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Topical Workshop on Electronics for Particle Physics (TWEPP)

作者： Herwig, C. Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA

The upgrade of the CMS detector for the high-luminosity LHC will include track-finding for the first time in the Level-1 trigger, enabling Particle Flow reconstruction of every event in addition to comprehensive pileup mitigation. The Correlator trigger will reconstruct isolated leptons and photons, hadronic jets, and energy sums, assisted in many cases by machine learning to benefit from the complete particle-level event record. We present the logic of these algorithms, possible implementations using large FPGAs and their demonstration in prototype hardware, in addition to the expected physics performance.

关键词： Trigger algorithms Trigger concepts and systems (hardware and software) data processing methods

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System design and prototyping for the CMS Level-1 Trigger at the High-Luminosity LHC

System design and prototyping for the CMS Level-1 Trigger at...

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Topical Workshop on Electronics for Particle Physics (TWEPP)

作者： Herwigon, C. Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA

For the High-Luminosity Large Hadron Collider, the trigger and data acquisition system of the CMS experiment will be entirely replaced. Novel design choices have been explored, including ATCA prototyping platforms with SoC controllers and newly available interconnect technologies with serial optical links with data rates up to 28 Gb/s. Trigger analyses will be performed through sophisticated algorithms, including widespread use of Machine Learning, in large FPGAs, such as the Xilinx Ultrascale family. The systemwill process over 50 Tb/s of detector data with an event rate of 750 kHz. We describe system design and prototyping and review trigger algorithm exemplars.

关键词： Trigger concepts and systems (hardware and software) Trigger algorithms data processing methods

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Classification of pulsar signals using ensemble gradient boosting algorithms based on asymmetric under-sampling method

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JOURNAL OF INSTRUMENTATION 2022年第3期17卷 P03020页

作者： Tariq, I Qiao, M. Wei, L. Yao, S. Zhou, C. Ali, Z. Azeem, S. W. Spanakis-Misirlis, A. Southeast Univ Sch Informat Sci & Engn Nanjing Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Southeast Univ Sch Elect Engn Nanjing Peoples R China Univ Piraeus Dept Informat Piraeus Greece

The detection of pulsar signals is a highly intensive task. Numerous artificial intelligence (AI) and machine learning techniques (ML) have been proposed to classify pulsar and non-pulsar signals. While existing machine learning techniques improve classification efficiency, these methods are limited when it comes to dealing with large volumes of astronomical data, the extreme problem of class imbalance , and the polarization of high recall and precision. In this paper, to accurately classify pulsar and non-pulsar signals, extreme Gradient Boosting (XgBoost), and Light Gradient Boosting Machine (LightGBM) algorithms based on an asymmetric undersampling method are proposed. Firstly, the proposed model uses an asymmetric undersampling technique that divides the benchmark datasets into 75 subsets for the LOFAR Tied-Array All-Sky Survey (LOTAAS-1) and 9 subsets for the High Time Resolution Universe Pulsar Survey (HTRU2), eluding the class imbalance problem. Finally, XgBoost and LightGBM algorithms have been adopted for each new data subset, with a majority voting classifier being used to integrate the output of proposed models against each subset. Results of the proposed method were compared to state-of-the-art baseline models, which showed that our models significantly performed better than existing methods and gained almost 2% and 2.5% better F-score and precision results, respectively.

关键词： data analysis data processing data processing methods

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3D track reconstruction of low-energy electrons in the MIGDAL low pressure optical time projection chamber

3D track reconstruction of low-energy electrons in the MIGDA...

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7th International Conference on Micro Pattern Gaseous Detectors

作者： Tilly, E. Handley, M. Univ New Mexico Dept Phys & Astron 210 Yale Blvd NE Albuquerque NM 87106 USA Univ Cambridge Cavendish Lab JJ Thomson Ave Cambridge CB3 0HE England STFC Rutherford Appleton Lab Particle Phys Dept Didcot OX11 0QX Oxon England

We demonstrate three-dimensional track reconstruction of electrons in a low pressure (50 Torr) optical TPC consisting of two glass GEMs with an ITO strip readout in CF4 and CF4/Ar mixtures. The reconstructed tracks show a variety of event topologies, including short tracks from photoelectrons induced by Fe-55 5.9 keV X-rays and long tracks from gamma ray interactions and beta decays. Algorithms for event identification and track ridge detection are discussed as well as multiple methods for integrating information from the camera image and ITO waveforms with the goal of full 3D reconstruction of the track.

关键词： data processing methods Gaseous imaging and tracking detectors Optical detector readout concepts Time projection chambers

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Reconstruction of missing data of the PandaX-III experiment TPC using neural network

Reconstruction of missing data of the PandaX-III experiment ...

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7th International Conference on Micro Pattern Gaseous Detectors

作者： Lobasenko, A. Univ Paris Saclay 15 Rue Georges Clemenceau Orsay France CEA IRFU DPhN Orme Merisiers Bat 703 Gif Sur Yvette France

The PandaX-III is a Neutrinoless Double-Beta Decay (NLDBD) experiment which uses a Time Projection Chamber (TPC) detector with a readout plane formed of Micromegas modules, which allows reconstruction of track topology for the background discrimination as well as reconstruction of the energy of the events. In NLDBD experiments, in order to achieve the highest sensitivity to the decay, it is necessary for the detector to have a high energy resolution, the background level should be low, and techniques for background discrimination must be applied as well. In reality, inhomogeneous signal gain at each module and the presence of missing channels lead to an incorrect energy reconstruction of the events. In this work, a method based on a Convolutional Neural Networks (CNN) aiming to reconstruct the kinematics of the event from imperfect data with missing channels is presented. Preliminary results of the reconstruction of the missing data using CNN are showing an increase in detection efficiency. The detection efficiency was evaluated on the simulated data with three channels randomly chosen per Micromegas module and artificially set as missing. Direct reconstruction of the energy gives the efficiency of 78%, while after applying CNN it increases to 86%, providing a promising application of this technique.

关键词： data processing methods Double-beta decay detectors Micropattern gaseous detec-tors (MSGC GEM THGEM RETHGEM MHSP MICROPIC MICROMEGAS InGrid etc) Simulation methods and programs

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Modern C++17 data pre-processing HLS Datflow Template Library

Modern C++17 data pre-processing HLS Datflow Template Librar...

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244th Electrochemical Society Meeting (ECS)

作者： Janson, T. Kebschull, U. Goethe Univ Frankfurt Main Infrastruktur & Rechnersyst Informationsverarbeitu Max Von Laue Str 12 Frankfurt Germany

Developing and implementing algorithms for detector read-out using FPGAs is traditionally done by using a hardware description language like VHDL, Verilog, or System Verilog. In the proposed approach here, we discuss an alternative way using higher level lan-guages like the Intel HLS Compiler. Intel HLS supports C++17 standard and is ideal to apply methods from Modern C++ to implement complex algorithms more easily. In this work, we have developed a dataflow template library. This enables a shorter development time for increasingly complex algorithm requirements, which is also important for next-generation experiments in the future.

关键词： data processing methods Digital signal processing (DSP) Front-end electronics for detector readout

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Trigger-less readout and unbiased data quality monitoring of the CMS drift tubes muon detector

Trigger-less readout and unbiased data quality monitoring of...

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Topical Workshop on Electronics for Particle Physics (TWEPP)

作者： Migliorini, M. Pazzini, J. Triossi, A. Zanetti, M. Zucchetta, A. Padova Univ Dept Phys & Astron Galileo Galilei Via Marzolo 8 I-35131 Padua Italy Natl Inst Nucl Phys Padova Div Via Marzolo 8 I-35131 Padua Italy

The CMS experiment 40 MHz data scouting project is aimed at intercepting the data produced at the level of the detectors' front-end without the filters induced by hardware-based triggers. A first implementation is realized by the trigger-less reading and processing of a fraction of the Drift Tube (DT) muon detector, equipped with a preliminary version of the so-called Phase-2 Upgrade on-detector electronics boards. The data are transferred via high-speed optical links to back-end boards independently from the central experiment data acquisition (DAQ), permitting real-time detector status monitoring via receiving all the signals produced at the front-end level, and providing an unbiased estimate of the CMS DT hit-rate under various data-taking conditions.

关键词： data acquisition concepts data processing methods Trigger concepts and systems (hardware and software)

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A machine learning approach in the estimation of a radioactive source position using a coded aperture device

A machine learning approach in the estimation of a radioacti...

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23rd International Workshop on Radiation Imaging Detectors

作者： Karafasoulis, K. Kaissas, I. Papadimitropoulos, C. Potiriadis, K. Lambropoulos, C. P. Hellen Army Acad Dept Nat Sci & Applicat Vari Attikis 16673 Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Fac Engn Thessaloniki 54124 Greece Natl & Kapodistrian Univ Athens Dept Aerosp Sci & Technol Psahna Evia 34400 Greece Greek Atom Energy Commiss Agia Paraskevi Atikis Greece

Coded Aperture gamma-cameras have been used for more than three decades for imaging radioactive source distributions encountered in astrophysics, in decommissioning of nuclear facilities, and in nuclear medicine. These devices enable the identification of the coordinates of gamma-emitters located within their Field of View (FOV) with the use of the coded-aperture shadow projected on pixelated detectors. In this work we have developed machine learning algorithms based on Gradient Boosted Decision Trees (BDTG) and Deep Neural Networks (DNN). The algorithms have been trained using 21000 shadowgrams created with simulation. A custom fast simulation tool was used to produce the shadowgrams due to sources placed randomly at different positions within the FOV at distances from 20 cm up to 20m from the detector plane. The performance of the algorithms has been evaluated with the aid of a different independent simulation sample of shadowgrams and verified with real data.

关键词： Analysis and statistical methods Computing (architecture, farms, GRID for recording, storage, archiving, and distribution of data) data processing methods

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Semiempirical criterion for eliminating wraparound artifacts in single-grid-based phase-contrast X-ray imaging

Semiempirical criterion for eliminating wraparound artifacts...

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244th Electrochemical Society Meeting (ECS)

作者： Lee, H. Lee, M. Lim, H. Jeon, D. Kim, W. Cho, H. Dept Radiat Convergence Engn 1 Yonseidae gil Wonju 26493 South Korea

Single-grid-based phase-contrast X-ray imaging (SG-PCXI) is a non-interferometric method for the simultaneous retrieval of absorption, phase-contrast (differential), and dark-field images from a single exposure with a common X-ray grid using a spatial harmonic imaging (SHI) technique. However, the retrieved images are often distorted by the wraparound artifacts that are caused by the spectral overlap of adjacent harmonic peaks in the Fourier domain, resulting in the degradation of image quality. In this study, a semiempirical criterion required for eliminating wraparound artifacts in SG-PCXI was established by considering two constraints: one to resolve periodic grid strips by the system resolution to use the SHI technique, and the other to ensure that the spectral contents of adjacent harmonic peaks are separated (not overlapped) to the greatest possible extent during data processing. Further, we assumed that the system resolution can be experimentally determined as the spatial frequency at a 10% modulation transfer function. To verify the efficacy of the semiempirical criterion, we conducted an experiment using a table-top setup, which primarily comprised a microfocus X-ray source with a 20 mu m focal spot size, X-ray grids with strip densities of 150, 200, and 254 lines/inch, and a CMOS flat-panel detector with a 49.5 mu m pixel size. The experimental measurements of the wraparound artifacts in our system setup were in good agreement with the semiempirical criterion over the entire range of test conditions, thus verifying its efficacy for SG-PCXI. Accordingly, the semiempirical criterion established in this study will be useful for designing a wraparound artifact-free system layout in SG-PCXI.

关键词： X-ray radiography and digital radiography (DR) data processing methods X-ray detectors

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Complexity-Reduced Variational Auto Encoders With Bayesian Optimization for Anomaly Detection in High-Dimensional Medical data 1

Complexity-Reduced Variational Auto Encoders With Bayesian O...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Batta, Priya Shete, Amol S. Jadhav, Ranjit S Kumra, Neha Chandigarh University Department of Cse Mohali India Krishna Institute of Pharmacy Krishna Vishwa Vidyapeeth Karad India Chitkara University Department of Applied Sciences Punjab India

ISBN: (纸本)9798350330915

Recent advances in imaging technology, along with the wealth of information provided by genetic sequencing and complete electronic health records, are radically altering the diagnostic landscape in medicine. High-dimensional medical data allows for previously inconceivable possibilities, such as early disease detection, a better understanding of subtle patient distinctions, and the creation of medicines that are specifically suited to each person's genetic makeup and state of health. However, there are still challenges associated with these innovations. As a result of their sheer size and complexity, such datasets often challenge the limits of traditional data processing methods. Conventional methods, which were developed for less complex data sets, have trouble exploiting this wealth of information to its full potential. This study looks into new ways of dealing with these issues. The goal is quite clear: to not only make sense of this data's complexity but also to fully capitalize on its revolutionary potential for the next generation of healthcare. © 2023 IEEE.

关键词： data processing methods Disease Detection Genetic Sequencing High-dimensional Medical data Imaging Technology Personalized Medicine

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