Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering
- URL: http://arxiv.org/abs/2405.17325v1
- Date: Mon, 27 May 2024 16:23:50 GMT
- Title: Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering
- Authors: Uraz Odyurt, Nadezhda Dobreva, Zef Wolffs, Yue Zhao, Antonio Ferrer Sánchez, Roberto Ruiz de Austri Bazan, José D. Martín-Guerrero, Ana-Lucia Varbanescu, Sascha Caron,
- Abstract summary: Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments.
We utilise a simplified simulator (REDVID) to generate training data that is specifically composed for simplicity.
We treat a hit sequence as a hit sequence to track sequence translation problem.
- Score: 2.7999949281820276
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance the algorithmic design effort by utilising a simplified simulator (REDVID) to generate training data that is specifically composed for simplicity. We demonstrate the effectiveness of this data in guiding the development of optimal network architectures. Additionally, we investigate the application of image segmentation networks for this task, exploring their potential for accurate track reconstruction. Moreover, we approach the task from a different perspective by treating it as a hit sequence to track sequence translation problem. Specifically, we explore the utilisation of Transformer architectures for tracking purposes. Our preliminary findings are covered in detail. By considering this novel approach, we aim to uncover new insights and potential advancements in track reconstruction. This research sheds light on previously unexplored methods and provides valuable insights for the field of particle track reconstruction and hit clustering in HEP.
Related papers
- TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era [2.9052912091435923]
High-Energy Physics experiments are facing a multi-fold data increase with every new iteration.
One such step in need of an overhaul is the task of particle track reconstruction, a.k.a., tracking.
A Machine Learning-assisted solution is expected to provide significant improvements.
arXiv Detail & Related papers (2024-07-09T18:47:25Z) - Simultaneous Map and Object Reconstruction [66.66729715211642]
We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR.
We take inspiration from recent novel view synthesis methods and pose the reconstruction problem as a global optimization.
By careful modeling of continuous-time motion, our reconstructions can compensate for the rolling shutter effects of rotating LiDAR sensors.
arXiv Detail & Related papers (2024-06-19T23:53:31Z) - Data Augmentations in Deep Weight Spaces [89.45272760013928]
We introduce a novel augmentation scheme based on the Mixup method.
We evaluate the performance of these techniques on existing benchmarks as well as new benchmarks we generate.
arXiv Detail & Related papers (2023-11-15T10:43:13Z) - Leveraging the Power of Data Augmentation for Transformer-based Tracking [64.46371987827312]
We propose two data augmentation methods customized for tracking.
First, we optimize existing random cropping via a dynamic search radius mechanism and simulation for boundary samples.
Second, we propose a token-level feature mixing augmentation strategy, which enables the model against challenges like background interference.
arXiv Detail & Related papers (2023-09-15T09:18:54Z) - Dyna-DepthFormer: Multi-frame Transformer for Self-Supervised Depth
Estimation in Dynamic Scenes [19.810725397641406]
We propose a novel Dyna-Depthformer framework, which predicts scene depth and 3D motion field jointly.
Our contributions are two-fold. First, we leverage multi-view correlation through a series of self- and cross-attention layers in order to obtain enhanced depth feature representation.
Second, we propose a warping-based Motion Network to estimate the motion field of dynamic objects without using semantic prior.
arXiv Detail & Related papers (2023-01-14T09:43:23Z) - The Preliminary Results on Analysis of TAIGA-IACT Images Using
Convolutional Neural Networks [68.8204255655161]
The aim of the work is to study the possibility of the machine learning application to solve the tasks set for TAIGA-IACT.
The method of Convolutional Neural Networks (CNN) was applied to process and analyze Monte-Carlo events simulated with CORSIKA.
arXiv Detail & Related papers (2021-12-19T15:17:20Z) - Is Deep Image Prior in Need of a Good Education? [57.3399060347311]
Deep image prior was introduced as an effective prior for image reconstruction.
Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques.
We develop a two-stage learning paradigm to address the computational challenge.
arXiv Detail & Related papers (2021-11-23T15:08:26Z) - Light Field Reconstruction Using Convolutional Network on EPI and
Extended Applications [78.63280020581662]
A novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views.
We demonstrate the high performance and robustness of the proposed framework compared with state-of-the-art algorithms.
arXiv Detail & Related papers (2021-03-24T08:16:32Z) - Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for
Particle Imaging Detectors [0.0]
This paper introduces an end-to-end, ML-based data reconstruction chain for Liquid Time Projection Chambers (LArTPCs)
It is the first implementation to handle the unprecedented pile-up of dozens of high energy neutrino interactions.
arXiv Detail & Related papers (2021-02-01T18:10:00Z) - Deep Non-Line-of-Sight Reconstruction [18.38481917675749]
In this paper, we employ convolutional feed-forward networks for solving the reconstruction problem efficiently.
We devise a tailored autoencoder architecture, trained end-to-end reconstruction maps transient images directly to a depth map representation.
We demonstrate that our feed-forward network, even though it is trained solely on synthetic data, generalizes to measured data from SPAD sensors and is able to obtain results that are competitive with model-based reconstruction methods.
arXiv Detail & Related papers (2020-01-24T16:05:50Z)
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.