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

Desai, Shantanu (2023) The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification. Monthly Notices of the Royal Astronomical Society, 518 (1). pp. 1106-1127. ISSN 0035-8711

Full text not available from this repository. (Request a copy)

Abstract

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 using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7-99.5 percent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC ('BEAMS with Bias Correction'), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian Omega(M) prior of 0.311 +/- 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 percent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet's criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015-0.03). Finally, we measure biases due to contamination on w(0) and w(a) (assuming a flat universe), and find these to be <0.009 in w(0) and <0.108 in w(a), 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Desai, Shantanuhttp://orcid.org/0000-0002-0466-3288
Item Type: Article
Uncontrolled Keywords: IA SUPERNOVAE; LEGACY SURVEY; SDSS-II; SYSTEMATIC UNCERTAINTIES; LIGHT CURVES; SNE IA; CONSTRAINTS; SIMULATIONS; PAN-STARRS1; PARAMETERS
Subjects: Physics
Physics > Classical mechanics
Physics > Astronomy Astrophysics
Divisions: Department of Physics
Depositing User: Mr Nigam Prasad Bisoyi
Date Deposited: 18 Aug 2023 08:42
Last Modified: 18 Aug 2023 08:42
URI: http://raiith.iith.ac.in/id/eprint/11572
Publisher URL: https://doi.org/10.1093/mnras/stac1404
OA policy: https://www.sherpa.ac.uk/id/publication/24618
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 11572 Statistics for this ePrint Item