2020
DOI: 10.1088/1748-0221/15/04/p04021
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Machine learning technique to improve anti-neutrino detection efficiency for the ISMRAN experiment

Abstract: A: The Indian Scintillator Matrix for Reactor Anti-Neutrino detection -ISMRAN experiment aims to detect electron anti-neutrinos (ν e ) emitted from a reactor via inverse beta decay reaction (IBD). The setup, consisting of 1 ton segmented Gadolinium foil wrapped plastic scintillator array, is planned for remote reactor monitoring and sterile neutrino search. The detection of prompt positron and delayed neutron from IBD will provide the signature of ν e event in ISMRAN. The number of segments with energy deposit… Show more

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Cited by 7 publications
(5 citation statements)
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“…6 (a)-(d), which are constructed from the sum energy, number of bars hit and energy depositions profile (E max and E 1 ) on event by event basis. The prompt signal from IBD and fast neutron exhibit characteristic differences in energy deposition profile [16]. These variables are selected from a variety of other variables and gives the best discriminatory powers.…”
Section: Status Of Ismran Experimentsmentioning
confidence: 99%
“…6 (a)-(d), which are constructed from the sum energy, number of bars hit and energy depositions profile (E max and E 1 ) on event by event basis. The prompt signal from IBD and fast neutron exhibit characteristic differences in energy deposition profile [16]. These variables are selected from a variety of other variables and gives the best discriminatory powers.…”
Section: Status Of Ismran Experimentsmentioning
confidence: 99%
“…The measured timing resolution of ∼4 ns leads to a parameterized position resolution of ∼20 cm in a single PSB [29,35] and is almost uniform among the different bars. Due to the segmented geometry of the ISMRAN detector, we can exploit the timing and position information for the localization of an event in the ISMRAN array to differentiate an ν e event from a background event arising due to fast neutron [36,37]. The complex absorption and re-emission processes of optical photons are known to be an important source of the non-linear and non-uniform response of the charge deposition in the PSBs.…”
Section: Ismran Detector Responsementioning
confidence: 99%
“…Stricter selection criteria based on event topology in segmented geometry and timing distribution among the PS bars can further improve the efficiency of prompt IBD events while rejecting the fast neutron background events. A machine learning approach using multi-layer perceptrons, the fast neutron background is discriminated with an efficiency of 80% from the prompt IBD signal [24]. However, due to small number of bars used in the current experimental setup, results from such a approach would be difficult to interpret and hence are not discussed in present work.…”
Section: Jinst 16 P08029mentioning
confidence: 99%
“…Hence, a detailed knowledge of the fast neutron deposited energy spectrum and response from fast neutrons in the PS bars would enable better optimization of efforts to identify such background events from true IBD events. A machine learning approach, using monte-carlo based simulated IBD events in ISMRAN detector, is used to demonstrate the discriminating capabilities of various backgrounds from real IBD events [24]. Fast neutron response using 𝐷-𝐷 and 𝐷-𝑇 reactions with plastic scintillator bars are earlier performed to estimate the energy response and sensitivity to the neutron flux in deep underground experiments [25].…”
Section: Introductionmentioning
confidence: 99%