skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Probing the consistency of cosmological contours for supernova cosmology

Journal Article · · Publications of the Astronomical Society of Australia
DOI:https://doi.org/10.1017/pasa.2023.40· OSTI ID:2007195
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [5]; ORCiD logo [6]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [7]
  1. Australian National University, Canberra, ACT (Australia)
  2. University of Pennsylvania, Philadelphia, PA (United States)
  3. Boston University, MA (United States)
  4. University of Queensland, Brisbane, QLD (Australia)
  5. University of Chicago, IL (United States)
  6. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  7. Australian National University, Canberra, ACT (Australia); ARC Centre of Excellence for All-Sky Astrophysics in 3 Dimensions (ASTRO 3D), Canberra, ACT (Australia)

As the scale of cosmological surveys increases, so does the complexity in the analyses. This complexity can often make it difficult to derive the underlying principles, necessitating statistically rigorous testing to ensure the results of an analysis are consistent and reasonable. This is particularly important in multi-probe cosmological analyses like those used in the Dark Energy Survey (DES) and the upcoming Legacy Survey of Space and Time, where accurate uncertainties are vital. In this paper, we present a statistically rigorous method to test the consistency of contours produced in these analyses and apply this method to the Pippin cosmological pipeline used for type Ia supernova cosmology with the DES. We make use of the Neyman construction, a frequentist methodology that leverages extensive simulations to calculate confidence intervals, to perform this consistency check. A true Neyman construction is too computationally expensive for supernova cosmology, so we develop a method for approximating a Neyman construction with far fewer simulations. We find that for a simulated dataset, the 68% contour reported by the Pippin pipeline and the 68% confidence region produced by our approximate Neyman construction differ by less than a percent near the input cosmology; however, they show more significant differences far from the input cosmology, with a maximal difference of 0.05 in $$Ω$$M and 0.07 in w. In conclusion, this divergence is most impactful for analyses of cosmological tensions, but its impact is mitigated when combining supernovae with other cross-cutting cosmological probes, such as the cosmic microwave background.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of Chicago, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP); National Science Foundation (NSF)
Grant/Contract Number:
AC02-05CH11231; FOA-0002424; AST-2108094; SC0009924
OSTI ID:
2007195
Alternate ID(s):
OSTI ID: 2228832
Journal Information:
Publications of the Astronomical Society of Australia, Vol. 40; ISSN 1323-3580
Publisher:
CSIROCopyright Statement
Country of Publication:
United States
Language:
English

References (22)

The Dark Energy Survey: more than dark energy – an overview journal March 2016
ChainConsumer journal August 2016
Unity: Confronting Supernova Cosmology’S Statistical and Systematic Uncertainties in a Unified Bayesian Framework journal November 2015
A hierarchical Bayesian SED model for Type Ia supernovae in the optical to near-infrared journal December 2021
A MORE GENERAL MODEL FOR THE INTRINSIC SCATTER IN TYPE Ia SUPERNOVA DISTANCE MODULI journal September 2011
Pippin: A pipeline for supernova cosmology journal March 2020
Array programming with NumPy journal September 2020
The Pantheon+ Analysis: SuperCal-fragilistic Cross Calibration, Retrained SALT2 Light-curve Model, and Calibration Systematic Uncertainty journal October 2022
Binning is Sinning (Supernova Version): The Impact of Self-calibration in Cosmological Analyses with Type Ia Supernovae journal May 2021
»Smooth test» for goodness of fit journal July 1937
SNANA: A Public Software Package for Supernova Analysis
  • Kessler, Richard; Bernstein, Joseph P.; Cinabro, David
  • Publications of the Astronomical Society of the Pacific, Vol. 121, Issue 883 https://doi.org/10.1086/605984
journal September 2009
SuperNNova: an open-source framework for Bayesian, neural network-based supernova classification journal December 2019
Matplotlib: A 2D Graphics Environment journal January 2007
BEAMS: Separating the Wheat from the Chaff in Supernova Analysis book October 2012
Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) journal July 2019
SCONE: Supernova Classification with a Convolutional Neural Network journal July 2021
The benefits of public transport journal September 2019
astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation journal April 2017
A revised SALT2 surface for fitting Type Ia supernova light curves journal April 2021
Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples journal February 2017
Asteroseismology of the DAV Star L19-2 journal July 2022
The Year in Industry at work journal January 1998