744 citations · 6.5k across the 102 of their papers we have counts for
9 papers · 1 filter
Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
Jesus Arjona Martinez, Thong Q Nguyen, Maurizio Pierini +2
We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model…
The DNNLikelihood: enhancing likelihood distribution with Deep Learning
Andrea Coccaro, Maurizio Pierini, Luca Silvestrini +1
We introduce the DNNLikelihood, a novel framework to easily encode, through Deep Neural Networks (DNN), the full experimental information contained in complicated likelihood functi…
Learning Multivariate New Physics
Raffaele Tito D'Agnolo, Gaia Grosso, Maurizio Pierini +2
We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any…
: a Code for the Combination of Indirect and Direct Constraints on High Energy Physics Models
Jorge de Blas, Debtosh Chowdhury, Marco Ciuchini +15
is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to fit the model parameters to a given set of experi…
Interaction networks for the identification of boosted decays
Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant +6
We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinar…
JEDI-net: a jet identification algorithm based on interaction networks
Eric A. Moreno, Olmo Cerri, Javier M. Duarte +7
We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced…
FPGA-accelerated machine learning inference as a service for particle physics computing
Javier Duarte, Philip Harris, Scott Hauck +20
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large pote…
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Shah Rukh Qasim, Jan Kieseler, Yutaro Iiyama +1
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, grap…
LHC analysis-specific datasets with Generative Adversarial Networks
Bobak Hashemi, Nick Amin, Kaustuv Datta +2
Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kin…