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research article

Assessing tension metrics with dark energy survey and Planck data

Lemos, P.
•
Raveri, M.
•
Campos, A.
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August 1, 2021
Monthly Notices Of The Royal Astronomical Society

Quantifying tensions - inconsistencies amongst measurements of cosmological parameters by different experiments - has emerged as a crucial part of modern cosmological data analysis. Statistically significant tensions between two experiments or cosmological probes may indicate new physics extending beyond the standard cosmological model and need to be promptly identified. We apply several tension estimators proposed in the literature to the dark energy survey (DES) large-scale structure measurement and Planck cosmic microwave background data. We first evaluate the responsiveness of these metrics to an input tension artificially introduced between the two, using synthetic DES data. We then apply the metrics to the comparison of Planck and actual DES Year 1 data. We find that the parameter differences, Eigentension, and Suspiciousness metrics all yield similar results on both simulated and real data, while the Bayes ratio is inconsistent with the rest due to its dependence on the prior volume. Using these metrics, we calculate the tension between DES Year 1 3 x 2pt and Planck, finding the surveys to be in similar to 2.3 sigma tension under the Lambda CDM paradigm. This suite of metrics provides a toolset for robustly testing tensions in the DES Year 3 data and beyond.

  • Details
  • Metrics
Type
research article
DOI
10.1093/mnras/stab1670
Web of Science ID

WOS:000698554000084

Author(s)
Lemos, P.
Raveri, M.
Campos, A.
Park, Y.
Chang, C.
Weaverdyck, N.
Huterer, D.
Liddle, A. R.
Blazek, J.  
Cawthon, R.
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Date Issued

2021-08-01

Published in
Monthly Notices Of The Royal Astronomical Society
Volume

505

Issue

4

Start page

6179

End page

6194

Subjects

Astronomy & Astrophysics

•

Astronomy & Astrophysics

•

methods: statistical

•

cosmological parameters

•

cosmology: observations

•

cosmological constant

•

universe

•

robustness

•

inference

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LASTRO  
Available on Infoscience
October 23, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/182559
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