Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2307.07747 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911974214860800 |
|---|---|
| author | Lange, Torben Nandan, Saswati Pata, Joosep Tani, Laurits Veelken, Christian |
| author_facet | Lange, Torben Nandan, Saswati Pata, Joosep Tani, Laurits Veelken, Christian |
| contents | Identifying and reconstructing hadronic $τ$ decays ($τ_{\textrm{h}}$) is an important task at current and future high-energy physics experiments, as $τ_{\textrm{h}}$ 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 $τ_{\textrm{h}}$ 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 $τ_{\textrm{h}}$ 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 $τ_{\textrm{h}}$ from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art $τ_{\textrm{h}}$ reconstruction and identification algorithms currently in use at the Large Hadron Collider. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_07747 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Tau lepton identification and reconstruction: a new frontier for jet-tagging ML algorithms Lange, Torben Nandan, Saswati Pata, Joosep Tani, Laurits Veelken, Christian High Energy Physics - Experiment Identifying and reconstructing hadronic $τ$ decays ($τ_{\textrm{h}}$) is an important task at current and future high-energy physics experiments, as $τ_{\textrm{h}}$ 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 $τ_{\textrm{h}}$ 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 $τ_{\textrm{h}}$ 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 $τ_{\textrm{h}}$ from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art $τ_{\textrm{h}}$ reconstruction and identification algorithms currently in use at the Large Hadron Collider. |
| title | Tau lepton identification and reconstruction: a new frontier for jet-tagging ML algorithms |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2307.07747 |