Tagging ultra-boosted jets at FCC-hh using machine learning techniques
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arXiv
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| Format: | Preprint |
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2025
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| author | Bhattacharyya, Sanchari Bhattacherjee, Biplob Bose, Camellia Chowdhury, Debtosh Mukherjee, Swagata |
| author_facet | Bhattacharyya, Sanchari Bhattacherjee, Biplob Bose, Camellia Chowdhury, Debtosh Mukherjee, Swagata |
| contents | The Future Circular Hadron Collider (FCC-hh) will probe unprecedented energy regimes, enabling direct searches for new elementary particles at a scale of tens of TeV. FCC-hh is currently in the planning stage, and one of its primary physics goals is to search for physics beyond the Standard Model by exploring a previously inaccessible kinematic domain. While venturing into uncharted high-energy territories promises excitement, reconstructing objects with enormous transverse momenta will require overcoming major experimental challenges. This work investigates the identification of boosted $W$ bosons and boosted top quarks in the context of three beyond the Standard Model scenarios: heavy vector-like quark ($B'$), heavy neutral gauge boson ($Z'$), and heavy neutral Higgs boson ($H$). We employ machine learning techniques, including eXtreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN), to identify these ultra-boosted objects in the collider from their SM background counterpart. We evaluate the performance of these techniques in distinguishing $W$ jets and top jets from QCD jets at extremely high transverse momenta ($p_{T}$) values, demonstrating their potential for future FCC-hh analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tagging ultra-boosted jets at FCC-hh using machine learning techniques Bhattacharyya, Sanchari Bhattacherjee, Biplob Bose, Camellia Chowdhury, Debtosh Mukherjee, Swagata High Energy Physics - Phenomenology High Energy Physics - Experiment The Future Circular Hadron Collider (FCC-hh) will probe unprecedented energy regimes, enabling direct searches for new elementary particles at a scale of tens of TeV. FCC-hh is currently in the planning stage, and one of its primary physics goals is to search for physics beyond the Standard Model by exploring a previously inaccessible kinematic domain. While venturing into uncharted high-energy territories promises excitement, reconstructing objects with enormous transverse momenta will require overcoming major experimental challenges. This work investigates the identification of boosted $W$ bosons and boosted top quarks in the context of three beyond the Standard Model scenarios: heavy vector-like quark ($B'$), heavy neutral gauge boson ($Z'$), and heavy neutral Higgs boson ($H$). We employ machine learning techniques, including eXtreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN), to identify these ultra-boosted objects in the collider from their SM background counterpart. We evaluate the performance of these techniques in distinguishing $W$ jets and top jets from QCD jets at extremely high transverse momenta ($p_{T}$) values, demonstrating their potential for future FCC-hh analyses. |
| title | Tagging ultra-boosted jets at FCC-hh using machine learning techniques |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2501.06702 |