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Khosa, C. K., Sanz, V., & Soughton, M. (2022). A simple guide from machine learning outputs to statistical criteria in particle physics. SciPost Phys. Core, 5(4), 050–31pp.
Abstract: In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-pT hadronic activity, and boosted Higgs in association with a massive vector boson.
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van Beekveld, M., Beenakker, W., Caron, S., Kip, J., Ruiz de Austri, R., & Zhang, Z. Y. (2023). Non-standard neutrino spectra from annihilating neutralino dark matter. SciPost Phys. Core, 6(1), 006–23pp.
Abstract: Neutrino telescope experiments are rapidly becoming more competitive in indirect de-tection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simulta-neously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation.
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Herrero-Garcia, J., Patrick, R., & Scaffidi, A. (2022). A semi-supervised approach to dark matter searches in direct detection data with machine learning. J. Cosmol. Astropart. Phys., 02, 039–19pp.
Abstract: The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.
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Liptak, Z. et al, & Marinas, C. (2022). Measurements of beam backgrounds in SuperKEKB Phase 2. Nucl. Instrum. Methods Phys. Res. A, 1040, 167168–19pp.
Abstract: The high design luminosity of the SuperKEKB electron–positron collider will result in challenging levels of beam-induced backgrounds in the interaction region. Understanding and mitigating these backgrounds is critical to the success of the Belle II experiment. We report on the first background measurements performed after roll-in of the Belle II detector, a period known as SuperKEKB Phase 2, utilizing both the BEAST II system of dedicated background detectors and the Belle II detector itself. We also report on first revisions to the background simulation made in response to our findings. Backgrounds measured include contributions from synchrotron radiation, beam-gas, Touschek, and injection backgrounds. At the end of Phase 2, single-beam backgrounds originating from the 4 GeV positron Low Energy Ring (LER) agree reasonably well with simulation, while backgrounds from the 7 GeV electron High Energy Ring (HER) are approximately one order of magnitude higher than simulation. We extrapolate these backgrounds forward and conclude it is safe to install the Belle II vertex detector.
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Middeldorf-Wygas, M. M., Oldengott, I. M., Bödeker, D., & Schwarz, D. J. (2022). Cosmic QCD transition for large lepton flavor asymmetries. Phys. Rev. D, 105, 123533–10pp.
Abstract: We study the impact of large lepton flavor asymmetries on the cosmic QCD transition. Scenarios of unequal lepton flavor asymmetries are observationally almost unconstrained and therefore open up a whole new parameter space for the cosmic QCD transition. We find that for large asymmetries, the formation of a Bose-Einstein condensate of pions can occur and identify the corresponding parameter space. In the vicinity of the QCD transition scale, we express the pressure in terms of a Taylor expansion with respect to the complete set of chemical potentials. The Taylor coefficients rely on input from lattice QCD calculations from the literature. The domain of applicability of this method is discussed.
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