Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning
The GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to MeV-scale gamma rays over previous telescopes. The novel pixel-based...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4357/aca329 |
| _version_ | 1827762745664077824 |
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| author | M. Buuck A. Mishra E. Charles N. Di Lalla O. A. Hitchcock M. E. Monzani N. Omodei T. Shutt |
| author_facet | M. Buuck A. Mishra E. Charles N. Di Lalla O. A. Hitchcock M. E. Monzani N. Omodei T. Shutt |
| author_sort | M. Buuck |
| collection | DOAJ |
| description | The GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to MeV-scale gamma rays over previous telescopes. The novel pixel-based charge readout allows for imaging of the tracks of electrons scattered by Compton interactions of incident gamma rays. The two primary contributors to the accuracy of a Compton telescope in reconstructing an incident gamma-ray’s original direction are its energy and position resolution. In this work, we focus on using deep learning to optimize the reconstruction of the initial position and direction of electrons scattered in Compton interactions, including using probabilistic models to estimate predictive uncertainty. We show that the deep-learning models are able to predict locations of Compton scatters of MeV-scale gamma rays from simulated 500 μ m pixel-based data to better than 1 mm rms error and are sensitive to the initial direction of the scattered electron. We compare and contrast different deep-learning uncertainty estimation algorithms for reconstruction applications. Additionally, we show that event-by-event estimates of the uncertainty of the locations of the Compton scatters can be used to select those events that were reconstructed most accurately, leading to improvement in locating the origin of gamma-ray sources on the sky. |
| first_indexed | 2024-03-11T10:35:00Z |
| format | Article |
| id | doaj.art-d60db15199874bdbbf0b729ab31e8784 |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-11T10:35:00Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-d60db15199874bdbbf0b729ab31e87842023-11-14T11:30:21ZengIOP PublishingThe Astrophysical Journal1538-43572023-01-0194227710.3847/1538-4357/aca329Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep LearningM. Buuck0https://orcid.org/0000-0001-5751-4326A. Mishra1E. Charles2https://orcid.org/0000-0002-3925-7802N. Di Lalla3https://orcid.org/0000-0002-7574-1298O. A. Hitchcock4M. E. Monzani5https://orcid.org/0000-0002-8254-5308N. Omodei6https://orcid.org/0000-0002-5448-7577T. Shutt7SLAC National Accelerator Laboratory , Menlo Park, CA 94025, USA ; mbuuck@slac.stanford.edu; Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305 , USASLAC National Accelerator Laboratory , Menlo Park, CA 94025, USA ; mbuuck@slac.stanford.eduSLAC National Accelerator Laboratory , Menlo Park, CA 94025, USA ; mbuuck@slac.stanford.edu; Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305 , USAKavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305 , USA; Physics Department, Stanford University , Stanford, CA 94305, USA; Hansen Experimental Physics Laboratory , Stanford, CA 94305, USAPhysics Department, Stanford University , Stanford, CA 94305, USASLAC National Accelerator Laboratory , Menlo Park, CA 94025, USA ; mbuuck@slac.stanford.edu; Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305 , USA; Vatican Observatory , Castel Gandolfo, V-00120, Vatican City StateKavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305 , USA; Physics Department, Stanford University , Stanford, CA 94305, USA; Hansen Experimental Physics Laboratory , Stanford, CA 94305, USASLAC National Accelerator Laboratory , Menlo Park, CA 94025, USA ; mbuuck@slac.stanford.edu; Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305 , USAThe GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to MeV-scale gamma rays over previous telescopes. The novel pixel-based charge readout allows for imaging of the tracks of electrons scattered by Compton interactions of incident gamma rays. The two primary contributors to the accuracy of a Compton telescope in reconstructing an incident gamma-ray’s original direction are its energy and position resolution. In this work, we focus on using deep learning to optimize the reconstruction of the initial position and direction of electrons scattered in Compton interactions, including using probabilistic models to estimate predictive uncertainty. We show that the deep-learning models are able to predict locations of Compton scatters of MeV-scale gamma rays from simulated 500 μ m pixel-based data to better than 1 mm rms error and are sensitive to the initial direction of the scattered electron. We compare and contrast different deep-learning uncertainty estimation algorithms for reconstruction applications. Additionally, we show that event-by-event estimates of the uncertainty of the locations of the Compton scatters can be used to select those events that were reconstructed most accurately, leading to improvement in locating the origin of gamma-ray sources on the sky.https://doi.org/10.3847/1538-4357/aca329Gamma-ray telescopesGamma-ray transient sourcesConvolutional neural networksGamma-ray detectorsAstronomy image processing |
| spellingShingle | M. Buuck A. Mishra E. Charles N. Di Lalla O. A. Hitchcock M. E. Monzani N. Omodei T. Shutt Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning The Astrophysical Journal Gamma-ray telescopes Gamma-ray transient sources Convolutional neural networks Gamma-ray detectors Astronomy image processing |
| title | Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning |
| title_full | Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning |
| title_fullStr | Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning |
| title_full_unstemmed | Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning |
| title_short | Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning |
| title_sort | low energy electron track imaging for a liquid argon time projection chamber telescope concept using probabilistic deep learning |
| topic | Gamma-ray telescopes Gamma-ray transient sources Convolutional neural networks Gamma-ray detectors Astronomy image processing |
| url | https://doi.org/10.3847/1538-4357/aca329 |
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