We study different one-parametric models of type Ia Supernova magnitude evolution on cosmic time scales. Constraints on cosmological and Supernova evolution parameters are obtained by combined fits on the actual data coming from Supernovae, the cosmic microwave background, and baryonic acoustic oscillations. We find that data prefer a magnitude evolution such that high-redshift Supernova are brighter than would be expected in a standard cosmos with a dark energy component. Data however are consistent with non-evolving magnitudes at the one-sigma level, except special cases.
We simulate a future data scenario where SN magnitude evolution is allowed for, and neglect the possibility of such an evolution in the fit. We find the fiducial models for which the wrong model assumption of non-evolving SN magnitude is not detectable, and for which at the same time biases on the fitted cosmological parameters are introduced. Of the cosmological parameters the overall mass density has the strongest chances to be biased due to the wrong model assumption. Whereas early-epoch models with a magnitude offset ~z^2 show up to be not too dangerous when neglected in the fitting procedure, late epoch models with magnitude offset ~sqrt(z) have high chances to bias the fit results.
The Supernova Legacy Survey (SNLS) has produced a high-quality, homogeneous sample of Type Ia supernovae (SNe Ia) out to redshifts greater than z=1. In its first four years of full operation (to June 2007), the SNLS discovered more than 3000 transient candidates, 373 of which have been confirmed spectroscopically as SNe Ia. Use of these SNe Ia in precision cosmology critically depends on an analysis of the observational biases incurred in the SNLS survey due to the incomplete sampling of the underlying SN Ia population. This paper describes our real-time supernova detection and analysis proced…
Simulations of Type Ia Supernovae (SNIa) surveys are a critical tool for correcting biases in the analysis of SNIa to infer cosmological parameters. Large scale Monte Carlo simulations include a thorough treatment of observation history, measurement noise, intrinsic scatter models and selection effects. In this paper, we improve simulations with a robust technique to evaluate the underlying populations of SNIa color and stretch that correlate with luminosity. In typical analyses, the standardized SNIa brightness is determined from linear `Tripp' relations between the light curve color and lumi…
We present a new technique to create a bin-averaged Hubble Diagram (HD) from photometrically identified SN~Ia data. The resulting HD is corrected for selection biases and contamination from core collapse (CC) SNe, and can be used to infer cosmological parameters. This method, called "BBC" (BEAMS with Bias Corrections), includes two fitting stages. The first BBC fitting stage uses a posterior distribution that includes multiple SN likelihoods, a Monte Carlo simulation to bias-correct the fitted SALT-II parameters, and CC probabilities determined from a machine learning technique. The BBC fit de…
Cosmological analyses of samples of photometrically-identified Type Ia supernovae (SNe Ia) depend on understanding the effects of 'contamination' from core-collapse and peculiar SN Ia events. We employ a rigorous analysis on state-of-the-art simulations of photometrically identified SN Ia samples and determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-year SN sample. As part of the analysis, we test on our DES simulations the performance of SuperNNova, a photometric SN classifier based on recurrent neural networks. Depending on the choice of non-I…
Supernova measurements have become a key ingredient in current determinations of cosmological parameters. These sources can however be used as standard candles only after correcting their apparent brightness for a number of effects. In this paper we discuss some limitations imposed by the formalism currently used for such corrections and investigate the impact on cosmological constraints. We show that color corrections are, in general, expected to be biased. In addition, color excesses which do not add a significant scatter to the observed SN brightnesses affect the value of cosmological param…
In the next decade, transient searches from the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope will increase the sample of known Type Ia Supernovae (SN Ia) from $\sim10^3$ to $10^5$. With this reduction of statistical uncertainties on cosmological measurements, new methods are needed to reduce systematic uncertainties. Characterizing the underlying spectroscopic evolution of SN Ia remains a major systematic uncertainty in current cosmological analyses, motivating a new simulation tool for the next era of SN Ia cosmology: Build Your Own Spectral Energy Distribution (BYOSED)…
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…
We use simulated SN Ia samples, including both photometry and spectra, to perform the first direct validation of cosmology analysis using the SALT-II light curve model. This validation includes residuals from the light curve training process, systematic biases in SN Ia distance measurements, and the bias on the dark energy equation of state parameter w. Using the SN-analysis package SNANA, we simulate and analyze realistic samples corresponding to the data samples used in the SNLS3 analysis: 120 low-redshift (z < 0.1) SNe Ia, 255 SDSS SNe Ia (z < 0.4), and 290 SNLS SNe Ia (z <= 1). To probe sy…
We describe catalog-level simulations of Type Ia supernova (SN~Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light curve analysis, and to determine bias corrections for SN~Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves, the simulation uses a detailed SN~Ia model, incorporates information from observations (PSF, sky noise, zero point), an…
Peculiar velocities of type Ia supernova (SNIa) host galaxies affect the dark-energy parameter constraints in a small but very specific way: the parameters are biased in a single direction in parameter space that is a-priori knowable for a given SNIa dataset. We demonstrate the latter fact with a combination of inference from a cosmological N-body simulation with overwhelming statistics applied to the Pantheon SNIa data set, then confirm it by simple quantitative arguments. We quantify small modifications to the current analyses that would ensure that the effect of cosmological parameters is e…
The original formulation of BEAMS - Bayesian Estimation Applied to Multiple Species - showed how to use a dataset contaminated by points of multiple underlying types to perform unbiased parameter estimation. An example is cosmological parameter estimation from a photometric supernova sample contaminated by unknown Type Ibc and II supernovae. Where other methods require data cuts to increase purity, BEAMS uses all of the data points in conjunction with their probabilities of being each type. Here we extend the BEAMS formalism to allow for correlations between the data and the type probabilities…
New supernova surveys such as the Dark Energy Survey, Pan-STARRS and the LSST will produce an unprecedented number of photometric supernova candidates, most with no spectroscopic data. Avoiding biases in cosmological parameters due to the resulting inevitable contamination from non-Ia supernovae can be achieved with the BEAMS formalism, allowing for fully photometric supernova cosmology studies. Here we extend BEAMS to deal with the case in which the supernovae are correlated by systematic uncertainties. The analytical form of the full BEAMS posterior requires evaluating 2^N terms, where N is …
The case of SN Ia Hubble diagrams from photometrically selected samples using photometric SN host galaxy redshifts is investigated. The host redshift uncertainties and the contamination by core collapse SNe are addressed. As a test, we use the 3-year photometric SN Ia sample of the SuperNova Legacy Survey (SNLS), made of 437 objects between 0.1 and 1.05 in redshift. We combine this sample with non-SNLS objects of the JLA spectroscopic sample, made of 501 objects mostly below 0.4 in redshift. We study two options for the origin of the redshifts of the photometric sample, either provided entirel…
Gamma-Ray Bursts (GRBs) are very energetic cosmological transients. Long GRBs are usually associated with Type Ib/c Supernovae (SNe), and we refer to them as GRB-SNe. Since the associated SN for a given GRB is observed only at low redshift, a possible selection effect exists when we consider intrinsically faint sources which cannot be observed at high redshift. Thus, it is important to explore the possible relationships between GRB and SN parameters after these have been corrected for astrophysical biases due to the instrumental selection effects and redshift evolution of the variables involve…
We show how spectra of Type Ia supernovae (SNe Ia) at maximum light can be used to improve cosmological distance estimates. In a companion article, we used manifold learning to build a three-dimensional parameterization of the intrinsic diversity of SNe Ia at maximum light that we call the "Twins Embedding". In this article, we discuss how the Twins Embedding can be used to improve the standardization of SNe Ia. With a single spectrophotometrically-calibrated spectrum near maximum light, we can standardize our sample of SNe Ia with an RMS of $0.101 \pm 0.007$ mag, which corresponds to $0.084 \…
Supernova cosmology without spectroscopic confirmation is an exciting new frontier which we address here with the Bayesian Estimation Applied to Multiple Species (BEAMS) algorithm and the full three years of data from the Sloan Digital Sky Survey II Supernova Survey (SDSS-II SN). BEAMS is a Bayesian framework for using data from multiple species in statistical inference when one has the probability that each data point belongs to a given species, corresponding in this context to different types of supernovae with their probabilities derived from their multi-band lightcurves. We run the BEAMS a…
More luminous Type Ia supernovae (SNe Ia) prefer less massive hosts and regions of higher star formation. This correlation is inverted during width-color-luminosity light curve standardization resulting in step-like biases of distance measurements with respect to host properties. Using the PISCO supernova host sample and SDSS, GALEX, and 2MASS photometry, we compare host stellar mass and specific star formation rate (sSFR) from different observation methods, including local vs. global, and fitting techniques to measure their impact on the host step biases. Mass step measurements for all our ma…
FlowSN: Neural Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology
University of Cambridge（剑桥大学）；National Center for Supercomputing Applications（国家超级计算应用中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Chicago（芝加哥大学）
We present a new Bayesian hierarchical model (BHM) named Steve for performing type Ia supernova (SNIa) cosmology fits. This advances previous works by including an improved treatment of Malmquist bias, accounting for additional sources of systematic uncertainty, and increasing numerical efficiency. Given light curve fit parameters, redshifts, and host-galaxy masses, we fit Steve simultaneously for parameters describing cosmology, SNIa populations, and systematic uncertainties. Selection effects are characterised using Monte-Carlo simulations. We demonstrate its implementation by fitting realis…
We use two different methods, i.e., dipole-fitting (DF) and hemisphere comparison (HC), to search for the anisotropic signals hiding in the Union2.1 data set. We find that the directions of maximum matter density derived using these two methods are about $114^{\circ}$ away from each other. We construct four Union2.1-like mock samples to test the statistical significance of these two methods. It is shown that DF method is statistically significant, while HC method is strongly biased by the distribution of data points in the sky. Then we assume that the anisotropy of distance modulus is mainly i…
All 21-cm signal experiments rely on electronic receivers that affect the data via both multiplicative and additive biases through the receiver's gain and noise temperature. While experiments attempt to remove these biases, the residuals of their imperfect calibration techniques can still confuse signal extraction algorithms. In this paper, the fourth and final installment of our pipeline series, we present a technique for fitting out receiver effects as efficiently as possible. The fact that the gain and global signal, which are multiplied in the observation equation, must both be modeled imp…
For future surveys, spectroscopic follow-up for all supernovae will be extremely difficult. However, one can use light curve fitters, to obtain the probability that an object is a Type Ia. One may consider applying a probability cut to the data, but we show that the resulting non-Ia contamination can lead to biases in the estimation of cosmological parameters. A different method, which allows the use of the full dataset and results in unbiased cosmological parameter estimation, is Bayesian Estimation Applied to Multiple Species (BEAMS). BEAMS is a Bayesian approach to the problem which include…
When photons from distant galaxies and stars pass through our neighboring environment, the wavelengths of the photons would be shifted by our local gravitational potential. This local gravitational redshift effect can potentially have an impact on the measurement of cosmological distance-redshift relation. Using available supernovae data, Wojtak et al [1] found seemingly large biases of cosmological parameters for some extended models (non-flat $Λ$CDM, $w$CDM, etc.). Huang [2] pointed out that, however, the biases can be reduced to a negligible level if cosmic microwave background (CMB) data a…
To constrain the properties of the first stars with the chemical abundance patterns observed in metal-poor stars, one must identify any non-trivial effects that the hydrodynamics of metal dispersal can imprint on the abundances. We use realistic cosmological hydrodynamic simulations to quantify the distribution of metals resulting from one Population III supernova and from a small number of such supernovae exploding in close succession. Overall, supernova ejecta are highly inhomogeneously dispersed throughout the simulations. When the supernova bubbles collapse, quasi-virialized metal-enriched…
Only a few cases of type Ic supernovae (SNe) with high-velocity ejecta have been discovered and studied. Here we present our analysis of radio and X-ray observations of a Type Ic SN, PTF12gzk. The radio emission rapidly declined less than 10 days after explosion, suggesting SN ejecta expanding at high velocity (~0.3c). The radio data also indicate that the density of the circumstellar material (CSM) around the supernova is lower by a factor of ~10 than the CSM around normal Type Ic SNe. Our observations of this rapidly declining radio SN at a distance of 58 Mpc demonstrates the potential to de…
In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts ($0.2<z<1.1$). Our method consists of two stages: feature extraction (obtaining …
A recent article by Wojtak {\it et al} (arXiv:1504.00718) pointed out that the local gravitational redshift, despite its smallness ($\sim 10^{-5}$), can have a noticeable ($\sim 1\%$) systematic effect on our cosmological parameter measurements. The authors studied a few extended cosmological models (nonflat $Λ$CDM, $w$CDM, and $w_0$-$w_a$CDM) with a mock supernova data set. We repeat this calculation and find that the $\sim 1\%$ biases are due to strong degeneracy between cosmological parameters. When cosmic microwave background (CMB) data are added to break the degeneracy, the biases due to …
We present BVRI and unfiltered light curves of 93 Type Ia supernovae (SNe Ia) from the Lick Observatory Supernova Search (LOSS) follow-up program conducted between 2005 and 2018. Our sample consists of 78 spectroscopically normal SNe Ia, with the remainder divided between distinct subclasses (three SN 1991bg-like, three SN 1991T-like, four SNe Iax, two peculiar, and three super-Chandrasekhar events), and has a median redshift of 0.0192. The SNe in our sample have a median coverage of 16 photometric epochs at a cadence of 5.4 days, and the median first observed epoch is ~4.6 days before maximum…
