Related papers: Calibrating Bayesian Tension Statistics using Neur…
We present a physics-informed Bayesian neural-network framework to infer neutron-star equations of state from theoretical priors and to propagate the associated uncertainties to stellar observables. Trained on a large and representative…
Neural Stochastic Differential Equations (NSDEs) model the drift and diffusion functions of a stochastic process as neural networks. While NSDEs are known to make accurate predictions, their uncertainty quantification properties have been…
Recent DESI BAO measurements, combined with CMB data, reveal a tension within the $\Lambda$CDM model that leads to a discrepancy between cosmological and laboratory bounds on the summed neutrino mass. We show that a recoupled interacting…
The Planck measurement of the cosmic microwave background (CMB) has established the $\Lambda$-cold-dark-matter ($\Lambda$CDM) model as the concordant model along with other observations. However, recent measurements of baryon acoustic…
The normalized stress metric measures how closely distances between vertices in a graph drawing match the graph-theoretic distances between those vertices. It is one of the most widely employed quality metrics for graph drawing, and is even…
Consistent experiment data are crucial to adjust parameters of physics models and to determine best estimates of observables. However, often experiment data are not consistent due to unrecognized systematic errors. Standard methods of…
Local measurements of the Hubble constant currently disagree with the high-precision value that is inferred from the CMB under the assumption of a $\Lambda$CDM cosmology. The significance of this tension clearly motivates studying…
The recent local measurements of the Hubble constant $H_0$, indicate a significant discrepancy of over 5$\sigma$ compared to the value inferred from \textit{Planck} observations of the cosmic microwave background (CMB). In this paper, we…
Understanding the dense matter equation of state at extreme conditions is an important open problem. Astrophysical observations of neutron stars promise to solve this, with NICER poised to make precision measurements of mass and radius for…
Searching for gravitational-wave signals is a challenging and computationally intensive endeavor undertaken by multiple independent analysis pipelines. While detection depends only on observed noisy data, it is sometimes inconsistently…
We present a further observational analysis of the $\Lambda_{\rm s}$CDM model proposed in Akarsu et al. [Phys. Rev. D 104, 123512 (2021)]. This model is based on the recent conjecture suggesting the Universe has transitioned from anti-de…
We assess dataset agreement and late-time predictive adequacy in $\Lambda$CDM and its sign-switching extension, $\Lambda_{\rm s}$CDM, using a suite of Gaussian and exact non-Gaussian consistency diagnostics. Both models are constrained with…
We present a Bayesian analysis of cosmological parameter constraints from early- and late-universe observations, focusing on the matter density parameter ($\Omega_m$) and the amplitude of matter fluctuations ($\sigma_8$) within the…
In this paper, we conduct a statistical analysis of various cosmological models within the framework of f (R) gravity theories, motivated by persistent challenges in modern cosmology, such as the unknown mechanisms driving the late-time…
We present a unified Bayesian assessment of model comparison and data-set consistency for LCDM (cold dark matter plus a cosmological constant) and minimal extensions (neutrino mass, spatial curvature, constant or evolving dark energy) using…
We define Baryon Acoustic Oscillation (BAO) distances $\hat{d}_\alpha(z, z_c)$, $\hat{d}_z(z, z_c)$, and $\hat{d}_/(z, z_c)$ that do not depend on cosmological parameters. These BAO distances are measured as a function of redshift $z$ with…
Cosmological parameter uncertainties are often stated assuming a particular model, neglecting the model uncertainty, even when Bayesian model selection is unable to identify a conclusive best model. Bayesian model averaging is a method for…
Looking at the well-known Hubble tension as a tension in the calibrators of the cosmic distance ladder, i.e. the absolute magnitude $M$ of standard candles such as supernovae of Type Ia (SNIa) and the standard ruler represented by the…
The Hubble tension and the recently reported anomaly in data from the Dark Energy Spectroscopic Instrument (DESI) are considered to pose serious challenges to the standard $\Lambda$CDM model of cosmology. In this work, we show that…
Calibrating neural networks is of utmost importance when employing them in safety-critical applications where the downstream decision making depends on the predicted probabilities. Measuring calibration error amounts to comparing two…