Detecting hidden uncertainties in standard candles and clocks via improved hypothesis test

TL;DR

This paper presents a new sensitive method to detect tiny systematic uncertainties caused by cosmological hypotheses, which are difficult to identify due to observational errors and indistinguishability from other errors.

Contribution

The paper introduces an efficient hypothesis test to detect hidden systematic errors in cosmological data, demonstrated on SN Ia and cosmic chronometer datasets.

Findings

Detected slight evolutional features in residuals with SN Ia data.

Cosmic chronometer data showed no noticeable features, likely due to larger uncertainties.

Method is useful for high-precision independent observational samples.

Abstract

Tiny systematic uncertainty caused by cosmological hypotheses is hard to be detected, not only because the present observational errors are relatively large but also because hypothesis-induced uncertainty is indistinguishable from other sources of systematic errors. We introduce an efficient and sensitive method for detecting tiny systematic errors, which contain the cosmological-hypothesis-induced uncertainty and other secondary systematic errors, hidden behind residuals of chi-square analysis. In this paper, we apply our analysis to JLA compilation of SN Ia observations and latest cosmic chronometer data-set. We find slight but noticeable evolutional feature in residuals of chi-square analysis under present systematic uncertainty control, when combining JLA samples with standard cosmological model. Meanwhile, cosmic chronometer observation has no noticeable similar feature with various cosmological models, which may be covered up by relatively large observational uncertainties. Our method can be useful when various independent observational samples with high observational precision are available, since the cosmological hypothesis-induced error appears unbiasedly in all related data-sets.