{"doi":"10.1103/physrevd.111.092015","title":"Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders","abstract":"<jats:p>We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the model on a large full simulation dataset from one detector design, and subsequently fine-tune the model on a sample with a different collider and detector design. Specifically, we use the Compact Linear Collider detector (CLICdet) model for the initial training set and demonstrate successful knowledge transfer to the CLIC-like detector (CLD) proposed for the Future Circular Collider in electron-positron mode. We show that with an order of magnitude less samples from the second dataset, we can achieve the same performance as a costly training from scratch, across particle-level and event-level performance metrics, including jet and missing transverse momentum resolution. Furthermore, we find that the fine-tuned model achieves comparable performance to the traditional rule-based particle-flow approach on event-level metrics after training on 100,000 CLD events, whereas a model trained from scratch requires at least 1 million CLD events to achieve similar reconstruction performance. To our knowledge, this represents the first full-simulation cross-detector transfer learning study for particle-flow reconstruction. These findings offer valuable insights towards building large foundation models that can be fine-tuned across different detector designs and geometries, helping to accelerate the development cycle for new detectors and opening the door to rapid detector design and optimization using machine learning.</jats:p>","journal":"Physical Review D","year":2025,"id":12680,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0496,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-05-29","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":98868,"name":"Joosep Pata","orcid":"0000-0002-5191-5759","position":1,"is_corresponding":false},{"id":98869,"name":"Dolores Garcia","orcid":"0000-0002-0120-8757","position":2,"is_corresponding":false},{"id":98870,"name":"Eric Wulff","orcid":"0000-0002-4681-8516","position":3,"is_corresponding":false},{"id":98871,"name":"Mengke Zhang","orcid":"0009-0004-7492-4895","position":4,"is_corresponding":false},{"id":9162,"name":"Michael Kagan","orcid":"0000-0002-3386-6869","position":5,"is_corresponding":false},{"id":7506,"name":"Javier Duarte","orcid":"0000-0002-5076-7096","position":6,"is_corresponding":false},{"id":44396,"name":"M. Zhang","orcid":null,"position":7,"is_corresponding":false},{"id":98867,"name":"Farouk Mokhtar","orcid":"0000-0003-2533-3402","position":0,"is_corresponding":true}],"reference_count":65,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}