Related papers: Neural Filters for Jet Analysis
The deep learning technique has been applied for the first time to investigate the possibility of centrality determination in terms of the number of participants ($N_{\mathrm{part}}$) in high-energy heavy-ion collisions. For this purpose,…
The possible application of boosted neural network to particle classification in high energy physics is discussed. A two-dimensional toy model, where the boundary between signal and background is irregular but not overlapping, is…
Our work aims to reproduce the set of findings published in "Network Deconvolution" by Ye et al. (2020)[1]. That paper proposes an optimization technique for model training in convolutional neural networks. The proposed technique "network…
We propose a new method for the discrimination of sub-micron nuclear recoil tracks from an instrumental background in fine-grain nuclear emulsions used in the directional dark matter search. The proposed method uses a 3D Convolutional…
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure $CP$-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional…
Recent results connected to nuclear collision dynamics, from low up to relativistic energies, are reviewed. Heavy ion reactions offer the unique opportunity to probe the complex nuclear many-body dynamics and to explore, in laboratory…
In this work, we explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime.…
Jet quenching measurements using leading particles and their correlations suffer from known biases, which can be removed via direct reconstruction of jets in central heavy ion collisions. In this talk, we discuss several modern jet…
The suppression of the yield of high transverse momentum $p_{T}$ hadrons in heavy-ion collisions, referred to as "jet-quenching", has now developed into a comprehensive science. Jets are now used as probes of a variety of properties of the…
High energy quarks and gluons play essential roles in the tomographic study of relativistic heavy-ion collisions and of the quark-gluon plasma. They interact with the traversed dense matter through elastic and inelastic collisions, and lose…
Neural networks (NNs) provide a powerful and flexible tool for selecting a signal from a larger background. The D0 collaboration has used them extensively in studying t-tbar decays. NNs were essential to the measurement of the t-tbar…
n these proceedings I discuss several recent developments in the physics of heavy flavor jets in heavy ion collisions. i) The dijet mass modification in nucleus-nucleus reactions has been proposed as a new observable with enhanced…
Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos. Three types of machine learning techniques are tested for their ability to…
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high…
We introduce a novel deep convolutional neural network (NN) -enhanced Bayesian global analysis of bulk observables in highest-energy heavy-ion collisions, using relativistic 2+1 D second-order viscous hydrodynamics with a dynamical…
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train…
We explore the potential to use machine learning methods to search for heavy neutrinos, from their hadronic final states including a fat-jet signal, via the processes $pp \rightarrow W^{\pm *}\rightarrow \mu^{\pm} N \rightarrow \mu^{\pm}…
The most extreme deformations that can be explored in heavy-ion collisions at Fermi-energies are collimated flows of nuclear matter which recall jet dynamics. From microphysics to the cosmological scale, jets are rather common topologies.…
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning…
Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either on images or on 4-vectors, they identify jets from decays of arbitrary heavy resonances. To control the…