arXiv subjects

Roberto Ruiz de Austri Bazan

Publications and source records attributed to Roberto Ruiz de Austri Bazan.

5 recordsLinked to original sources

arXiv2026

TrackFormers Part 2: Enhanced Transformer-Based Models for High-Energy Physics Track Reconstruction

High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing pipeline, with particle track reconstruction being a prime candidate for improvement. In our previous work, we introduced "TrackFormers", a collection of Transformer-based one-shot encoder-only models that effectively associate hits with expected tracks. In this study, we extend our earlier efforts by conducting detailed investigations into more custom Transformer attention mechanisms, a new design combining geometric projection and lightweight clustering, and a joint model conditioning classification on a regressor's predictions. Furthermore, we discuss new datasets that allow the training on hit level for a range of physics processes. These developments collectively aim to boost both the accuracy and potentially the efficiency of our tracking models, offering a robust solution to meet the demands of next-generation high-energy physics experiments.

hep-ex↗

arXiv2025

TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era

High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing requirements forces revisions to almost every step of the data processing pipeline. 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, since the most time-consuming step in tracking is the assignment of hits to particles or track candidates. This is the topic of this paper. We take inspiration from large language models. As such, we consider two approaches: the prediction of the next word in a sentence (next hit point in a track), as well as the one-shot prediction of all hits within an event. In an extensive design effort, we have experimented with three models based on the Transformer architecture and one model based on the U-Net architecture, performing track association predictions for collision event hit points. In our evaluation, we consider a spectrum of simple to complex representations of the problem, eliminating designs with lower metrics early on. We report extensive results, covering both prediction accuracy (score) and computational performance. We have made use of the REDVID simulation framework, as well as reductions applied to the TrackML data set, to compose five data sets from simple to complex, for our experiments. The results highlight distinct advantages among different designs in terms of prediction accuracy and computational performance, demonstrating the efficiency of our methodology. Most importantly, the results show the viability of a one-shot encoder-classifier based Transformer solution as a practical approach for the task of tracking.

hep-ex↗

arXiv2024

Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering

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.

hep-ex↗

arXiv2022

LHC bounds on MUED after Run 2

We present updated LHC limits on the minimal universal extra dimensions (MUED) model from the Run 2 searches. We scan the parameter space against a number of searches implemented in the public code \textsc{CheckMATE} and derive up-to-date limits on the MUED parameter space from 13 TeV searches. The strongest constraints come from a search dedicated to squarks and gluinos with one isolated lepton, jets and missing transverse energy. In the procedure we take into account initial state radiation and stress its importance in the MUED searches, which is not always appreciated.

hep-ph↗

arXiv2021

Constraining electroweak and strongly charged long-lived particles with CheckMATE

Long-lived particles have become a new frontier in the exploration of physics beyond the Standard Model. In this paper, we present the implementation of four types of long-lived particle searches, viz. displaced leptons, disappearing track, displaced vertex (together with muons or with missing energy), and heavy charged tracks. These four categories cover the signatures of a large range of physics models. We illustrate their potential for exclusion and discuss their mutual overlaps in mass-lifetime space for two simple phenomenological models involving either a U(1)-charged or a coloured scalar.

hep-ph↗