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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.

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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 OSTI2023

Performance of the ALICE Electromagnetic Calorimeter

The performance of the electromagnetic calorimeter of the ALICE experiment during operation in 2010–2018 at the Large Hadron Collider is presented. After a short introduction into the design, readout, and trigger capabilities of the detector, the procedures for data taking, reconstruction, and validation are explained. The methods used for the calibration and various derived corrections are presented in detail. Subsequently, the capabilities of the calorimeter to reconstruct and measure photons, light mesons, electrons and jets are discussed. The performance of the calorimeter is illustrated mainly with data obtained with test beams at the Proton Synchrotron and Super Proton Synchrotron or in proton-proton collisions at $\sqrt{s}$ = 13 TeV, and compared to simulations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Beam-induced backgrounds measured in the ATLAS detector during local gas injection into the LHC beam vacuum

Inelastic beam-gas collisions at the Large Hadron Collider (LHC), within a few hundred metres of the ATLAS experiment, are known to give the dominant contribution to beam backgrounds. These are monitored by ATLAS with a dedicated Beam Conditions Monitor (BCM) and with the rate of fake jets in the calorimeters. These two methods are complementary since the BCM probes backgrounds just around the beam pipe while fake jets are observed at radii of up to several metres. In order to quantify the correlation between the residual gas density in the LHC beam vacuum and the experimental backgrounds recorded by ATLAS, several dedicated tests were performed during LHC Run 2. Local pressure bumps, with a gas density several orders of magnitude higher than during normal operation, were introduced at different locations. The changes of beam-related backgrounds, seen in ATLAS, are correlated with the local pressure variation. In addition the rates of beam-gas events are estimated from the pressure measurements and pressure bump profiles obtained from calculations. Using these rates, the efficiency of the ATLAS beam background monitors to detect beam-gas events is derived as a function of distance from the interaction point. These efficiencies and characteristic distributions of fake jets from the beam backgrounds are found to be in good agreement with results of beam-gas simulations performed with theFluka Monte Carlo programme.

43 PARTICLE ACCELERATORS↗
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

Comparison of point cloud and image-based models for calorimeter fast simulation

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Furthermore, point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.

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 OSTI2020

Overview of CMOS Sensors for Future Tracking Detectors

Depleted Complementary Metal-Oxide-Semiconductor (CMOS) sensors are emerging as one of the main candidate technologies for future tracking detectors in high luminosity colliders. Their capability of integrating the sensing diode into the CMOS wafer hosting the front-end electronics allows for reduced noise and higher signal sensitivity, due to the direct collection of the sensor signal by the readout electronics. They are suitable for high radiation environments due to the possibility of applying high depletion voltage and the availability of relatively high resistivity substrates. The use of a CMOS commercial fabrication process leads to their cost reduction and allows faster construction of large area detectors. In this contribution, a general perspective of the state of the art of CMOS detectors for High Energy Physics experiments is given. The main developments carried out with regard to these devices in the framework of the CERN RD50 collaboration are summarized.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Parametrizing the detector response with neural networks

In high energy physics, characterizing the response of a detector to radiation is one of the most important and fundamental experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper reports on how to parameterize summary statistics of the detector response using neural networks. Specifically, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

TTDAQ: A Continuous Flow, Timing and Trigger DAQ System

Final Scientific/Technical Report for DOE Award DE-SC0019581, “TTDAQ: A Continuous Flow, Timing and Trigger DAQ System.” The report summarizes Telluric Labs’ Phase II STTR work developing silicon-photonic building blocks for a software-defined, continuous-flow, trigger-less data acquisition system for next-generation high-energy and nuclear-physics detectors. The project focused on radiation-hard photonic integrated circuits, remote optical illumination, dense wavelength-division multiplexing, and a differential microring-resonator transceiver architecture designed to improve high-speed optical link stability and bandwidth. The report describes project objectives, technical accomplishments, AIM Photonics tape-outs, bench characterization, radiation-hardness testing, deferred integration work, and potential applications beyond physics readout.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Highly efficient photon detection systems for noble liquid detectors based on perovskite quantum dots

Abstract Wavelength shifting photon detection systems (PDS) are the critical functioning components in noble liquid detectors used for high energy physics (HEP) experiments and dark matter search. The vacuum ultraviolet (VUV) scintillation light emitted by these Liquid argon (LAr) and liquid Xenon (LXe) detectors are shifted to higher wavelengths resulting in its efficient detection using the state-of-the-art photodetectors such as silicon photomultipliers (SiPM). The currently used organic wavelength shifting materials [such as 1,1,4,4 Tetraphenyl Butadiene (TPB)] have several disadvantages and are unreliable for longterm use. In this study, we demonstrate the application of the inorganic perovskite cesium lead bromide (CsPbBr 3 ) quantum dots (QDs) as highly efficient wavelength shifters. The absolute photoluminescence quantum yield of the PDS fabricated using these QDs exceeds 70%. CsPbBr 3 -based PDS demonstrated an enhancement in the SiPM signal enhancement by up to 3 times when compared to a 3 µm-thick TPB-based PDS. The emission spectrum from the QDs was optimized to match the highest quantum efficiency region of the SiPMs. In addition, we have demonstrated the deposition of the QD-based wavelength shifting material on a large area PDS substrate using low capital cost and widely scalable solution-based techniques providing a pathway appropriate for meter-scale PDS fabrication and widespread use for other wavelength shifting applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A large area 100-channel PICOSEC Micromegas detector with time resolution at the 20 ps level

Abstract The PICOSEC Micromegas precise timing detector is based on a Cherenkov radiator coupled to a photocathode operating in a semi-transparent mode and a Micromegas amplification structure. The first proof of concept single-channel prototype was able to achieve a time resolution below 25 ps. One of the crucial aspects in the development of precise timing gaseous detectors applicable in high-energy physics experiments is a modular design that enables large area coverage. The first 19-channel multi-pad prototype with an active area of approximately 10 cm 2 suffered from degraded timing resolution due to the non-uniformity of the preamplification gap thickness. A new 100 cm 2 detector module with 100 channels based on a rigid hybrid ceramic/FR4 Micromegas board for improved drift gap uniformity was developed. Initial measurements with 80 GeV/c muons showed improvements in timing response over the measured pads and a time resolution below 25 ps. More recent measurements with a thinner drift gap detector module and newly developed RF pulse amplifiers show that the pad centre resolution can be enhanced to the level of 17 ps. This work will present the development of the detector from structural simulations, design, and beam test commissioning with a focus on the timing performance of a thinner drift gap detector module in combination with new electronics using an automated timing scan method.

Utrobicic, A.↗
DOE OSTI2025

Impact of proton irradiation on 4H-SiC Low Gain Avalanche Detectors (LGADs)

Silicon carbide (SiC) particle detectors have the potential to provide time resolutions and robust performance in extreme environments which exceed that of silicon detectors. In this work 4H-SiC low gain avalanche detectors (LGADs) and complementary PiN diodes were irradiated with 2.5 GeV protons at fluences up to 3.33 × 10 14 p/cm 2 . The electrostatic performance of both irradiated and non-irradiated devices was evaluated using I-V and C-V measurements. Moreover, charge collection measurements using α particles were also conducted. SiC LGADs displayed a loss in rectification, high ON-state resistances >10 10 Ω-cm 2 , and the complete removal of gain when exposed to the highest proton fluence. Additionally, the reductions in capacitance and OFF-state current pointed to compensation of the gain layer as a gain reducing mechanism. The introduction of radiation-induced defects also hinders carrier acceleration, which reduces impact ionization and further gain reduction. LGADs exposed to a fluence of 1 × 10 14 p/cm 2 experienced a partial recovery in gain (originally having a value of ∼ 2) with a value of ∼ 1.65 when applying a bias of 500 V. Charge trapping was also seen in the reduction of charge collection efficiency (CCE) to ∼ 65% for the PiN diode. However, despite the reduction in device performance, the demonstration of a measurable signal after irradiation points to the potential of SiC LGAD detectors for future high energy physics applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Electron pulse-dilation diagnostic instruments

During the past decade, a number of diagnostic instruments have been developed that utilize electron pulse-dilation to achieve temporal resolution in the 5–30 ps range. These development efforts were motivated by the need for advanced diagnostics for high-energy density physics experiments around the world. The new instruments include single- and multi-frame gated imagers and non-imaging detectors that record continuous data streams. Electron pulse-dilation provides high-speed detection capability by converting incoming signals into a free electron cloud and manipulating the electron signal with electric and magnetic fields. In this paper we discuss design details and applications of these instruments along with issues and challenges associated with employing the electron pulse-dilation technique. Additionally, methods to characterize instrument performance and improve tolerance to gamma and neutron background radiation are discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Improving unfolding and systematic uncertainty estimation using generative diffusion networks (Final Technical Report)

This final technical report summarizes the key accomplishments on the unfolding using diffusion model project, a DOE award received by PI Pierre-Hugues Beauchemin at Tufts University. This project main goal was to investigate the potential of diffusion models for unfolding experimental High Energy Physics data from detector effects while controlling systematics uncertainties. The project accomplished its goals by completing the following objectives: 1) Performing an object-by-object, event-by-event unfolding of various kinematic distributions reconstructed from detector data in HEP in a way that keeps correlations between unfolded observables while demonstrating competitive performance compared to standard algorithms used in the field; 2) Address the generalization problem by developing an unfolding algorithm capable to correctly infer the underlying distributions of observables and processes never seen before, while controlling the dominant theoretical uncertainties affecting the process, therefore increasing the effectiveness, the precision, and the applicability of the developed algorithm; 3) Understand the theoretical foundations between the developed algorithm so to extend it to applications beyond experimental HEP, for broader benefits to the society. This report provides an overview of the accomplishments related to each of these key objectives.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Design of detectors at the electron ion collider with artificial intelligence

Abstract Artificial Intelligence (AI) for design is a relatively new but active area of research across many disciplines. Surprisingly when it comes to designing detectors with AI this is an area at its infancy. The electron ion collider is the ultimate machine to study the strong force. The EIC is a large-scale experiment with an integrated detector that extends for about ±35 meters to include the central, far-forward, and far-backward regions. The design of the central detector is made by multiple sub-detectors, each in principle characterized by a multidimensional design space and multiple design criteria also called objectives. Simulations with Geant4 are typically compute intensive, and the optimization of the detector design may include non-differentiable terms as well as noisy objectives. In this context, AI can offer state of the art solutions to solve complex combinatorial problems in an efficient way. In particular, one of the proto-collaborations, ECCE, has explored during the detector proposal the possibility of using multi-objective optimization to design the tracking system of the EIC detector. This document provides an overview of these techniques and recent progress made during the EIC detector proposal. Future high energy nuclear physics experiments can leverage AI-based strategies to design more efficient detectors by optimizing their performance driven by physics criteria and minimizing costs for their realization.

Instruments & Instrumentation↗
DOE OSTI2022

A Massive, Position-Resolving, High-Energy-Resolution Detector for Non-Accelerator Cosmic and Intensity Frontier Particle Physics

We proposed to take the first steps in the development of a detector that promises energy resolution of tens of eV FWHM combined with robust nuclear-recoil discrimination and high fidelity, mm-precision position reconstruction for applications in non-accelerator particle physics at the Cosmic and Intensity Frontiers. The detector would obtain these excellent resolutions by sensing athermal phonons produced by particle interactions in crystalline, dielectric targets using a sensitive, highly multiplexable superconducting phonon sensor, the kinetic inductance detector (KID). This detector would be applicable to: the search for low-mass particle dark matter candidates with masses below 5~GeV via direct detection of scattering of dark matter particles with terrestrial nuclei; detection of coherent elastic neutrino-nucleus scattering to test for new physics such as a non-standard value of the weak nuclear charge, non-standard neutrino interactions (perhaps driven by a neutrino magnetic moment), or the existence of sterile neutrinos; and, searches for neutrinoless double-beta decay. During the funding period, we demonstrated scaling up of the detector concept from a 22-mm by 22-mm by 1-mm, 1-g prototype with 0.9~keV FWHM energy resolution to a 75-mm diameter by 1-mm, 9-g prototype while improving the inferred energy resolution to 0.7~keV~FWHM. In the process, we solved many problems associated with scaling device fabrication to large wafers and vastly reduced the fraction of the surface occupied by inactive but energy-absorbing metal. We also demonstrated a new technique that substantially simplifies the process of characterizing a new detector. These results provide a good foundation for future work scaling up the design to 4-mm thickness substrates and improving the resolution to reach 0.035~keV~FWHM, yielding a detector with compelling potential for the above applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Neural conditional reweighting

There is a growing use of neural network classifiers as unbinned, high-dimensional (and variable-dimensional) reweighting functions. To date, the focus has been on marginal reweighting, where a subset of features are used for reweighting while all other features are integrated over. There are some situations, though, where it is preferable to condition on auxiliary features instead of marginalizing over them. Here, we introduce neural conditional reweighting, which extends neural marginal reweighting to the conditional case. This approach is particularly relevant in high-energy physics experiments for reweighting detector effects conditioned on particle-level truth information. Furthermore we leverage a custom loss function that not only allows us to achieve neural conditional reweighting through a single training procedure, but also yields sensible interpolation even in the presence of phase space holes. As a specific example, we apply neural conditional reweighting to the energy response of high-energy jets, which could be used to improve the modeling of physics objects in parametrized fast simulation packages.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗