimation of uncertainties, better separation of color and light-curve stretch, and a publicly available training code. We present the application of our training method on a cross-calibrated compilation of 1083 SNe with 1207 spectra. Our compilation is 2.5× larger than the SALT2 training sample and has greatly reduced calibration uncertainties. The resulting trained SALT3.K21 model has an extended wavelength range 2000–11,000 Å (1800 Å redder) and reduced uncertainties compared to SALT2, enabling accurate use of low-z I and iz photometric bands. Including these previously discarded bands, SALT3.K21 reduces the Hubble scatter of the low-z Foundation and CfA3 samples by 15% and 10%, respectively. To check for potential systematic uncertainties, we compare distances of low (0.01 < z < 0.2) and high (0.4 < z < 0.6) redshift SNe in the training compilation, finding an insignificant 3 ± 14 mmag shift between SALT2.4 and SALT3.K21. While the SALT3.K21 model was trained on optical data, our method can be used to build a model for rest-frame NIR samples from the Roman Space Telescope. Our open-source training code, public training data, model, and documentation are available at https://saltshaker.readthedocs.io/en/latest/, and the model is integrated into the sncosmo and SNANA software packages.
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