744 citations · 6.5k across the 102 of their papers we have counts for
14 papers · 1 filter
New UTfit Analysis of the Unitarity Triangle in the Cabibbo-Kobayashi-Maskawa scheme
UTfit Collaboration, Marcella Bona, Marco Ciuchini +14
Flavour mixing and CP violation as measured in weak decays and mixing of neutral mesons are a fundamental tool to test the Standard Model (SM) and to search for new physics. New an…
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…
FIT: A Metric for Model Sensitivity
Ben Zandonati, Adrian Alan Pol, Maurizio Pierini +2
Model compression is vital to the deployment of deep learning on edge devices. Low precision representations, achieved via quantization of weights and activations, can reduce infer…
LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
Zhiqiang Que, Hongxiang Fan, Marcus Loo +5
This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performanc…
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini +8
In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Consi…
Impact of the recent measurements of the top-quark and W-boson masses on electroweak precision fits
J. de Blas, M. Pierini, L. Reina +1
We assess the impact of the very recent measurement of the top-quark mass by the CMS Collaboration on the fit of electroweak data in the Standard Model and beyond, with particular…
Learning new physics efficiently with nonparametric methods
Marco Letizia, Gianvito Losapio, Marco Rando +5
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, n…
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…
A Muon Collider Facility for Physics Discovery
D. Stratakis, N. Mokhov, M. Palmer +66
Muon colliders provide a unique route to deliver high energy collisions that enable discovery searches and precision measurements to extend our understanding of the fundamental law…
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…
Machine Learning for Particle Flow Reconstruction at CMS
Joosep Pata, Javier Duarte, Farouk Mokhtar +5
We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on ca…
Lightweight Jet Reconstruction and Identification as an Object Detection Task
Adrian Alan Pol, Thea Aarrestad, Ekaterina Govorkova +8
We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN Large Hadron Collider (LHC…
Autoencoders for Semivisible Jet Detection
Florencia Canelli, Annapaola de Cosa, Luc Le Pottier +3
The production of dark matter particles from confining dark sectors may lead to many novel experimental signatures. Depending on the details of the theory, dark quark production in…