arXiv subjects

Dylan Sheldon Rankin

Publications and source records attributed to Dylan Sheldon Rankin.

5 recordsLinked to original sources

arXiv2026

Chopping and distilling variational autoencoders for real-time anomaly detection in high energy physics

Anomaly detection (AD) has recently emerged as an exciting alternative to conventional search strategies in high energy physics using artificial intelligence (AI) and machine learning (ML). The integration of these techniques into trigger systems is even more recent, but represents a crucial step in expanding the coverage of LHC triggers. In this paper, we explore the direct comparison, as well as combination, of two compression techniques for variational autoencoder (VAE) AD trigger algorithms: utilizing only latent-space derived variables and therefore requiring only half of the VAE that we call "chopping" and applying knowledge distillation (KD) to distill the VAE into a student architecture that we call "distillation." We demonstrate the feasibility of deploying such techniques on an FPGA within the resource and latency constraints of an LHC trigger environment and further find that a combination of the two leads to the smallest models that maintain, and in some cases, improve, performance with respect to the original VAE architecture.

physics.ins-det↗

arXiv2024

MACK: Mismodeling Addressed with Contrastive Knowledge

The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics.

hep-ph↗

arXiv2024

Graph Neural Network-based Tracking as a Service

Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient computation, a requirement not met by all computing facilities used for particle physics experiments, particularly those lacking advanced GPUs. Furthermore, deploying the GNN-based track-finding algorithm in a production environment requires the installation of all dependent software packages, exclusively utilized by this algorithm. These computing challenges must be addressed for the successful implementation of GNN-based track-finding algorithm into production settings. In response, we introduce a ``GNN-based tracking as a service'' approach, incorporating a custom backend within the NVIDIA Triton inference server to facilitate GNN-based tracking. This paper presents the performance of this approach using the Perlmutter supercomputer at NERSC.

physics.comp-ph↗

arXiv2020

FPGAs-as-a-Service Toolkit (FaaST)

Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs are an extremely promising option as well. A series of workflows are developed to establish the performance capabilities of FPGAs as a service. Multiple different devices and a range of algorithms for use in high energy physics are studied. For a small, dense network, the throughput can be improved by an order of magnitude with respect to GPUs as a service. For large convolutional networks, the throughput is found to be comparable to GPUs as a service. This work represents the first open-source FPGAs-as-a-service toolkit.

physics.comp-ph↗

arXiv2019

An approach to constraining the Higgs width at the LHC and HL-LHC

Despite the discovery of the Higgs boson decay in five separate channels many parameters of the Higgs boson remain largely unconstrained. In this paper, we present a new approach to constraining the Higgs total width by requiring the Higgs to be resolved as a single high p$_T$ jet and measuring the inclusive Higgs boson cross section. To measure the inclusive Higgs boson cross section, we rely on new approaches from machine learning and a modified jet reconstruction. This approach is found to be complementary to the existing off-shell width measurement and, with the full HL-LHC luminosity, is capable of yielding similar sensitivity to the off-shell projections. We outline the theoretical and experimental limitations and present a path towards making this approach a truly model-independent measurement of the Higgs boson total width.

hep-ph↗