744 citations · 6.5k across the 102 of their papers we have counts for
12 papers · 1 filter
Construction and commissioning of CMS CE prototype silicon modules
B. Acar, G. Adamov, C. Adloff +327
As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling cal…
The DAQ system of the 12,000 Channel CMS High Granularity Calorimeter Prototype
B. Acar, G. Adamov, C. Adloff +327
The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant wit…
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
Aneesh Heintz, Vesal Razavimaleki, Javier Duarte +18
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a fram…
Anomaly Detection With Conditional Variational Autoencoders
Adrian Alan Pol, Victor Berger, Gianluca Cerminara +2
Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open rese…
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
Cheng Chen, Olmo Cerri, Thong Q. Nguyen +2
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 pr…
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta +19
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering…
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
Claudionor N. Coelho, Aki Kuusela, Shan Li +7
Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore r…
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
Oliver Knapp, Guenther Dissertori, Olmo Cerri +3
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Ano…
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
Giuseppe Di Guglielmo, Javier Duarte, Philip Harris +13
We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with FPG…
Les Houches 2019 Physics at TeV Colliders: New Physics Working Group Report
G. Brooijmans, A. Buckley, S. Caron +87
This report presents the activities of the `New Physics' working group for the `Physics at TeV Colliders' workshop (Les Houches, France, 10--28 June, 2019). These activities includ…
Fast inference of Boosted Decision Trees in FPGAs for particle physics
Sioni Summers, Giuseppe Di Guglielmo, Javier Duarte +10
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion p…
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
Dawit Belayneh, Federico Carminati, Amir Farbin +13
Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of particles produced i…