1 code implementation • 4 Dec 2024 • Aaron Wang, Abhijith Gandrakota, Jennifer Ngadiuba, Vivekanand Sahu, Priyansh Bhatnagar, Elham E Khoda, Javier Duarte
Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC.
no code implementations • 29 Nov 2024 • Abhijith Gandrakota
We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3.
no code implementations • 16 Jan 2024 • Abhijith Gandrakota, Lily Zhang, Aahlad Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics.
1 code implementation • 28 Nov 2023 • Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba, Maria Spiropulu, Jean-Roch Vlimant
Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics.
1 code implementation • 23 Nov 2023 • Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba, Maria Spiropulu, Jean-Roch Vlimant
The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed.
no code implementations • 30 Mar 2022 • Philip Harris, Erik Katsavounidis, William Patrick McCormack, Dylan Rankin, Yongbin Feng, Abhijith Gandrakota, Christian Herwig, Burt Holzman, Kevin Pedro, Nhan Tran, Tingjun Yang, Jennifer Ngadiuba, Michael Coughlin, Scott Hauck, Shih-Chieh Hsu, Elham E Khoda, Deming Chen, Mark Neubauer, Javier Duarte, Georgia Karagiorgi, Mia Liu
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges.