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A combined approach of performance evaluation and position distortion correction for a multitube position-sensitive neutron detector

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JOURNAL OF INSTRUMENTATION 2025年第1期20卷

作者： Zuo, Jingxin Hu, Sen Sun, Liangwei Zhou, Rong Yang, Chaowen Zhang, Xiang Sichuan Univ Coll Phys Chengdu 610065 Peoples R China 10th Res Inst China Elect Technol Grp Corp Chengdu 610036 Peoples R China China Acad Engn Phys Mianyang 621000 Peoples R China

A new multitube position-sensitive detector (PSD) for small-angle neutron scattering spectrometer at the Chinese Mianyang Research Reactor was assembled in 2021. This paper presents the experimental design and implementation aimed at noise reduction, spatial resolution testing, and position correction for the multitube PSD. In addition, the analysis results indicate that deviation of the linear fitting correction increases linearly with the gain difference between the ends of the PSD, ultimately hindering effective position correction. To address this issue, position correction method for multitube PSDs has been proposed that is simpler, more accurate, and broadly applicable to similar detectors.

关键词： data processing methods Detector alignment and calibration methods (lasers, sources, particle-beams) Neutron detectors (cold, thermal, fast neutrons)

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Development of the firmware logic validation system using the FPGA accelerator

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JOURNAL OF INSTRUMENTATION 2025年第4期20卷 C04011-C04011页

作者： Mizuhiki, Ryugo Maeda, Junpei Marumoto, Seiya Kobe Univ Grad Sch Sci Dept Phys Kobe Hyogo 6578501 Japan

Validating FPGA firmware logic used in particle physics is becoming increasingly difficult as the implementation logic scales and becomes more complex with the expansion of FPGA resources. In order to address this issue efficiently, we have developed a firmware validation system utilizing an FPGA embedded on the PCI-express board, referred to as FPGA accelerators, produced by FPGA vendors. We developed a system that controls communication with DDR memory and runs logic on the FPGA. This system supports the use of firmware written in Hardware Description Language as well as High-Level Synthesis firmware, enabling rapid validation. This paper details the developed system and its significant improvements in validation speed and flexibility compared to traditional methods.

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

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New efficient data-driven reduced order models for oscillatory dynamics

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JOURNAL OF COMPUTATIONAL PHYSICS 2025年 533卷

作者： Terragni, Filippo Vega, Jose M. Univ Carlos III Madrid Dept Math Leganes 28911 Spain Univ Carlos III Madrid G Millan Inst Leganes 28911 Spain Univ Politecn Madrid ETSI Aeronaut & Espacio Madrid 28040 Spain

A new methodology is presented to construct data-driven reduced order models able to efficiently approximate periodic attractors of dynamical systems depending on one or more parameters. In an offline stage, some sets of temporally equidistant snapshots are computed for a limited number of parameter values. Such computation is performed using a standard numerical solver when the problem is governed by a low-dimensional system of ordinary differential equations. If, instead, the underlying dynamical system is high-dimensional (i.e., governed by partial differential equations), the snapshots are computed relying on a physics-based reduced order model obtained via proper orthogonal decomposition plus Galerkin projection. The snapshot sets are then treated using a recent, very robust data processing method, the higher order dynamic mode decomposition, which permits describing the periodic attractors as synchronized expansions in terms of spatial modes and associated temporal frequencies. Modes and frequencies are effectively interpolated at new parameter values, different from those involved in the offline stage. The online operation of the resulting data-driven reduced order model is very fast, since it requires carrying out only a small number of algebraic computations. The performance of the new method is tested for two representative dynamical systems, namely the three-dimensional Lorenz system and a high-dimensional system describing the electron transport in a semiconductor superlattice.

关键词： Reduced order models Periodic attractors data processing methods Higher order dynamic mode decomposition Proper orthogonal decomposition Galerkin projection Lorenz system Semiconductor superlattices

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X-ray scattering tensor tomography with a random wavefront modulator to study 3D microstructures in archaeological skeletal remains

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JOURNAL OF INSTRUMENTATION 2025年第2期20卷 C02028-C02028页

作者： Lautizi, Ginevra Lemmers, Simone A. M. Di Trapani, Vittorio Schmeltz, Margaux Zdora, Marie-Christine Broche, Ludovic Studer, Alain Marone, Federica Stampanoni, Marco Thibault, Pierre Univ Trieste Dept Phys I-34127 Trieste Italy Elettra Sincrotrone Trieste I-34149 Basovizza Italy Paul Scherrer Inst Swiss Light Source Villigen 5232 Switzerland Monash Univ Sch Phys & Astron Clayton Campus Melbourne Vic 3800 Australia ESRF European Synchrotron F-38043 Grenoble 9 France Paul Scherrer Inst Data Proc Dev & Consulting Grp CH-5232 Villigen Switzerland Swiss Fed Inst Technol Inst Biomed Engn CH-8092 Zurich Switzerland

We present an application of X-ray scattering tensor tomography with a random wavefront modulator to resolve the orientation of microstructures in archaeological skeletal remains. We experimentally investigated two fragments from different tissue types - cortical bone and a root dentine - commonly analyzed in archaeological and palaeoanthropological research. This study hints at the potential to advance methodologies for both archaeological research and clinical applications.

关键词： Computerized Tomography (CT) and Computed Radiography (CR) data processing methods Image reconstruction in medical imaging Inspection with x-rays

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Optimizing spin dressing sensitivity for the nEDMSF experiment

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JOURNAL OF INSTRUMENTATION 2025年第1期20卷

作者： Cianciolo, V. Oak Ridge Natl Lab Oak Ridge TN 37830 USA

nEDMSF aims to measure the neutron electric dipole moment (da) with unprecedented precision. In this paper we explore the experiment's sensitivity when operating with an implementation of the critical dressing method in which the angle between the neutron and Helium-3 spins (cb3a) is subjected to a square modulation by an amount cbd (the "dressing angle"). Several parameters can be tuned to optimize sensitivity. We find roughly 10% improvement over a previous estimate, resulting primarily from the addition of a waiting period between the ir/2 pulse that initiates da-driven cb3a growth and the start of cb3a modulation. We find negligible further improvement by allowing cbd to vary continuously over the course of a run, and no degradation resulting from the addition of an in situ background measurement into each cb3a modulation sequence. A complete simulation confirms a 300 live-day sensitivity of O = 1.45 x 10-28 e . cm. At this level of sensitivity, O phi 3n0 = 1 mrad precision on the initial n/3He angle difference is not negligible.

关键词： Analysis and statistical methods data processing methods

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Particle identification with machine learning from incomplete data in the ALICE experiment

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JOURNAL OF INSTRUMENTATION 2024年第7期19卷 C07013页

作者： Karwowska, Maja Graczykowski, Lukasz Deja, Kamil Kasak, Milosz Janik, Malgorzata Warsaw Univ Technol Fac Phys Koszykowa 75 PL-00662 Warsaw Poland CERN European Org Nucl Res Espl Particules 1 CH-1211 Geneva Switzerland Warsaw Univ Technol Fac Elect & Informat Technol Nowowiejska 15-19 PL-00665 Warsaw Poland IDEAS NCBR Chmielna 69 PL-00801 Warsaw Poland

The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several detectors for particles with momentum from about 100 MeV/c up to 20 GeV/c. Traditionally, particles are selected with rectangular cuts. A much better performance can be achieved with machine learning (ML) methods. Our solution uses multiple neural networks (NN) serving as binary classifiers. Moreover, we extended our particle classifier with Feature Set Embedding and attention in order to train on data with incomplete samples. We also present the integration of the ML project with the ALICE analysis software, and we discuss domain adaptation, the ML technique needed to transfer the knowledge between simulated and real experimental data.

关键词： Analysis and statistical methods data processing methods Particle identification methods

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Parallel CPU- and GPU-based connected component algorithms for event building for hybrid pixel detectors

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JOURNAL OF INSTRUMENTATION 2025年第1期20卷

作者： Celko, T. Mraz, F. Bergmann, B. Manek, P. Charles Univ Prague Dept Comp Sci Educ Malostranske Nam 25 Prague 1 Czech Republic Czech Tech Univ Inst Expt & Appl Phys Husova 240-5 Prague 1 Czech Republic UCL Dept Phys & Astron Gower St London WC1E 6BT England

The latest generation of Timepix series hybrid pixel detectors enhance particle tracking with high spatial and temporal resolution. However, their high hit-rate capability poses challenges for data processing, particularly in multidetector configurations or systems like Timepix4. Storing and processing each hit offline is inefficient for such high data throughput. To efficiently group partly unsorted pixel hits into clusters for particle event characterization, we explore parallel approaches for online clustering to enable real-time data reduction. Although using multiple CPU cores improved throughput, scaling linearly with the number of cores, load-balancing issues between processing and I/O led to occasional data loss. We propose a parallel connected component labeling algorithm using a union-find structure with path compression optimized for zero-suppression data encoding. Our GPU implementation achieved a throughput of up to 300 million hits per second, providing a two-order-of-magnitude speedup over compared CPU-based methods while also freeing CPU resources for I/O handling and reducing the data loss.

关键词： data processing methods data reduction methods Pattern recognition cluster finding calibration and fitting methods

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Mitigating multiple single-event upsets during deep neural network inference using fault-aware training

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JOURNAL OF INSTRUMENTATION 2025年第2期20卷 C02044-C02044页

作者： Vinck, Toon Jonckers, Nain Dekkers, Gert Prinzie, Jeffrey Karsmakers, Peter Magics Technol Cipalstr 3 B-2440 Geel Belgium Katholieke Univ Leuven ESAT ADVISE Kleinhoefstr 4 B-2440 Geel Belgium Katholieke Univ Leuven Dept Comp Sci Leuven AI Celestijnenlaan 200a B-3001 Leuven Belgium Flanders Make MPRO Gaston Geenslaan 8 B-3001 Leuven Belgium

Deep neural networks (DNNs) are increasingly used in safety-critical applications. Reliable fault analysis and mitigation are essential to ensure their functionality in harsh environments that contain high radiation levels. This study analyses the impact of multiple single-bit single-event upsets in DNNs by performing fault injection at the level of a DNN model. Additionally, a fault-aware training (FAT) methodology is proposed that improves the DNNs' robustness to faults without any modification to the hardware. Experimental results show that the FAT methodology improves the tolerance to faults up to a factor 3.

关键词： Simulation methods and programs Analysis and statistical methods data processing methods Software architectures (event data models, frameworks and databases)

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Machine learning for real-time processing of ATLAS liquid argon calorimeter signals with FPGAs

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JOURNAL OF INSTRUMENTATION 2025年第2期20卷 C02012-C02012页

作者： Voigt, Johann Christoph TUD Dresden Univ Technol Inst Nucl & Particle Phys Dresden Germany

To deal with the higher pileup after the high-luminosity upgrade of the LHC, the ATLAS liquid argon calorimeter readout electronics will be replaced. 556 Intel Agilex-7 FPGAs will be installed as part of the off-detector electronics, enabling a more sophisticated digital energy reconstruction. Instead of using a linear optimal filter like the current readout, we evaluated the use of recurrent and convolutional neural networks. These artificial neural networks are trained on signal-enriched simulated detector pulses for a set of representative cells. The network size is limited to approximately 500 multiply-accumulate operations per detector cell. They show improvements in energy reconstruction especially for the case of overlapping signal pulses, as they will appear more often under high-luminosity conditions. An FPGA prototype firmware has been developed for both RNNs and CNNs, that supports processing the required number of 384 detector cells on one FPGA. This uses a low-level VHDL approach to minimize the resource usage. The ANN architecture is fixed at compile time, but the weights are configurable per cell. The compilation results show that such a neural network based readout is feasible with the available hardware. This is possible through the use of time multiplexing, i.e. by running the FPGA at a multiple of the 40 MHz input data frequency.

关键词： Calorimeters data processing methods Electronic detector readout concepts (gas, liquid)

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Computational microscopy with the PERCIVAL detector system at TwinMic beamline

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JOURNAL OF INSTRUMENTATION 2025年第1期20卷

作者： Guzzi, Francesco Gianoncelli, Alessandra Stebel, Luigi Giuressi, Dario Cautero, Giuseppe Borghes, Roberto Gregori, Iztok Bille, Fulvio Scarcia, Martin Bonanni, Valentina Zizic, Milan Hyun, Hyojung Nichols, William Correa, Jonathan Marras, Alessandro Wunderer, Cornelia B. Graafsma, Heinz Menk, Ralf Hendrik Kourousias, George Elettra Sincrotrone Trieste SCpA Str Statale 14 Trieste Italy Pohang Accelerator Lab PAL 80 Jigokro 127 Beongil Pohang South Korea Harwell Sci & Innovat Campus Diamond Light Source Didcot Oxon England Deutsch Elektronen Synchrotron DESY Notkestr 85 Hamburg Germany Ctr Free Electron Laser Sci CFEL Luruper Ch 149 Hamburg Germany Mid Sweden Univ S-85230 Sundsvall Holmgatan Sweden

PERCIVAL is a novel soft X-ray detection system designed for the needs of modern microscopy. By integrating it into the TwinMic end-station at Elettra Sincrotrone Trieste, we conducted an exploratory computational microscopy experiment on biological samples, aiming at evaluating the entire system in a real use-case scenario. We present the methodology to convert the RAW data and our high-resolution image reconstructions.

关键词： data processing methods Image reconstruction in medical imaging X-ray detectors

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