arXiv subjects

Torben Lange

Publications and source records attributed to Torben Lange.

4 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↗

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↗

arXiv2024

Higgs cross-section ( including di-Higgs ) with CMS and ATLAS

Since the discovery of the Higgs boson in 2012 by the ATLAS and CMS Collaborations a lot of progress has been made in verifying the nature of this new bosonic particle. Still, questions remain as to whether this new particle is the standard model (SM) Higgs boson, and whether it gives us hints of where physics beyond the SM (BSM) might be hidden. This overview tries to tackle these questions by looking at three types of analyses 1) Total cross section measurements of the various Higgs boson production and decay modes, allowing us access to the various Higgs boson couplings to SM bosons and fermions, 2) Differential Higgs boson cross section measurements such as in the STXS framework allowing for a model independent search for BSM and, finally, 3) The searches for di-Higgs production which give access to the trilinear Higgs self coupling $\lambda$ and the Higgs potential itself.

hep-ex↗

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.

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