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The increasing richness of data related to cold dense matter, from laboratory experiments to neutron-star observations, requires a framework for constraining the properties of such matter that makes use of all relevant information. Here, we…
We investigate constraints on the Hubble constant ($H_0$) using Baryon Acoustic Oscillations (BAO) and baryon density measurements from Big Bang Nucleosynthesis (BBN). We start by investigating the tension between galaxy BAO measurements…
Standard cosmological models rely on an approximate treatment of gravity, utilizing solutions of the linearized Einstein equations as well as physical approximations. In an era of precision cosmology, we should ask: are these approximate…
Numerical simulations are widely used to predict the behavior of physical systems, with Bayesian approaches being particularly well suited for this purpose. However, experimental observations are necessary to calibrate certain simulator…
Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use…
Estimated uncertainty by approximate posteriors in Bayesian neural networks are prone to miscalibration, which leads to overconfident predictions in critical tasks that have a clear asymmetric cost or significant losses. Here, we extend the…
We present an analysis that investigates the $H_0$ and $S_8$ tensions by considering a dark energy model. The latter is a late-time model characterized by a future abrupt event known as the Little Rip (LR) model and characterised by one…
The nuclear symmetry energy is intimately connected with nuclear astrophysics. This contribution focuses on the estimation of the symmetry energy from experiment and how it is related to the structure of neutron stars. The most important…
Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space. This is motivated by the problem in neuroscience of calculating the mutual…
Recent studies have presented evidence for tension between the constraints on Omega_m and sigma_8 from the cosmic microwave background (CMB) and measurements of large-scale structure (LSS). This tension can potentially be resolved by…
In order to investigate the potential Hubble tension, we compile a catalogue of 216 measurements of the Hubble--Lema\^itre constant $H_0$ between 2012 and 2022, which includes 109 model-independent measurements and 107 $\Lambda$CDM…
Weak galaxy lensing surveys have consistently reported low values of the $S_8$ parameter compared to the $\textit{Planck}\ \Lambda\rm{CDM}$ cosmology. Amon & Efstathiou (2022) used KiDS-1000 cosmic shear measurements to propose that this…
The {\it Linear Point} (LP), defined as the midpoint between the BAO peak and the associated left dip of the two-point correlation function (2PCF), $\xi(s)$, is proposed as a new standard ruler which is insensitive to nonlinear effects. In…
The DESI Collaboration reports a significant preference for a dynamic dark energy model ($w_0w_a$CDM) over the cosmological constant ($\Lambda$CDM) when their data are combined with other frontier cosmological probes. We present a direct…
Calibration precision is currently a limiting systematic in 21 cm cosmology experiments. While there are innumerable calibration approaches, most can be categorized as either `sky-based,' relying on an extremely accurate model of…
Over the past decade, the disparity between the value of the cosmic expansion rate directly determined from measurements of distance and redshift or instead from the standard $\Lambda$CDM cosmological model calibrated by measurements from…
Bayesian model selection is a tool to decide whether the introduction of a new parameter is warranted by data. I argue that the usual sampling statistic significance tests for a null hypothesis can be misleading, since they do not take into…
We present a Bayesian analysis to constrain the equation of state of dense nucleonic matter by exploiting the available data from symmetric nuclear matter at saturation and from observations of compact X-ray sources and from the…
We have conducted an extensive study using a diverse set of equations of state (EoSs) to uncover strong relationships between neutron star (NS) observables and the underlying EoS parameters using symbolic regression method. These EoS…
With recent advances in neutron star observations, major progress has been made in determining the pressure of neutron star matter at high density. This pressure is constrained by the neutron star deformability, determined from…