# Neural Scaling Laws for Boosted Jet Tagging

arXiv: https://arxiv.org/abs/2602.15781 · HTML: https://mlsanity.com/paper/2602.15781/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [Scaling Data-Constrained Language Models](https://mlsanity.com/paper/2305.16264/) (2023)
- [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)
- [A Lorentz-Equivariant Transformer for All of the LHC](https://mlsanity.com/paper/2411.00446/) (2024)
- [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://mlsanity.com/paper/2307.16506/) (2023)
- [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)
- [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)
- [EveNet: A Foundation Model for Particle Collision Data Analysis](https://mlsanity.com/paper/2601.17126/) (2026)
- [Scaling laws for amplitude surrogates](https://mlsanity.com/paper/2601.13308/) (2026)
- [Towards Foundation Models for Experimental Readout Systems Combining Discrete and Continuous Data](https://mlsanity.com/paper/2505.08736/) (2025)
- [Differentiable Vertex Fitting for Jet Flavour Tagging](https://mlsanity.com/paper/2310.12804/) (2023)
- [SURFing to the Fundamental Limit of Jet Tagging](https://mlsanity.com/paper/2511.15779/) (2025)
- [Enhancing next token prediction based pre-training for jet foundation models](https://mlsanity.com/paper/2512.04149/) (2025)
- [Double Descent and Overparameterization in Particle Physics Data](https://mlsanity.com/paper/2509.01397/) (2025)

## Built on by (most cited first, 12 of 12)

- [Neural Scaling Laws for Jet Generation](https://mlsanity.com/paper/2605.28940/) (2026)
- [Towards Engineering Scaling Laws with Pretraining Data Composition](https://mlsanity.com/paper/2606.19781/) (2026)
- [Statistical Properties of Training & Generalization](https://mlsanity.com/paper/2606.20299/) (2026)
- [Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks](https://mlsanity.com/paper/2609.31862/) (2026)
- [Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives](https://mlsanity.com/paper/2606.14870/) (2026)
- [Scaling Collider Event Generation with Residual-Quantized Tokens](https://mlsanity.com/paper/2610.00569/) (2026)
- [Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics](https://mlsanity.com/paper/2605.07471/) (2026)
- [Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction](https://mlsanity.com/paper/2605.21483/) (2026)
- [Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders](https://mlsanity.com/paper/2608.26090/) (2026)
- [Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design](https://mlsanity.com/paper/2609.23385/) (2026)
- [Machine learning for the LHC physics program: a 2025-2026 stocktake](https://mlsanity.com/paper/2609.32874/) (2026)
- [How to scale your HEP ML models: A recipe for robust architecture comparisons at scale](https://mlsanity.com/paper/2610.06784/) (2026)

## Abstract (arXiv)

The success of Large Language Models (LLMs) has established that scaling compute, through joint increases in model capacity and dataset size, is the primary driver of performance in modern machine learning. While machine learning has long been an integral component of High Energy Physics (HEP) data analysis workflows, the compute used to train state-of-the-art HEP models remains orders of magnitude below that of industry foundation models. With scaling laws only beginning to be studied in the field, we investigate neural scaling laws for boosted jet classification using the public JetClass dataset. We derive compute optimal scaling laws and identify an effective performance limit that can be consistently approached through increased compute. We study how data repetition, common in HEP where simulation is expensive, modifies the scaling yielding a quantifiable effective dataset size gain. We then study how the scaling coefficients and asymptotic performance limits vary with the choice of input features and particle multiplicity, demonstrating that increased compute reliably drives performance toward an asymptotic limit, and that more expressive, lower-level features can raise the performance limit and improve results at fixed dataset size.