126 citations · 362 across the 19 of their papers we have counts for
19 papers
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows
Breno Orzari, Nadezda Chernyavskaya, Raphael Cobe +7
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 gen…
Knowledge Distillation for Anomaly Detection
Adrian Alan Pol, Ekaterina Govorkova, Sonja Gronroos +5
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model siz…
Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
The CMS ECAL Collaboration
The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online Data Quality Monitoring of the CMS electromagnetic calorimeter is a…
Autoencoders for Real-Time SUEP Detection
Simranjit Singh Chhibra, Nadezda Chernyavskaya, Benedikt Maier +2
Confining dark sectors with pseudo-conformal dynamics can produce Soft Unclustered Energy Patterns (SUEP), at the Large Hadron Collider: the production of dark quarks in proton-pro…
Triggering Dark Showers with Conditional Dual Auto-Encoders
Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier +2
We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types…
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier
Sonja Grönroos, Maurizio Pierini, Nadezda Chernyavskaya
More than a thousand 8" silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Ca…
Di-Higgs searches at the LHC
Nadezda Chernyavskaya
An overview of the recent searches for Higgs boson pair production at the LHC was presented. The searches were based on approximately 140 of data collected by th…
Lorentz group equivariant autoencoders
Zichun Hao, Raghav Kansal, Javier Duarte +1
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection…
Evaluating generative models in high energy physics
Raghav Kansal, Anni Li, Javier Duarte +4
There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP). In or…
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler +4
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-lum…
GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter
Saptaparna Bhattacharya, Nadezda Chernyavskaya, Saranya Ghosh +11
We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Ne…
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
Mary Touranakou, Nadezda Chernyavskaya, Javier Duarte +6
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…
Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya +5
We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a wo…
Higgs boson potential at colliders: status and perspectives
B. Di Micco, M. Gouzevitch, J. Mazzitelli +104
This document summarises the current theoretical and experimental status of the di-Higgs boson production searches, and of the direct and indirect constraints on the Higgs boson se…
Two components in charged particle production in heavy-ion collisions
A. A. Bylinkin, N. S. Chernyavskaya, A. A. Rostovtsev
Transverse momentum spectra of charged particle production in heavy-ion collisions are considered in terms of a recently introduced Two Component parameterization combining exponen…
Predictions on the transverse momentum spectra for charged particle production at LHC-energies from a two component model
Alexander Bylinkin, Nadezda Chernyavskaya, Andrei Rostovtsev
Transverse momentum spectra, , of charged hadron production in -collisions are considered in terms of a recently introduced two component model. The shapes of…
Hydrodynamic extension of the two component model for hadroproduction in heavy-ion collisions
A. A. Bylinkin, N. S. Chernyavskaya, A. A. Rostovtsev
The dependence of the spectra shape of produced charged hadrons on the size of a colliding system is discussed using a two component model. As a result, the hierarchy by the system…
Hadroproduction in heavy-ion collisions
A. A. Bylinkin, A. A. Rostovtsev, N. S. Chernyavskaya
The shapes of invariant differential cross section for charged particle production as function of transverse momentum measured in heavy-ion collisions are analyzed. The data measur…