744 citations · 6.5k across the 102 of their papers we have counts for
13 papers · 1 filter
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…
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…
Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
Ekaterina Govorkova, Ema Puljak, Thea Aarrestad +11
In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely ch…
Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks
Shah Rukh Qasim, Kenneth Long, Jan Kieseler +2
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events…
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig +15
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off…
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…
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…
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…
Fast inference of deep neural networks in FPGAs for particle physics
Javier Duarte, Song Han, Philip Harris +8
Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learni…