{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/efficient-and-robust-jet-tagging-at-the-lhc","title":"Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation","arxiv_id":"2311.14160","date":"2023-11-23","proceeding":null,"authors":["Ryan Liu","Abhijith Gandrakota","Jennifer Ngadiuba","Maria Spiropulu","Jean-Roch Vlimant"],"abstract":"The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed. For deep learning models, this implies that only models with low computational complexity that have weak inductive bias are feasible. To address this issue, we utilize knowledge distillation to leverage both the performance of large models and the reduced computational complexity of small ones. In this paper, we present an implementation of knowledge distillation, demonstrating an overall boost in the student models' performance for the task of classifying jets at the LHC. Furthermore, by using a teacher model with a strong inductive bias of Lorentz symmetry, we show that we can induce the same inductive bias in the student model which leads to better robustness against arbitrary Lorentz boost.","url_abs":"https://arxiv.org/abs/2311.14160v1","url_pdf":"https://arxiv.org/pdf/2311.14160v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"efficient-and-robust-jet-tagging-at-the-lhc","repo_url":"https://github.com/ryanliu30/kd4jets","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"jet-tagging","task_name":"Jet Tagging"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"}],"methods":[{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2311.14160","atlas_url":"https://app.syntology.ai/?focus=2311.14160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14160"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ryanliu30/kd4jets","reach":{"status":"ok"}}],"summary":{"ran":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"cc9972cf651fa1a6","entry":"boost_jets","repo":"ryanliu30/kd4jets","repo_kind":"official","path":"kd4jets/knowledge_distillation_base.py","file_url":"https://github.com/ryanliu30/kd4jets/blob/HEAD/kd4jets/knowledge_distillation_base.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cc9972cf651fa1a6"}},{"code_sha256_prefix":"63559bc480bea732","entry":"norm","repo":"ryanliu30/kd4jets","repo_kind":"official","path":"kd4jets/models/deepset.py","file_url":"https://github.com/ryanliu30/kd4jets/blob/HEAD/kd4jets/models/deepset.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"63559bc480bea732"}},{"code_sha256_prefix":"a03e808a79bcbfe1","entry":"boost_batch","repo":"ryanliu30/kd4jets","repo_kind":"official","path":"kd4jets/knowledge_distillation_base.py","file_url":"https://github.com/ryanliu30/kd4jets/blob/HEAD/kd4jets/knowledge_distillation_base.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a03e808a79bcbfe1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}