Related papers: Unbinned extraction of $\gamma$ from $B\to DK$ wit…
Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the…
A width difference of the order of 20\% has previously been predicted for the two mass eigenstates of the $B_s$ meson. The dominant contributor to the width difference is the $b\rightarrow c\bar c s$ transition, with final states common to…
The extraction of the CKM angle $\alpha$ from the asymmetry in $B^0 \to \pi^+\pi^-$ vs ${\bar B^0} \to \pi^+\pi^-$ suffers from a currently unknown penguin contribution. Experimentally, one can determine the magnitude and phase of the CP…
We first review the methods for determining gamma from B->D K decays that appeared after CKM 2008. We then discuss the theoretical errors in gamma extraction. The errors due to neglected D-Dbar and B_{d,s}-Bbar_{d,s} mixing can be avoided…
A status report on the theory and phenomenology of rare radiative $B$ decays in the standard model is presented with emphasis on the measured decays $B \to X_s \gamma$ and $B \to K^* \gamma$. Standard model is in agreement with experiments…
We study the normalization of perturbative QCD corrections to the inclusive $B \rightarrow X_s \gamma$ decay. We propose to set the renormalization scale using the Brodsky-Lepage-Mackenzie (BLM) method. In the proposed method the scale is…
Unnormalized (or energy-based) models provide a flexible framework for capturing the characteristics of data with complex dependency structures. However, the application of standard Bayesian inference methods has been severely limited…
The first observation of the $B^0_s \to D_s^{*\mp} K^{\pm}$ decay is reported using 3.0$fb^{-1}$ of proton-proton collision data collected by the LHCb experiment. The $D_s^{*\mp}$ mesons are reconstructed through the decay chain $D_s^{*\mp}…
The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics. While classical stochastic sampling methods such as Markov Chain Monte Carlo…
Estimating physical parameters from data is a crucial application of machine learning (ML) in the physical sciences. However, systematic uncertainties, such as detector miscalibration, induce data distribution distortions that can erode…
Using a statistical model for the normally deformed states and for their coupling to a member of the superdeformed band, we calculate the ensemble average and the fluctuations of the intensity for decay out of the superdeformed band and of…
I present a measurement of the CKM angle gamma from a combination of three LHCb measurements using the tree decays B -> DK and B -> Dpi. These measurements are based on a dataset corresponding to 1.0fb-1, collected in 2011. In contrast to…
The normalization constraint on probability density poses a significant challenge for solving the Fokker-Planck equation. Normalizing Flow, an invertible generative model leverages the change of variables formula to ensure probability…
Denoising generative models, such as diffusion and flow-based models, produce high-quality samples but require many denoising steps due to discretization error. Flow maps, which estimate the average velocity between timesteps, mitigate this…
We present a deep machine learning algorithm to extract crystal field (CF) Stevens parameters from thermodynamic data of rare-earth magnetic materials. The algorithm employs a two-dimensional convolutional neural network (CNN) that is…
Normalizing Flows (NF) are powerful generative models with increasing applications in augmenting Monte Carlo algorithms due to their high flexibility and expressiveness. In this work we explore the integration of NF in Diagrammatic Monte…
The sensitivity of the CKM angle $\gamma$ in $\Lambda_b^0 \to D \Lambda$ decays has been studied using the decay parameter $\alpha$ as an observable in addition to the decay rate asymmetry. Feasibility studies show that adding this…
We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron…
We discuss the exclusive radiative decays $B\to K^{*}\gamma$, $B \to\rho\gamma$, and $B\to\omega\gamma$ in QCD factorization within the Standard Model. The analysis is based on the heavy-quark limit of QCD. Our results for these decays are…
This paper presents a parameter scan technique for BSM signal models based on normalizing flow. Normalizing flow is a type of deep learning model that transforms a simple probability distribution into a complex probability distribution as…