Investigating Improvements to the NOvA Event Selection Efficiency for Events in the Mass-hierarchy-sensitive Energy Range
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Sullivan, Cullen↗
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ThickBrick: optimal event selection and categorization in high energy physics. Part I. Signal discovery
We provide a prescription called ThickBrick to train optimal machine-learning-based event selectors and categorizers that maximize the statistical significance of a potential signal excess in high energy physics (HEP) experiments, as quantified by any of six different performance measures. For analyses where the signal search is performed in the distribution of some event variables, our prescription ensures that only the information complementary to those event variables is used in event selection and categorization. This eliminates a major misalignment with the physics goals of the analysis (maximizing the significance of an excess) that exists in the training of typical ML-based event selectors and categorizers. In addition, this decorrelation of event selectors from the relevant event variables prevents the background distribution from becoming peaked in the signal region as a result of event selection, thereby ameliorating the challenges imposed on signal searches by systematic uncertainties. Our event selectors (categorizers) use the output of machine-learning-based classifiers as input and apply optimal selection cutoffs (categorization thresholds) that are functions of the event variables being analyzed, as opposed to flat cutoffs (thresholds). These optimal cutoffs and thresholds are learned iteratively, using a novel approach with connections to Lloyd’s k-means clustering algorithm. We provide a public, Python implementation of our prescription, also called ThickBrick, along with usage examples.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Event Selections in the NOvA 2024 Analysis
The NuMI Off-axis $\nu_e$ Experiment (NOvA) is a long-baseline neutrino oscillation experiment that studies a neutrino beam produced by the Neutrinos at the Main Injector (NuMI) facility at Fermilab to constrain the PNFS mixing angles, the neutrino mass hierarchy, and the CP-violating phase $\delta_{CP}$. These parameters are extracted by comparing the spectra of muon neutrinos and electron neutrinos measured at the near and far detectors, using an extrapolation of the Near Detector spectra. Accurate event selection is critical to this process, as only interactions with well-reconstructed energies and interaction types should be included in the oscillation analysis to reduce systematic uncertainties. In this work, we describe the selection criteria used for muon and electron neutrinos in the Near and Far Detector. We demonstrate the effectiveness of the criteria by examining the efficiency and purity of the event selections as well as their impact on key variables used in the oscillation analysis.
Chen, Hanyi [Indiana U.]↗
Event Selection at the DUNE Near Detector
The Deep Underground Neutrino Experiment (DUNE) is a next generation long-baseline experiment designed with a primary goal of providing definitive measurements of the neutrino mass ordering and the Charge Parity (CP)-violating phase. A key component in reaching these goals is the DUNE Near Detector (ND) complex, placed 574 m from the beam target, and tasked with characterising the neutrino beam and constraining systematic uncertainties before oscillation effects occur. We will present primary findings from the development of an event selection for $\nu_\mu$ CC interactions within the ND liquid argon time projection chamber (ND-LAr). These findings will include a discussion of the importance of defining metrics, such as efficiency and purity, such that we can meet the required sensitivity for DUNE's physics measurements.
Sabater Andres, Eva [Sussex U.] (ORCID:00090001748↗
Light dark matter constraints from SuperCDMS HVeV detectors operated underground with an anticoincidence event selection
This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of 7.63 g−days is used to set upper limits on the dark-matter-electron scattering cross section for dark matter masses between 0.5 and 1000 MeV/𝑐 2 , as well as upper limits on dark photon kinetic mixing and axionlike particle axioelectric coupling for masses between 1.2 and 23.3 eV/𝑐 2 . Compared to an earlier HVeV search, sensitivity was improved as a result of an increased overburden of 225 meters of water equivalent, an anticoincidence event selection, and better pile-up rejection. In the case of dark-matter-electron scattering via a heavy mediator, an improvement by up to a factor of 25 in cross section sensitivity was achieved.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Neutrino event selection in the MicroBooNE liquid argon time projection chamber using Wire-Cell 3D imaging, clustering, and charge-light matching
An accurate and efficient event reconstruction is required to realize the full scientific capability of liquid argon time projection chambers (LArTPCs). The current and future neutrino experiments that rely on massive LArTPCs create a need for new ideas and reconstruction approaches. Wire-Cell, proposed in recent years, is a novel tomographic event reconstruction method for LArTPCs. The Wire-Cell 3D imaging approach capitalizes on charge, sparsity, time, and geometry information to reconstruct a topology-agnostic 3D image of the ionization electrons prior to pattern recognition. A second novel method, the many-to-many charge-light matching, then pairs the TPC charge activity to the detected scintillation light signal, thus enabling a powerful rejection of cosmic-ray muons in the MicroBooNE detector. A robust processing of the scintillation light signal and an appropriate clustering of the reconstructed 3D image are fundamental to this technique. In this paper, we describe the principles and algorithms of these techniques and their successful application in the MicroBooNE experiment. A quantitative evaluation of the performance of these techniques is presented. Using these techniques, a 95% efficient pre-selection of neutrino charged-current events is achieved with a 30-fold reduction of non-beam-coincident cosmic-ray muons, and about 80% of the selected neutrino charged-current events are reconstructed with at least 70% completeness and 80% purity.
3D imaging↗
Event Selection in the MicroBooNE Deep Learning Based Low Energy Excess Analysis Using Two-Body Scattering Criteria
The uniquely detailed neutrino event information from liquid argon time projection chambers allows reconstruction of a set of kinematic quantities that over-constrain the expectations for charged current quasielastic scattering (CCQE). MicroBooNE makes use of the CCQE consistency requirements in a deep-learning-based search for the MiniBooNE low energy excess analysis. This requirement rejects backgrounds as well as events with poorly reconstructed neutrino energy due to final state interactions of the outgoing proton. The results presented here demonstrate the quality of the selection of $\nu_e$ and $\nu_µ$ events. We show excellent agreement between the data and the simulation across many data sets. This positions us to be ready to unblind the MicroBooNE low energy excess analysis in the very near future.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Machine learning on FPGA for event selection
Real-time data processing is a frontier field in experimental particle physics. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented Machine Learning (ML) elements for real-time data processing with a triggered readout system that runs most of the ML algorithms on a computer farm. The work described in this article aims to test the ML-FPGA algorithms for streaming data acquisition. Herein, there are many experiments working in this area and they have a lot in common, but there are many specific solutions for detector and accelerator parameters that are worth exploring further. This report describes the purpose of the work and progress in evaluating the ML-FPGA application.
47 OTHER INSTRUMENTATION↗
Risk-Informed Approach for Event Selection and Component Classification
Develop a report summarizing a structured approach for the incorporation of risk insights when making facility safety assessments and regulatory decisions is proposed to supplement the deterministic approach, with the following expected benefits: Increased confidence that key safety issues have been fully identified and addressed Better understanding of safety margins Facilitates more structured dialogue among international regulators Reduced reliance on “expert judgement” through a rigorous, pre-defined process with more predictable outcomes Proposed outcome is development of a report summarizing the structure and key considerations for applying this approach that: Is inclusive of advanced reactor technologies Provides for flexible implementation recognizing unique and varying sovereign regulatory structures Builds upon existing/current GIF safety approaches (e.g., GIF Basic Safety Approach, Integrated Safety Assessment Methodology)
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
A Search for Sterile Neutrino Oscillations in the MicroBooNE Experiment Using Deep Learning Reconstruction and Event Selection
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46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Performance of the missing transverse momentum triggers for the ATLAS detector during Run-2 data taking
The factor of four increase in the LHC luminosity, from 0.5 × 10 34 cm -2 s -1 to 2.0 × 10 34 cm -2 s -1 , and the corresponding increase in pile-up collisions during the 2015–2018 data-taking period, presented a challenge for the ATLAS trigger, particularly for those algorithms that select events with missing transverse momentum. The output data rate at fixed threshold typically increases exponentially with the number of pile-up collisions, so the legacy algorithms from previous LHC data-taking periods had to be tuned and new approaches developed to maintain the high trigger efficiency achieved in earlier operations. A study of the trigger performance and comparisons with simulations show that these changes resulted in event selection efficiencies of > 98% for this period, meeting and in some cases exceeding the performance of similar triggers in earlier run periods, while at the same time keeping the necessary bandwidth within acceptable limits.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Search for supersymmetry in final states with missing transverse momentum and three or more b-jets in 139 fb$^{-1}$ of proton–proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector
A search for supersymmetry involving the pair production of gluinos decaying via off-shell third-generation squarks into the lightest neutralino ($\tilde {χ}_{1}^{0}$) is reported. It exploits LHC proton–proton collision data at a centre-of mass energy $\sqrt{s} = 13$ TeV with an integrated luminosity of 139 fb -1 collected with the ATLAS detector from 2015 to 2018. The search uses events containing large missing transverse momentum, up to one electron or muon, and several energetic jets, at least three of which must be identified as containing b-hadrons. Both a simple kinematic event selection and an event selection based upon a deep neural network are used. No significant excess above the predicted background is found. In simplified models involving the pair production of gluinos that decay via off-shell top (bottom) squarks, gluino masses less than 2.44 TeV (2.35 TeV) are excluded at 95% CL for a massless $\tilde {χ}_{1}^{0}$. Limits are also set on the gluino mass in models with variable branching ratios for gluino decays to $b$$\overline{b}$$\tilde {χ}_{1}^{0}$, $t$$\overline{t}$$\tilde {χ}_{1}^{0}$ and $t$$\overline{b}$$\tilde {χ}_{1}^{-}$ / $t$$\overline{b}$$\tilde {χ}_{1}^{+}$.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Improving the NOvA 3-Flavour Neutrino Oscillation Analysis
NOvA is a long-baseline neutrino experiment studying neutrino oscillations, a quantum mechanical interference phenomena where the observed neutrino flavour differs from that measured earlier, stemming from neutrino mass and flavour states mixing. NOvA consists of two functionally identical tracking calorimeter detectors deployed in the Fermilab NuMI beam. Both detectors are placed 14.6 mrad off the beam axis to achieve a narrow energy peak at 1.8 GeV at an oscillation maximum. The NOvA 3-flavour oscillation analysis measures the neutrino oscillation parameters sin2θ23 and ∆m232 as well as sets limit to δCP , the octant of θ23 and the sign of ∆m32. The event selection for 3-flavour neutrino oscillation analysis ensures the maximum quantity of signal is made available and a minimal amount of background is present. The current disappearance analysis selection has an efficiency of 80% for selecting νμ CC events, and with some improvements additional events could be recovered into the analysis to improve the sensitivity to the aforementioned oscillation parameters. In an effort to recover the currently rejected signal event to the analysis, these events were trained in a classification neural network. The aim of the network was to divide the data into signal (νμ CC events) and background (NC and νe CC events). The highest performing network gave an additional figure of merit gain of 2.34 increasing the sensitivity by 4.3% in effective POT equal to 33 days of additional data taking. This was compared to changing the current particle identification event selection cuts, the best result out of the tested cut combinations gave an additional FOM of 3.36 equal to 3.7% increase in effective POT equivalent to 28 days of data taking.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Manipulating strong electromagnetic fields with the average transverse momentum of relativistic nuclear collisions
Abstract We show that an event-shape engineering based on the mean transverse momentum of charged hadrons, $$[p_t]$$ [ p t ] , provides an optimal handle on the strength of the magnetic field created in central heavy-ion collisions at high energy. This is established through quantitative evaluations of the correlation existing between the event-by-event magnetic field produced by the spectator protons in 5.02 TeV Pb + Pb collisions and the event-by-event $$[p_t]$$ [ p t ] at a given collision centrality. We argue that the event selection based on $$[p_t]$$ [ p t ] provides a better handle on the magnetic field than the more traditional selection based on the event ellipticities. Advantages brought by this new method for the experimental search of the chiral magnetic effect are discussed.
Giacalone, Giuliano (ORCID:0000000287761034)↗
Regional Earthquake Ground Motion Simulations for Southern California With EQSIM: Insights From the 2008 Chino Hills, 2024 Highland Park, and 2021 Carson Earthquakes
This study presents physics-based, 3D simulations using the EQSIM framework for several earthquakes in the Los Angeles region. The primary objective was to assess the ability of deterministic physics-based ground motion simulations to reproduce the observed motions from historical events. The selected events included the mathematical equation M w 5.4 2008 Chino Hills, the mathematical equation M w 4.4 2024 Highland Park, and the mathematical equation M w 4.3 2021 Carson events. The simulated motions were evaluated by comparing the recorded and simulated seismograms, as well as the Fourier amplitude spectra, across multiple seismic stations. The SCEC 3D velocity model, CVM-S4.26.M01, was used to represent the regional geology, and ground motion simulations were carried out with a resolution of up to 5 Hz. The results indicate that the simulated motions captured the recorded motions up to approximately 4 Hz. While careful iterations regarding source parameters and corner frequencies were required, and, for the case of the Highland Park event, some of the near-source stations had relatively low accuracy, the present study established a positive step toward the utilization of physics-based simulations in practical applications. The computational efficiencies exhibited by EQSIM, especially on GPU clusters, further supported this assertion, as wall-clock times of simulations involving more than 10 billion grid points were as low as mathematical equation minutes. This permits ensemble simulations for a considered scenario event so that modeling uncertainties (e.g., source and geology) can be bracketed.
EQSIM↗
Training data selection for event classification in a highly variable environment
A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.
Iyer, A↗
Search for the chiral magnetic effect through beam energy dependence of charge separation using event shape selection
High-energy, heavy-ion collisions can create local domains of chirality-imbalanced quarks, reflecting the topological features of quantum chromodynamics. The chiral magnetic effect (CME) predicts an electric charge separation of quarks in such topological domains along the magnetic field ($\vec{B}$) generated by the passing of two high-Z nuclei. Here, we use a correlation observable Δ𝛾$^{112}$ between charged meson pairs to detect the CME-induced charge separation and a novel event shape selection (ESS) method to mitigate the background effects related to elliptic flow (𝑣 2 ). The ESS method classifies events based on the emission pattern of final-state particles and determines Δ𝛾$^{112}_{ESS}$ from the zero-flow limit. We reconstruct the $\vec{B}$ field direction from the spectator nucleons, which minimizes backgrounds unrelated to the collective motion of the system. In this work, we report the measurements of Δ𝛾$^{112}$ and a background indicator Δ𝛾$^{132}$ in Au+Au collisions from the Brookhaven National Laboratory Relativistic Heavy Ion Collider (RHIC) Beam Energy Scan phase II and at the top RHIC energy. After background suppression, Δ𝛾$^{132}_{ESS}$ aligns with zero, and Δ𝛾$^{112}_{ESS}$ is reduced to no more than 20% of Δ𝛾$^{112}$. We observe a finite residual charge separation with 2.5𝜎, 3𝜎, and 3.2𝜎 significance in the 20–50% centrality range of Au + Au collisions at 11.5, 14.6, and 19.6 GeV. The results at 17.3 and 27 GeV also show positive values but with a lower significance of 1.3𝜎 and 1.1𝜎, respectively. The corresponding Δ𝛾$^{112}_{ESS}$ values at 7.7, 9.2, and 200 GeV are consistent with zero within uncertainties.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
PopSyCLE: A New Population Synthesis Code for Compact Object Microlensing Events
We present a new Milky Way microlensing simulation code, dubbed PopSyCLE (Population Synthesis for Compact object Lensing Events). PopSyCLE is the first resolved microlensing simulation to include a compact object distribution derived from numerical supernovae explosion models and both astrometric and photometric microlensing effects. We demonstrate the capabilities of PopSyCLE by investigating the optimal way to find black holes (BHs) with microlensing. Candidate BHs have typically been selected from wide-field photometric microlensing surveys, such as OGLE, by selecting events with long Einstein crossing times (tE > 120 days). These events can be selected at closest approach and monitored astrometrically in order to constrain the mass of each lens; PopSyCLE predicts a BH detection rate of ~40% for such a program. We find that the detection rate can be enhanced to ~ 85% by selecting events with both tE > 120 days and a microlensing parallax of πE < 0.08. Unfortunately, such a selection criterion cannot be applied during the event as πE requires both pre- and post-peak photometry. However, historical microlensing events from photometric surveys can be revisited using this new selection criteria in order to statistically constrain the abundance of BHs in the Milky Way. The future WFIRST microlensing survey provides both precise photometry and astrometry and will yield individual masses of O(100 - 1000) black holes, which is at least an order of magnitude more than is possible with individual candidate follow-up with current facilities. The resulting sample of BH masses from WFIRST will begin to constrain the shape of the black hole present-day mass function, BH multiplicity, and BH kick velocity distributions
79 ASTRONOMY AND ASTROPHYSICS↗