Related papers: Das ist der HAMMER: Consistent new physics interpr…
Hybrid Monte-Carlo (HMC) sampling smoother is a fully non-Gaussian four-dimensional data assimilation algorithm that works by directly sampling the posterior distribution formulated in the Bayesian framework. The smoother in its original…
We present a new version of the truncated harmonic mean estimator (THAMES) for univariate or multivariate mixture models. The estimator computes the marginal likelihood from Markov chain Monte Carlo (MCMC) samples, is consistent,…
We argue that a detailed analysis of semileptonic decays can test the possibility of a massive neutrino. The key observable, related to the forward-backward asymmetry, is exactly zero for a massless neutrino but becomes non-zero if the…
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the fact that completely random measures, which commonly used…
In phenomenological preparation for new measurements one searches for the carriers of quality signatures. Often, the first approach quantities may be difficult to measure or to provide sufficiently precise predictions for comparisons.…
We consider leptonic $B^-\to \ell^- \bar\nu_\ell$ and semileptonic $\bar B \to \pi \ell^- \bar\nu_\ell$, $\bar B \to \rho \ell^- \bar\nu_\ell$ decays and present a strategy to determine short-distance coefficients of New-Physics operators…
We calculate the $\alpha_s$ corrections to the form factors which parameterize the hadronic tensor relevant for inclusive semileptonic $B \rightarrow X \tau\bar\nu_{\tau}$ and $\Lambda_b \rightarrow X \tau\bar\nu_{\tau}$ decays. We apply…
This paper presents an in-depth mathematical analysis of the Monte Carlo replica method, commonly used in global fitting studies within the high-energy physics theory field. For the first time, we offer a rigorous derivation of the…
Recent experimental measurements of several observables in semileptonic B meson decays have pointed towards the possibility of new physics. The LHCb collaboration has reported a significant deviation, exceeding $3.2\sigma$, in the combined…
We rely on Monte Carlo (MC) simulations to interpret searches for new physics at the Large Hadron Collider (LHC) and elsewhere. These simulations result in noisy and approximate estimators of selection efficiencies and likelihoods. In this…
We present a nonlinear (in the sense of McKean) generalization of Hamiltonian Monte Carlo (HMC) termed nonlinear HMC (nHMC) capable of sampling from nonlinear probability measures of mean-field type. When the underlying confinement…
The Belle Collaboration has recently measured the complete set of angular coefficient functions for the exclusive decays $\bar B \to D^* (D \pi) \ell \bar \nu_\ell$, with $\ell=e,\,\mu$, in four bins of the parameter…
Searches for a Heavy Sterile Neutrino $N$ profit from precise predictions of inclusive decay rates, entering predictions for branching fractions and lifetime. Once decay channels into semi-hadronic final states are open, a reliable…
Precision measurements in allowed nuclear beta decays and neutron decay are reviewed and analyzed both within the Standard Model and looking for new physics. The analysis incorporates the most recent experimental and theoretical…
A general analysis of possible violation of CP in processes like $\tau \to K\pi \nu$, for unpolarized $\tau$ is presented. In this paper, we derive the new contributions to the effective Hamiltonian governs $\vert\Delta S \vert=1$…
Self-learning Monte Carlo method (SLMC), using a trained effective model to guide Monte Carlo sampling processes, is a powerful general-purpose numerical method recently introduced to speed up simulations in (quantum) many-body systems. In…
The R-parity conserving MSSM in light of the decay $B_s\rightarrow\mu^+\mu^-$ with near-SM branching ratio is an interesting platform for studying the complementarity between direct and indirect searches for beyond the SM physics. Based on…
The Hamiltonian Monte Carlo (HMC) algorithm is a powerful Markov Chain Monte Carlo (MCMC) method that uses Hamiltonian dynamics to generate samples from a target distribution. To fully exploit its potential, we must understand how…
In order to determine the ratio of CKM matrix elements |V_{ub}/V_{cb}| (and |V_{ub}|), we propose a new model-independent method based on the heavy quark effective theory, which is theoretically described by the phase space factor and the…
This article introduces the Modified Parameterized Leapfrog Hamiltonian Monte Carlo (MPL-HMC) method, a novel extension of HMC addressing key limitations through tunable integration parameters $\alpha(\delta t)$ and $\beta(\delta t)$,…