PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions

• URL: http://arxiv.org/abs/2309.08630v5
• Date: Thu, 13 Jun 2024 00:48:39 GMT
• Title: PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions
• Authors: Yash Semlani, Mihir Relan, Krithik Ramesh,
• Abstract summary: Jet tagging is a classification problem in high-energy physics experiments. Current approaches use deep learning to uncover hidden patterns in complex collision data. We propose a graph-based representation of a jet that encodes the most information possible.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Jet tagging is a classification problem in high-energy physics experiments that aims to identify the collimated sprays of subatomic particles, jets, from particle collisions and tag them to their emitter particle. Advances in jet tagging present opportunities for searches of new physics beyond the Standard Model. Current approaches use deep learning to uncover hidden patterns in complex collision data. However, the representation of jets as inputs to a deep learning model have been varied, and often, informative features are withheld from models. In this study, we propose a graph-based representation of a jet that encodes the most information possible. To learn best from this representation, we design Particle Chebyshev Network (PCN), a graph neural network (GNN) using Chebyshev graph convolutions (ChebConv). ChebConv has been demonstrated as an effective alternative to classical graph convolutions in GNNs and has yet to be explored in jet tagging. PCN achieves a substantial improvement in accuracy over existing taggers and opens the door to future studies into graph-based representations of jets and ChebConv layers in high-energy physics experiments. Code is available at https://github.com/YVSemlani/PCN-Jet-Tagging.

Related papers

• EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction [0.0]
  We consider a one-shot OC approach that reconstructs particle tracks directly from a set of hits. This approach iteratively updates the graphs and can better facilitate the message passing across each graph. Preliminary studies on the TrackML dataset show better track performance compared to the methods that require a fixed input graph.
  arXiv  Detail & Related papers  (2024-07-18T22:29:24Z)
• GOODAT: Towards Test-time Graph Out-of-Distribution Detection [103.40396427724667]
  Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. Recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN. This paper introduces a data-centric, unsupervised, and plug-and-play solution that operates independently of training data and modifications of GNN architecture.
  arXiv  Detail & Related papers  (2024-01-10T08:37:39Z)
• RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design [70.59828655929194]
  Deep Graph Variational Autoencoders are among the most powerful machine learning tools with which it is possible to address this problem. We propose RGCVAE, an efficient and effective Graph Variational Autoencoder based on: (i) an encoding network exploiting a new powerful Graph Isomorphism Network; (ii) a novel probabilistic decoding component.
  arXiv  Detail & Related papers  (2023-05-19T14:23:48Z)
• GraphHD: Efficient graph classification using hyperdimensional computing [58.720142291102135]
  We present a baseline approach for graph classification with HDC. We evaluate GraphHD on real-world graph classification problems. Our results show that when compared to the state-of-the-art Graph Neural Networks (GNNs) the proposed model achieves comparable accuracy.
  arXiv  Detail & Related papers  (2022-05-16T17:32:58Z)
• Quantum Machine Learning for $b$-jet charge identification [0.0]
  We present a new approach to identify if a jet contains a hadron formed by a $b$ or $barb quark at the moment of production, based on a Variational Quantum applied to simulated data of the LHCb experiment.
  arXiv  Detail & Related papers  (2022-02-28T16:48:27Z)
• Graph Kernel Neural Networks [53.91024360329517]
  We propose to use graph kernels, i.e. kernel functions that compute an inner product on graphs, to extend the standard convolution operator to the graph domain. This allows us to define an entirely structural model that does not require computing the embedding of the input graph. Our architecture allows to plug-in any type of graph kernels and has the added benefit of providing some interpretability.
  arXiv  Detail & Related papers  (2021-12-14T14:48:08Z)
• Graph Neural Networks with Adaptive Frequency Response Filter [55.626174910206046]
  We develop a graph neural network framework AdaGNN with a well-smooth adaptive frequency response filter. We empirically validate the effectiveness of the proposed framework on various benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-26T19:31:21Z)
• Jet tagging in the Lund plane with graph networks [0.0]
  LundNet is a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet. We show significantly improved performance for top tagging compared to existing state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-12-15T19:00:01Z)
• Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics [1.6417409087671928]
  We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC) We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton collisions like those at the LHC.
  arXiv  Detail & Related papers  (2020-11-30T23:53:45Z)
• Dirichlet Graph Variational Autoencoder [65.94744123832338]
  We present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors. Motivated by the low pass characteristics in balanced graph cut, we propose a new variant of GNN named Heatts to encode the input graph into cluster memberships.
  arXiv  Detail & Related papers  (2020-10-09T07:35:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.