Speaker
Description
Neural Simulation Based Inference (NSBI) is a collection of statistical machine learning methods that learn the likelihood or posterior based on high-dimensional input data. One flavor of NSBI, Neural Likelihood Ratio Estimation (NLRE), has emerged as a powerful method in the domain of High Energy Physics (HEP), having recently been used in Higgs-boson measurement [1] at the Large Hadron Collider (LHC). NLRE uses the likelihood ratio trick [2] to transform the Bayes optimal binary classifier to a ratio of probability densities, as a surrogate for the ratio that can be used directly in a per-event maximum likelihood process to perform unbinned measurements. It has the inherent advantage over binned template histogram fits as it doesn't incur loss of information by forced dimensionality reduction nor the aggregation process of binning. However, due to the direct usage of neural surrogates, NLRE requires highly reliable classifiers. This fact can prevent the utilization of more powerful, deep networks, which are known to suffer from calibration shortcomings [3]. Leveraging the core infrastructural element of the NEEDLE project [4], needle-sbi, an efficient workflow orchestrator for large-scale deployment and tracking of neural surrogates enabling practicable studying of NSBI, we investigate this problem from two perspectives: improving the estimator and restructuring the question posed to the model.
We present an extensive validation and calibration suite for NLRE classifiers, integrated into, and automated by needle-sbi, with which we demonstrate the insufficiency of common binary classifier post-hoc calibration approaches for improving estimated probability ratios in the NLRE setting. Those methods, like temperature scaling, isotonic regression, and Platt scaling rely [5] on remapping classifier scores, but no function of the score alone can repair a residual that differs between events sharing the same score. Therefore, we further investigate more expressive methods such as modular model extension [6] acting on the learned representation and adapter-based fine-tuning [7]. Studies use transformer models trained on FAIR Universe Higgs uncertainty challenge dataset [8] as a proxy for realistic HEP data analyses, and a custom three-body decay model [9] for its tractable likelihood and generator-level densities, the latter of which we also investigate as a direct augmentation of the training objective.
Restructuring the question begins from what NLRE does well: classifiers are simple and not too computationally costly to train. However, HEP statistical workflows frequently require combining multiple neural probability ratio surrogates which further compounds small classifier errors. Neural Likelihood Estimation (NLE) directly produces absolute densities that don't require composition, but solver-based NLE with diffusion models [10] or flow matching [11] is much more computationally expensive. We introduce Density Ratio Diffusion Probabilistic Models (DRDPM) that hybridize the two approaches: they deliver NLE, while retaining classifier-training simplicity of NLRE. DRDPM hinges on a multi-class classifier trained to discern the discrete timesteps of a forward diffusion process as it interpolates between the data distribution and a standard Gaussian anchor, from which conditional log-likelihood is obtained as the difference of two boundary logits, combined with the closed-form Gaussian term. Demonstrated on the three-body decay model, DRDPM yields absolute likelihoods at the cost of a single forward pass, increasing the speed of NLE inferencing by an order of magnitude or more compared to solver-based NLE.
Because DRDPM relies on differences between boundary-class outputs, its calibration becomes the central methodological challenge and a natural direction for future work merging these two research tracks.
References:
[1] The ATLAS Collaboration, Measurement of off-shell Higgs boson production in the $H^*\rightarrow ZZ\rightarrow 4\ell$ decay channel using a neural simulation-based inference technique in 13TeV pp collisions with the ATLAS detector, Reports on Progress in Physics, vol. 88, p. 057803, 2025.
[2] J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, A guide to constraining effective field theories
with machine learning, Phys. Rev. D, vol. 98, p. 052004, 2018.
[3] C. Guo, G. Pleiss, Y. Sun, and K. Weinberger, On calibration of modern neural networks, ICML, 2017.
[4] NEEDLE open-source release version GitHub: https://github.com/needle-sbi/needle-sbi/
[5] T. Silva Filho, H. Song, M. Perello-Nieto, R. Santos-Rodriguez, M. Kull, and P. Flach, Classifier calibration: a survey on how to assess and improve predicted class probabilities, Machine Learning, vol. 112, pp. 3211--3260, 2023.
[6] A. Rahimi, T. Mensink, K. Gupta, T. Ajanthan, C. Sminchisescu, and R. Hartley, Post-hoc calibration of neural networks by g-layers, arXiv:2006.12807, 2022.
[7] E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, LoRA: Low-rank adaptation of large language models, ICLR, 2022.
[8] FAIR universe - HiggsML Uncertainty Challenge: https://www.codabench.org/competitions/2977/
[9] Three-Body Decay Monte Carlo package: https://sjiggins.github.io/VegasThreeBodyDecay-docs/
[10] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, Score-based generative modeling through stochastic differential equations, ICLR, 2021.
[11] Y. Lipman, R. T. Q. Chen, H. Ben-Hamu, M. Nickel, and M. Le, Flow matching for generative modeling, ICLR, 2023.