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DOE OSTI2021

Enhanced Cross-Correlation Inter-Detector Timing

Inter-detector timing is one of the key steps for compositing signals obtained by multiple detectors. Conventionally this is done via inversing time gamma functions of individual detectors and then matching their times at the same gamma level. The applicability of the gamma inverse approach is often restricted to the monotonic gamma functions. This presentation presents a novel approach for inter-detector timing. This new approach introduced an enhanced cross-correlation of gamma values between two time intervals of two detectors. If this enhanced cross-correlation is close to 1, then both intervals should cover the same gamma segment of the signal. Consequently the time shift between the two detector can be derived from the time shift of the these two time intervals. This work will be presented at the 4th Annual Reaction History Analysis Workshop in North Las Vegas.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays

Abstract We present an interpretable implementation of the autoencoding algorithm, used as an anomaly detector, built with a forest of deep decision trees on FPGA, field programmable gate arrays. Scenarios at the Large Hadron Collider at CERN are considered, for which the autoencoder is trained using known physical processes of the Standard Model. The design is then deployed in real-time trigger systems for anomaly detection of unknown physical processes, such as the detection of rare exotic decays of the Higgs boson. The inference is made with a latency value of 30 ns at percent-level resource usage using the Xilinx Virtex UltraScale+ VU9P FPGA. Our method offers anomaly detection at low latency values for edge AI users with resource constraints.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Searches for direct slepton production in the compressed-mass corridor in $\sqrt{\textrm{s}}$ = 13 TeV pp collisions with the ATLAS detector

This paper presents searches for the direct pair production of charged light-flavour sleptons, each decaying into a stable neutralino and an associated Standard Model lepton. The analyses focus on the challenging ``corridor'' region, where the mass difference, $Δm$, between the slepton ($\tilde{e}$ or $\tildeμ$) and the lightest neutralino ($\tildeχ^{0}_{1}$) is less or similar to the mass of the $W$ boson, $m(W)$, with the aim to close a persistent gap in sensitivity to models with $Δm \lesssim m(W)$. Events are required to contain a high-energy jet, significant missing transverse momentum, and two same-flavour opposite-sign leptons ($e$ or $μ$). The analysis uses $pp$ collision data at $\sqrt{s} = 13$ TeV recorded by the ATLAS detector, corresponding to an integrated luminosity of 140 fb$^{-1}$. Several kinematic selections are applied, including a set of boosted decision trees. These are each optimised for different $Δm$ to provide expected sensitivity for the first time across the full $Δm$ corridor. The results are generally consistent with the Standard Model, with the most significant deviations observed with a local significance of 2.0 $σ$ in the selectron search, and 2.4 $σ$ in the smuon search. While these deviations weaken the observed exclusion reach in some parts of the signal parameter space, the previously present sensitivity gap to this corridor is largely reduced. Constraints at the 95% confidence level are set on simplified models of selectron and smuon pair production, where selectrons (smuons) with masses up to 300 (350) GeV can be excluded for $Δm$ between 2 GeV and 100 GeV.

hadron-hadron scattering↗

DOE OSTI2023

Charged particle reconstruction in CLAS12 using Machine Learning

In this work, we present studies of track parameter reconstruction from raw information in CLAS12 detector's Drift Chambers, using Machine Learning (ML). We study the resolution of tracks reconstructed with different types of ML models/algorithms, including Multi-Layer Perceptron (MLP), Extremely Randomized Trees (ERT) and Gradient Boosting Trees (GBT) using simulated data. We find that the resulting ML model is capable of reconstructing track parameters (particle momentum, and polar and azimuthal angles) with accuracy similar to Hit Based (HB) tracking code, but $150$ times faster. Moreover, physics reactions can be identified using the particles reconstructed by the neural network in real-time (with a rate of about $34~kHz$) during experimental data collection. The developed model can be used in numerous applications, such as triggering specific physics reactions in real-time, detector performance monitoring, and real-time detector calibration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Baryon Number Violation Search

Understanding the fundamental forces and symmetries of nature has long been a central goal of particle physics. While the Standard Model (SM) provides a successful framework, it does not guarantee the conservation of baryon number B or lepton number L, thus motivating searches for their violation. Proton decay, a fundamental process violating B, has been at the forefront of experimental searches for decades.The discovery of the weak neutral current in 1973 unified the electromagnetic and weak forces and inspired the creation of Grand Unified Theories (GUTs) that also unify the strong force. In 1974, the first-ever GUT, proposed by Georgi and Glashow, naturally predicted proton decay. This prediction led to an experimental push to validate these theories, and a large underground detector boom was born. Initially designed for proton decay searches, these detectors later proved invaluable to neutrino physics.Although no evidence for proton decay has yet been observed, next-generation large detectors, such as the Deep Underground Neutrino Experiment (DUNE), offer the opportunity to improve on current experimental limits. Utilizing its Liquid Argon Time Projection Chamber (LArTPC) technology, DUNE is positioned to probe rare processes such as proton decay with increased sensitivity.This thesis presents a sensitivity study for the dominant proton decay mode predicted by Supersymmetric GUTs, p → K+ν, utilizing machine learning approaches. Two methods are explored in this thesis: a Boosted Decision Tree (BDT) analysis and a Graphical Neural Network (GNN) analysis with NuGraph. A lifetime limit of 5.36 ± 0.69 × 1033 years for 400 kt-yrs is found using the BDT, while the GNN achieves a lifetime limit of 6.19±1.26×1033 years for 400-kt-yrs. The NuGraph result offers better sensitivity compared to the current limit set by Super-Kamiokande of 5.90 × 1033 while the BDT result offers a slightly lower sensitivity.Additionally, this thesis discusses cross-section work, a first-ever foray into proton decay and atmospheric neutrinos in a vertical drift (VD) DUNE detector, and extensive hardware contributions to the DUNE Far Detector (FD) 1 Module-0, ProtoDUNE-2, which serves as a testbed for the final detector design and installation.

Stokes, Tyler D. [Louisiana State U.] (ORCID:00000↗

DOE OSTI2024

Photon classification with Gradient Boosted Trees at CLAS12

Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, π + π 0 and π - π 0 , were analyzed, requiring the reconstruction of diphoton pairs. Here, in this analysis, we addressed the problem of false neutral particles being reconstructed by CLAS12's event builder, polluting the otherwise physical combinatorial background underneath the π 0 peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background π 0 's. We show that the nearest-neighbor features learned by the model lead to a substantial increase in signal vs. background discrimination compared to previous CLAS12 π^0 analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗

DOE OSTI2022

Event driven readout architecture with non-priority arbitration for radiation detectors

A novel event driven readout architecture, EDWARD (Event Driven with Access and 8 Reset Decoder) architecture, for highly granular pixel detectors is presented. It incorporates, inter alia, an asynchronous arbitration tree based on Seitz' arbiters, removing the need for an imposed prioritization scheme. It also provides protection against glitches during readout. The system allows not only reading pixel activities, but also retrieving additional data, both analog and digital, from the pixels. A novel in-channel logic allows the entire readout process to be split into consecutive phases for additional flexibility. All operations are controlled by only one edge of the clock signal, seen as an acknowledge token, so there is no dead time between readouts.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Searching for Neutrino Tridents in the NOvA Near Detector

This dissertation presents a search for neutrino trident production in the NOvA near detector through the coherent ``dimuon" channel: $\nu_\mu +\hspace{1pt}\text{X} \rightarrow \nu_\mu + \mu^- + \mu^+ +\hspace{1pt}\text{X}$. Trident production is a rare, purely electroweak process with sensitivity to physics beyond the Standard Model. The theoretical background, motivation for studying the process, and previous experimental measurements are reviewed. The analysis uses data collected by the NOvA near detector (ND) from Fermilab's Neutrinos at the Main Injector (NuMI) beam between November 2014 and February 2024, corresponding to an exposure of $25.5\times 10^{20}$ protons on target. The ND is a segmented tracking calorimeter located 800~m from the beam target, receiving neutrinos with a mean energy of 2~GeV. A multi-pass background reduction strategy is implemented, including the development of a novel dimuon-specific tracking technique. Trident candidates are identified using a boost ed decision tree classifier trained on simulated signal and background events. Limited background Monte Carlo statistics necessitate the use of functional fits to sideband data, which are extrapolated to estimate backgrounds in the signal region. The unblinded data contain 9 trident-like events, with an estimated background of 5.66 $\pm$ 5.15 events. This yields a best fit estimate of 3.34 tridents compared to the Standard Model prediction of 4.66. A profiled Feldman-Cousins method is used to determine a 90\% confidence interval of [0,9.1] on the number of signal events, corresponding to an upper limit of 1.95$\times$ the Standard Model prediction. This result represents the lowest energy search for trident events to date, and the first experimental contribution to the process in 27 years.

Bowles, Reed Scott [Indiana U.]↗

DOE OSTI2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

42 ENGINEERING↗

DOE OSTI2025

Multi-channel, multi-template event reconstruction for SuperCDMS data using machine learning

SuperCDMS SNOLAB uses kilogram-scale germanium and silicon detectors to search for dark matter. Each detector has Transition Edge Sensors (TESs) patterned on the top and bottom faces of a large crystal substrate, with the TESs electrically grouped into six phonon readout channels per face. Noise correlations are expected among a detector's readout channels, in part because the channels and their readout electronics are located in close proximity to one another. Moreover, owing to the large size of the detectors, energy deposits can produce vastly different phonon propagation patterns depending on their location in the substrate, resulting in a strong position dependence in the readout-channel pulse shapes. Both of these effects can degrade the energy resolution and consequently diminish the dark matter search sensitivity of the experiment if not accounted for properly. We present a new algorithm for pulse reconstruction, mathematically formulated to take into account correlated noise and pulse shape variations. This new algorithm fits N readout channels with a superposition of M pulse templates simultaneously - hence termed the N$\times$M filter. We describe a method to derive the pulse templates using principal component analysis (PCA) and to extract energy and position information using a gradient boosted decision tree (GBDT). We show that these new N$\times$M and GBDT analysis tools can reduce the impact from correlated noise sources while improving the reconstructed energy resolution for simulated mono-energetic events by more than a factor of three and for the 71Ge K-shell electron-capture peak recoils measured in a previous version of SuperCDMS called CDMSlite to $<$ 50 eV from the previously published value of $\sim$100 eV. These results lay the groundwork for position reconstruction in SuperCDMS with the N$\times$M outputs.

Albakry, M. F. [British Columbia U.; TRIUMF]↗

DOE OSTI2023

A Search for Heavy Neutral Leptons Decaying to Electron-Positron Pairs and Neutral Pions in the MicroBooNE Detector

This thesis presents a search for heavy neutral leptons (HNLs) decaying inside the MicroBooNE detector. The MicroBooNE detector is a liquid-argon time projection chamber located at Fermilab that receives flux from two neutrino beams; the booster neutrino beam (BNB) and the neutrinos at the main injector (NuMI) beam. The search uses data taken when the NuMI beam was on, with a total exposure of $7.01\times10^{20}$ protons on target. HNLs would be produced in kaon decays, and the source of HNLs targeted in this search is kaons decaying at rest in the NuMI hadron absorber. The kinematics of the HNLs produced in this way allow their decays to be distinguished from the majority of beam-neutrino interactions and cosmic-ray activity that constitute the background. Two HNL decay channels are studied, $\nu$\ee and \vpi. Boosted decision trees (BDTs) are trained to separate the simulated HNL decays from the expected background events. The BDT score distributions are used to calculate upper limits on the mixing angle \umusq in the mass range $10\le m_{\rm HNL}\le 245$~MeV. This is the first direct search for HNL decays to the \vpi channel, and the limits provide the most stringent constraints on \umusq in the mass range $35<\mhnl<175$~MeV.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

A Pattern Dictionary Method for Anomaly Detection

In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel–Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

ETROC1: the first full chain precision timing prototype ASIC for CMS MTD endcap timing layer upgrade

We present the design and characterization of the first fullchain precision timing prototype ASIC, named ETL Readout Chipversion 1 (ETROC1) for the CMS MTD endcap timing layer (ETL)upgrade. The ETL utilizes Low Gain Avalanche Diode (LGAD) sensors todetect charged particles, with the goal to achieve a time resolutionof 40–50 ps per hit, and 30–40 ps per track with hits from twodetector layers. The ETROC1 is composed of a 5 × 5 pixelarray and peripheral circuits. The pixel array includes a4 × 4 active pixel array with an H-tree shaped networkdelivering clock and charge injection signals. Each active pixel iscomposed of various components, including a bump pad, a chargeinjection circuit, a pre-amplifier, a discriminator, adigital-to-analog converter, and a time-to-digital converter. Thesecomponents play essential roles as the front-end link in processingLGAD signals and measuring timing-related information. Theperipheral circuits provide clock signals and readoutfunctionalities. The size of the ETROC1 chip is7 mm× 9 mm. ETROC1 has been fabricated in a 65 nmCMOS process, and extensively tested under stimuli of chargeinjection, infrared laser, and proton beam. The time resolution ofbump-bonded ETROC1 + LGAD chipsets reaches 42–46 ps per hit in thebeam test.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Search for resonant production of strongly coupled dark matter in proton-proton collisions at 13 TeV

The first collider search for dark matter arising from a strongly coupled hidden sector is presented and uses a data sample corresponding to 138 fb$^{-1}$, collected with the CMS detector at the CERN LHC, at $ \sqrt{s} $ = 13 TeV. The hidden sector is hypothesized to couple to the standard model (SM) via a heavy leptophobic Z' mediator produced as a resonance in proton-proton collisions. The mediator decay results in two “semivisible” jets, containing both visible matter and invisible dark matter. The final state therefore includes moderate missing energy aligned with one of the jets, a signature ignored by most dark matter searches. No structure in the dijet transverse mass spectra compatible with the signal is observed. Assuming the Z' boson has a universal coupling of 0.25 to the SM quarks, an inclusive search, relevant to any model that exhibits this kinematic behavior, excludes mediator masses of 1.5–4.0 TeV at 95% confidence level, depending on the other signal model parameters. To enhance the sensitivity of the search for this particular class of hidden sector models, a boosted decision tree (BDT) is trained using jet substructure variables to distinguish between semivisible jets and SM jets from background processes. When the BDT is employed to identify each jet in the dijet system as semivisible, the mediator mass exclusion increases to 5.1 TeV, for wider ranges of the other signal model parameters. These limits exclude a wide range of strongly coupled hidden sector models for the first time.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Searching for Clues for a Matter Dominated Universe in Liquid Argon Time Projection Chambers

Liquid Argon Time Projection Chambers (LArTPCs) represent one of the most widely utilized neutrino detection techniques in neutrino experiments, for instance, in the Short Baseline Neutrino (SBN) program and the future large-scale LArTPC: Deep Underground Neutrino Experiment (DUNE). The high-end technique, facilitating excellent spatial and calorimetric reconstruction resolution, also enables testing exotic Beyond Standard Model (BSM) theories, such as baryon number violation (BNV) processes (e.g., proton-decay, neutron-antineutron oscillation). At the same time, Machine Learning (ML) techniques have demonstrated their ubiquitous use in recent decades; ML techniques have also become some of the most powerful tools in high-energy physics (HEP) analyses. Furthermore, the development of algorithms to cater to the needs of problems in HEP (i.e., triggering, reconstruction, improving sensitivity, etc.) has also become an active area of research. By developing a combined approach using Convolutional Neural Network (CNN) and Boosted Decision Tree (BDT) techniques, the sensitivity of neutron-antineutron oscillation in DUNE is evaluated for a projected exposure of 400kton·years. Additionally, to meet the triggering requirement to select such rare events in DUNE, such a search is only supported with highly efficient self-triggering algorithms. An ML-based self-triggering scheme for large-scale LArTPCs, such as DUNE, is also developed with the intention of implementation on field-programmable gate arrays (FPGAs). The ML-based approach for searching for neutron-antineutron oscillation can be demonstrated and validated on the current LArTPC MicroBooNE. The analysis in MicroBooNE represents the first-ever search for neutron-antineutron oscillation in a LArTPC. DUNE's projected 90% C.L. sensitivity to the neutron antineutron oscillation lifetime is 6.45×10³² years, assuming 1.327×10³⁵ neutron·years, equivalent to 10 years of DUNE far detector exposure (400kton·years). For MicroBooNE, assuming 372 seconds of exposure (equivalent to 3.13×10³⁶ neutron·years), the 90% C.L. lifetime sensitivity is found at 3.07×10²⁵ yrs, after accounting for Monte-Carlo statistical uncertainty and systematic uncertainty from detector effects.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗