[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125958-en":3,"doc-seo-125958-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125958,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Pulse Shape Simulation and Discrimination using Machine Learning Techniques","Statistical power is essential for particle-identification experiments to distinguish signal from background. Pulse shape discrimination (PSD) is widely used in nuclear and rare-event searches employing scintillation detectors, leveraging differences in pulse decay times and signal properties tied to radiation type. Conventional PSD works well only when sufficient light emission produces a usable pulse profile, which rare-event searches may not always provide. This work evaluates Dense Neural Network and Recurrent Neural Network methods for PSD and compares their discrimination performance with conventional approaches.","arXiv :2206 . 15156v2 [physics .ins-det] 15 May 2024  \nPulse Shape Simulation and Discrimination using Machine Learning Techniques  \nS. Dutta 1* , S. Ghosh3 , S. Bhattacharya 1 , and S. Saha2  \n1 High Energy Nuclear and Particle Physics Division, Saha Institute of Nuclear Physics - a CI of Homi Bhabha National Institute, Kolkata - 700064, INDIA  \n2 Applied Nuclear Physics Division, Saha Institute of Nuclear Physics - a CI of Homi Bhabha National Institute, Kolkata - 700064, INDIA  \n3 Department of Physics and Astronomy, Purdue University, West Lafayette, IN, 47907, USA  \n* shubhamdutta   [16@yahoo.com](16@yahoo.com)  \nAbstract  \nAn essential metric for the quality of a particle-identification experiment is its statistical power to discriminate between signal and background. Pulse shape discrimination (PSD) is a basic method for this purpose in many nuclear, high-energy and rare-event search experiments where scintillation detectors are used. Conventional techniques exploit the difference between decaytimes of the pulses from signal and background events or pulse signals caused by different types of radiation quanta to achieve good discrimination. However, such techniques are efficient only when the total light-emission is sufficient to get a proper pulse profile. This is only possible when adequate amount of energy is deposited from recoil of the electrons or the nuclei of the scintillator materials caused by the incident particle on the detector. But, rare-event search experiments like direct search for dark matter do not always satisfy these conditions. Hence, it becomes imperative to have a method that can deliver a very efficient discrimination in these scenarios. Neural network based machine-learning algorithms have been used for classification problems in many areas of physics especially in high-energy experiments and have given better results compared to conventional techniques. We present the results of our investigations of two network based methods viz. Dense Neural Network and Recurrent Neural Network, for pulse shape discrimination and compare the same with conventional methods.  \n1 Introduction  \nPulse shape discrimination (PSD) have been widely used to discriminate between the different radiation quanta, such as photons (X-rays, gamma rays etc), electrons, neutrons, protons, alpha particles, etc. As these particles interact with the medium, they leave traces of signals (pulses) that differ in shape characterized by rise time, fall time, charge content and various other parameters. In a mixed radiation field experiment, PSD is considered as indispensable method to discriminate between the signal from the radiation quanta of interest and the background. One of the of first techniques for PSD was developed using the time domain information [1] . Since then various PSD techniques have been evolved and utilized, mostly in the time domain, such as charge integration[2], mean-time[3], zero cross-over[4], pulse gradient[5], time-over-threshold[6][7], etc. In addition, PSD in the frequency domain has also been demonstrated by digital signal processing techniques such as discrete wavelet transform and found to perform better than conventional time domain technique for discrimination between neutrons and gamma-rays using liquid organic scintillator[8] .  \nInorganic scintillators are quite often used in radiation detection for their much higher light output as compared to the organic scintillators. Manifestation of photo-peaks in inorganic scintillators helps in carrying out nuclear spectroscopic investigation in the energy domain. Thallium doped Cesium Iodide [CsI(Tl)] scintillator has been used across the energy domain for spectroscopic as well as  \ncalorimetric investigation exploiting the PSD techniques for improved particle identification. This method has been widely used for discrimination between light charged particles at E ≤ 20MeV in nuclear reaction studies at low and intermediate energy domain [9] and proposed to ","cbCaiofZsjLMkaa9","https://ap.wps.com/l/cbCaiofZsjLMkaa9","pdf",2643671,6,1,19,"English","en",105,"# Introduction\n## Pulse Shape Discrimination overview\n## Conventional time-domain and frequency-domain PSD methods\n## Scintillators and detector context\n## Motivation for rare-event searches and limitations of conventional PSD\n## Neural-network approaches for PSD","[{\"question\":\"Why is pulse shape discrimination important in particle-identification experiments?\",\"answer\":\"PSD helps discriminate between signal and background radiation quanta by exploiting measurable differences in pulse shape parameters such as rise/fall time and charge content.\"},{\"question\":\"What limits conventional PSD techniques in rare-event search experiments?\",\"answer\":\"Conventional methods require enough total light emission to form a proper pulse profile, which may not be available when deposited energy is very small.\"},{\"question\":\"Which machine-learning models are investigated for PSD in this work?\",\"answer\":\"The study investigates two network-based methods: Dense Neural Network and Recurrent Neural Network, and compares them with conventional PSD approaches.\"}]","Pulse Shape Simulation and Discrimination using Machine Learning Techniques | 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is pulse shape discrimination important in particle-identification experiments?","Question",{"text":77,"@type":78},"PSD helps discriminate between signal and background radiation quanta by exploiting measurable differences in pulse shape parameters such as rise/fall time and charge content.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What limits conventional PSD techniques in rare-event search experiments?",{"text":82,"@type":78},"Conventional methods require enough total light emission to form a proper pulse profile, which may not be available when deposited energy is very small.",{"name":84,"@type":75,"acceptedAnswer":85},"Which machine-learning models are investigated for PSD in this work?",{"text":86,"@type":78},"The study investigates two network-based methods: Dense Neural Network and Recurrent Neural Network, and compares them with conventional PSD 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