相关论文: Internal Robustness: systematic search for systema…
Evaluating the performance of heuristic optimisation algorithms is essential to determine how well they perform under various conditions. Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in…
Measurement of the growth rate of structures ($\fsig$) with Type Ia supernovae (\sns) will improve our understanding of the nature of dark energy and enable tests of general relativity. In this paper, we generate simulations of the 10 year…
Type Ia Supernovae hold great promise to measure the cosmic deceleration. The diversity observed among these explosions, however, complicates their ability to measure cosmological parameters considerably. The comparison of near and distant…
The standard cosmology strongly relies upon the Cosmological Principle, which consists on the hypotheses of large scale isotropy and homogeneity of the Universe. Testing these assumptions is, therefore, crucial to determining if there are…
For low-redshift (z < 0.1) Type Ia supernovae (SN Ia) samples used in several cosmological analyses over the past decade, we probe for systematic bias by looking for correlations between surface brightness (SB) measurements and Hubble…
We compare two different probes of the expansion history of the universe, namely, luminosity distances from type Ia supernovae and angular diameter distances from galaxy clusters, using the Bayesian interpretation of Crossing statistic [1,…
We present a systematic analysis of the cosmological constraints from the "Pantheon Sample" of 1048 Type Ia Supernovae (SNe Ia) in the redshift range $0.01<z<2.3$ compiled by Scolnic et al. (2018). Applying the flux-averaging method for…
We perform an Internal Robustness analysis (iR) to a compilation of the most recent $f\sigma_8(z)$ data, using the framework of 1209.1897. The method analyzes combinations of subsets in the data set in a Bayesian model comparison way,…
Bayesian graphical models are an efficient tool for modelling complex data and derive self-consistent expressions of the posterior distribution of model parameters. We apply Bayesian graphs to perform statistical analyses of Type Ia…
We have developed a frequentist approach for model selection which determines the consistency between any cosmological model and the data using the distribution of likelihoods from the iterative smoothing method. Using this approach, we…
Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can…
The systems that statisticians are asked to assess, such as nuclear weapons, infrastructure networks, supercomputer codes and munitions, have become increasingly complex. It is often costly to conduct full system tests. As such, we present…
For future surveys, spectroscopic follow-up for all supernovae will be extremely difficult. However, one can use light curve fitters, to obtain the probability that an object is a Type Ia. One may consider applying a probability cut to the…
When using Bayesian inference to support conservative software reliability assessments, it is useful to consider a collection of Bayesian inference problems, with the aim of determining the worst-case value (from this collection) for a…
Mapping the recent expansion history of the universe offers the best hope for uncovering the characteristics of the dark energy believed to be responsible for the acceleration of the expansion. In determining cosmological and dark-energy…
It is now common practice to constrain cosmological parameters using supernovae (SNe) catalogues constructed from several different surveys. Before performing such a joint analysis, however, one should check that parameter constraints…
Inter-survey calibration remains an important systematic uncertainty in cosmological studies using type Ia supernova (SNe Ia). Ideally, each survey would measure its system throughputs, for instance with bandpass measurements combined with…
We analyze the three-year SDSS-II Superernova (SN) Survey data and identify a sample of 1070 photometric SN Ia candidates based on their multi-band light curve data. This sample consists of SN candidates with no spectroscopic confirmation,…
We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to check their consistency using cosmology blind statistical analyses as well as cosmological parameter fitting. We find that Panstarrs dataset is the most stable of the…
As an increasing number of well measured type Ia supernovae (SNe Ia) become available, the statistical uncertainty on w has been reduced to the same size as the systematic uncertainty. The statistical error will decrease further in the near…