Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter
The PADME apparatus was built at the Frascati National Laboratory of INFN to search for a dark photon (<inline-formula><math display="inline"><semantics><mrow><msup><mi>A</mi><mo>′</mo></msup></mrow></semantics></ma...
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MDPI AG
2022-09-01
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| Series: | Instruments |
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| Online Access: | https://www.mdpi.com/2410-390X/6/4/46 |
| _version_ | 1797457162689052672 |
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| author | Kalina Dimitrova on behalf of the PADME collaboration |
| author_facet | Kalina Dimitrova on behalf of the PADME collaboration |
| author_sort | Kalina Dimitrova |
| collection | DOAJ |
| description | The PADME apparatus was built at the Frascati National Laboratory of INFN to search for a dark photon (<inline-formula><math display="inline"><semantics><mrow><msup><mi>A</mi><mo>′</mo></msup></mrow></semantics></math></inline-formula>) produced via the process <inline-formula><math display="inline"><semantics><mrow><msup><mi>e</mi><mo>+</mo></msup><msup><mi>e</mi><mo>−</mo></msup><mo>→</mo><msup><mi>A</mi><mo>′</mo></msup><mi>γ</mi></mrow></semantics></math></inline-formula>. The central component of the PADME detector is an electromagnetic calorimeter composed of 616 BGO crystals dedicated to the measurement of the energy and position of the final state photons. The high beam particle multiplicity over a short bunch duration requires reliable identification and measurement of overlapping signals. A regression machine-learning-based algorithm has been developed to disentangle with high efficiency close-in-time events and precisely reconstruct the amplitude of the hits and the time with sub-nanosecond resolution. The performance of the algorithm and the sequence of improvements leading to the achieved results are presented and discussed. |
| first_indexed | 2024-03-09T16:17:15Z |
| format | Article |
| id | doaj.art-e3e552d5583a4d2a9b41e60c26c8b4a8 |
| institution | Directory Open Access Journal |
| issn | 2410-390X |
| language | English |
| last_indexed | 2024-03-09T16:17:15Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
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| series | Instruments |
| spelling | doaj.art-e3e552d5583a4d2a9b41e60c26c8b4a82023-11-24T15:40:32ZengMDPI AGInstruments2410-390X2022-09-01644610.3390/instruments6040046Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic CalorimeterKalina Dimitrova0on behalf of the PADME collaboration1Faculty of Physics, Sofia University “St. Kliment Ohridski”, 5 J. Bourchier Blvd., 1164 Sofia, BulgariaFaculty of Physics, Sofia University “St. Kliment Ohridski”, 5 J. Bourchier Blvd., 1164 Sofia, BulgariaThe PADME apparatus was built at the Frascati National Laboratory of INFN to search for a dark photon (<inline-formula><math display="inline"><semantics><mrow><msup><mi>A</mi><mo>′</mo></msup></mrow></semantics></math></inline-formula>) produced via the process <inline-formula><math display="inline"><semantics><mrow><msup><mi>e</mi><mo>+</mo></msup><msup><mi>e</mi><mo>−</mo></msup><mo>→</mo><msup><mi>A</mi><mo>′</mo></msup><mi>γ</mi></mrow></semantics></math></inline-formula>. The central component of the PADME detector is an electromagnetic calorimeter composed of 616 BGO crystals dedicated to the measurement of the energy and position of the final state photons. The high beam particle multiplicity over a short bunch duration requires reliable identification and measurement of overlapping signals. A regression machine-learning-based algorithm has been developed to disentangle with high efficiency close-in-time events and precisely reconstruct the amplitude of the hits and the time with sub-nanosecond resolution. The performance of the algorithm and the sequence of improvements leading to the achieved results are presented and discussed.https://www.mdpi.com/2410-390X/6/4/46dark photoncalorimetrysignal reconstructionmachine learning |
| spellingShingle | Kalina Dimitrova on behalf of the PADME collaboration Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter Instruments dark photon calorimetry signal reconstruction machine learning |
| title | Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter |
| title_full | Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter |
| title_fullStr | Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter |
| title_full_unstemmed | Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter |
| title_short | Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter |
| title_sort | using artificial intelligence in the reconstruction of signals from the padme electromagnetic calorimeter |
| topic | dark photon calorimetry signal reconstruction machine learning |
| url | https://www.mdpi.com/2410-390X/6/4/46 |
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