The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

Vincenzi, M.; Sullivan, M.; Möller, A.; Armstrong, P.; Bassett, Bruce A.; Brout, D.; Carollo, D.; Carr, Anthony; Davis, Tamara M.; Frohmaier, C.; Galbany, L.; Glazebrook, Karl; Graur, Or; Kelsey, L.; Keßler, R.; Kovacs, E.; Lewis, Geraint F.; Lidman, C.; Malik, U.; Nichol, R. C.; Popovic, Brodie; Šako, M.; Scolnic, Dan; Smith, M.; Taylor, Georgie; Tucker, B.; Wiseman, P.; Aguena, M.; Allam, S.; Annis, J.; Asorey, J.; Bacon, David; Bertin, E.; Burke, D. L.; Rosell, A. Carnero; Carretero, J.; Castander, F. J.; Costanzi, M.; Costa, L. N. da; Pereira, M. E. S.; Vicente, J. De; Desai, S.; Diehl, H. T.; Doel, P.; Everett, S.; Ferrero, I.; Flaugher, B.; Fosalba, P.; Frieman, Joshua A.; García-Bellido, J.; Gerdes, D. W.; Gruen, D.; Gutiérrez, G.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; James, D. J.; Kuêhn, K.; Kuropatkin, N.; Lahav, O.; Li, T S; Lima, M.; Maia, M. A. G.; Marshall, J. L.; Miquel, R.; Morgan, R.; Ogando, R. L. C.; Palmese, A.; Paz-Chinchón, F.; Pieres, A.; Malagón, A. A. Plazas; Reil, K.; Sánchez, E.; Schubnell, M.; Serrano, S.; Sevilla-Noarbe, I.; Suchyta, E.; Tarlè, G.; To, C.; Varga, T. N.; Weller, J.; Wilkinson, R. D.

doi:10.1093/mnras/stac1404

Cited by 18 publications

(7 citation statements)

References 137 publications

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“…The next major effort is to develop the cosmology analysis for samples that include non-SN Ia contamination, host galaxy misassociation, and a more complete list of systematic uncertainties that includes host galaxy photo-z models and intrinsic scatter of the SN brightness. Cosmology analyses using photometric classification and spectroscopic redshifts have been well developed on real data from PS1 (Jones et al 2018) and DES (Vincenzi et al 2023). Here we have developed and demonstrated a complimentary analysis using photometric redshifts and a spectroscopically confirmed sample.…”

Section: Discussionmentioning

confidence: 99%

“…The BEAMS framework, combined with photometric classification, was first used to obtain SN Ia cosmology results from Pan-STARRS1 data (Jones et al 2018). An extension to BEAMS, BEAMS with Bias Corrections (BBC; Kessler & Scolnic 2017, hereafter KS17), was used in Jones et al (2018) and is currently used in the analysis of data from the Dark Energy Survey (DES; Vincenzi et al 2023).…”

Section: Introductionmentioning

confidence: 99%

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

Mitra

Kessler

Hložek

2023

ApJ

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We perform a rigorous cosmology analysis on simulated Type Ia supernovae (SNe Ia) and evaluate the improvement from including photometric host galaxy redshifts compared to using only the “z spec” subset with spectroscopic redshifts from the host or SN. We use the Deep Drilling Fields (∼50 deg2) from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) in combination with a low-z sample based on Data Challenge2. The analysis includes light-curve fitting to standardize the SN brightness, a high-statistics simulation to obtain a bias-corrected Hubble diagram, a statistical+systematics covariance matrix including calibration and photo-z uncertainties, and cosmology fitting with a prior from the cosmic microwave background. Compared to using the z spec subset, including events with SN+host photo-z results in (i) more precise distances for z > 0.5, (ii) a Hubble diagram that extends 0.3 further in redshift, and (iii) a 50% increase in the Dark Energy Task Force figure of merit (FoM) based on the w 0 w a CDM model. Analyzing 25 simulated data samples, the average bias on w 0 and w a is consistent with zero. The host photo-z systematic of 0.01 reduces FoM by only 2% because (i) most z < 0.5 events are in the z spec subset, (ii) the combined SN+host photo-z has ×2 smaller bias, and (iii) the anticorrelation between fitted redshift and color self-corrects distance errors. To prepare for analyzing real data, the next SN Ia cosmology analysis with photo-zs should include non–SN Ia contamination and host galaxy misassociations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mitra

Kessler

Hložek

2023

ApJ

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“…We test the unbinning and rebinning procedure (Section 3) by analyzing 50 simulated data-sized samples that closely follow Vincenzi et al (2023). Each simulation corresponds to the 5 yr DES photometric sample for events with an accurate spectroscopic redshift of the host galaxy, combined with a spectroscopically confirmed low-redshift (LOWZ) sample (z < 0.1).…”

Section: Validation I: Simulation and Analysismentioning

confidence: 99%

“…BBC has been used in the SN Ia cosmology analysis for spectroscopic samples from Pantheon (Scolnic et al 2018), DES (DES Collaboration 2019), and Pantheon+ (Brout et al 2022a). BBC has also been used on a photometric sample from PS1 (Jones et al 2019) and to examine contamination biases for the photometric DES sample (Vincenzi et al 2023).…”

Section: Introductionmentioning

confidence: 99%

“…We validate the unbinning and rebinning methods using simulations of DES that include SNe Ia, SNCC, and peculiar SNe Ia. The simulation and analysis presented here are similar to those in Vincenzi et al (2023), and all analysis software used in this analysis is publicly available. The software for simulations, light-curve fitting, BBC, and cosmology fitting is from the SuperNova ANAlysis package (SNANA; Kessler et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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

Kessler

Vincenzi

Armstrong

2023

ApJL

Self Cite

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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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The DEHVILS in the details: Type Ia supernova Hubble residual comparisons and mass step analysis in the near-infrared

Peterson,

Scolnic,

Jones

et al. 2024

A&A

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Measurements of type Ia supernovae (SNe Ia) in the near-infrared (NIR) have been used both as an alternate path to cosmology compared to optical measurements and as a method of constraining key systematics for the larger optical studies. With the DEHVILS sample, the largest published NIR sample with consistent NIR coverage of maximum light across three NIR bands ($Y$, $J$, and $H$), we check three key systematics: (i) the reduction in Hubble residual scatter as compared to the optical, (ii) the measurement of a ``mass step'' or lack thereof and its implications, and (iii) the ability to distinguish between various dust models by analyzing slopes and correlations between Hubble residuals in the NIR and optical. We produce SN Ia simulations of the DEHVILS sample and find that it is harder to differentiate between various dust models than previously understood. Additionally, we find that fitting with the current SALT3-NIR model does not yield accurate wavelength-dependent stretch-luminosity correlations, and we propose a limited solution for this problem. From the data, we see that (i) the standard deviation of Hubble residual values from NIR bands treated as standard candles are 0.007--0.042 mag smaller than those in the optical, (ii) the NIR mass step is not constrainable with the current sample size of 47 SNe Ia from DEHVILS, and (iii) Hubble residuals in the NIR and optical are correlated in the data. We test a few variations on the number and combinations of filters and data samples, and we observe that none of our findings or conclusions are significantly impacted by these modifications.

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The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

Cited by 18 publications

References 137 publications

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

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

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

The DEHVILS in the details: Type Ia supernova Hubble residual comparisons and mass step analysis in the near-infrared

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