77 citations · 164 across the 7 of their papers we have counts for
8 papers
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs
Alberto Coppi, Ema Puljak, Lorenzo Borella +6
We present a systematic study of Tensor Network (TN) models $\unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $\unicode{x2013}$ for real-time jet tagging…
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
Ema Puljak, Maurizio Pierini, Artur Garcia-Saez
The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on t…
tn4ml: Tensor Network Training and Customization for Machine Learning
Ema Puljak, Sergio Sanchez-Ramirez, Sergi Masot-Llima +3
Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their application…
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Julian Schuhmacher, Laura Boggia, Vasilis Belis +7
Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadr…
Quantum anomaly detection in the latent space of proton collision events at the LHC
Vasilis Belis, Kinga Anna Woźniak, Ema Puljak +7
The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along w…
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…
LHC physics dataset for unsupervised New Physics detection at 40 MHz
Ekaterina Govorkova, Ema Puljak, Thea Aarrestad +3
In particle detectors at the Large Hadron Collider, tens of terabytes of data are produced every second from proton-proton collisions occurring at a rate of 40 megahertz. This data…