Related papers: Das ist der HAMMER: Consistent new physics interpr…
We calculate the $\Lambda_b \to \Lambda_c \ell \nu$ form factors and decay rates for all possible $b\to c \ell\bar\nu$ four-Fermi interactions beyond the Standard Model, including nonzero charged lepton masses and terms up to order…
Measurement of $CP$-violating observables in semileptonic decays is a sensitive null-test of the Standard Model: any $CP$ violation would be an unambiguous sign of New Physics effects. The model-independent technique to measure parity and…
Hamiltonian Monte Carlo (HMC) is a Markov chain algorithm for sampling from a high-dimensional distribution with density $e^{-f(x)}$, given access to the gradient of $f$. A particular case of interest is that of a $d$-dimensional Gaussian…
The coalescence of binary neutron stars are one of the main sources of gravitational waves for ground-based gravitational wave detectors. As Bayesian inference for binary neutron stars is computationally expensive, more efficient and faster…
Hamiltonian Monte-Carlo (HMC) and its auto-tuned variant, the No U-Turn Sampler (NUTS) can struggle to accurately sample distributions with complex geometries, e.g., varying curvature, due to their constant step size for leapfrog…
Hamiltonian Monte Carlo (HMC) has been progressively incorporated within the statistician's toolbox as an alternative sampling method in settings when standard Metropolis-Hastings is inefficient. HMC generates a Markov chain on an augmented…
In high-energy particle physics, complex Monte Carlo (MC) simulations are needed to compare theory predictions to measurable quantities. Many and large MC samples are needed to be generated to take into account all the systematics.…
The b -> ssdbar and b -> ddsbar decays are highly suppressed in the SM, and are thus good probes of new physics (NP) effects. We discuss in detail the structure of the relevant SM effective Hamiltonian pointing out the presence of nonlocal…
We investigate the discovery potential of semileptonic hyperon decays in terms of searches of new physics at teraelectronvolt scales. These decays are controlled by a small $SU(3)$-flavor breaking parameter that allows for systematic…
It is well known that estimating cosmological parameters from cosmic microwave background (CMB) data alone results in a significant degeneracy between the total neutrino mass and several other cosmological parameters, especially the Hubble…
We present a comprehensive analysis of the decay $\Lambda_c^- \to \Lambda(\to p\pi)\,\mu^- \bar\nu_\mu$ within a model-independent effective field theory framework. Previous studies have been restricted to the three-body decay $\Lambda_c^+…
Inspired by the recently observed tensions between the experimental data and the theoretical predictions, based on QCD factorisation, for several colour-allowed non-leptonic $B$-meson decays, we study the potential size of new physics (NP)…
This article analyses the available inputs in $\btopilnu$ and $\btorholnu$ decays which include the measured values of differential rate in different $q^2$-bins (lepton invariant mass spectrum), lattice, and the newly available inputs on…
Traditional Markov chain Monte Carlo (MCMC) sampling of hidden Markov models (HMMs) involves latent states underlying an imperfect observation process, and generates posterior samples for top-level parameters concurrently with nuisance…
Using the \texttt{lalinference} Markov-chain Monte Carlo parameter estimation code, we examine two distinct nonprecessing black hole-neutron star (BH-NS) binaries with and without higher-order harmonics. Our simulations suggest that higher…
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) approach that exhibits favourable exploration properties in high-dimensional models such as neural networks. Unfortunately, HMC has limited use in large-data regimes and…
Discrete data are abundant and often arise as counts or rounded data. These data commonly exhibit complex distributional features such as zero-inflation, over-/under-dispersion, boundedness, and heaping, which render many parametric models…
Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction. Due to its simplicity and superior performance as compared to its Quasi Monte…
In Hamiltonian Monte Carlo sampling, the shape of the potential and the choice of the momentum distribution jointly give rise to the Hamiltonian dynamics of the sampler. An efficient sampler propagates quickly in all regions of the…
Hamiltonian Monte Carlo (HMC) algorithms which combine numerical approximation of Hamiltonian dynamics on finite intervals with stochastic refreshment and Metropolis correction are popular sampling schemes, but it is known that they may…