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Register a new userImproved Treatment of Host-Galaxy Correlations in Cosmological Analyses With Type Ia Supernovae
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Improving the use of Type Ia supernovae (SNIa) as standard candles requires a better approach to incorporate the relationship between SNIa and the properties of their host galaxies. Using a spectroscopically-confirmed sample of $sim$1600 SNIa, we develop the first empirical model of underlying populations for SNIa light-curve properties that includes their dependence on host-galaxy stellar mass. These populations are important inputs to simulations that are used to model selection effects and correct distance biases within the BEAMS with Bias Correction (BBC) framework. Here we improve BBC to also account for SNIa-host correlations, and we validate this technique on simulated data samples. We recover the input relationship between SNIa luminosity and host-galaxy stellar mass (the mass step, $gamma$) to within 0.004 mags, which is a factor of 5 improvement over the previous method that results in a $gamma$-bias of ${sim}0.02$. We adapt BBC for a novel dust-based model of intrinsic brightness variations, which results in a greatly reduced mass step for data ($gamma = 0.017 pm 0.008$), and for simulations ($gamma =0.006 pm 0.007$). Analysing simulated SNIa, the biases on the dark energy equation-of-state, $w$, vary from $Delta w = 0.006(5)$ to $0.010(5)$ with our new BBC method; these biases are significantly smaller than the $0.02(5)$ $w$-bias using previous BBC methods that ignore SNIa-host correlations.
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From Sloan Digital Sky Survey ugriz imaging, we estimate the stellar masses of the host galaxies of 70 low redshift SN Ia (0.015 < z < 0.08) from the hosts absolute luminosities and mass-to-light ratios. These nearby SN were discovered largely by searches targeting luminous galaxies, and we find that their host galaxies are substantially more massive than the hosts of SN discovered by the flux-limited Supernova Legacy Survey. Testing four separate light curve fitters, we detect ~2.5{sigma} correlations of Hubble residuals with both host galaxy size and stellar mass, such that SN Ia occurring in physically larger, more massive hosts are ~10% brighter after light curve correction. The Hubble residual is the deviation of the inferred distance modulus to the SN, calculated from its apparent luminosity and light curve properties, away from the expected value at the SN redshift. Marginalizing over linear trends in Hubble residuals with light curve parameters shows that the correlations cannot be attributed to a light curve-dependent calibration error. Combining 180 higher-redshift ESSENCE, SNLS, and HigherZ SN with 30 nearby SN whose host masses are less than 10^10.8 solar masses in a cosmology fit yields 1+w=0.22 +0.152/-0.143, while a combination where the 30 nearby SN instead have host masses greater than 10^10.8 solar masses yields 1+w=-0.03 +0.217/-0.108. Progenitor metallicity, stellar population age, and dust extinction correlate with galaxy mass and may be responsible for these systematic effects. Host galaxy measurements will yield improved distances to SN Ia.
Recent cosmological analyses (e.g., JLA, Pantheon) of Type Ia Supernova (SNIa) have propagated systematic uncertainties into a covariance matrix and either binned or smoothed the systematic vectors in redshift space. We demonstrate that systematic error budgets of these analyses can be improved by a factor of $sim1.5times$ with the use of unbinned and unsmoothed covariance matrices. To understand this, we employ a separate approach that simultaneously fits for cosmological parameters and additional self-calibrating scale parameters that constrain the size of each systematic. We show that the covariance-matrix approach and scale-parameter approach yield equivalent results, implying that in both cases the data can self-calibrate certain systematic uncertainties, but that this ability is hindered when information is binned or smoothed in redshift space. We review the top systematic uncertainties in current analyses and find that the reduction of systematic uncertainties in the unbinned case depends on whether a systematic is consistent with varying the cosmological model and whether or not the systematic can be described by additional correlations between SN properties and luminosity. Furthermore, we show that the power of self-calibration increases with the size of the dataset, which presents a tremendous opportunity for upcoming analyses of photometrically classified samples, like those of Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (NGRST). However, to take advantage of self-calibration in large, photometrically-classified samples, we must first address the issue that binning is required in currently-used photometric methodologies.
Using the largest single-survey sample of Type Ia supernovae (SNe Ia) to date, we study the relationship between properties of SNe Ia and those of their host galaxies, focusing primarily on correlations with Hubble residuals (HR). Our sample consists of 345 photometrically-classified or spectroscopically-confirmed SNeIa discovered as part of the SDSS-II Supernova Survey (SDSS-SNS). This analysis utilizes host-galaxy spectroscopy obtained during the SDSS-I/II spectroscopic survey and from an ancillary program on the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) that obtained spectra for nearly all host galaxies of SDSS-II SN candidates. In addition, we use photometric host-galaxy properties from the SDSS-SNS data release (Sako et al. 2014) such as host stellar mass and star-formation rate. We confirm the well-known relation between HR and host-galaxy mass and find a 3.6{sigma} significance of a non-zero linear slope. We also recover correlations between HR and host-galaxy gas-phase metallicity and specific star-formation rate as they are reported in the literature. With our large dataset, we examine correlations between HR and multiple host-galaxy properties simultaneously and find no evidence of a significant correlation. We also independently analyze our spectroscopically-confirmed and photometrically-classified SNe Ia and comment on the significance of similar combined datasets for future surveys.
While Type Ia Supernovae (SNe Ia) are one of the most mature cosmological probes, the next era promises to be extremely exciting in the number of different ways SNe Ia are used to measure various cosmological parameters. Here we review the experiments in the 2020s that will yield orders of magnitudes more SNe Ia, and the new understandings and capabilities to constrain systematic uncertainties at a level to match these statistics. We then discuss five different cosmological probes with SNe Ia: the conventional Hubble diagram for measuring dark energy properties, the distance ladder for measuring the Hubble constant, peculiar velocities and weak lensing for measuring sigma8 and strong-lens measurements of H0 and other cosmological parameters. For each of these probes, we discuss the experiments that will provide the best measurements and also the SN Ia-related systematics that affect each one.
We study the feasibility of detecting weak lensing spatial correlations between Supernova (SN) Type Ia magnitudes with present (Dark Energy Survey, DES) and future (Large Synoptic Survey Telescope, LSST) surveys. We investigate the angular auto-correlation function of SN magnitudes (once the background cosmology has been subtracted) and cross-correlation with galaxy catalogues. We examine both analytical and numerical predictions, the latter using simulated galaxy catalogues from the MICE Grand Challenge Simulation. We predict that we will be unable to detect the SN auto-correlation in DES, while it should be detectable with the LSST SN deep fields (15,000 SNe on 70 deg^2) at ~6sigma level of confidence (assuming 0.15 magnitudes of intrinsic dispersion). The SN-galaxy cross-correlation function will deliver much higher signal-to-noise, being detectable in both surveys with an integrated signal-to-noise of ~100 (up to 30 arcmin separations). We predict joint constraints on the matter density parameter (Omega_m) and the clustering amplitude (sigma_8) by fitting the auto-correlation function of our mock LSST deep fields. When assuming a Gaussian prior for Omega_m, we can achieve a 25% measurement of sigma_8 from just these LSST supernovae (assuming 0.15 magnitudes of intrinsic dispersion). These constraints will improve significantly if the intrinsic dispersion of SNe Ia can be reduced.
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