A5086280868- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Quantum Chromodynamics and Particle Interactions
- Particle Detector Development and Performance
- Dark Matter and Cosmic Phenomena
- Computational Physics and Python Applications
- Cosmology and Gravitation Theories
- Neutrino Physics Research
- Radiation Detection and Scintillator Technologies
- Astrophysics and Cosmic Phenomena
- Black Holes and Theoretical Physics
- CCD and CMOS Imaging Sensors
- Particle Accelerators and Free-Electron Lasers
- Distributed and Parallel Computing Systems
- Medical Imaging Techniques and Applications
- Gamma-ray bursts and supernovae
- Nuclear reactor physics and engineering
- Atomic and Subatomic Physics Research
- advanced mathematical theories
- Radiation Effects in Electronics
- Nuclear Physics and Applications
- Laser-induced spectroscopy and plasma
- Noncommutative and Quantum Gravity Theories
- Radioactive contamination and transfer
- Parallel Computing and Optimization Techniques
University of Illinois Chicago
2018-2025
Institute of High Energy Physics
2021-2024
University of Chicago
2023-2024
University of Antwerp
2024
A. Alikhanyan National Laboratory
2022-2024
University of Iowa
2023
University of Illinois Urbana-Champaign
2023
University of Rochester
2022
University of Edinburgh
2014-2019
AGH University of Krakow
2012-2017
High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate 40\,MHz, these detectors create massive amounts data. Signal processing that handles data incoming those and intelligently reduces within pixelated region detector \textit{at rate} will enhance performance enable analyses not currently possible. Using shape charge clusters deposited in array small pixels, physical properties traversing can be extracted with...
Abstract Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation require that sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High- Luminosity Large Hadron Collider. Signal processing handles incoming a rate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi class="MJX-tex-calligraphic">O</mml:mi> </mml:mrow> </mml:math> (40 MHz) and...
This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at High Luminosity Large Hadron Collider (HL-LHC). We present our approach for developing a compact model that filters out based on particle's transverse momentum with goal reducing amount being sent downstream electronics. The incoming charge waveforms are converted streams binary-valued events,...
Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event require highly granular silicon pixel detectors with billions readout channels. With rates as 40 MHz, these generate petabytes data per second. To enable discovery within strict bandwidth and latency constraints, trackers must be capable fast, power efficient, radiation hard data-reduction source. We are developing a integrated circuit...
We will present a program to establish the first development and manufacturing of HEP-specific sensors monolithically integrated into standard CMOS process using US-based foundry. In collaboration with several US universities project aims develop Monolithic Active Pixel Sensors (MAPS) designs implemented in 90 nm technology node, including simple test structures multi-pixel arrays, monolithic readout circuits, perform detailed characterization detector prototypes quantify their performance...
The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), remains so the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information charged particle passing through from pixel-cluster properties. This detector technology immediately improves situation tracking, but any major improvements physics reach are unrealized since they dominated by lowest-level hardware...
This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at High Luminosity Large Hadron Collider. We present our approach for developing a compact model that filters out based on particle's transverse momentum with goal reducing amount being sent downstream electronics. The incoming charge waveforms are converted streams binary-valued events, which then...
Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation require that sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High Luminosity Large Hadron Collider. Signal processing handles incoming a rate O(40MHz) and intelligently reduces within pixelated region detector enhance physics performance high luminosity enable analyses are not currently possible. Using shape charge clusters...
The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), remains so the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information charged particle passing through from pixel-cluster properties. This detector technology immediately improves situation tracking, but any major improvements physics reach are unrealized since they dominated by lowest-level hardware...
The first measurements of the W and Z cross sections in proton-proton collisions at sqrt{s} = 7 TeV using ATLAS detector Large Hadron Collider (LHC) have been completed. Cross electron muon channels, as well combined section, are presented for both boson. charge asymmetry production a function psuedorapidity lepton has also measured.
Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) ASIC design for low-latency By implementing 5-stage pipelined CNN model 3D technology, demonstrate that the distributed on two dies utilizing face-to-face (F2F) integration achieves superior performance. Our experimental results show based 43% better energy-delay product when compared to traditional 2D technology.