We present a design methodology that enables the semi-automatic generation of a hardware-accelerated graph building architectures for locally constrained graphs based on formally described detector definitions. In addition, we define a similarity measure in order to compare our locally constrained graph building approaches with commonly used k-nearest neighbour building approaches. To demonstrate the feasibility of our solution for particle physics applications, we implemented a real-time graph building approach in a case study for the Belle~II central drift chamber using Field-Programmable Gate Arrays~(FPGAs). Our presented solution adheres to all throughput and latency constraints currently present in the hardware-based trigger of the Belle~II experiment. We achieve constant time complexity at the expense of linear space complexity and thus prove that our automated methodology generates online graph building designs suitable for a wide range of particle physics applications. By enabling an hardware-accelerated pre-processing of graphs, we enable the deployment of novel Graph Neural Networks~(GNNs) in first level triggers of particle physics experiments.

Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics

A bstract Extensions of the Standard Model with masses at or below the GeV scale are motivated by searches for dark matter and precision measurements in the quark and lepton flavour sectors, including that of the muon anomalous magnetic moment. An excellent experimental environment to test such light new physics is given by the Belle II experiment, which foresees to take up to 50 ab −1 of data. Here we consider a model with an additional gauged $$ \textrm{U}{(1)}_{L_{\mu }-{L}_{\tau }} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>U</mml:mi> <mml:msub> <mml:mfenced> <mml:mn>1</mml:mn> </mml:mfenced> <mml:mrow> <mml:msub> <mml:mi>L</mml:mi> <mml:mi>μ</mml:mi> </mml:msub> <mml:mo>−</mml:mo> <mml:msub> <mml:mi>L</mml:mi> <mml:mi>τ</mml:mi> </mml:msub> </mml:mrow> </mml:msub> </mml:math> symmetry that introduces a neutral gauge boson, a Dark Photon, with possibly large couplings to muon- and tau-flavored leptons, including neutrinos. Dark Photon mixing with the Standard Model photon is loop induced, allowing it to couple to electrically charged fermions other than muons and taus. We systematically investigate the possible search strategies for Dark Photons with four fermion final states. We identified search channels with muons as the most promising ones, and we analyse the kinematic distributions to obtain cuts that optimise the sensitivity of Belle II searches for the Dark Photon. Summarising the sensitivities from the most promising search channels we provide a comprehensive overview of future searches at Belle II. 

pdf/systematic-analysis-of-search-strategies-for-lm-lt-gauge-4xbgzqiyre5a.pdf

Systematic analysis of search strategies for Lμ − Lτ gauge bosons at Belle II

We present the study of a fuzzy clustering algorithm for the Belle II electromagnetic calorimeter using Graph Neural Networks. We use a realistic detector simulation including simulated beam backgrounds and focus on the reconstruction of both isolated and overlapping photons. We find significant improvements of the energy resolution compared to the currently used reconstruction algorithm for both isolated and overlapping photons of more than 30% for photons with energies E<0.5 GeV and high levels of beam backgrounds. Overall, the GNN reconstruction improves the resolution and reduces the tails of the reconstructed energy distribution and therefore is a promising option for the upcoming high luminosity running of Belle II.

pdf/photon-reconstruction-in-the-belle-ii-calorimeter-using-1gwynm14.pdf

Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Isabel Haide

Author Tools

Chat about Author

Papers

Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks