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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Characterization of the polysilicon resistor in silicon strip sensors for ATLAS inner tracker as a function of temperature, pre- and post-irradiation

The high luminosity upgrade of the Large Hadron Collider, foreseen for 2029, requires the replacement of the ATLAS Inner Detector with a new all-silicon Inner Tracker (ITk). The expected ultimate total integrated luminosity of 4000 fb –1 means that the strip part of the ITk detector will be exposed to the total particle fluences and ionizing doses reaching the values of 1.6 · 10 15 1 MeV n eq /cm 2 and 0.66 MGy, respectively, including a safety factor of 1.5. Radiation hard n + -in-p micro-strip sensors were developed by the ATLAS ITk strip collaboration and are produced by Hamamatsu Photonics K.K. The active area of each ITk strip sensor is delimited by the n-implant bias ring, which is connected to each individual n + implant strip by a polysilicon bias resistor. The total resistance of the polysilicon bias resistor should be within a specified range to keep all the strips at the same potential, prevent the signal discharge through the grounded bias ring and avoid the readout noise increase. While the polysilicon is a ubiquitous semiconductor material, the fluence and temperature dependence of its resistance is not easily predictable, especially for the tracking detector with the operational temperature significantly below the values typical for commercial microelectronics. Dependence of the resistance of polysilicon bias resistor on the temperature, as well as on the total delivered fluence and ionizing dose, was studied on the specially-designed test structures called ATLAS Testchips, both before and after their irradiation by protons, neutrons, and gammas to the maximal expected fluence and ionizing dose. The resistance has an atypical negative temperature dependence. It is different from silicon, which shows that the grain boundary has a significant contribution to the resistance. Finally, we discuss the contributions by parameterizing the activation energy of the polysilicon resistance as a function of the temperature for unirradiated and irradiated ATLAS Testchips.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Construction and testing of sMDT tubes at the University of Michigan for the ATLAS Muon Spectrometer upgrade

This paper reports on the design and construction of infrastructure and test stations for small-diameter monitored drift tube (sMDT) assembly and testing at the University of Michigan (UM) to prepare for the ATLAS Muon Spectrometer upgrade for the high-luminosity program of the Large Hadron Collider. Procedures of the tube assembly and quality assurance and control (QA/QC) tests are described in detail. More than 99% of the tubes meet the tube QA/QC specifications based on 2100 tubes built at UM. The UM test stations are also used for QA/QC testing on the tubes constructed at Michigan State University. Furthermore, these tubes are being used to construct the sMDT chambers which will replace the current MDT chambers of the barrel inner station of the Muon Spectrometer.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Ultrafast jet classification at the HL-LHC

Abstract Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN large hadron collider during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that O ( 100 ) ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Application of Distributed Fiber Optic Strain Sensors to LMQXFA Cold Mass Welding

The future High Luminosity upgrade of the Large Hadron Collider (HL-LHC) at CERN will include the low-beta inner triplets (Q1, Q2a/b, Q3) for two LHC insertion regions. The Q1, Q3 components consist of eight 10 m-long LMQXFA cryo-assemblies fabricated by the HL-LHC Accelerator Upgrade Project. Each LMQXFA Cold mass contains two Nb 3 Sn magnets connected in series. A stainless-steel shell is welded around the two magnets before the insertion into the cryostat. There is a limit on how much coil preload increase induced by the shell welding is allowed. Distributed Rayleigh backscattering fiber optics sensors were used for the first time to obtain a strain map over a wide area of a Nb 3 Sn magnet cold mass shell. Finally, data were collected during welding of the first LMQXFA cold mass and the results confirm that the increase of the coil pole azimuthal pre-stress after welding do not exceed requirements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Quench Performance of the First Pre-series AUP Cryo-assembly

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) at CERN will include eight cryo-assemblies that are expected to be fabricated and delivered to CERN by the US HL-LHC Accelerator Upgrade Project (AUP) as part of the U.S. contributions to the HL-LHC. These cryostat assemblies are the quadrupole magnetic components of the HL-LHC Q1 and Q3 inner triplet optical elements in front of the two interaction points. Each cryo-assembly consists of two 4.2 m long Nb 3 Sn quadrupole magnets with aperture 150 mm and operating gradient 132.6 T/m. The first pre-series cryo-assembly has been fabricated and successfully tested at the horizontal test facility at Fermi National Accelerator Laboratory. Here in this manuscript we report the quench test results of the LQXFA/B-01 cryo-assembly. The primary objective of the horizontal test is full cryo-assembly qualification and validation of the performance requirements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

AUP First Pre-Series Cryo-Assembly Design Production and Test Overview

New high field and large-aperture quadrupole magnets for the low-beta inner triplets (Q1, Q2, Q3) have been built and tested as part of the high-luminosity upgrade of the Large Hadron Collider (HL-LHC). These new quadrupole magnets are based on Nb3Sn superconducting technology. The US Accelerator Upgrade Project (US-AUP) is producing the Q1 and Q3 Cryo-Assemblies: a pair of ~5 m long magnet structures installed in a stainless-steel helium vessel (Cold Mass) and surrounded by cryostat shields, piping, and a vacuum vessel. In conclusion, this paper gives an overview of the design, production, and the results of the horizontal test of the first pre-series Q1/Q3 Cryo-Assembly.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production

One of the forthcoming major challenges in particle physics is the experimental determination of the Higgs trilinear self-coupling. While efforts have largely focused on on-shell double- and single-Higgs production in proton-proton collisions, off-shell Higgs production has also been proposed as a valuable complementary probe. In this article, we design a hybrid neural simulation-based inference (NSBI) approach to construct a likelihood of the Higgs signal incorporating modifications from the Standard Model effective field theory (SMEFT), relevant background processes, and quantum interference effects. It leverages the training efficiency of matrix-element-enhanced techniques, which are vital for robust SMEFT applications, while also incorporating the practical advantages of classification-based methods for effective background estimates. We demonstrate that our NSBI approach achieves sensitivity close to the theoretical optimum and provide expected constraints for the high-luminosity upgrade of the Large Hadron Collider. While we primarily concentrate on the Higgs trilinear self-coupling, we also consider constraints on other SMEFT operators that affect off-shell Higgs production.

Ghosh, Aishik [Univ. of California, Irvine, CA (Un↗
DOE OSTI2024

Test Beam Results of Planar Pixel Sensor for the CMS Phase 2 Inner Tracker Upgrade

Results of the test beam measurements that characterise the performance of CMS Readout Chip (CROC) sensors to be used in the High Luminosity era of the Large Hadron Collider (HL-LHC) are presented. The HL-LHC peak instantaneous luminosity of $7.5 \times 10^{34} \ \text{cm}^{-2} s^{-1}$ corresponds to an average of around 200 inelastic proton-proton collisions per beam-crossing every 25 ns. In order to efficiently reconstruct and track particles in this extreme and challenging conditions, the present CMS tracking detector will be completely replaced. The new tracking detector consists of an Inner Tracker closest to the beamline and an Outer Tracker surrounding it. These are populated with modules that comprise of readout chips and silicon sensors. The test beam measurements of these modules are vital to understand the performance of the related technologies. Using a primary 120 GeV proton beam from the Main Injector at Fermilab, data was collected at the Fermilab Test Beam Facility (FTBF) using the silicon tracker telescope that provides a precision position measurement of the track impact point with less than 5 $\mu$m uncertainty. The proton beam was incident on a 1x2 planar CROC module developed by Hamamatsu. The sensor has 100x25 $\mu m^2$ standard pixels and also a smaller number of 225x25 $\mu m^2$ longer pixels at the boundary between the two ROCs. We present characterization of these modules that includes pixel efficiency, resolution, cluster size and charge distributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Analysis and Grading of the Test Performance of PS Modules for the CMS Phase-II Outer Tracker Upgrade

The Outer Tracker detector of the Compact Muon Solenoid (CMS) experiment provides information about the trajectory of charged particles produced in proton-proton collisions at the Large Hadron Collider (LHC). During the High Luminosity LHC upgrade, scheduled for the late 2020s, the Outer Tracker will be replaced with new modules capable of transmitting data to the L1 Trigger. These modules are being assembled at several facilities around the world, including Fermilab, necessitating coordinated standards of module quality. Here I discuss the development of POTATO (Phase-II Outer Tracker Analyzer of Test Outputs), a C++ software which provides a standardized procedure for analyzing and grading test results of the Outer Tracker modules. The particular focus of this paper is on the analysis and grading of the PS (pixel-strip) modules in POTATO.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

A pixel detector ROIC with a 3-bit, 40 Msps synchronous ADC per pixel for HL LHC

The prototype Fermi Compact Muon Solenoid (CMS) Pixel (FCP130), manufactured in a 130 nm process, is a readout integrated circuit (ROIC) designed for the High Luminosity upgrade of the Large Hadron Collider. It employs a synchronous analog to digital converter (ADC) for the front-end design, with signal processing and data conversion within a single bunch crossing of 25 ns. It is therefore capable of accurately detecting hits occurring in consecutive bunch crossings, without off-time registration of events, making it particularly suitable for the inner most layers of the vertex detector. The ROIC consists of a matrix of 48 × 160 pixels, each 100 × 30 μm2 in size. Each pixel contains a charge sensitive preamplifier with self-biasing leakage current compensation, eight auto-zero comparators and a Gray code encoder for a 3-bit flash-type ADC. The total power consumption is approximately 24 μW per pixel. The measured noise at the output of all the hit comparators across the ROIC is < 80 e- with threshold dispersion < 110 e-RMS, which allows an in-time threshold setting of ~ 1000 e-.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

In-Pixel Readout IC with compact in-pixel ADC for Pixel detectors at HL LHC

The prototype Smart Pixel concept test chip (SP28), designed in a CMOS 28 nm bulk process, is a proof of concept readout integrated circuit (ROIC) designed for a future Phase III High Luminosity upgrade of the Large Hadron Collider. It employs a synchronous analog to digital converter (ADC) for the front-end design, with signal processing and data conversion within a single bunch crossing of 25 ns. It is therefore capable of accurately detecting hits occurring in consecutive bunch crossings, without off-time registration of events and pileup insensitivity, making it particularly suitable for the inner most layers of the vertex detector. The ROIC consists of a matrix of 32 × 16 pixels, each 25 × 25 µm 2 in size. Each pixel contains a charge sensitive preamplifer with leakage current compensation, three auto-zero comparators for a 2-bit fash-type ADC. The total power consumption is approximately 5 µW per pixel. The measured noise at the output of all the hit comparators across the ROIC is < 90 e- with threshold dispersion < 45 e-RMS, which allows an in-time threshold setting of ≈ 475 e-.

Parpillon, Benjamin↗
DOE OSTI2022

Performance in beam tests of irradiated Low Gain Avalanche Detectors for the ATLAS High Granularity Timing Detector

The High Granularity Timing Detector (HGTD) will be installed in the ATLAS detector to mitigate pile-up effects during the High Luminosity (HL) upgrade of the Large Hadron Collider (LHC) at CERN. The design of the HGTD is based on the use of Low Gain Avalanche Detectors (LGADs), with an active thickness of 50 μm, that allow to measure with high-precision the time of arrival of particles. The HGTD will improve the particle-vertex assignment by measuring the track time with a resolution ranging from approximately 30 ps at the beginning of the HL-LHC operations to 50 ps at the end. Performances of several unirradiated, as well as neutron- and proton-irradiated, LGAD sensors from different vendors have been measured in beam test campaigns during the years 2018 and 2019 at CERN SPS and DESY. This paper presents the results obtained with data recorded by an oscilloscope synchronized with a beam telescope which provides particle position information within a resolution of a few μm. Collected charge, time resolution and hit efficiency are presented. In addition to these properties, the charge uniformity is also studied as a function of the position of the incident particle inside the sensor pad.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Beyond 4D tracking: using cluster shapes for track seeding

Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However, present and future hardware already have additional information that is largely unused by existing track seeding algorithms. The shape of pixel-clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding. We use neural networks to show that cluster shapes can reduce significantly the rate of fake combinatorical backgrounds while preserving a high efficiency. We demonstrate this using the information in cluster singlets, doublets and triplets. Numerical results are presented with simulations from the TrackML challenge.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline

Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic about global track positions. In this work, we study the performance of the Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in the event, the pipeline simultaneously reconstructs prompt tracks and large radius tracks with high efficiencies. This new capability offered by the Exa.TrkX pipeline may enable us to search for new physics in real time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Evolution of the ATLAS event data model for the HL-LHC

The upcoming high-luminosity run of the CERN Large Hadron Collider (HL-LHC) will yield an unprecedented volume of data. In order to process this data, the ATLAS collaboration is evolving its offline software to be able to use heterogeneous resources such as graphical processing units (GPUs) and field-programmable gate arrays (FPGAs). To reduce conversion overheads, the event data model (EDM) should be compatible with the requirements of these resources. While the ATLAS EDM has long allowed representing data as a structure of arrays, further evolution of the EDM can enable more efficient sharing of data between CPU and GPU resources. Some of this work will be summarized here, including extensions to allow controlling how memory for event data is allocated and the implementation of jagged vectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Electronics integration for the GE2/1 and ME0 GEM detector systems for the CMS phase-2 muon system upgrade

Abstract The Large Hadron Collider is currently undergoing its High Luminosity upgrade, which is set to increase the instantaneous luminosity by about a factor of five. Consequently, the Compact Muon Solenoid experiment is upgrading its muon spectrometer to cope with the increased muon flux in the forward region. The GE2/1 triple-gas electron multiplier detector, which has recently entered the mass production phase, and the ME0 triple-GEM detector system, which is in the late prototyping phase, are undergoing electronics integration. These proceedings briefly discuss the frontend electronics for the GE2/1 and ME0 detector systems, the electronics integration testing process, and the future plans for the frontend electronics of these two detector systems by the CMS GEM collaboration.

Instruments & Instrumentation↗
DOE OSTI2023

Semi-supervised graph neural networks for pileup noise removal

Abstract The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton–proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the neutral particle pileup label information from simulation, and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.

43 PARTICLE ACCELERATORS↗