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At least 37 records · Page 2

Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors

Detectors in next-generation high-energy physics experiments face several daunting requirements, such as high data rates, damaging radiation exposure, and stringent constraints on power, space, and latency. To address these challenges, machine learning in readout electronics can be leveraged for smart detector designs, enabling intelligent inference and data reduction at-source. Variational autoencoders (VAEs) offer a variety of benefits for front-end readout; an on-sensor encoder can perform efficient lossy data compression while simultaneously providing a latent space representation that can be used for anomaly detection. Results are presented from low-latency and resource-efficient VAEs for front-end data processing in a futuristic silicon pixel detector. Encoder-based data compression is found to preserve good performance of off-detector analysis while significantly reducing the off-detector data rate as compared to a similarly sized data filtering approach. Furthermore, the latent space information is found to be a useful discriminator in the context of real-time sensor defect monitoring. Together, these results highlight the multifaceted utility of autoencoder-based front-end readout schemes and motivate their consideration in future detector designs.

47 OTHER INSTRUMENTATION↗

Embedded FPGA developments in 130 nm and 28 nm CMOS for machine learning in particle detector readout

Embedded field programmable gate array (eFPGA) technology allows the implementation of reconfigurable logic within the design of an application-specific integrated circuit (ASIC). This approach offers the low power and efficiency of an ASIC along with the ease of FPGA configuration, particularly beneficial for the use case of machine learning in the data pipeline of next-generation collider experiments. An open-source framework called "FABulous" was used to design eFPGAs using 130 nm and 28 nm CMOS technology nodes, which were subsequently fabricated and verified through testing. The capability of an eFPGA to act as a front-end readout chip was assessed using simulation of high energy particles passing through a silicon pixel sensor. A machine learning-based classifier, designed for reduction of sensor data at the source, was synthesized and configured onto the eFPGA. A successful proof-of-concept was demonstrated through reproduction of the expected algorithm result on the eFPGA with perfect accuracy. Finally, further development of the eFPGA technology and its application to collider detector readout is discussed.

47 OTHER INSTRUMENTATION↗

Design and Characterization of a Phonon-Mediated Cryogenic Particle Detector with an eV-Scale Threshold and 100 keV-Scale Dynamic Range

We present the design and characterization of a cryogenic phonon-sensitive Si detector exploiting the Neganov-Trofimov-Luke effect to detect single-charge excitations. This device achieved 2.65(2) eV phonon energy resolution when operated without a voltage bias across the crystal and a corresponding charge resolution of 0.03 electron-hole pairs at 100 V bias. With a continuous-readout data acquisition system and an offline optimum-filter trigger, we obtain a 9.2 eV threshold with a trigger rate of the order of 20 Hz. The detector's energy scale is calibrated up to 120 keV using an energy estimator based on the pulse area. The high performance of this device allows its application to different fields where excellent energy resolution, low threshold, and large dynamic range are required, including dark matter searches, precision measurements of coherent neutrino-nucleus scattering, and ionization yield measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

An in-situ movable calibration source for cryogenic particle detectors

A prototype device capable of moving a radioactive calibration source to multiple positions was operated at millikelvin temperatures using a modified commercial stepper motor. It was developed as an in-situ calibration strategy for cryogenic dark matter detectors. Data taken by scanning a calibration source across multiple radial positions of a prototype dark matter detector demonstrated its functionality. Construction, heat load, and operation of the device are discussed, as is the effect of the motor on the detector operation. A sample dataset taken over multiple positions of a SuperCDMS detector is presented as an example of the utility of such a device.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Introducing a Markov chain-based time calibration procedure for multi-channel particle detectors: application to the SuperFGD and ToF detectors of the T2K experiment

Inter-channel mis-synchronisation can be a limiting factor to the time resolution of high performance timing detectors with multiple readout channels and independent electronics units. In these systems, time calibration methods employed must be able to efficiently correct for minimal mis-synchronisation between channels and achieve the best detector performance. We present an iterative time calibration method based on Markov Chains, suitable for detector systems with multiple readout channels. Starting from correlated hit pairs alone, and without requiring an external reference time measurement, the method solves for fixed per-channel offsets, with precision limited only by the intrinsic single-channel resolution. A mathematical proof that the method is able to find the correct time offsets to be assigned to each detector channel in order to achieve inter-channel synchronisation is given, and it is shown that the number of iterations to reach convergence within the desired precision is controllable with a single parameter. Numerical studies are used to confirm unbiased recovery of true offsets. Finally, the application of the calibration method to the Super Fine-Grained Detector (SuperFGD) and the Time of Flight (TOF) detector at the upgraded T2K near detector (ND280) shows good improvement in overall timing resolution, demonstrating the effectiveness in a real-world scenario and scalability.

calibration and fitting methods↗

Design and characterization of a phonon-mediated cryogenic particle detector with an eV-scale threshold and 100 keV-scale dynamic range

We present the design and characterization of a cryogenic phonon-sensitive Si detector exploiting the Neganov-Tromov-Luke effect to detect single-charge excitations. This device achieved 2.65(2) eV phonon energy resolution when operated without a voltage bias across the crystal and a corresponding charge resolution of 0.03 electron-hole pairs at 100 V bias. With a continuous-readout data acquisition system and an oine optimum-filter trigger, we obtain a 9.2 eV threshold with a trigger rate of the order of 20 Hz. The detector's energy scale is calibrated up to 120 keV using an energy estimator based on the pulse area. The high performance of this device allows its application to different fields where excellent energy resolution, low threshold, and large dynamic range are required, including dark matter searches, precision measurements of coherent neutrino-nucleus scattering, and ionization yield measurements.

Ren, R↗

First operation of superconducting quantum bits as particle detectors

Transmon qubits are among the leading technologies for the development of future quantum computers. However these superconducting devices are sensitive to ionizing radiation, which reduces their coherence time and leads to correlated errors. This study represents the first quantitative estimation of cosmic rays and ambient radiation impact on transmon qubits. In the shielded underground facility at the Laboratori Nazionali del Gran Sasso (LNGS), errors induced by the interaction of a radioactive source were reconstructed with an efficiency of about 10%. In contrast, in the unshielded above-ground facility at the Fermi National Accelerator Laboratory (FNAL), cosmic and ambient radiation impacts were detected with an efficiency ranging from 10% to 20% by modifying the acquisition protocol. Lastly, the simultaneous readout of three transmons in coincidence was demonstrated, achieving an event reconstruction efficiency of 90% when the devices were illuminated with an LED.

Bonomo, Camilla [Rome U.; INFN, Rome]↗

Using machine learning for particle track identification in the CLAS12 detector

Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively tests track measurements ("hits") to identify those that form an actual particle trajectory. In this article, we describe the development of four machine learning (ML) models that assist the tracking algorithm by identifying valid track candidates from the measurements in drift chambers. Several types of machine learning models were tested, including: Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Extremely Randomized Trees (ERT) and Recurrent Neural Networks (RNN). As a result of this work, an MLP network classifier was implemented as part of the CLAS12 reconstruction software to provide the tracking code with recommended track candidates. The resulting software achieved accuracy of greater than 99% and resulted in an end-to-end speedup of 35% compared to existing algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Building the Eyes of Discovery

My presentation focuses on the development and enhancement of sensor modules that help particle detectors “see” particles invisible to the human eye, specifically the Compact Muon Shield detector, which is part of the High-Luminosity Large Hadron Collider. As a CCI intern at Fermilab’s Silicon Detector Facility, I worked on testing and visual inspection of different module parts before assembly and on testing the modules after assembly. My talk highlights how small sensors carry the capability to collect tiny particle signals within the detector and the reliability of control checks pre-assembly for scientists to produce reliable data for future discoveries.

Siddiqui, Hooriya [Fermilab] (ORCID:00090001510973↗

Determining Optimal Running Conditions for TinyTPC Detector

Liquid argon time projection chambers, (LArTPCs), are particle detectors used to collect ionization charge information from particle trajectories, facilitating detailed particle track analysis. They are currently used as particle detectors in major physics projects such as the deep underground neutrino experiment (DUNE), to detect and study the nature of the elusive neutrino particle. TinyTPC is a small scale LArTPC featuring a pixelated readout system (LArPix) that we will use to study liquid argon doping. We expect this doping to enhance the resolution of LAr detectors, especially for low energy particles below 10 MeV which would expand the capabilities of currently running experiments. Housed within a cryostat filled with liquid argon, the TinyTPC will rely on a high and a low voltage system to collect data. In preparation for deployment we found and resolved issues in the HV and LV systems and we determined optimal running conditions in a test vessel. This presentation will go over the detector commissioning process that enabled data taking with the TinyTPC.

Gonzalez, Rebecca↗

A parallel-plate avalanche counter for the prompt fission neutron spectrum measurement

Neutrons, gamma’s, and fission fragments are among the prompt fission observables. Their precision measurements are fundamental to advance our understanding of fission and any application of fission. For our programmatic need, the χ matrix, which defines as the outgoing neutron spectrum as a function of incoming neutron energy, is sparsely populated and poorly measured for 233,235,238 U and 239,240 Pu. To improve the quality of those χ matrices, a joint LANLLLNL project was undertaken to measure their prompt fission neutron spectra at the LANSCE/WNR facility using neutron detector arrays and a charged-particle detector for the fission-fragment detection. A typical setup is shown in Fig. 1, where one of neutron detector array with a charged-particle detector is given.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors

We present the first physical realization of in-pixel signal processing with integrated AI-based data filtering for particle tracking detectors. Building on prior work that demonstrated a physics-motivated edge-AI algorithm suitable for ASIC implementation, this work marks a significant milestone toward intelligent silicon trackers. Our prototype readout chip performs real-time data reduction at the sensor level while meeting stringent requirements on power, area, and latency. The chip is taped-out in 28nm TSMC CMOS bulk process, which has been shown to have sufficient radiation hardness for particle experiments. This development represents a key step toward enabling fully on-detector edge AI, with broad implications for data throughput and discovery potential in high-rate, high-radiation environments such as the High-Luminosity LHC.

Parpillon, Benjamin [Fermilab; Illinois U., Chicag↗