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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. 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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4. Searching for (sharp) thresholds in random structures: Where are we now?

   https://doi.org/10.1090/bull/1857  

   Perkins, Will (January 2025, Bulletin of the American Mathematical Society)

   We survey the current state of affairs in the study of thresholds and sharp thresholds in random structures on the occasion of the recent proof of the Kahn–Kalai conjecture by Park and Pham and the fairly recent proof of the satisfiability conjecture for large k by Ding, Sly, and Sun. Random discrete structures appear as fundamental objects of study in many scientific and mathematical fields including statistical physics, combinatorics, algorithms and complexity, social choice theory, coding theory, and statistics. While the models and properties of interest in these fields vary widely, much progress has been made through the development of general tools applicable to large families of models and properties all at once. Historically, these tools originated to solve or make progress on specific difficult conjectures in the areas mentioned above. We will survey recent progress on some of these hard problems and describe some challenges for the future. This survey was prepared in conjunction with a talk for the Current Events Bulletin at the 2024 Joint Mathematics Meetings (JMM) in San Francisco, California. 

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5. How Augmented Reality Affects Collaborative Learning of Physics: a Qualitative Analysis

   Unahalekhaka, A.; Radu, I.; Schneider, B. (July 2019, Computer-supported collaborative learning)

   Augmented reality (AR) is a powerful visualization tool to support learning of scientific concepts across learners of various ages. AR can make information otherwise invisible visible in the physical world in real-time. In this study, we are looking at a subset of data from a larger study (N=120), in which participant pairs interacted with an augmented sound producing speaker. We explored the learning behaviors in eight pairs of learners (N=16) who participated in an unstructured physics activity under two conditions: with or without AR. Comparing behaviors between the two experimental conditions, we found that AR affected learning in four different ways: participants in the AR condition (1) learned more about visual concepts (ex: magnetic field structures) but learned less about nonvisual content (ex: relationship between electricity and physical movement); (2) stopped exploring the system faster than NonAR participants; (3) used less aids in exploration and teaching; and (4) spent less time in teaching their collaborators. We discuss implications of those results for designing collaborative learning activities with augmented reality. 

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