# Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders arXiv: https://arxiv.org/abs/2503.00131 · HTML: https://mlsanity.com/paper/2503.00131/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 89 references that are in the map. - First posted on arXiv 28 Feb 2025. ## Builds on (most cited first, 29 of 29) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022) - [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023) - [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023) - [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020) - [Particle Transformer for Jet Tagging](https://mlsanity.com/paper/2202.03772/) (2022) - [Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models](https://mlsanity.com/paper/2401.13537/) (2024) - [OmniJet-α: The first cross-task foundation model for particle physics](https://mlsanity.com/paper/2403.05618/) (2024) - [MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks](https://mlsanity.com/paper/2101.08578/) (2021) - [Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data](https://mlsanity.com/paper/2002.03605/) (2020) - [Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models](https://mlsanity.com/paper/2403.07066/) (2024) - [Is Tokenization Needed for Masked Particle Modelling?](https://mlsanity.com/paper/2409.12589/) (2024) - [Finetuning Foundation Models for Joint Analysis Optimization](https://mlsanity.com/paper/2401.13536/) (2024) - [Towards a Computer Vision Particle Flow](https://mlsanity.com/paper/2003.08863/) (2020) - [Machine Learning for Particle Flow Reconstruction at CMS](https://mlsanity.com/paper/2203.00330/) (2022) - [Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics](https://mlsanity.com/paper/2412.10504/) (2024) - [OmniJet-αC: Learning point cloud calorimeter simulations using generative transformers](https://mlsanity.com/paper/2501.05534/) (2025) - [Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture](https://mlsanity.com/paper/2412.05333/) (2024) - [Anomalies, Representations, and Self-Supervision](https://mlsanity.com/paper/2301.04660/) (2023) - [Machine Learning Methods for Track Classification in the AT-TPC](https://mlsanity.com/paper/1810.10350/) (2018) - [Progress towards an improved particle flow algorithm at CMS with machine learning](https://mlsanity.com/paper/2303.17657/) (2023) - [The Pandora Software Development Kit for Pattern Recognition](https://mlsanity.com/paper/1506.05348/) (2015) - [Leveraging universality of jet taggers through transfer learning](https://mlsanity.com/paper/2203.06210/) (2022) - [Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors](https://mlsanity.com/paper/2309.06782/) (2023) - [Improving the performance of weak supervision searches using transfer and meta-learning](https://mlsanity.com/paper/2312.06152/) (2023) ## Built on by (most cited first, 9 of 9) - [RINO: Renormalization Group Invariance with No Labels](https://mlsanity.com/paper/2509.07486/) (2025) - [End-to-End Optimal Detector Design with Mutual Information Surrogates](https://mlsanity.com/paper/2503.14342/) (2025) - [Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning](https://mlsanity.com/paper/2507.23297/) (2025) - [End-to-end event reconstruction for precision physics at future colliders](https://mlsanity.com/paper/2603.04084/) (2026) - [Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks](https://mlsanity.com/paper/2609.31862/) (2026) - [Machine-learned particle flow as a foundation model for collider physics](https://mlsanity.com/paper/2606.14373/) (2026) - [Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics](https://mlsanity.com/paper/2605.07471/) (2026) - [Meta-learning accelerates detector design optimization](https://mlsanity.com/paper/2609.35827/) (2026) - [RINO: Renormalization Group Distillation with No Labels](https://mlsanity.com/paper/2610.15919/) (2026) ## Abstract (arXiv) 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.