arXiv subjects

Laurits Tani

Publications and source records attributed to Laurits Tani.

8 recordsLinked to original sources

arXiv2026

ParticleTransformer is all you need for reconstructing hadronic tau leptons

The large number of $Z \rightarrow \tau\tau$ events expected during the TeraZ program at FCC-ee will allow for precision measurements and searches for physics beyond the Standard Model, requiring accurate reconstruction of hadronically decaying tau leptons. This reconstruction is particularly challenging due to the presence of undetected neutrinos and the diverse topology of hadronic tau decays, making the design of robust heuristic reconstruction algorithms challenging. In this work, we present the first fully machine learned hadronic tau reconstruction approach tuned for FCC-ee studies. The reconstruction is formulated as a set of complementary tasks, including tau identification, decay mode classification, charge reconstruction, and full four-momentum regression. The algorithms are evaluated on fully simulated electron-positron collision samples with realistic detector effects using the CLD detector setup. We compare dedicated task-specific models with a unified multi-task model and quantify their performance in a granular manner across all reconstruction tasks. Both approaches achieve per-mille-level tau mis-identification rates at high signal efficiency, decay mode classification F1 scores of up to 0.95 for the dominant channels, and sub-per-mille charge mis-identification rates, outperforming a conventional jet-charge estimator by up to two orders of magnitude. For the full kinematic reconstruction, the models achieve per-mille-level angular resolution and percent-level visible transverse momentum resolution, exceeding the performance of reconstruction-level jet observables. The resulting models provide a realistic high-performance solution for hadronic tau reconstruction at FCC-ee, offering identification, charge discrimination, decay mode analysis and full kinematic reconstruction.

hep-ex↗

arXiv2025

Reconstructing hadronically decaying tau leptons with a jet foundation model

The limited availability and accuracy of simulated data has motivated the use of foundation models in high energy physics, with the idea to first train a task-agnostic model on large and potentially unlabeled datasets. This enables the subsequent fine-tuning of the learned representation for specific downstream tasks, potentially requiring much smaller dataset sizes to reach the performance of models trained from scratch. We study how OmniJet-$\alpha$, one of the proposed foundation models for particle jets, can be used on a new set of tasks, and in a new dataset, in order to reconstruct hadronically decaying $\tau$ leptons. We show that the pretraining can successfully be utilized for this multi-task problem, improving the resolution of momentum reconstruction by about 50\% when the pretrained weights are fine-tuned, compared to training the model from scratch. While much work remains ahead to develop generic foundation models for high-energy physics, this early result of generalizing an existing model to a new dataset and to previously unconsidered tasks highlights the importance of testing the approaches on a diverse set of datasets and tasks.

hep-ex↗

arXiv2024

A unified machine learning approach for reconstructing hadronically decaying tau leptons

Tau leptons serve as an important tool for studying the production of Higgs and electroweak bosons, both within and beyond the Standard Model of particle physics. Accurate reconstruction and identification of hadronically decaying tau leptons is a crucial task for current and future high energy physics experiments. Given the advances in jet tagging, we demonstrate how tau lepton reconstruction can be decomposed into tau identification, kinematic reconstruction, and decay mode classification in a multi-task machine learning setup. Based on an electron-positron collision dataset with full detector simulation and reconstruction, we show that common jet tagging architectures can be effectively used for these sub-tasks. We achieve comparable momentum resolutions of 2-3% with all the tested models, while the precision of reconstructing individual decay modes is between 80-95%. We find ParticleTransformer to be the best-performing approach, significantly outperforming the heuristic baseline. This paper also serves as an introduction to a new publicly available $\mathtt{Fu}\tau\mathtt{ure}$ dataset for the development of tau reconstruction algorithms. This allows to further study the resilience of ML models to domain shifts and the efficient use of foundation models for such tasks.

hep-ex↗

arXiv2023

Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics

When using machine learning (ML) techniques, users typically need to choose a plethora of algorithm-specific parameters, referred to as hyperparameters. In this paper, we compare the performance of two algorithms, particle swarm optimisation (PSO) and Bayesian optimisation (BO), for the autonomous determination of these hyperparameters in applications to different ML tasks typical for the field of high energy physics (HEP). Our evaluation of the performance includes a comparison of the capability of the PSO and BO algorithms to make efficient use of the highly parallel computing resources that are characteristic of contemporary HEP experiments.

physics.data-an↗

arXiv2023

Tau lepton identification and reconstruction: a new frontier for jet-tagging ML algorithms

Identifying and reconstructing hadronic $\tau$ decays ($\tau_{\textrm{h}}$) is an important task at current and future high-energy physics experiments, as $\tau_{\textrm{h}}$ represent an important tool to analyze the production of Higgs and electroweak bosons as well as to search for physics beyond the Standard Model. The identification of $\tau_{\textrm{h}}$ can be viewed as a generalization and extension of jet-flavour tagging, which has in the recent years undergone significant progress due to the use of deep learning. Based on a granular simulation with realistic detector effects and a particle flow-based event reconstruction, we show in this paper that deep learning-based jet-flavour-tagging algorithms are powerful $\tau_{\textrm{h}}$ identifiers. Specifically, we show that jet-flavour-tagging algorithms such as LorentzNet and ParticleTransformer can be adapted in an end-to-end fashion for discriminating $\tau_{\textrm{h}}$ from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art $\tau_{\textrm{h}}$ reconstruction and identification algorithms currently in use at the Large Hadron Collider.

hep-ex↗

arXiv2021

Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics

The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices need to be made by the user when training the ML algorithm. In addition to deciding which ML algorithm to use and choosing suitable observables as inputs, users typically need to choose among a plethora of algorithm-specific parameters. We refer to parameters that need to be chosen by the user as hyperparameters. These are to be distinguished from parameters that the ML algorithm learns autonomously during the training, without intervention by the user. The choice of hyperparameters is conventionally done manually by the user and often has a significant impact on the performance of the ML algorithm. In this paper, we explore two evolutionary algorithms: particle swarm optimization (PSO) and genetic algorithm (GA), for the purposes of performing the choice of optimal hyperparameter values in an autonomous manner. Both of these algorithms will be tested on different datasets and compared to alternative methods.

hep-ex↗

arXiv2020

Monitoring the optical quality of the FACT Cherenkov Telescope

In gamma ray astronomy muon events have a distinct feature of casting ring-like images on the sensor plane, thus forming a well known signal class for Cherenkov telescopes. These ring-like images can then be used to deduce the optical point spread function (PSF) which is an important measure of the optical quality of the imaging-reflector. In this thesis the observed 'fuzziness' of muon rings is used as a measure to infer the PSF. However to have a good estimate for this 'fuzziness' parameter, the reconstruction of the ring center and ring radius itself needs to be accurate, so different methods of ring feature extraction are studied. To check for the accuracy of the methods a simulation and analysis is performed. Measuring the evolution of the PSF over time allows to identify its effects and take them into account for the reconstruction of gamma-rays postliminary. As a further benefit of the methods presented here no additional observations are needed to measure the PSF nor any human activity on site is required. The accuracy of the method, and the PSF of FACT vs. time are presented.

astro-ph.IM↗

arXiv2019

5.5 years multi-wavelength variability of Mrk 421: evidences of leptonic emission from the radio to TeV

Mrk 421 is a high-synchrotron-peaked blazar featuring bright and persistent GeV and TeV emission. We use the longest and densest ongoing unbiased observing campaign obtained at TeV and GeV energies during 5.5 years with the FACT telescope and the Fermi-LAT detector. The contemporaneous multi-wavelength observations were used to characterize the variability of the source and to constrain the underlying physical mechanisms. We study and correlate light curves obtained by nine different instruments from radio to gamma rays and found two significant results. The TeV and X-ray light curves are very well correlated with lag, if any, shorter than a day. The GeV light curve varies independently and accurately leads the variations observed at long wavelengths, in particular in the radio band. We find that the observations match the predictions of leptonic models and suggest that the physical conditions vary along the jet, when the emitting region moves outwards.

astro-ph.HE↗