Breno Orzari
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View article: Search for new physics in the final state with a single photon and large missing transverse momentum in proton-proton collisions at $\sqrt{s}$ = 13 TeV
Search for new physics in the final state with a single photon and large missing transverse momentum in proton-proton collisions at $\sqrt{s}$ = 13 TeV Open
A search for new physics in events featuring a single photon and missing transverse momentum is presented, using proton-proton $\sqrt{s}$ = 13 TeV collision data corresponding to an integrated luminosity of 101 fb$^{-1}$ collected by the C…
View article: Search for heavy H$γ$ and Z$γ$ resonances with a bottom quark-antiquark pair in the final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV
Search for heavy H$γ$ and Z$γ$ resonances with a bottom quark-antiquark pair in the final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV Open
A search for heavy resonances decaying into a Higgs boson (H) or a Z boson and a photon ($γ$), with the H or Z bosons decaying to a bottom quark-antiquark pair ($\mathrm{b\bar{b}}$) is presented. The analysis is performed using proton-prot…
View article: Search for a new neutral gauge boson produced in association with one or two b jets and decaying into a pair of muons in proton-proton collisions at $\sqrt{s}$ = 13 TeV
Search for a new neutral gauge boson produced in association with one or two b jets and decaying into a pair of muons in proton-proton collisions at $\sqrt{s}$ = 13 TeV Open
A search for a new neutral gauge boson, Z', produced in association with one or two jets, including at least one b jet, and decaying into a pair of muons is presented. The analysis uses proton-proton collision data collected with the CMS d…
View article: Search for low-mass hidden-valley dark showers with non-prompt muon pairs in proton-proton collisions at $\sqrt{s}$ = 13 TeV
Search for low-mass hidden-valley dark showers with non-prompt muon pairs in proton-proton collisions at $\sqrt{s}$ = 13 TeV Open
A search for signatures of a dark analog to quantum chromodynamics is performed. The analysis targets long-lived dark mesons that decay into standard-model particles, with a high branching fraction of the dark mesons decaying into muons. T…
View article: Jet fragmentation function and groomed substructure of bottom quark jets in proton-proton collisions at 5.02 TeV
Jet fragmentation function and groomed substructure of bottom quark jets in proton-proton collisions at 5.02 TeV Open
A measurement of the substructure of bottom quark jets (b jets) in proton-proton (pp) collisions is presented. The measurement uses data collected in pp collisions at $\sqrt{s}$ = 5.02 TeV recorded by the CMS experiment in 2017, correspond…
View article: Search for New Physics in Jet Multiplicity Patterns of Multilepton Events at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> <mml:mtext> </mml:mtext> <mml:mtext> </mml:mtext> <mml:mi>TeV</mml:mi> </mml:math>
Search for New Physics in Jet Multiplicity Patterns of Multilepton Events at Open
A first search for beyond the standard model physics in jet multiplicity patterns of multilepton events is presented, using a data sample corresponding to an integrated luminosity of 138 fb − 1 of 13 TeV proton-proton collisions …
View article: Measurement of event shapes in minimum-bias events from proton-proton collisions at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> <mml:mtext> </mml:mtext> <mml:mtext> </mml:mtext> <mml:mi>TeV</mml:mi> </mml:math>
Measurement of event shapes in minimum-bias events from proton-proton collisions at Open
A measurement of event-shape variables is presented, using a data sample produced in a special run with approximately one inelastic proton-proton collision per bunch crossing. The data were collected with the CMS detector at a center-of-ma…
View article: Search for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>γ</mml:mi> <mml:mi>H</mml:mi> </mml:math> production and constraints on the Yukawa couplings of light quarks to the Higgs boson
Search for production and constraints on the Yukawa couplings of light quarks to the Higgs boson Open
A search for γ H production is performed with data from the CMS experiment at the LHC corresponding to an integrated luminosity of 138 fb − 1 at a proton-proton center-of-mass collision energy of 13 TeV . The analysis f…
View article: Inclusive and differential measurements of the $\mathrm{t\bar{t}}γ$ cross section and the $\mathrm{t\bar{t}}γ/\mathrm{t\bar{t}}$ cross section ratio in proton-proton collisions at $\sqrt{s}$ = 13 TeV
Inclusive and differential measurements of the $\mathrm{t\bar{t}}γ$ cross section and the $\mathrm{t\bar{t}}γ/\mathrm{t\bar{t}}$ cross section ratio in proton-proton collisions at $\sqrt{s}$ = 13 TeV Open
Inclusive and differential cross section measurements of top quark pair ($\mathrm{t\bar{t}}$) production in association with a photon ($γ$) are performed as a function of lepton, photon, top quark, and $\mathrm{t\bar{t}}$ kinematic observa…
View article: Characterizing the initial state and dynamical evolution in XeXe and PbPb collisions using multiparticle cumulants
Characterizing the initial state and dynamical evolution in XeXe and PbPb collisions using multiparticle cumulants Open
For the first time, correlations among mixed-order moments of two or three flow harmonics $-$($v_{n}^{k},v_{m}^{l}$) and ($v_{n}^{k},v_{m}^{l}, v_{p}^{q}$), with $k$, $l$, and $q$ denoting the respective orders$-$are measured in xenon-xeno…
View article: Identification of tau leptons using a convolutional neural network with domain adaptation
Identification of tau leptons using a convolutional neural network with domain adaptation Open
A tau lepton identification algorithm, DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons ($τ_\mathrm{h}$) from quark or gluon je…
View article: LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows
LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows Open
In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. Howe…
View article: LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows
LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows Open
In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. Howe…
View article: Evaluating generative models in high energy physics
Evaluating generative models in high energy physics Open
There has been a recent explosion in research into machine-learning-based\ngenerative modeling to tackle computational challenges for simulations in high\nenergy physics (HEP). In order to use such alternative simulators in practice,\nwe n…
View article: JetNet150
JetNet150 Open
Gluon (g), Top Quark (t), Light Quark (q), W boson (w), and Z boson (z) jets of ~1 TeV transverse momentum (\(p_T\)), as introduced in Ref. [1], but up to 150 particles. Each file has particle_features; and jet_features; arrays, containing…
View article: JetNet150
JetNet150 Open
Gluon (g), Top Quark (t), Light Quark (q), W boson (w), and Z boson (z) jets of ~1 TeV transverse momentum (\(p_T\)), as introduced in Ref. [1], but up to 150 particles. Each file has particle_features; and jet_features; arrays, containing…
View article: JetNet
JetNet Open
Gluon (g), Top Quark (t), Light Quark (q), W boson (w), and Z boson (z) jets of ~1 TeV transverse momentum (\(p_T\)), as introduced in Ref. [1]. Each file has particle_features; and jet_features; arrays, containing the list of particles' f…
View article: JetNet
JetNet Open
Gluon (g), Top Quark (t), Light Quark (q), W boson (w), and Z boson (z) jets of ~1 TeV transverse momentum (\(p_T\)), as introduced in Ref. [1]. Each file has particle_features; and jet_features; arrays, containing the list of particles' f…
View article: Particle-based fast jet simulation at the LHC with variational autoencoders
Particle-based fast jet simulation at the LHC with variational autoencoders Open
We study how to use deep variational autoencoders (VAEs) for a fast simulation of jets of particles at the Large Hadron Collider. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of th…
View article: Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders Open
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector eff…
View article: JetNet150
JetNet150 Open
Gluon (g), Top Quark (t), Light Quark (q) jets/particle clouds saved in hdf5 format. The shape of particles features is [N, 150, 4], where N is the total number of jets, 150 is the number of particles per jet, and 4 is the number of partic…
View article: Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset Open
Jets at generator and reconstruction level saved in .npy format. Each jet is represented as an array of jet constituents characterized by their particle momentum in Cartesian coordinates, i.e., (px, py, pz). For both generator-level and re…
View article: Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset Open
Jets at generator and reconstruction level saved in .npy format. Each jet is represented as an array of jet constituents characterized by their particle momentum in Cartesian coordinates, i.e., (px, py, pz). For both generator-level and re…
View article: arXiv : Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
arXiv : Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC Open
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of ha…
View article: Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC Open
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of ha…
View article: Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC Open
We develop a generative neural network for the generation of sparse data in particle physics using a permutation invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of ha…
View article: Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC Open
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of ha…
View article: Particle Cloud Generation with Message Passing Generative Adversarial Networks
Particle Cloud Generation with Message Passing Generative Adversarial Networks Open
In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those at the CERN Large Hadron Collider (LHC). Machine learning (ML)-based generative models, such as generativ…
View article: Particle Cloud Generation with Message Passing Generative Adversarial\n Networks
Particle Cloud Generation with Message Passing Generative Adversarial\n Networks Open
In high energy physics (HEP), jets are collections of correlated particles\nproduced ubiquitously in particle collisions such as those at the CERN Large\nHadron Collider (LHC). Machine learning (ML)-based generative models, such as\ngenera…
View article: JetNet
JetNet Open
Gluon (g), Top Quark (t), Light Quark (q) jets saved in .csv and .hdf5 formats. The shape is [N, 30 * 4] for .csv and [N, 30, 4] for .hdf5, where N is the total number of jets, 30 is the number of particles per jet, and 4 is the number of …