6 citations · 8 across the 5 of their papers we have counts for
5 papers
Cluster Reconstruction in Electromagnetic Calorimeters Using Machine Learning Methods
Kalina Dimitrova, Venelin Kozhuharov, Ruslan Nastaev +1
Machine-learning-based methods can be developed for the reconstruction of clusters in segmented detectors for high energy physics experiments. Convolutional neural networks with au…
Development and Explainability of Models for Machine-Learning-Based Reconstruction of Signals in Particle Detectors
Kalina Dimitrova, Venelin Kozhuharov, Peicho Petkov
Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics experiments. We present the development and applicati…
Applicability Evaluation of Selected xAI Methods for Machine Learning Algorithms for Signal Parameters Extraction
Kalina Dimitrova, Venelin Kozhuharov, Peicho Petkov
Machine learning methods find growing application in the reconstruction and analysis of data in high energy physics experiments. A modified convolutional autoencoder model was empl…
Status and Prospects of PADME
Susanna Bertelli, Fabio Bossi, Riccardo De Sangro +28
The Positron Annihilation to Dark Matter Experiment (PADME) was designed and constructed to search for dark photons () in the process , using the positron…
Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter
Kalina Dimitrova
The PADME apparatus was built at the Frascati National Laboratory of INFN to search for a dark photon () produced via the process . The central compon…