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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. Debates: Does Information Theory Provide a New Paradigm for Earth Science? Emerging Concepts and Pathways of Information Physics

   https://doi.org/10.1029/2019WR025270  

   Perdigão, Rui A. P. ; Ehret, Uwe ; Knuth, Kevin H. ; Wang, Jingfeng ( February 2020 , Water Resources Research)

   Entropy and Information are key concepts not only in Information Theory but also in Physics: historically in the fields of Thermodynamics, Statistical and Analytical Mechanics, and, more recently, in the field of Information Physics. In this paper we argue that Information Physics reconciles and generalizes statistical, geometric, and mechanistic views on information. We start by demonstrating how the use and interpretation of Entropy and Information coincide in Information Theory, Statistical Thermodynamics, and Analytical Mechanics, and how this can be taken advantage of when addressing Earth Science problems in general and hydrological problems in particular. In the second part we discuss how Information Physics provides ways to quantify Information and Entropy from fundamental physical principles. This extends their use to cases where the preconditions to calculate Entropy in the classical manner as an aggregate statistical measure are not met. Indeed, these preconditions are rarely met in the Earth Sciences due either to limited observations or the far‐from‐equilibrium nature of evolving systems. Information Physics therefore offers new opportunities for improving the treatment of Earth Science problems.

    

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4. 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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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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