Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernovae in the LSST Era

Mitra, Ayan; Kessler, R.; Hložek, Renée

doi:10.3847/1538-4357/acb057

Cited by 13 publications

(9 citation statements)

References 56 publications

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“…While our unbiased results are encouraging, we note that Mitra et al (2023) reported a significant cosmology bias using an unbinned HD from a simulated LSST data sample of pure SNe Ia. We therefore recommend repeating our bias tests on simulated data for future analyses.…”

Section: Discussionmentioning

confidence: 58%

Binning is Sinning: Redemption for Hubble Diagram Using Photometrically Classified Type Ia Supernovae

Kessler

Vincenzi

Armstrong

2023

ApJL

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Bayesian Estimation Applied to Multiple Species (BEAMS) is implemented in the BEAMS with Bias Corrections (BBC) framework to produce a redshift-binned Hubble diagram (HD) for Type Ia supernovae (SNe Ia). BBC corrects for selection effects and non–SN Ia contamination, and systematic uncertainties are described by a covariance matrix with dimension matching the number of BBC redshift bins. For spectroscopically confirmed SN Ia samples, a recent “Binning is Sinning” article showed that an unbinned HD and covariance matrix reduces the systematic uncertainty by a factor of ∼1.5 compared to the binned approach. Here we extend their analysis to obtain an unbinned HD for a photometrically identified sample processed with BBC. To test this new method, we simulate and analyze 50 samples corresponding to the Dark Energy Survey (DES) with a low-redshift anchor; the simulation includes SNe Ia, and contaminants from core-collapse SNe and peculiar SNe Ia. The analysis includes systematic uncertainties for calibration and measures the dark energy equation of state parameter (w). Compared to a redshift-binned HD, the unbinned HD with nearly 2000 events results in a smaller systematic uncertainty, in qualitative agreement with BHS21, and averaging results among the 50 samples we find no evidence for a w-bias. To reduce computation time for fitting an unbinned HD with large samples, we propose an HD-rebinning method that defines the HD in bins of redshift, color, and stretch; the rebinned HD results in similar uncertainty as the unbinned case, and shows no evidence for a w-bias.

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Section: Discussionmentioning

confidence: 58%

Binning is Sinning: Redemption for Hubble Diagram Using Photometrically Classified Type Ia Supernovae

Kessler

Vincenzi

Armstrong

2023

ApJL

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“…Cosmological inference frameworks that account for the inflated uncertainties from photometric redshifts are currently being developed. Mitra et al (2023) and Dai et al (2018) show promising results with simulated LSST samples and SN photometric redshifts fitted using host galaxy redshift priors. In particular, Dai et al (2018) recover a fitted Ω m value consistent with the input cosmology when using SN photo-zs fitted with a host galaxy photo-z prior.…”

Section: Introductionmentioning

confidence: 82%

“…In particular, Dai et al (2018) recover a fitted Ω m value consistent with the input cosmology when using SN photo-zs fitted with a host galaxy photo-z prior. Mitra et al (2023) show a 2% effect on fitted cosmological parameters of an assumed systematic uncertainty due to the use of SN photo-zs of 0.01. Using observed data from the Dark Energy Survey, Chen et al (2022) performed a cosmological analysis using a subset of ∼100 SNe Ia hosted by galaxies in the redMaGiC catalog, which have both photometric and spectroscopic redshifts.…”

Section: Introductionmentioning

confidence: 92%

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Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning

Qu,

Sako

2023

ApJ

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Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the scientific return of these observations in the absence of spectroscopic information, we must accurately extract key parameters, such as SN redshifts, with photometric information alone. We present Photo-zSNthesis, a convolutional neural network-based method for predicting full redshift probability distributions from multi-band supernova lightcurves, tested on both simulated Sloan Digital Sky Survey (SDSS) and Vera C. Rubin Legacy Survey of Space and Time data as well as observed SDSS SNe. We show major improvements over predictions from existing methods on both simulations and real observations as well as minimal redshift-dependent bias, which is a challenge due to selection effects, e.g., Malmquist bias. Specifically, we show a 61× improvement in prediction bias 〈Δz〉 on PLAsTiCC simulations and 5× improvement on real SDSS data compared to results from a widely used photometric redshift estimator, LCFIT+Z. The PDFs produced by this method are well constrained and will maximize the cosmological constraining power of photometric SNe Ia samples.

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“…Most of the M22 SNe Ia are lost due to lack of convergence of the SNphoto-z. If new host-galaxy photo-z are available, combining the SNphoto-z method with a host-galaxy photo-z prior is expected to significantly improve photo-z estimates and the fitting efficiency (Mitra et al 2023).…”

Section: Discussionmentioning

confidence: 99%

The dark energy survey 5-yr photometrically identified type Ia supernovae

Möller¹,

Smith²,

Šako³

et al. 2022

Monthly Notices of the Royal Astronomical Society

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As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multi-band light-curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNova trained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1,863 SNe Ia from which we select 1,484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically-selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).

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Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernovae in the LSST Era

Cited by 13 publications

References 56 publications

Binning is Sinning: Redemption for Hubble Diagram Using Photometrically Classified Type Ia Supernovae

Binning is Sinning: Redemption for Hubble Diagram Using Photometrically Classified Type Ia Supernovae

Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning

The dark energy survey 5-yr photometrically identified type Ia supernovae

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