Publications (6)
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Tommaso Dorigo, Andrea Giammanco, Pietro Vischia +33
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensi…
Application of Inferno to a Top Pair Cross Section Measurement with CMS Open Data
Lukas Layer, Tommaso Dorigo, Giles C. Strong
In recent years novel inference techniques have been developed based on the construction of non-linear summary statistics with neural networks by minimising inferencemotivated loss…
Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?
M. Saquib Sarfraz, Mei-Yen Chen, Lukas Layer +2
The current state of machine learning scholarship in Timeseries Anomaly Detection (TAD) is plagued by the persistent use of flawed evaluation metrics, inconsistent benchmarking pra…
Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm
T. Dorigo, Sofia Guglielmini, Jan Kieseler +2
Within the context of studies for novel measurement solutions for future particle physics experiments, we developed a performant kNN-based regressor to infer the energy of highly-r…
Calorimetric Measurement of Multi-TeV Muons via Deep Regression
Jan Kieseler, Giles C. Strong, Filippo Chiandotto +2
The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find improved solutions for the…
Muon Energy Measurement from Radiative Losses in a Calorimeter for a Collider Detector
Tommaso Dorigo, Jan Kieseler, Lukas Layer +1
The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find new solutions for the dete…