Publication Date
2026
Document Type
Dissertation/Thesis
First Advisor
Adelman, Jahred A.
Degree Name
M.S. (Master of Science)
Legacy Department
Department of Physics
Abstract
With the advent of the High-Luminosity Large Hadron Collider (HL-LHC), the demand for modern approaches to high-throughput data processing has increased substantially. The HL-LHC will deliver far higher collision rates (pile-up reaching ⟨μ⟩ ∼200), producing a dramatic rise in detector activity and placing significant pressure on real-time reconstruction algorithms. One strategy for meeting this challenge is to leverage commodity hardware such as field-programmable gate arrays (FPGAs) and graphics processing units (GPUs), which can accelerate bandwidth-intensive portions of the reconstruction chain. In particular, FPGAs programmed through high-level synthesis (HLS) offer a promising balance between flexibility and performance, allowing physicists to design logic in a C-like environment rather than relying exclusively on low-level firmware specialists—a bottleneck when designs must rapidly evolve.
Within the ATLAS experiment, the trigger system performs real-time event selection using a sequence of reconstruction algorithms that transform raw detector readout into physics objects. As part of the trigger upgrade program for the HL-LHC, the trigger-level tracking upgrade effort investigates hardware-accelerated solutions for charged-particle tracking, of which FPGA-based clustering is a key component. The local strip clustering kernel converts detector readout into cluster objects by identifying adjacent hits, forming the first stage of data reduction for downstream tracking algorithms. The overarching objective is to develop a resource-efficient, low-latency clustering implementation that can be adapted for integration within a scalable, fully reconfigurable tracking pipeline.
Recommended Citation
Shaddix, Hayden, "High-Throughput Strip Clustering on FPGAS for the HL-LHC" (2026). Graduate Research Theses & Dissertations. 8245.
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/8245
Extent
98 pages
Language
en
Publisher
Northern Illinois University
Rights Statement
In Copyright
Rights Statement 2
NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.
Media Type
Text
