Related papers: Improvement of $q^2$ resolution in semileptonic de…
In general a decay with a missing (not detected) particle can not be fully reconstructed apart from a few exceptions. For example, if the momentum of the decaying particle is known or if the missing energy in an event is measured precisely,…
A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods…
It is well known that in $b$ hadron decays with a single unreconstructible final state particle, the decay kinematics can be solved up to a quadratic ambiguity, without any knowledge of the $b$ hadron momentum. We present a method to infer…
Decay-frame Kinematics (DK) has previously been introduced as a technique to reconstruct neutralino masses from their three-body decays to leptons. This work is an extension to the case of two-body decays through on-shell sleptons, with…
In this work, we propose a machine learning-based approach to address a specific aspect of the Quantum Marginal Problem: reconstructing a global density matrix compatible with a given set of quantum marginals. Our method integrates a…
Study of methods of resolved top quarks kinematic reconstruction in the $t\bar{t} \rightarrow \ell+$jets channel is presented at the particle level as well as the fast-simulation detector level. Previous and current pseudo-top quark…
Artificial neural networks are trained by a standard backpropagation learning algorithm with regularization to model and predict the systematics of -decay of heavy and superheavy nuclei. This approach to regression is implemented in two…
The need to map parton-level processes to color-neutral hadrons in a way that respects quark-hadron duality arises in several areas of physics, including in the semileptonic decays of $B$ mesons. Integrated over large regions of phase…
High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatial tensor models. MMT leverages the property that spatial…
We study in detail the kinematics of semileptonic $M_{l3}$ ($M \rightarrow M^\prime l \nu$) decay, considering only the new models preserving all the gauge symmetries of the Standard Model (SM), and assuming that neutrinos are massless and…
The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations. In this work, we present a machine…
Errors in surface code have typically been decoded by Minimum Weight Perfect Matching (MWPM) based method. Recently, neural-network-based Machine Learning (ML) techniques have been employed for this purpose. Here we propose a two-level (low…
We propose a novel method to reconstruct the 3D shapes of transparent objects using hand-held captured images under natural light conditions. It combines the advantage of explicit mesh and multi-layer perceptron (MLP) network, a hybrid…
This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections,…
Tau leptons serve as an important tool for studying the production of Higgs and electroweak bosons, both within and beyond the Standard Model of particle physics. Accurate reconstruction and identification of hadronically decaying tau…
We critically reexamine the standard applications of the method of kinematical endpoints for sparticle mass determination. We consider the typical decay chain in supersymmetry (SUSY) squark -> neutralino -> slepton -> LSP, which yields a…
Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to…
We present a new approach for representing and reconstructing multidimensional magnetic resonance imaging (MRI) data. Our method builds on a novel, learned feature-based image representation that disentangles different types of features,…
Inverse medium scattering is an ill-posed, nonlinear wave-based imaging problem arising in medical imaging, remote sensing, and non-destructive testing. Machine learning (ML) methods offer increased inference speed and flexibility in…
We evaluate semileptonic and two--meson nonleptonic decays of the $B_c^-$ meson in the framework of a nonrelativistic quark model. The former are done in spectator approximation using one--body current operators at the quark level. Our…