{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T04:32:44Z","timestamp":1773894764508,"version":"3.50.1"},"reference-count":48,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"crossref","award":["Sonderforschungsbereich 953 \/ 182849149"],"award-info":[{"award-number":["Sonderforschungsbereich 953 \/ 182849149"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"crossref","award":["Advanced Grant AccelOnChip"],"award-info":[{"award-number":["Advanced Grant AccelOnChip"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"crossref","award":["DE-SC0012447"],"award-info":[{"award-number":["DE-SC0012447"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100022731","name":"Max Planck School of Photonics","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100022731","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown\u2013Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad3d2d","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T22:43:25Z","timestamp":1712789005000},"page":"025019","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5103-089X","authenticated-orcid":true,"given":"Marco","family":"Knipfer","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9888-2042","authenticated-orcid":true,"given":"Stefan","family":"Meier","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5847-2683","authenticated-orcid":true,"given":"Tobias","family":"Volk","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7931-8454","authenticated-orcid":true,"given":"Jonas","family":"Heimerl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4757-5410","authenticated-orcid":true,"given":"Peter","family":"Hommelhoff","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6222-8102","authenticated-orcid":true,"given":"Sergei","family":"Gleyzer","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"mlstad3d2dbib1","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.nima.2006.05.221","article-title":"On the history of photomultiplier tube invention","volume":"567","author":"Lubsandorzhiev","year":"2006","journal-title":"Nucl. Instrum. Methods Phys. Res. A"},{"key":"mlstad3d2dbib2","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.nima.2014.12.044","article-title":"Micro-channel plates and vacuum detectors","volume":"787","author":"Gys","year":"2015","journal-title":"Nucl. Instrum. Methods Phys. Res. A"},{"key":"mlstad3d2dbib3","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/s0168-9002(01)01839-3","article-title":"A broad-application microchannel-plate detector system for advanced particle or photon detection tasks: large area imaging, precise multi-hit timing information and high detection rate","volume":"477","author":"Jagutzki","year":"2002","journal-title":"Nucl. Instrum. Methods Phys. Res. A"},{"key":"mlstad3d2dbib4","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1088\/0034-4885\/66\/9\/203","article-title":"Recoil-ion and electron momentum spectroscopy: reaction-microscopes","volume":"66","author":"Ullrich","year":"2003","journal-title":"Rep. Prog. Phys."},{"key":"mlstad3d2dbib5","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1038\/35015033","article-title":"Correlated electron emission in multiphoton double ionization","volume":"405","author":"Weber","year":"2000","journal-title":"Nature"},{"key":"mlstad3d2dbib6","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1038\/nature05513","article-title":"Comparison of the hanbury brown\u2013twiss effect for bosons and fermions","volume":"445","author":"Jeltes","year":"2007","journal-title":"Nature"},{"key":"mlstad3d2dbib7","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1085\/2\/022008","article-title":"Machine learning in high energy physics community white paper","volume":"1085","author":"Albertsson","year":"2018","journal-title":"J. Phys.: Conf. Ser."},{"key":"mlstad3d2dbib8","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1146\/annurev-nucl-101917-021019","article-title":"Deep learning and its application to LHC physics","volume":"68","author":"Guest","year":"2018","journal-title":"Annu. Rev. Nucl. Part. Sci."},{"key":"mlstad3d2dbib9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2019.11.001","article-title":"Jet substructure at the large hadron collider: a review of recent advances in theory and machine learning","volume":"841","author":"Larkoski","year":"2020","journal-title":"Phys. Rep."},{"key":"mlstad3d2dbib10","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41586-018-0361-2","article-title":"Machine learning at the energy and intensity frontiers of particle physics","volume":"560","author":"Radovic","year":"2018","journal-title":"Nature"},{"key":"mlstad3d2dbib11","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.91.045002","article-title":"Machine learning and the physical sciences","volume":"91","author":"Carleo","year":"2019","journal-title":"Rev. Mod. Phys."},{"key":"mlstad3d2dbib12","doi-asserted-by":"publisher","DOI":"10.1142\/S0217751X19300199","article-title":"Machine and deep learning applications in particle physics","volume":"34","author":"Bourilkov","year":"2020","journal-title":"Int. J. Mod. Phys. A"},{"key":"mlstad3d2dbib13","doi-asserted-by":"crossref","DOI":"10.1162\/99608f92.beeb1183","article-title":"Modern machine learning and particle physics","author":"Schwartz","year":"2021"},{"key":"mlstad3d2dbib14","article-title":"Machine learning in the search for new fundamental physics","author":"Karagiorgi","year":"2021"},{"key":"mlstad3d2dbib15","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.94.031003","article-title":"Colloquium: machine learning in nuclear physics","volume":"94","author":"Boehnlein","year":"2022","journal-title":"Rev. Mod. Phys."},{"key":"mlstad3d2dbib16","article-title":"Bridging physics-based and data-driven modeling for learning dynamical systems","author":"Wang","year":"2020"},{"key":"mlstad3d2dbib17","first-page":"1457","article-title":"Towards physics-informed deep learning for turbulent flow prediction","author":"Wang","year":"2020"},{"key":"mlstad3d2dbib18","doi-asserted-by":"crossref","DOI":"10.1109\/ICRA.2019.8794351","article-title":"Neural lander: stable drone landing control using learned dynamics","author":"Shi","year":"2019"},{"key":"mlstad3d2dbib19","article-title":"Deepgleam: a hybrid mechanistic and deep learning model for covid-19 forecasting","author":"Wu","year":"2021"},{"key":"mlstad3d2dbib20","first-page":"777","article-title":"Neural point process for learning spatiotemporal event dynamics","author":"Zhou","year":"2022"},{"key":"mlstad3d2dbib21","doi-asserted-by":"publisher","first-page":"492","DOI":"10.3390\/e24040492","article-title":"Estimation of the covariance matrix in hierarchical bayesian spatio-temporal modeling via dimension expansion","volume":"24","author":"Sun","year":"2022","journal-title":"Entropy"},{"key":"mlstad3d2dbib22","doi-asserted-by":"publisher","first-page":"321","DOI":"10.3390\/e24030321","article-title":"Spatial modeling of precipitation based on data-driven warping of gaussian processes","volume":"24","author":"Agou","year":"2022","journal-title":"Entropy"},{"key":"mlstad3d2dbib23","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1038\/s41598-020-57897-9","article-title":"Predicting clustered weather patterns: a test case for applications of convolutional neural networks to spatio-temporal climate data","volume":"10","author":"Chattopadhyay","year":"2020","journal-title":"Sci. Rep."},{"key":"mlstad3d2dbib24","article-title":"Graph-based deep modeling and real time forecasting of sparse spatio-temporal data","author":"Wang","year":"2018"},{"key":"mlstad3d2dbib25","article-title":"Cross-city transfer learning for deep spatio-temporal prediction","author":"Wang","year":"2018"},{"key":"mlstad3d2dbib26","article-title":"Bridging physics-based and data-driven modeling for learning dynamical systems","author":"Wang","year":"2020"},{"key":"mlstad3d2dbib27","article-title":"Deep machine learning with spatio-temporal inference","author":"Karnowski","year":"2012"},{"key":"mlstad3d2dbib28","article-title":"Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector","author":"CMS Collaboration","year":"2022"},{"key":"mlstad3d2dbib29","article-title":"9.6.0.1072779 (R2019a)","author":"MATLAB","year":"2019"},{"key":"mlstad3d2dbib30","author":"Van Rossum","year":"1995"},{"key":"mlstad3d2dbib31","article-title":"Untersuchung von Korrelationseffekten in der Doppelphotoemission von normal- und supraleitendem Blei","author":"Wallauer","year":"2011"},{"key":"mlstad3d2dbib32","doi-asserted-by":"publisher","DOI":"10.1063\/1.4931684","article-title":"Note: an improved 3D imaging system for electron-electron coincidence measurements","volume":"86","author":"Lin","year":"2015","journal-title":"Rev. Sci. Instrum."},{"key":"mlstad3d2dbib33","article-title":"Koinzidente Photoelektronenspektroskopie an Kuprat-Hochtemperatursupraleitern","author":"Bauer","year":"2015"},{"key":"mlstad3d2dbib34","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"mlstad3d2dbib35","doi-asserted-by":"crossref","DOI":"10.3115\/v1\/W14-4012","article-title":"On the properties of neural machine translation: encoder-decoder approaches","author":"Cho","year":"2014"},{"key":"mlstad3d2dbib36","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"mlstad3d2dbib37","article-title":"Keras Tuner","author":"O\u2019Malley","year":"2019"},{"key":"mlstad3d2dbib38","article-title":"Hyperband: a novel bandit-based approach to hyperparameter optimization","author":"Li","year":"2016"},{"key":"mlstad3d2dbib39","doi-asserted-by":"publisher","first-page":"2477","DOI":"10.1109\/TNS.2002.803889","article-title":"Multiple hit readout of a microchannel plate detector with a three-layer delay-line anode","volume":"49","author":"Jagutzki","year":"2002","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"mlstad3d2dbib40","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1103\/RevModPhys.84.1011","article-title":"Theories of photoelectron correlation in laser-driven multiple atomic ionization","volume":"84","author":"Becker","year":"2012","journal-title":"Rev. Mod. Phys."},{"key":"mlstad3d2dbib41","doi-asserted-by":"publisher","DOI":"10.1063\/1.4770120","article-title":"Advance in multi-hit detection and quantization in atom probe tomography","volume":"83","author":"Costa","year":"2012","journal-title":"Rev. Sci. Instrum."},{"key":"mlstad3d2dbib42","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1038\/nphoton.2017.79","article-title":"High-temporal-resolution electron microscopy for imaging ultrafast electron dynamics","volume":"11","author":"Hassan","year":"2017","journal-title":"Nat. Photon."},{"key":"mlstad3d2dbib43","doi-asserted-by":"publisher","DOI":"10.1103\/physrevlett.128.235301","article-title":"Quantum-coherent light-electron interaction in a scanning electron microscope","volume":"128","author":"Shiloh","year":"2022","journal-title":"Phys. Rev. Lett."},{"key":"mlstad3d2dbib44","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1038\/nature00911","article-title":"Observation of hanbury brown\u2013twiss anticorrelations for free electrons","volume":"418","author":"Kiesel","year":"2002","journal-title":"Nature"},{"key":"mlstad3d2dbib45","doi-asserted-by":"publisher","DOI":"10.1103\/physrevlett.126.125501","article-title":"Intensity interference in a coherent spin-polarized electron beam","volume":"126","author":"Kuwahara","year":"2021","journal-title":"Phys. Rev. Lett."},{"key":"mlstad3d2dbib46","doi-asserted-by":"publisher","DOI":"10.1080\/00268976.2021.1931722","article-title":"The lack of electron momentum correlation in strong-field triple ionisation of molecules","volume":"120","author":"Basnayake","year":"2021","journal-title":"Mol. Phys."},{"key":"mlstad3d2dbib47","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.1038\/s41567-023-02059-7","article-title":"Few-electron correlations after ultrafast photoemission from nanometric needle tips","volume":"19","author":"Meier","year":"2023","journal-title":"Nat. Phys."},{"key":"mlstad3d2dbib48","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1038\/s41567-023-02067-7","article-title":"Coulomb-correlated electron number states in a transmission electron microscope beam","volume":"19","author":"Haindl","year":"2023","journal-title":"Nat. Phys."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3d2d","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3d2d\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3d2d","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3d2d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3d2d\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3d2d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3d2d\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3d2d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T05:33:31Z","timestamp":1713850411000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad3d2d"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,23]]},"references-count":48,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,4,23]]},"published-print":{"date-parts":[[2024,6,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad3d2d","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,23]]},"assertion":[{"value":"Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2023-06-16","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-04-10","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-04-23","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}