3 papers
cs.LG2026
Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning
Saraa Ali, Vladimir Bocharnikov, Fedor Ratnikov +3
The quality of recorded data depends on the stability of the sensor system that acquires it. Sensor motion and aging can degrade the performance and stability of downstream data-dr…
hep-ex2026
Production of Î hyperons in 4.0A GeV and 4.5A GeV carbon-nucleus interactions at the Nuclotron
S. Afanasiev, G. Agakishiev, A. Aleksandrov +207
The BM@N experiment (Baryonic Matter at the Nuclotron) is the first fixed-target experiment at the JINR NICA accelerator complex. In this work, data on the interactions of a carbon…
cs.LG2024
Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning
S. Ali, A. S. Ryzhikov, D. A. Derkach +2
In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters…