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Aarrestad, T. et al, Mamuzic, J., & Ruiz de Austri, R. (2022). Benchmark data and model independent event classification for the large hadron collider. SciPost Phys., 12(1), 043–57pp.
Abstract: We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.
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Baamara, Y., Gessner, M., & Sinatra, A. (2023). Quantum-enhanced multiparameter estimation and compressed sensing of a field. SciPost Phys., 14(3), 050–18pp.
Abstract: We show that a significant quantum gain corresponding to squeezed or over-squeezed spin states can be obtained in multiparameter estimation by measuring the Hadamard coefficients of a 1D or 2D signal. The physical platform we consider consists of twolevel atoms in an optical lattice in a squeezed-Mott configuration, or more generally by correlated spins distributed in spatially separated modes. Our protocol requires the possibility to locally flip the spins, but relies on collective measurements. We give examples of applications to scalar or vector field mapping and compressed sensing.
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Banerjee, P., Coutinho, A., Engel, T., Gurgone, A., Signer, A., & Ulrich, Y. (2023). High-precision muon decay predictions for ALP searches. SciPost Phys., 15(1), 021–38pp.
Abstract: We present an improved theoretical prediction of the positron energy spectrum for the polarised Michel decay & mu;+ & RARR; e+ & nu;e & nu; over bar & mu;. In addition to the full next-to-next-to-leading order correction of order & alpha;2 in the electromagnetic coupling, we include logarithmically enhanced terms at even higher orders. Logarithms due to collinear emission are included at next-to-leading accuracy up to order & alpha;4. At the endpoint of the Michel spectrum, soft photon emission results in large logarithms that are resummed up to next-to-next-to leading logarithmic accuracy. We apply our results in the context of the MEG II and Mu3e experiments to estimate the impact of the theory error on the branching ratio sensitivity for the lepton-flavour-violating decay & mu;+ & RARR; e+X of a muon into an axion-like particle X.
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Barenboim, G., Hirn, J., & Sanz, V. (2021). Symmetry meets AI. SciPost Phys., 11(1), 014–11pp.
Abstract: We explore whether Neural Networks (NNs) can discover the presence of symmetries as they learn to perform a task. For this, we train hundreds of NNs on a decoy task based on well-controlled Physics templates, where no information on symmetry is provided. We use the output from the last hidden layer of all these NNs, projected to fewer dimensions, as the input for a symmetry classification task, and show that information on symmetry had indeed been identified by the original NN without guidance. As an interdisciplinary application of this procedure, we identify the presence and level of symmetry in artistic paintings from different styles such as those of Picasso, Pollock and Van Gogh.
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Cranmer, K. et al, & Sanz, V. (2022). Publishing statistical models: Getting the most out of particle physics experiments. SciPost Phys., 12(1), 037–55pp.
Abstract: The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases – including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits – we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.
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