744 citations · 3.4k across the 80 of their papers we have counts for
85 papers
Data Preservation in High Energy Physics
Alexandre Arbey, Jamie Boyd, Daniel Britzger +45
Data preservation significantly increases the scientific output of high-energy physics experiments during and after data acquisition. For new and ongoing experiments, the careful c…
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…
Symbolic Regression on FPGAs for Fast Machine Learning Inference
Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar +7
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitiv…
Speeding up the CMS track reconstruction with a parallelized and vectorized Kalman-filter-based algorithm during the LHC Run 3
Sophie Berkman, Giuseppe Cerati, Peter Elmer +17
One of the most challenging computational problems in the Run 3 of the Large Hadron Collider (LHC) and more so in the High-Luminosity LHC (HL-LHC) is expected to be finding and fit…
IRIS-HEP Strategic Plan for the Next Phase of Software Upgrades for HL-LHC Physics
Brian Bockelman, Peter Elmer, Gordon Watts
The quest to understand the fundamental building blocks of nature and their interactions is one of the oldest and most ambitious of human scientific endeavors. CERN's Large Hadron…
Data Preservation in High Energy Physics
T. Basaglia, M. Bellis, J. Blomer +51
Data preservation is a mandatory specification for any present and future experimental facility and it is a cost-effective way of doing fundamental research by exploiting unique da…