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Type Ia supernovae (SNe Ia) are standardizable candles that must be modeled empirically to yield cosmological constraints. To understand the robustness of this modeling to variations in the model-training procedure, we build an end-to-end pipeline to test the recently developed SALT3 model. We explore the consequences of removing pre-2000s low- z or poorly calibrated U -band data, adjusting the amount and fidelity of SN Ia spectra, and using a model-independent framework to simulate the training data. We find that the SALT3 model surfaces are improved by having additional spectra and U -band data, and can be shifted by ∼5% if host-galaxy contamination is not sufficiently removed from SN spectra. We find that resulting measurements of w are consistent to within 2.5% for all of the training variants explored in this work, with the largest shifts coming from variants that add color-dependent calibration offsets or host-galaxy contamination to the training spectra and those that remove pre-2000s low- z data. These results demonstrate that the SALT3 model-training procedure is largely robust to reasonable variations in the training data, but that additional attention must be paid to the treatment of spectroscopic data in the training process. We also find that the training procedure is sensitive to the color distributions of the input data—the resulting w measurement can be biased by ∼2% if the color distribution is not sufficiently wide. Future low- z data, particularly u -band observations and high signal-to-noise ratio SN Ia spectra, will help to significantly improve SN Ia modeling in the coming years.

Measurements of the dark energy equation-of-state parameter, w, have been limited by uncertainty in the selection effects and photometric calibration of z < 0.1 Type Ia supernovae (SNe Ia). The Foundation Supernova Survey is designed to lower these uncertainties by creating a new sample of z < 0.1 SNe Ia observed on the Pan-STARRS system. Here we combine the Foundation sample with SNe from the Pan-STARRS Medium Deep Survey and measure cosmological parameters with 1338 SNe from a single telescope and a single, well-calibrated photometric system. For the first time, both the low-z and high-z data are predominantly discovered by surveys that do not target preselected galaxies, reducing selection bias uncertainties. The z > 0.1 data include 875 SNe without spectroscopic classifications, and we show that we can robustly marginalize over CC SN contamination. We measure Foundation Hubble residuals to be fainter than the preexisting low-z Hubble residuals by 0.046 ± 0.027 mag (stat + sys). By combining the SN Ia data with cosmic microwave background constraints, we find w = −0.938 ± 0.053, consistent with ΛCDM. With 463 spectroscopically classified SNe Ia alone, we measure w = −0.933 ± 0.061. Using the more homogeneous and better-characterized Foundation sample gives a 55% reduction in the systematic uncertainty attributed to SN Ia sample selection biases. Although use of just a single photometric system at low and high redshift increases the impact of photometric calibration uncertainties in this analysis, previous low-z samples may have correlated calibration uncertainties that were neglected in past studies. The full Foundation sample will observe up to 800 SNe to anchor the LSST and WFIRST Hubble diagrams. [ABSTRACT FROM AUTHOR]

We present simulated observations to assess the ability of the Large Synoptic Survey Telescope (LSST) and the wide-fast-deep (WFD) survey to detect and characterize kilonovae—the optical emission associated with binary neutron star (and possibly black hole–neutron star) mergers. We expand on previous studies in several critical ways by exploring a range of kilonova models and several choices of cadence, as well as by evaluating the information content of the resulting light curves. We find that, depending on the precise choice of cadence, the WFD survey will achieve an average kilonova detection efficiency of ≈1.6%–2.5% and detect only ≈3–6 kilonovae per year. The detected kilonovae will be within the detection volume of the Advanced LIGO/Virgo (ALV). By refitting the best resulting LSST light curves with the same model used to generate them, we find that the model parameters are generally weakly constrained, and are accurate to at best a factor of 2–3. Motivated by the finding that the WFD will yield a small number of kilonova detections, with poor light curves and marginal information content, and that the detections are in any case inside the ALV volume, we argue that target-of-opportunity follow-up of gravitational-wave triggers is a much more effective approach for kilonova studies. We outline the qualitative foundation for such a program with the goal of minimizing the impact on LSST operations. We argue that observations in the gz-bands with a total time investment per event of ≈1.5 hr per 10 deg2 of a search area is sufficient to rapidly detect and identify kilonovae with ≳90% efficiency. For an estimated event rate of ∼20 per year visible to LSST, this accounts for ∼1.5% of the total survey time. In this regime, LSST has the potential to be a powerful tool for kilonovae discovery, with detected events handed off to other narrow-field facilities for further monitoring. [ABSTRACT FROM AUTHOR]

Recent analyses suggest that distance residuals measured from Type Ia supernovae (SNe Ia) are correlated with local host galaxy properties within a few kiloparsecs of the SN explosion. However, the well-established correlation with global host galaxy properties is nearly as significant, with a shift of 0.06 mag across a low to high mass boundary (the mass step). Here, with 273 SNe Ia at z < 0.1, we investigate whether the stellar masses and rest-frame u − g colors of regions within 1.5 kpc of the SN Ia explosion site are significantly better correlated with SN distance measurements than global properties or properties measured at random locations in SN hosts. At ≲2σ significance, local properties tend to correlate with distance residuals better than properties at random locations, though despite using the largest low-z sample to date, we cannot definitively prove that a local correlation is more significant than a random correlation. Our data hint that SNe observed by surveys that do not target a pre-selected set of galaxies may have a larger local mass step than SNe from surveys that do, an increase of 0.071 ± 0.036 mag (2.0σ). We find a 3σ local mass step after global mass correction, evidence that SNe Ia should be corrected for their local mass, but we note that this effect is insignificant in the targeted low-z sample. Only the local mass step remains significant at >2σ after global mass correction, and we conservatively estimate a systematic shift in H0 measurements of −0.14 km s−1 Mpc−1 with an additional uncertainty of 0.14 km s−1 Mpc−1, ∼10% of the present uncertainty. [ABSTRACT FROM AUTHOR]

We present optical light curves, redshifts, and classifications for spectroscopically confirmed Type Ia supernovae (SNe Ia) discovered by the Pan-STARRS1 (PS1) Medium Deep Survey. We detail improvements to the PS1 SN photometry, astrometry, and calibration that reduce the systematic uncertainties in the PS1 SN Ia distances. We combine the subset of PS1 SNe Ia (0.03 < z < 0.68) with useful distance estimates of SNe Ia from the Sloan Digital Sky Survey (SDSS), SNLS, and various low-z and Hubble Space Telescope samples to form the largest combined sample of SNe Ia, consisting of a total of SNe Ia in the range of 0.01 < z < 2.3, which we call the “Pantheon Sample.” When combining Planck 2015 cosmic microwave background (CMB) measurements with the Pantheon SN sample, we find and for the wCDM model. When the SN and CMB constraints are combined with constraints from BAO and local H0 measurements, the analysis yields the most precise measurement of dark energy to date: and for the CDM model. Tension with a cosmological constant previously seen in an analysis of PS1 and low-z SNe has diminished after an increase of 2× in the statistics of the PS1 sample, improved calibration and photometry, and stricter light-curve quality cuts. We find that the systematic uncertainties in our measurements of dark energy are almost as large as the statistical uncertainties, primarily due to limitations of modeling the low-redshift sample. This must be addressed for future progress in using SNe Ia to measure dark energy. [ABSTRACT FROM AUTHOR]

We present an empirical study of contamination in wide-field optical follow-up searches of gravitational wave sources from Advanced LIGO/Virgo using dedicated observations with the Dark Energy Camera. Our search covered ∼56 deg2, with two visits per night, in the i and z bands, followed by an additional set of griz images three weeks later to serve as reference images for subtraction. We achieve 5σ point-source limiting magnitudes of i ≈ 23.5 and z ≈ 22.4 mag in the coadded single-epoch images. We conduct a search for transient objects that mimic the i − z color behavior of both red (i−z > 0.5 mag) and blue (i−z < 0 mag) kilonova emission, finding 11 and 10 contaminants, respectively. Independent of color, we identify 48 transients of interest. Additionally, we leverage the rapid cadence of our observations to search for sources with characteristic timescales of ≈1 day and ≈3 hr, finding no potential contaminants. We assess the efficiency of our search with injected point sources, finding that we are 90% (60%) efficient when searching for red (blue) kilonova-like sources to a limiting magnitude of i ≲ 22.5 mag. Using our efficiencies, we derive sky rates for kilonova contaminants of deg−2 and deg−2. The total contamination rate is deg−2. We compare our results to previous optical follow-up efforts and comment on the outlook for gravitational wave follow-up searches as additional detectors (e.g., KAGRA, LIGO India) come online in the next decade. [ABSTRACT FROM AUTHOR]

We use 1169 Pan-STARRS supernovae (SNe) and 195 low-z (z < 0.1) SNe Ia to measure cosmological parameters. Though most Pan-STARRS SNe lack spectroscopic classifications, in a previous paper we demonstrated that photometrically classified SNe can be used to infer unbiased cosmological parameters by using a Bayesian methodology that marginalizes over core-collapse (CC) SN contamination. Our sample contains nearly twice as many SNe as the largest previous SN Ia compilation. Combining SNe with cosmic microwave background (CMB) constraints from Planck, we measure the dark energy equation-of-state parameter w to be −0.989 ± 0.057 (stat+sys). If w evolves with redshift as w(a) = w0 + wa(1 − a), we find w0 = −0.912 ± 0.149 and wa = −0.513 ± 0.826. These results are consistent with cosmological parameters from the Joint Light-curve Analysis and the Pantheon sample. We try four different photometric classification priors for Pan-STARRS SNe and two alternate ways of modeling CC SN contamination, finding that no variant gives a w differing by more than 2% from the baseline measurement. The systematic uncertainty on w due to marginalizing over CC SN contamination, , is the third-smallest source of systematic uncertainty in this work. We find limited (1.6σ) evidence for evolution of the SN color-luminosity relation with redshift, a possible systematic that could constitute a significant uncertainty in future high-z analyses. Our data provide one of the best current constraints on w, demonstrating that samples with ∼5% CC SN contamination can give competitive cosmological constraints when the contaminating distribution is marginalized over in a Bayesian framework. [ABSTRACT FROM AUTHOR]

The Pan-STARRS (PS1) Medium Deep Survey discovered over 5000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3147 of these likely SNe and estimate that ∼1000 are Type Ia SNe (SNe Ia) with light-curve quality sufficient for a cosmological analysis. We use these data with simulations to determine the impact of core-collapse SN (CC SN) contamination on measurements of the dark energy equation of state parameter, w. Using the method of Bayesian Estimation Applied to Multiple Species (BEAMS), distances to SNe Ia and the contaminating CC SN distribution are simultaneously determined. We test light-curve-based SN classification priors for BEAMS as well as a new classification method that relies upon host galaxy spectra and the association of SN type with host type. By testing several SN classification methods and CC SN parameterizations on large SN simulations, we estimate that CC SN contamination gives a systematic error on w () of 0.014, 29% of the statistical uncertainty. Our best method gives , just 8% of the statistical uncertainty, but could be affected by incomplete knowledge of the CC SN distribution. This method determines the SALT2 color and shape coefficients, α and β, with ∼3% bias. However, we find that some variants require α and β to be fixed to known values for BEAMS to yield accurate measurements of w. Finally, the inferred abundance of bright CC SNe in our sample is greater than expected based on measured CC SN rates and luminosity functions. [ABSTRACT FROM AUTHOR]