Related papers: Calibrating Bayesian Tension Statistics using Neur…
We present baryon acoustic oscillation (BAO) measurements from more than 14 million galaxies and quasars drawn from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2), based on three years of operation. For cosmology…
Observations of neutron stars (NSs) by the LIGO-Virgo and NICER collaborations have provided reasonably precise measurements of their various macroscopic properties. In this paper, we employ a Bayesian framework to combine them and place…
The Dark Energy Spectroscopic Instrument (DESI) Collaboration has obtained robust measurements of baryon acoustic oscillations (BAO) in the redshift range, $0.1 < z < 4.2$, based on the Lyman-$\alpha$ forest and galaxies from Data Release 2…
With the increasing precision of recent cosmological surveys and the discovery of important tensions within the $\Lambda$CDM paradigm, it is becoming more and more important to develop tools to quantify accurately the discordance between…
This paper develops a Bayesian network-based method for the calibration of multi-physics models, integrating various sources of uncertainty with information from computational models and experimental data. We adopt the Kennedy and O'Hagan…
We discuss how to efficiently and reliably estimate the level of agreement and disagreement on parameter determinations from different experiments, fully taking into account non-Gaussianities in the parameter posteriors. We develop two…
Detection of millikelvin-level signals from the 'Cosmic Dawn' requires an unprecedented level of sensitivity and systematic calibration. We report the theory behind a novel calibration algorithm developed from the formalism introduced by…
The sensitivity of cosmology to the total neutrino mass scale $\Sigma m_\nu$ is approaching the minimal values required by oscillation data. We study quantitatively possible tensions between current and forecasted cosmological and…
We provide another look at the statistical calibration problem in computer models. This viewpoint is inspired by two overarching practical considerations of computer models: (i) many computer models are inadequate for perfectly modeling…
Modern neural networks have found to be miscalibrated in terms of confidence calibration, i.e., their predicted confidence scores do not reflect the observed accuracy or precision. Recent work has introduced methods for post-hoc confidence…
Discordance in the $\Lambda$CDM cosmological model can be seen by comparing parameters constrained by CMB measurements to those inferred by probes of large scale structure. Recent improvements in observations, including final data releases…
Model-independent approaches have gained increasing attention as powerful tools to investigate persistent tensions between cosmological observations and the predictions of $\Lambda$CDM. Notably, recent DESY5 Type Ia Supernovae (SNIa) and…
Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an…
The cosmic dipole measured in surveys of cosmologically distant sources is generally found to be in disagreement with the kinematic expectation of the Cosmic Microwave Background (CMB). This discrepancy represents severe tension with the…
The possibility that the $H_0$ tension is a sign of a physics beyond the $\Lambda$CDM model is one of the most exciting possibilities in modern cosmology. The challenge of solving this problem is complicated by several factors, including…
Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space. Motivated by the premise that the tuning of individual units may be important, there has…
It is found that a non-minimally coupled scalar tensor theory, Thawing Gravity (TG), can explain multiple tensions plaguing the standard cosmological model $\Lambda$CDM while fitting better to observations than the latter. Using the…
Bayesian methods have been very successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model…
Field experiments are often difficult and expensive to make. To bypass these issues, industrial companies have developed computational codes. These codes intend to be representative of the physical system, but come with a certain amount of…
Several studies in the literature have found a disagreement between data on Baryon Acoustic Oscillations (BAO) derived using two distinct methodologies: the two-dimensional (2D or angular) BAO, which extracts the BAO signal from the angular…