Type Ia supernovae (SNe Ia) are standardizable candles: their peak magnitudes can be corrected for correlations between light-curve properties and their luminosities to precisely estimate distances. Understanding SN Ia standardization across wavelength improves methods for correcting SN Ia magnitudes. Using 150 SNe Ia from the Foundation Supernova Survey and Young Supernova Experiment, we present the first study focusing on SN Ia standardization properties in the z band. Straddling the optical and near-infrared, SN Ia light in the z band is less sensitive to dust extinction and can be collected alongside the optical on CCDs. Pre-standardization, SNe Ia exhibit less residual scatter in z-band peak magnitudes than in the g and r bands. SNe Ia peak z-band magnitudes still exhibit a significant dependence on light-curve shape. Post-standardization, the z-band Hubble diagram has a total scatter of root mean square =0.195 mag. We infer a z-band mass step of  -0.105±0.031 mag, which is consistent within 1σ of that estimated from gri data, assuming R_v=2.61⁠. When assuming different R_v values for high and low mass host galaxies, the z band and optical mass steps remain consistent within 1⁠σ. Based on current statistical precision, these results suggest dust reddening cannot fully explain the mass step. SNe Ia in the z band exhibit complementary standardizability properties to the optical that can improve distance estimates. Understanding these properties is important for the upcoming Vera Rubin Observatory and Nancy G. Roman Space Telescope, which will probe the rest-frame z band to redshifts 0.1 and 1.8.

We present a simple method for assessing the predictive performance of high-dimensional models directly in data space when only samples are available. Our approach is to compare the quantiles of observables predicted by a model to those of the observables themselves. In cases where the dimensionality of the observables is large (e.g., multiband galaxy photometry), we advocate that the comparison is made after projection onto a set of principal axes to reduce the dimensionality. We demonstrate our method on a series of two-dimensional examples. We then apply it to results from a state-of-the-art generative model for galaxy photometry () that generates predictions of colors and magnitudes by forward simulating from a 16-dimensional distribution of physical parameters represented by a score-based diffusion model. We validate the predictive performance of this model directly in a space of nine broadband colors. Although motivated by this specific example, we expect that the techniques we present will be broadly useful for evaluating the performance of flexible, nonparametric population models of this kind, and other settings where two sets of samples are to be compared.

Context. Type Ia supernovae (SNe Ia) are a key probe in modern cosmology, as they can be used to measure luminosity distances at gigaparsec scales. Models of their light curves are used to project heterogeneous observed data onto a common basis for analysis. Aims. The SALT model currently used for SN Ia cosmology describes SNe as having two sources of variability, accounted for by a color parameter c, and a “stretch” parameter x1. We extend the model to include an additional parameter we label x2, to investigate the cosmological impact of currently unaddressed light-curve variability. Methods. We constructed a new SALT model, that we dub “SALT3+”. This model was trained by an improved version of the SALTshaker code, using training data combining a selection of the second data release of cosmological SNe Ia from the Zwicky Transient Facility and the existing SALT3 training compilation. Results. We find additional, coherent variability in supernova light curves beyond SALT3. Most of this variation can be described as phase-dependent variation in g − r and r − i color curves, correlated with a boost in the height of the secondary maximum in i-band. These behaviors correlate with spectral differences, particularly in line velocity. We find that fits with the existing SALT3 model tend to address this excess variation with the color parameter, leading to less informative measurements of supernova color. We find that neglecting the new parameter in light-curve fits leads to a trend in Hubble residuals with x2 of 0.039 ± 0.005 mag, representing a potential systematic uncertainty. However, we find no evidence of a bias in current cosmological measurements. Conclusions. We conclude that extended SN Ia light-curve models promise mild improvement in the accuracy of color measurements, and corresponding cosmological precision. However, models with more parameters are unlikely to substantially affect current cosmological results.

We present GAUSSN, a Bayesian semiparametric Gaussian Process (GP) model for time-delay estimation with resolved systems of gravitationally lensed supernovae (glSNe). GAUSSN models the underlying light curve non-parametrically using a GP. Without assuming a template light curve for each SN type, GAUSSN fits for the time delays of all images using data in any number of wavelength filters simultaneously. We also introduce a novel time-varying magnification model to capture the effects of microlensing alongside time-delay estimation. In this analysis, we model the time-varying relative magnification as a sigmoid function, as well as a constant for comparison to existing time-delay estimation approaches. We demonstrate that GAUSSN provides robust time-delay estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (Rubin-LSST). We find that up to 43.6 per cent of time-delay estimates from Roman and 52.9 per cent from Rubin-LSST have fractional errors of less than 5  per cent. We then apply GAUSSN to SN Refsdal and find the time delay for the fifth image is consistent with the original analysis, regardless of microlensing treatment. Therefore, GAUSSN maintains the level of precision and accuracy achieved by existing time-delay extraction methods with fewer assumptions about the underlying shape of the light curve than template-based approaches, while incorporating microlensing into the statistical error budget. GAUSSN is scalable for time-delay cosmography analyses given current projections of glSNe discovery rates from Rubin-LSST and Roman.

Supernova (SN) SN H0pe is a gravitationally lensed, triply imaged, Type Ia SN (SN Ia) discovered in James Webb Space Telescope imaging of the PLCK G165.7+67.0 cluster of galaxies. Well-observed multiply imaged SNe provide a rare opportunity to constrain the Hubble constant (H-0), by measuring the relative time delay between the images and modeling the foreground mass distribution. SN H0pe is located at z = 1.783 and is the first SN Ia with sufficient light-curve sampling and long enough time delays for an H-0 inference. Here we present photometric time-delay measurements and SN properties of SN H0pe. Using JWST/NIRCam photometry, we measure time delays of Delta t(ab) = -116.6(-9.3)(+10.8) observer-frame days and Delta t(cb) = -48.6(-4.0)(+3.6) observer-frame days relative to the last image to arrive (image 2b; all uncertainties are 1 sigma), which corresponds to a similar to 5.6% uncertainty contribution for H-0 assuming 70 km s(-1) Mpc(-1). We also constrain the absolute magnification of each image to mu(a) = 4.3(-1.8)(+1.6), mu(b) = 7.6(-2.6)(+3.6), mu(c) = 6.4(-1.5)(+1.6) by comparing the observed peak near-IR magnitude of SN H0pe to the nonlensed population of SNe Ia.

We present the Keck Infrared Transient Survey, a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types, and its first data release, consisting of 105 NIR spectra of 50 transients. Such a data set is essential as we enter a new era of IR astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman). NIR spectral templates will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. Between 2022 February and 2023 July, we systematically obtained 274 NIR spectra of 146 astronomical transients, representing a significant increase in the number of available NIR spectra in the literature. Here, we describe the first release of data from the 2022A semester. We systematically observed three samples: a flux-limited sample that includes all transients <17 mag in a red optical band (usually ZTF r or ATLAS o bands); a volume-limited sample including all transients within redshift z < 0.01 (D ≈ 50 Mpc); and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. The flux-limited sample is 39% complete (60% excluding SNe Ia), while the volume-limited sample is 54% complete and is 79% complete to z = 0.005. Transient classes observed include common Type Ia and core-collapse supernovae, tidal disruption events, luminous red novae, and the newly categorized hydrogen-free/helium-poor interacting Type Icn supernovae. We describe our observing procedures and data reduction using PypeIt, which requires minimal human interaction to ensure reproducibility.

We present pop-cosmos: a comprehensive model characterizing the galaxy population, calibrated to 140,938 (r < 25 selected) galaxies from the Cosmic Evolution Survey (COSMOS) with photometry in 26 bands from the ultraviolet to the infrared. We construct a detailed forward model for the COSMOS data, comprising: a population model describing the joint distribution of galaxy characteristics and its evolution (parameterized by a flexible score-based diffusion model); a state-of-the-art stellar population synthesis model connecting galaxies’ intrinsic properties to their photometry; and a data model for the observation, calibration, and selection processes. By minimizing the optimal transport distance between synthetic and real data, we are able to jointly fit the population and data models, leading to robustly calibrated population-level inferences that account for parameter degeneracies, photometric noise and calibration, and selection. We present a number of key predictions from our model of interest for cosmology and galaxy evolution, including the mass function and redshift distribution; the mass-metallicity-redshift and fundamental metallicity relations; the star-forming sequence; the relation between dust attenuation and stellar mass, star formation rate, and attenuation-law index; and the relation between gas-ionization and star formation. Our model encodes a comprehensive picture of galaxy evolution that faithfully predicts galaxy colors across a broad redshift (z < 4) and wavelength range.

We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pretrained population model (pop-cosmos) that was calibrated using purely photometric data. This model specifies a prior distribution over 16 stellar population synthesis (SPS) parameters using a score-based diffusion model, and includes a data model with detailed treatment of nebular emission. We use a GPU-accelerated affine-invariant ensemble sampler to achieve fast posterior sampling under this model for 292,300 individual galaxies in the COSMOS2020 catalog, leveraging a neural network emulator (Speculator) to speed up the SPS calculations. We apply both the pop-cosmos population model and a baseline prior inspired by Prospector-α, and compare these results to published COSMOS2020 redshift estimates from the widely used EAZY and LePhare codes. For the ∼12,000 galaxies with spectroscopic redshifts, we find that pop-cosmos yields redshift estimates that have minimal bias (∼10−4), high accuracy (σ MAD = 7 × 10−3), and a low outlier rate (1.6%). We show that the pop-cosmos population model generalizes well to galaxies fainter than its r < 25 mag training set. The sample we have analyzed is ≳3× larger than has previously been possible via posterior sampling with a full SPS model, with average throughput of 15 GPU-sec per galaxy under the pop-cosmos prior, and 0.6 GPU-sec per galaxy under the Prospector prior. This paves the way for principled modeling of the huge catalogs expected from upcoming Stage IV galaxy surveys.

We apply the hierarchical probabilistic spectral energy distribution (SED) model BAYESN to analyse a sample of 475 type Ia supernovae (0.015 < z < 0.4) from Foundation, DES3YR and PS1MD to investigate the properties of dust in their host galaxies. We jointly infer the dust law R_V population distributions at the SED level in high- and low-mass galaxies simultaneously with dust-independent, intrinsic differences. We find an intrinsic mass step of −0.049 ± 0.016 mag, at a significance of 3.1σ, when allowing for a constant intrinsic, achromatic magnitude offset. We additionally apply a model allowing for time- and wavelength-dependent intrinsic differences between SNe Ia in different mass bins, finding ∼2σ differences in magnitude and colour around peak and 4.5σ differences at later times. These intrinsic differences are inferred simultaneously with a difference in population mean R_V of ∼2σ significance, demonstrating that both intrinsic and extrinsic differences may play a role in causing the host galaxy mass step. We also consider a model which allows the mean of the R_V distribution to linearly evolve with redshift but find no evidence for any evolution – we infer the gradient of this relation η_R = −0.38 ± 0.70. In addition, we discuss in brief a new, GPU-accelerated PYTHON implementation of BAYESN suitable for application to large surveys which is publicly available and can be used for future cosmological analyses; this code can be found here: https://github.com/bayesn/bayesn.

We use rest-frame optical and near-infrared (NIR) observations of 42 Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project at low-z and 37 from the RAISIN (SNIA in the IR) Survey at high-z to investigate correlations between SN Ia host galaxy dust, host mass, and redshift. This is the first time the SN Ia host galaxy dust extinction law at high-z has been estimated using combined optical and rest-frame NIR data (YJ band). We use the BAYESN hierarchical model to leverage the data’s wide rest-frame wavelength range (extending to ∼1.0–1.2 μm for the RAISIN sample at 0.2 ≲ z ≲ 0.6). By contrasting the RAISIN and Carnegie Supernova Project (CSP) data, we constrain the population distributions of the host dust R_V parameter for both redshift ranges. We place a limit on the difference in population mean R_V between RAISIN and CSP of −1.16 < Δμ(R_V) < 1.38 with 95 per cent posterior probability. For RAISIN we estimate μ(R_V) = 2.58 ± 0.57, and constrain the population standard deviation to σ(R_V) < 0.90 [2.42] at the 68 [95] per cent level. Given that we are only able to constrain the size of the low- to high-z shift in μ(R_V) to ≲1.4 – which could still propagate to a substantial bias in the equation-of-state parameter w – these and other recent results motivate continued effort to obtain rest-frame NIR data at low- and high-redshifts (e.g. using the Roman Space Telescope).