Related papers: Das ist der HAMMER: Consistent new physics interpr…
Flavour Changing Neutral Current (FCNC) decays are forbidden at lowest perturbative order in the Standard Model (SM) and only allowed via quantum loops. These transitions are therefore heavily suppressed in the SM, and New Physics (NP) can…
In many problems, complex non-Gaussian and/or nonlinear models are required to accurately describe a physical system of interest. In such cases, Monte Carlo algorithms are remarkably flexible and extremely powerful approaches to solve such…
Machine learning and deep learning have revolutionized computational physics, particularly the simulation of complex systems. Equivariance is essential for simulating physical systems because it imposes a strong inductive bias on the…
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order…
This paper considers Bayesian parameter estimation of dynamic systems using a Markov Chain Monte Carlo (MCMC) approach. The Metroplis-Hastings (MH) algorithm is employed, and the main contribution of the paper is to examine and illustrate…
We present a new data-driven benchmark system to evaluate the performance of new MCMC samplers. Taking inspiration from the COCO benchmark in optimization, we view this task as having critical importance to machine learning and statistics…
A brief review of the current state of observed deviations of theoretical predictions from experimental data in semileptonic decays of $B$ and $B_c$ mesons is given. A theoretical analysis of these decays is carried out, taking into account…
Hierarchical Bayesian models based on Gaussian processes are considered useful for describing complex nonlinear statistical dependencies among variables in real-world data. However, effective Monte Carlo algorithms for inference with these…
Recent evidence for a CP violating asymmetry in the semileptonic decays of B_s mesons cannot be accommodated within the Standard Model. Such an asymmetry can be explained by new physics contributions to Delta B=2 components of either the…
Although Hamiltonian Monte Carlo (HMC) scales as O(d^(1/4)) in dimension, there is a large constant factor determined by the curvature of the target density. This constant factor can be reduced in most cases through preconditioning, the…
Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high…
Precision CP violation measurements in rare hadronic B decays could provide clean signatures of parity symmetric new physics, implying the existence of SU(2)_L x SU(2)_R x U(1)_{B-L} x P symmetry at high energies. New contributions to the…
Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be…
We update the branching ratios for the inclusive decays $B\to X_s \ell^+ \ell^-$ and the exclusive decays $B\to (K,K^*) \ell^+ \ell^-$, with $\ell=e, \m$, in the standard model by including the explicit $O(\a_s)$ and $\Lambda_{\hbox{\tiny…
Bayesian inference requires determining the posterior distribution, a task that becomes particularly challenging when the dimension of the parameter space is large and unknown. This limitation arises in many physics problems, such as…
Hamiltonian Monte Carlo (HMC) has emerged as a powerful Markov Chain Monte Carlo (MCMC) method to sample from complex continuous distributions. However, a fundamental limitation of HMC is that it can not be applied to distributions with…
Computer simulation models are widely used to study complex physical systems. A related fundamental topic is the inverse problem, also called calibration, which aims at learning about the values of parameters in the model based on…
The analysis of results from HEP experiments often involves the estimates of the composition of the binned data samples, based on Monte Carlo simulations of various sources. Due to a finite statistic of MC samples they have statistical…
The significant divergence between the SM predictions and experimental measurements for the ratios, $R_{D^{(*)}}\equiv \mathcal{B}(\bar{B}\to D^{(*)}\tau^- \bar{\nu}_\tau)/ \mathcal{B}(\bar{B}\to D^{(*)} \ell^{\prime-}…
Semileptonic and purely leptonic decays of B meson to $\tau$, such as $B\to D^{(\ast)}\tau\nu_{\tau}$ and $B\to\tau\nu_\tau$ are studied. Recognizing that there already were some weak hints of possible deviations from the SM in the…