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1. Learning to simulate high energy particle collisions from unlabeled data

   https://doi.org/10.1038/s41598-022-10966-7  

   Howard, Jessica N.; Mandt, Stephan; Whiteson, Daniel; Yang, Yibo (December 2022, Scientific Reports)

   Abstract In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly-described analytically. Instead, numerical simulations are used at great computational cost. We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Without the aid of current simulation information, OTUS trains a probabilistic autoencoder to transform directly between theoretical models and experimental data. Identifying the probabilistic autoencoder’s latent space with the space of theoretical models causes the decoder network to become a fast, predictive simulator with the potential to replace current, computationally-costly simulators. Here, we provide proof-of-principle results on two particle physics examples, Z -boson and top-quark decays, but stress that OTUS can be widely applied to other fields. 

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2. Reinterpretation of LHC Results for New Physics: Status and recommendations after Run 2

   https://doi.org/10.21468/SciPostPhys.9.2.022  

   Abdallah, Waleed; Salam, Shehu Abdus; Ahmadov, Azar; Ahriche, Amine; Alguero, Gaël; Allanach, Ben; Araz, Jack Y.; Arbey, Alexandre; Arina, Chiara; Athron, Peter; et al (January 2020, SciPost Physics)

   null (Ed.)

   We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data. 

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3. Statistical physics of large-scale neural activity with loops

   https://doi.org/10.1073/pnas.2426926122  

   Carcamo, David P; Lynn, Christopher W (October 2025, Proceedings of the National Academy of Sciences)

   As experiments advance to record from tens of thousands of neurons, statistical physics provides a framework for understanding how collective activity emerges from networks of fine-scale correlations. While modeling these populations is tractable in loop-free networks, neural circuitry inherently contains feedback loops of connectivity. Here, for a class of networks with loops, we present an exact solution to the maximum entropy problem that scales to very large systems. This solution provides direct access to information-theoretic measures like the entropy of the model and the information contained in correlations, which are usually inaccessible at large scales. In turn, this allows us to search for the optimal network of correlations that contains the maximum information about population activity. Applying these methods to 45 recordings of approximately 10,000 neurons in the mouse visual system, we demonstrate that our framework captures more information—providing a better description of the population—than existing methods without loops. For a given population, our models perform even better during visual stimulation than spontaneous activity; however, the inferred interactions overlap significantly, suggesting an underlying neural circuitry that remains consistent across stimuli. Generally, we construct an optimized framework for studying the statistical physics of large neural populations, with future applications extending to other biological networks. 

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4. Learning to validate generative models: a goodness-of-fit approach

   https://doi.org/10.1088/2632-2153/ae73e3  

   Cappelli, Pietro; Grosso, Gaia; Letizia, Marco; Reyes-González, Humberto; Zanetti, Marco (July 2026, Machine Learning: Science and Technology)

   Abstract Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous validation. Classic approaches struggle with scalability, statistical power, or interpretability when applied to high-dimensional data, making it difficult to certify the reliability of these models in realistic, high-dimensional scientific settings. Here, we propose the use of the New Physics Learning Machine (NPLM), a learning-based approach to goodness-of-fit testing inspired by the Neyman–Pearson construction, to test generative networks trained on high-dimensional scientific data. We demonstrate the performance of NPLM for validation in two benchmark cases: generative models trained on mixtures of Gaussian models with increasing dimensionality, and a public end-to-end model developed to generate high-energy physics collision events. We show that NPLM can serve as a powerful validation method while also providing a means to diagnose sub-optimally modeled regions of the data. 

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5. Recommended conventions for reporting results from direct dark matter searches

   https://doi.org/10.1140/epjc/s10052-021-09655-y  

   Baxter, D.; Bloch, I. M.; Bodnia, E.; Chen, X.; Conrad, J.; Di Gangi, P.; Dobson, J. E.; Durnford, D.; Haselschwardt, S. J.; Kaboth, A.; et al (October 2021, The European Physical Journal C)

   Abstract The field of dark matter detection is a highly visible and highly competitive one. In this paper, we propose recommendations for presenting dark matter direct detection results particularly suited for weak-scale dark matter searches, although we believe the spirit of the recommendations can apply more broadly to searches for other dark matter candidates, such as very light dark matter or axions. To translate experimental data into a final published result, direct detection collaborations must make a series of choices in their analysis, ranging from how to model astrophysical parameters to how to make statistical inferences based on observed data. While many collaborations follow a standard set of recommendations in some areas, for example the expected flux of dark matter particles (to a large degree based on a paper from Lewin and Smith in 1995), in other areas, particularly in statistical inference, they have taken different approaches, often from result to result by the same collaboration. We set out a number of recommendations on how to apply the now commonly used Profile Likelihood Ratio method to direct detection data. In addition, updated recommendations for the Standard Halo Model astrophysical parameters and relevant neutrino fluxes are provided. The authors of this note include members of the DAMIC, DarkSide, DARWIN, DEAP, LZ, NEWS-G, PandaX, PICO, SBC, SENSEI, SuperCDMS, and XENON collaborations, and these collaborations provided input to the recommendations laid out here. Wide-spread adoption of these recommendations will make it easier to compare and combine future dark matter results. 

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