Performance of Simulated Detector Responses in Training Neural Networks for Neutron Spectrum Unfolding
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Nuclear science and engineering↗
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In-pixel AI for lossy data compression at source for X-ray detectors
Integrating neural networks for data compression directly in the Read-Out Integrated Circuits (ROICs), i.e. the pixelated front-end, would result in a significant reduction in off-chip data transfer, overcoming the I/O bottleneck. Our ROIC test chip (AI-In-Pixel-65) is designed in a 65 nm Low Power CMOS process for the readout of pixelated X-ray detectors. Each pixel consists of an analog front-end for signal processing and a 10b analog-to-digital converter operating at 100KSPS. Here, we compare two non-reconfigurable techniques, Principal Component Analysis (PCA) and an AutoEncoder (AE) as lossy data compression engines implemented within the pixelated area. The PCA algorithm achieves 50$×$ compression, adds one clock cycle latency, and results in a 21% increase in the pixel area. The AE achieves 70$×$ compression, adds 30 clock cycle latency, and results in a similar area increase.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The synthesis of binary sequence detectors.
Synthesizing sequential network for detecting n-place nonperiodic binary sequence within data stream
Perlman, M.↗
Long-period cosmic ray variations and their altitude dependence
Long-period variations were studied from the data of ground-based cosmic ray (CR) observations. In spite of a large value of an 2-year variation, it is more difficult to obtain its spectrum than the spectrum of a solar diurnal variation. Serious obstacles are caused by changes in individual detectors and in the whole world wide network of CR detectors, by the absence of continuity and uniformity of data series, by various apparatus variations. In discrimination and investigation of long-period variations an important and determining point is preparation and preliminary analysis of data.
Belov, A. V.↗
Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors
Abstract Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid quadratic operations while achieving realistic reconstruction. We show that hyperparameter tuning significantly improves the performance of the models. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm. The resulting model is portable across Nvidia, AMD and Habana hardware. Accurate and fast machine-learning based reconstruction can significantly improve future measurements at colliders.
Physics↗
Gamma-ray burst localisation with microspacecraft
It is shown that gamma-ray burst detectors on a network of microspacecraft can be used to directly determine burst directions to a few arcsec in both angular coordinates. This level of accuracy is sufficient to conduct searches for the quiescent counterpart of the burster at other wavelengths. It is maintained that the strawman instrument for the microspacecraft network, consisting of two 1 kg Na I detectors on opposite ends of the spacecraft, is capable of providing a nearly omnidirectional FOV with adequate sensitivity to gamma-ray bursts.
Mahoney, W. A.↗
Definition of air quality measurements for monitoring space shuttle launches
A description of a recommended air quality monitoring network to characterize the impact on ambient air quality in the Kennedy Space Center (KSC) (area) of space shuttle launch operations is given. Analysis of ground cloud processes and prevalent meteorological conditions indicates that transient HCl depositions can be a cause for concern. The system designed to monitor HCl employs an extensive network of inexpensive detectors combined with a central analysis device. An acid rain network is also recommended. A quantitative measure of projected minimal long-term impact involves the limited monitoring of NOx and particulates. All recommended monitoring is confined ti KSC property.
Thorpe, R. D.↗
Improving ProtoDUNE pion cross-section measurements with NuGraph Michel-electron tagging
Understanding hadron-argon interactions is essential for precise neutrino energy reconstruction and final-state interaction modeling in liquid-argon time projection chamber (LArTPC) experiments such as DUNE. In particular, pion absorption and charge-exchange processes constitute significant sources of systematic uncertainty in neutrino oscillation measurements. ProtoDUNE-SP, a large-scale LArTPC prototype operated at the CERN Neutrino Platform and exposed to charged-particle test beams in the few-GeV range, enables direct measurements of these processes. This work focuses on the measurement of differential cross sections for pion absorption and charge exchange using the 2 GeV/c pion beam data from the ProtoDUNE-SP run. A key component of this analysis is the identification of Michel electrons from $\pi \rightarrow \mu \rightarrow e$ decay chains, which helps separate different interaction topologies and improves background rejection. Michel electron identification will also assist in reliably calibrating the electromagnetic response in ProtoDUNE-SP data and for the future DUNE detectors. In this analysis, we apply NuGraph to identify Michel electrons. NuGraph is a graph neural network that models detector hits as nodes connected by spatial and temporal edges for particle and topology classification in LArTPC detectors. We first benchmark NuGraph’s Michel electron classification performance using ICEBERG data, a small-scale LArTPC prototype used for DUNE electronics and reconstruction development, and then transfer the approach to ProtoDUNE-SP. This poster presents the analysis strategy, NuGraph-based classification studies, and discusses how these developments are expected to improve the pion cross-section measurement.
Razafinime, Soamasina Herilala [Cincinnati U.] (OR↗
Neural network tracking and extension of positive tracking periods
Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basically, if a picture says a thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.
feature extraction↗
Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks
The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from 1017eV up to more than 1020eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Beyond the Hype: An Evaluation of Commercially Available Machine-Learning-Based Malware Detectors
There is a lack of scientific testing of commercially available malware detectors, especially those that boast accurate classification of never-before-seen (i.e., zero-day) files using machine learning (ML). Consequently, efficacy of malware detectors is opaque, inhibiting end users from making informed decisions and researchers from targeting gaps in current detectors. In this paper, we present a scientific evaluation of four prominent commercial malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously and never-before-seen files? Is purchasing a network-level malware detector worth the cost? To investigate, we tested each tool against 3,536 total files (2,554 or 72% malicious, 982 or 28% benign) of a variety of file types, including hundreds of malicious zero-days, polyglots, and APT-style files, delivered on multiple protocols. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of the recent cost-benefit evaluation procedure of Iannacone & Bridges. Although the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool might still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files—37% of malware, including all polyglot files, were undetected. Priorities for researchers and takeaways for end users are given. Code for future use of the cost model is provided.
97 MATHEMATICS AND COMPUTING↗
Applying Automated Detectors for Regional Infrasound Signals to Global Networks: A Case Study Using the 2022 Hunga Tonga Volcanic Eruption
The January 15, 2022 Tonga Hunga volcanic eruption was the largest event to be recorded across the International Monitoring System infrasound network. Signals from the eruption were identified at all 53 operational stations by International Data Centre analysts. This report contains descriptors for signal detection bulletins produced by infrasound researchers at Sandia National Laboratories and Los Alamos National Laboratory. Manual detection bulletins were produced by laboratory staff. Automated detection bulletins were produced using automated infrasound processing tools developed at both laboratories. Initial results are provided to evaluate the utility of extending tools developed for regional infrasound event analysis to global-scale events.
58 GEOSCIENCES↗
Limits to the burster repetition rate as deduced from the 2nd catalog of the interplanetary network
The burster repetition rate is an important parameter in many gamma ray burst models. The localizations of the interplanetary network, which have a relatively small combined surface area, may be used to estimate the average repetition rate. The method consists of (1) estimating the number of random overlaps between error boxes expected in the catalog and comparing this number to that actually observed; (2) modeling the response of the detectors in the network, so that the probability of detecting a burst can be estimated; and (3) simulating the arrival of bursts at the network assuming that burster repetition is governed by a Poisson process. The application of this method for many different burster luminosity functions shows that (1) the lower limit to the burster repetition rate depends strongly upon the assumed luminosity function; (2) the best lower limit to the repetition period obtainable from the data of the network is about 100 months; and (3) that a luminosity function for all bursters similar to that of the 1979 Mar 5 burster is inconsistent with the data.
Atteia, J. L.↗
Implementation of Machine Learning Methods for Crater-Based Navigation
Terrain Relative Navigation methods require surface feature detectors to gain information from images used to improve on-board state estimates. This paper presents the development of a crater detection method based on Machine Learning that can extract data from optical images with different crater shapes and sizes, under varying lighting conditions. This work includes an automated capability for generating labeled training data and iterative testing of the neural network-based crater detector. Preliminary results are included to quantify the detector’s accuracy compared to a known crater catalog, given a set of real lunar images from the Lunar Reconnaissance Orbiter.
Sofia G Catalan↗
Characterization of Precipitation-Induced Radon Progeny Deposition Events Using a City-Scale Sensor Network
Networks of radiation detectors provide a platform for real-time radioactive source detection and identification in urban environments. Detection algorithms in these systems must adapt to naturally-occurring changes in background, which requires well-characterized relationships between precipitation events and their corresponding radiological signature. Here, we present a quantitative and qualitative description of rain-induced radon progeny deposition events occurring in Chicago from September 2023 to February 2024. We measure ambient gamma radiation levels, precipitation rate, temperature, pressure, and relative humidity in a network of sensor nodes. For each identified precipitation period, we decompose spectra into static- and radon-associated components as defined by a non-negative matrix factorization (NMF) algorithm. We find a consistent power-law relationship between a precipitation-dependent peak of the radon progeny proxy (RPP) and the peak strength of the radon-associated NMF component for most precipitation events. We conduct a case study of a rainfall period with abnormally high levels of implied radon progeny concentration and describe its temporal and spatial evolution. We hypothesize that this phenomenon is due to the air mass path that intersects a uranium-rich region of Wyoming. Finally, we cluster precipitation events into three distinct categories. One category roughly corresponds to events with deep low-pressure systems and high relative radon concentration, while another is characteristic of light stratiform rain with slightly higher temperatures and intermediate relative radon concentration. The third category appears to contain weak-gradient or lake breeze convection showers with intermittent precipitation and low relative radon concentration. These findings suggest that radiological anomaly detection could be improved by training unique background models corresponding to each category of meteorological event.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Analysis-Specific Fast Simulation at the LHC with Deep Learning
Abstract We present a fast-simulation application based on a deep neural network, designed to create large analysis-specific datasets. Taking as an example the generation of W + jet events produced in $$\sqrt{s}=$$ s = 13 TeV proton–proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. The adoption of this approach would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow. This strategy could help the high energy physics community to face the computing challenges of the future High-Luminosity LHC.
Chen, C.↗
Swift Follow-Up Observations of Candidate Gravitational-Wave Transient Events
We present the first multi-wavelength follow-up observations of two candidate gravitational-wave (GW) transient events recorded by LIGO and Virgo in their 2009-2010 science run. The events were selected with low latency by the network of GW detectors (within less than 10 minutes) and their candidate sky locations were observed by the Swift observatory (within 12 hr). Image transient detection was used to analyze the collected electromagnetic data, which were found to be consistent with background. Off-line analysis of the GW data alone has also established that the selected GW events show no evidence of an astrophysical origin; one of them is consistent with background and the other one was a test, part of a "blind injection challenge." With this work we demonstrate the feasibility of rapid follow-ups of GW transients and establish the sensitivity improvement joint electromagnetic and GW observations could bring. This is a first step toward an electromagnetic follow-up program in the regime of routine detections with the advanced GW instruments expected within this decade. In that regime, multi-wavelength observations will play a significant role in completing the astrophysical identification of GW sources. We present the methods and results from this first combined analysis and discuss its implications in terms of sensitivity for the present and future instruments.
Evans, P. A.↗