Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “cluster finding”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

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↗
DOE OSTI2022

Muon track reconstruction in a segmented bolometric array using multi-objective optimization

Recent advances in segmented solid-state detector arrays for rare-event searches have allowed the technology to approach the ton-scale in detector mass and the scale of meters in size. Often focused around searches for neutrinoless double-beta decay or direct dark matter detection, such experiments also have the capability to search for exotic particles that leave track-like signatures across their volume. However, the segmented nature of such detector arrays often sets the spatial resolution and makes the problem of reconstructing track-like paths non-trivial. Here, in this paper, we present an algorithm that improves reconstruction of track-like events in segmented detectors using multi-objective optimization — a computational technique that optimizes more than one cost function at a time without specifying a quantitative weighting between them. Such a technique allows the reconstruction of tracks through a detector and the determination of path-lengths through individual elements. When combined with the reconstructed energy depositions in each element this allows for a calculation of the stopping power of track-like particles and opens the door to searches for particles with abnormal stopping power like monopoles or lightly-ionizing particles (LIPs). Results are presented which evaluate the precision of the reconstruction tools as they currently stand against Monte Carlo generated data. The algorithm is presented in the context of the CUORE experiment, but has applications to other segmented calorimeter detectors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Heterogeneous Graph Neural Network for identifying hadronically decayed tau leptons at the High Luminosity LHC

Here, we present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algorithm is used. Instead, the algorithm represents jets as heterogeneous graphs with tracks and energy clusters as nodes and trains a Graph Neural Network to identify tau jets from other jets. Different attributed graph representations and different GNN architectures are explored. We propose to use differential track and energy cluster information as node features and a heterogeneous sequentially-biased encoding for the inputs to final graph-level classification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Electron and photon energy calibration with the ATLAS detector using LHC Run 2 data

This paper presents the electron and photon energy calibration obtained with the ATLAS detector using 140 fb -1 of LHC proton-proton collision data recorded at $\sqrt{s}$ = 13 TeV between 2015 and 2018. Methods for the measurement of electron and photon energies are outlined, along with the current knowledge of the passive material in front of the ATLAS electromagnetic calorimeter. The energy calibration steps are discussed in detail, with emphasis on the improvements introduced in this paper. The absolute energy scale is set using a large sample of Z-boson decays into electron-positron pairs, and its residual dependence on the electron energy is used for the first time to further constrain systematic uncertainties. The achieved calibration uncertainties are typically 0.05% for electrons from resonant Z-boson decays, 0.4% at E T ~ 10 GeV, and 0.3% at E T ~ 1 TeV; for photons at E T ~ 60 GeV, they are 0.2% on average. This is more than twice as precise as the previous calibration. The new energy calibration is validated using J/ψ → ee and radiative Z-boson decays.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Normalizing flows for domain adaptation when identifying Λ hyperon events

Here this study focuses on the application of a normalizing flow as a method of domain adaptation when classifying physics data. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a novel method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. After being transformed, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

A novel method to reconstruct the energy of hadronic showersin the CMS High Granularity Calorimeter (HGCAL) is presented. TheHGCAL is a sampling calorimeter with very fine transverse andlongitudinal granularity. The active media are silicon sensors andscintillator tiles readout by SiPMs and the absorbers are acombination of lead and Cu/CuW in the electromagnetic section, andsteel in the hadronic section. The shower reconstruction method isbased on graph neural networks and it makes use of a dynamicreduction network architecture. It is shown that the algorithm isable to capture and mitigate the main effects that normally hinderthe reconstruction of hadronic showers using classicalreconstruction methods, by compensating for fluctuations in themultiplicity, energy, and spatial distributions of the shower'sconstituents. The performance of the algorithm is evaluated usingtest beam data collected in 2018 prototype of the CMS HGCALaccompanied by a section of the CALICE AHCAL prototype. Thecapability of the method to mitigate the impact of energy leakagefrom the calorimeter is also demonstrated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Expected tracking performance of the ATLAS Inner Tracker at the High-Luminosity LHC

The high-luminosity phase of LHC operations (HL-LHC), will feature a large increase in simultaneous proton-proton interactions per bunch crossing up to 200, compared with a typical leveling target of 64 in Run 3. Such an increase will create a very challenging environment in which to perform charged particle trajectory reconstruction, a task crucial for the success of the ATLAS physics program, and will exceed the capabilities of the current ATLAS Inner Detector (ID). A new all-silicon Inner Tracker (ITk) will replace the current ID in time for the start of the HL-LHC. To ensure successful use of the ITk capabilities in Run 4 and beyond, the ATLAS tracking software has been successfully adapted to achieve state-of-the-art track reconstruction in challenging high-luminosity conditions with the ITk detector. This paper presents the expected tracking performance of the ATLAS ITk based on the latest available developments since the ITk technical design reports.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Particle hit clustering and identification using point set transformers in liquid argon time projection chambers

Liquid argon time projection chambers are often used in neutrino physics and dark-matter searches because of their high spatial resolution. The images generated by these detectors are extremely sparse, as the energy values detected by most of the detector are equal to 0, meaning that despite their high resolution, most of the detector is unused in a particular interaction. Instead of representing all of the empty detections, the interaction is usually stored as a sparse matrix, a list of detection locations paired with their energy values. Traditional machine learning methods that have been applied to particle reconstruction such as convolutional neural networks (CNNs), however, cannot operate over data stored in this way and therefore must have the matrix fully instantiated as a dense matrix. Operating on dense matrices requires a lot of memory and computation time, in contrast to directly operating on the sparse matrix. We propose a machine learning model using a point set neural network that operates over a sparse matrix, greatly improving both processing speed and accuracy over methods that instantiate the dense matrix, as well as over other methods that operate over sparse matrices. Compared to competing state-of-the-art methods, our method improves classification performance by 14%, segmentation performance by more than 22%, while taking 80% less time and using 66% less memory. Compared to state-of-the-art CNN methods, our method improves classification performance by more than 86%, segmentation performance by more than 71%, while reducing runtime by 91% and reducing memory usage by 61%.

calibration and fitting methods↗
DOE OSTI2025

Performance of heavy-flavour jet identification in Lorentz-boosted topologies in proton-proton collisions at √(s) = 13 TeV

Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to bb̅ or cc̅, have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at √(s) = 13 TeV during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.

Pattern recognition↗
DOE OSTI2025

Identification of tau leptons using a convolutional neural network with domain adaptation

A tau lepton identification algorithm,DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τ h ) from quark or gluon jets and electrons and muons that are misreconstructed as τ h candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τh candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30–50% in the probability for quark and gluon jets to be misidentified as τ h candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in theDeepTau algorithm and evaluates its performance in LHC proton-proton collision data at √(s) = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb -1 , respectively. Techniques to calibrate the performance of the τ h identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses.

Large detector-systems performance↗
DOE OSTI2026

Calibration plan for the SBC 10-kg liquid argon detector with 100 eV target threshold

The Scintillating Bubble Chamber (SBC) Collaboration is designing a new generation of low background, noble liquid bubble chamber experiments with sub-keV nuclear recoil threshold. These experiments combine the electronic recoil blindness of a bubble chamber with the energy resolution of noble liquid scintillation, and maintain electron recoil discrimination at higher degrees of superheat (lower nuclear recoil thresholds) than Freon-based bubble chambers. A 10-kg liquid argon bubble chamber has the potential to set world leading limits on the dark matter nucleon cross-section for 𝒪(GeV/c 2 ) masses, and to perform a high statistics coherent elastic neutrino nuclear scattering measurement with reactor neutrinos. This work presents a detailed calibration plan to measure the detector response of these experiments, combining photoneutron scattering with two new techniques to induce sub-keV nuclear recoils: nuclear Thomson scattering and thermal neutron capture.

Dark Matter detectors (WIMPs, axions, etc.)↗
DOE OSTI2022

Reaching for the Edge I: probing the outskirts of massive galaxies with HSC, DECaLS, SDSS, and Dragonfly

ABSTRACT The outer light (stellar haloes) of massive galaxies has recently emerged as a possible low scatter tracer of dark matter halo mass. To test the robustness of outer light measurements across different data sets, we compare the 1D azimuthally averaged surface brightness profiles of massive galaxies using four independent data sets: the Hyper Suprime-Cam survey (HSC), the Dark Energy Camera Legacy Survey (DECaLS), the Sloan Digital Sky Survey (SDSS), and the Dragonfly Wide Field Survey (Dragonfly). We test the sky subtraction and proposed corrections for HSC and DECaLS. For galaxies at z < 0.05, Dragonfly has the best control of systematics, reaching surface brightness levels of μr ≈ 30 mag arcsec−2. At 0.19 < z < 0.50, HSC can reliably recover individual surface brightness profiles to μr ≈ 28.5 mag arcsec−2 (R = 100–150 kpc in semimajor axis). In a statistical sense, DECaLS agrees with HSC to R > 200 kpc. DECaLS and HSC measurements of the stellar mass contained within 100 kpc agree within 0.05 dex. Finally, we use weak lensing to show that measurements of outer light with DECaLS at 0.19 < z < 0.50 show a similar promise as HSC as a low scatter proxy of halo mass. The tests and results from this paper represent an important step forward for accurate measurements of the outer light of massive galaxies and demonstrate that outer light measurements from DECam imaging will be a promising method for finding galaxy clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Constraints on S 8 from a full-scale and full-shape analysis of redshift-space clustering and galaxy–galaxy lensing in BOSS

ABSTRACT We present a novel simulation-based cosmological analysis of galaxy–galaxy lensing and galaxy redshift-space clustering. Compared to analysis methods based on perturbation theory, our simulation-based approach allows us to probe a much wider range of scales, $0.4 \, h^{-1} \, \mathrm{Mpc}$ to $63 \, h^{-1} \, \mathrm{Mpc}$, including highly non-linear scales, and marginalizes over astrophysical effects such as assembly bias. We apply this framework to data from the Baryon Oscillation Spectroscopic Survey LOWZ sample cross-correlated with state-of-the-art gravitational lensing catalogues from the Kilo Degree Survey and the Dark Energy Survey. We show that gravitational lensing and redshift-space clustering when analysed over a large range of scales place tight constraints on the growth-of-structure parameter $S_8 = \sigma _8 \sqrt{\Omega _{\rm m} / 0.3}$. Overall, we infer S8 = 0.792 ± 0.022 when analysing the combination of galaxy–galaxy lensing and projected galaxy clustering and S8 = 0.771 ± 0.027 for galaxy redshift-space clustering. These findings highlight the potential constraining power of full-scale studies over studies analysing only large scales and also showcase the benefits of analysing multiple large-scale structure surveys jointly. Our inferred values for S8 fall below the value inferred from the CMB, S8 = 0.834 ± 0.016. While this difference is not statistically significant by itself, our results mirror other findings in the literature whereby low-redshift large-scale structure probes infer lower values for S8 than the CMB, the so-called S8-tension.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Ab initio multishell valence-space Hamiltonians and the island of inversion

In the shell-model framework, valence-space Hamiltonians connecting multiple major-oscillator shells are of key interest for investigating the physics of neutron-rich nuclei, which have been the subject of intense experimental activity for decades. Here we present an extension of the ab initio valence-space in-medium similarity renormalization group, which allows the derivation of such Hamiltonians nonperturbatively. Starting from initial two- and three-nucleon forces from chiral effective field theory, we then calculate properties of nuclei in the important island-of-inversion region above oxygen, so far unexplored with ab initio methods. In this work, our results in the neon and magnesium isotopes indicate the importance of neutron excitation from the $\textit{sd}$ to $\textit{pf}$ shells and ground states dominated by intruder configurations around $\textit{N}$ = 20, consistent with the conclusions from phenomenological studies. We also benchmark the excitation spectrum of 16 O with coupled-cluster theory, finding generally good agreement, and discuss implications for ground-state energies and charge radii in oxygen and calcium isotopes. Finally we outline the proper procedure for treating the longstanding issue of center-of-mass contamination, and show that with a particular choice of valence space, these spurious states can be removed successfully.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Electromagnetic observables of open-shell nuclei from coupled-cluster theory

Here we develop a new method to describe electromagnetic observables of open-shell nuclei with two nucleons outside a closed shell. This approach combines the equation-of-motion coupled-cluster method for such systems and the Lorentz integral transform technique, expanding the applicability of coupled-cluster theory for electromagnetic observables beyond closed-(sub)shell nuclei. To validate this new approach, we compute the non-energy-weighted dipole sum rule and the dipole polarizability of 16,24 ⁢O in both the closed-(sub)shell and the new equation-of-motion coupled-cluster frameworks, finding agreement within error bars. We then analyze the evolution of the dipole polarizability along the oxygen and calcium isotopic chains. Our predictions agree well with available experimental data and other available theoretical calculations for the closed-(sub)shell 16,22 ⁢O and the open-shell 18 O. In the calcium isotopes, we observe that our dipole polarizability predictions for open-shell nuclei are lower than those of closed-(sub)shell nuclei. We expect that our predictions for 24 O and 54,56 ⁢Ca will motivate future experimental studies at the drip line.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Dark-matter-enhanced probe of relic neutrino clustering

We propose heavy decaying dark matter (DM) as a new probe of the cosmic neutrino background (C⁢𝜈⁢B). Heavy DM, with mass ≳ 10 9 GeV, decaying into neutrinos can be a new source of ultrahigh-energy (UHE) neutrinos. Including this contribution along with the measured astrophysical and predicted cosmogenic neutrino fluxes, we study the scattering of UHE neutrinos with the C⁢𝜈⁢B via standard weak interactions mediated by the 𝑍 boson. We solve the complete neutrino transport equation, taking into account both absorption and reinjection effects, to calculate the expected spectrum of UHE neutrino flux at future neutrino telescopes, such as the IceCube-Gen2 radio. We argue that such observations can be used to probe the C⁢𝜈⁢B properties and, in particular, local C⁢𝜈⁢B clustering. We find that, depending on the absolute neutrino mass and the DM mass and lifetime, a local C⁢𝜈⁢B overdensity ≳ 10 6 can be probed at the IceCube-Gen2 radio within ten years of data taking.

Dark matter↗
DOE OSTI2021

Reversible disorder-order transitions in atomic crystal nucleation

Nucleation in atomic crystallization remains poorly understood, despite advances in classical nucleation theory. The nucleation process has been described to involve a nonclassical mechanism that includes a spontaneous transition from disordered to crystalline states, but a detailed understanding of dynamics requires further investigation. In situ electron microscopy of heterogeneous nucleation of individual gold nanocrystals with millisecond temporal resolution shows that the early stage of atomic crystallization proceeds through dynamic structural fluctuations between disordered and crystalline states, rather than through a single irreversible transition. Our experimental and theoretical analyses support the idea that structural fluctuations originate from size-dependent thermodynamic stability of the two states in atomic clusters. These findings, based on dynamics in a real atomic system, reshape and improve our understanding of nucleation mechanisms in atomic crystallization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Spectral Peak Enhancement by Combining Trusted Response Elements via Machine Learning (SPECTRE-ML) v0.8.0

SPECTRE-ML (Spectral Peak Enhancement by Combining Trusted Response Elements via Machine Learning) is a machine learning based program for finding optimal clusters of radiation detector segments (i.e., pixels or voxels) in order to improve spectral performance. It provides facilities for pre-processing and analyzing training datasets, running ML algorithms, and evaluating and visualizing outputs. SPECTRE-ML outperforms simpler ad-hoc segmentation methods such as uniform depth clusters, learning detector performance trends such as dead layers, edge effects, and gain shifts. Although extensible to arbitrary highly-segmented spectroscopic radiation detectors, SPECTRE-ML currently focuses on improving spectral performance in highly-segmented CdZnTe (CZT) detectors for International Atomic Energy Agency (IAEA) non-destructive assay (NDA) safeguards tasks.

Vavrek, Jayson↗