A5009620496- Radiation Detection and Scintillator Technologies
- Dark Matter and Cosmic Phenomena
- Muon and positron interactions and applications
- Atomic and Subatomic Physics Research
- Seismic Waves and Analysis
- Seismology and Earthquake Studies
- Particle Detector Development and Performance
- Seismic Imaging and Inversion Techniques
- Particle physics theoretical and experimental studies
Sofia University "St. Kliment Ohridski"
2022-2024
The PADME apparatus was built at the Frascati National Laboratory of INFN to search for a dark photon ($A'$) produced via process $e^+ e^- \rightarrow A' \gamma$. central component detector is an electromagnetic calorimeter composed 616 BGO crystals dedicated measurement energy and position final state photons. high beam particle multiplicity over short bunch duration requires reliable identification overlapping signals. A regression machine-learning-based algorithm has been developed...
Abstract The PADME Experiment at the Laboratori Nationali di Frascati, INFN is used in search for a Dark photon, produced with an ordinary photon electron-positron annihilation events. energy of photons, emitted measured using segmented electromagnetic calorimeter. Machine learning methods consisting various convolutional neural networks are reconstruction close-in-time signals high resolution. These algorithms were on two-photon events e + − → γγ to calibrate values. In order network output...
A bstract This paper presents a detailed characterization of the positron beam delivered by Beam Test Facility at Laboratori Nazionali Frascati to PADME experiment during Run III, which took place from October December 2022. It showcases methodology used measure main parameters such as position in space, absolute momentum scale, energy spread, and its intensity through combination data analysis Monte Carlo simulations. The results achieved include an precision within ~1–2 MeV /c , relative...
Abstract Machine learning methods can be used for signal processing in different cases of physics research. A convolutional neural network was developed the task pulse counting particle detectors high energy physics. For extraction parameters a with autoencoder architecture and subsequent result reconstruction algorithm applied. also seismic studies identifying events seismograms. All algorithms their architecture, input output are presented discussed.