Huang, G. Y., Lindner, M., Martinez-Mirave, P., & Sen, M. (2022). Cosmology-friendly time-varying neutrino masses via the sterile neutrino portal. Phys. Rev. D, 106(3), 033004–18pp.
Abstract: We investigate a consistent scenario of time-varying neutrino masses, and discuss its impact on cosmology, beta decay, and neutrino oscillation experiments. Such time-varying masses are assumed to be generated by the coupling between a sterile neutrino and an ultralight scalar field, which in turn affects the light neutrinos by mixing. We demonstrate how various cosmological bounds, such as those coming from big bang nucleosynthesis, the cosmic microwave background, as well as large scale structures, can be evaded in this model. This scenario can be further constrained using multiple terrestrial experiments. In particular, for beta-decay experiments like KATRIN, nontrivial distortions to the electron spectrum can be induced, even when time-variation is fast and it gets averaged. Furthermore, the presence of time-varying masses of sterile neutrinos will alter the interpretation of light sterile neutrino parameter space in the context of the reactor and gallium anomalies. In addition, we also study the impact of such time-varying neutrino masses on results from the BEST collaboration, which have recently strengthened the gallium anomaly. If confirmed, we find that the time-varying neutrino mass hypothesis could give a better fit to the recent BEST data.
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Horak, J., Ihssen, F., Papavassiliou, J., Pawlowski, J. M., Weber, A., & Wetterich, C. (2022). Gluon condensates and effective gluon mass. SciPost Phys., 13(2), 042–40pp.
Abstract: Lattice simulations along with studies in continuum QCD indicate that non-perturbative quantum fluctuations lead to an infrared regularisation of the gluon propagator in covariant gauges in the form of an effective mass-like behaviour. In the present work we propose an analytic understanding of this phenomenon in terms of gluon condensation through a dynamical version of the Higgs mechanism, leading to the emergence of color condensates. Within the functional renormalisation group approach we compute the effective potential of covariantly constant field strengths, whose non-trivial minimum is related to the color condensates. In the physical case of an SU(3) gauge group this is an octet condensate. The value of the gluon mass obtained through this procedure compares very well to lattice results and the mass gap arising from alternative dynamical scenarios.
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HISPEC-DESPEC Collaboration(Polettini, M. et al), Algora, A., Morales, A. I., & Orrigo, S. E. A. (2022). Decay studies in the A similar to 225 Po-Fr region from the DESPEC campaign at GSI in 2021. Nuovo Cim. C, 45(5), 125–4pp.
Abstract: The HISPEC-DESPEC collaboration aims at investigating the struc-ture of exotic nuclei formed in fragmentation reactions with decay spectroscopymeasurements, as part of the FAIR Phase-0 campaign at GSI. This paper reportson first results of an experiment performed in spring 2021, with a focus on beta-decaystudies in the Po-Fr nuclei in the 220 < A <230 island of octupole deformationexploiting the DESPEC setup. Ion-beta correlations and fast-timing techniques arebeing employed, giving an insight into this difficult-to-reach region.
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Hirn, J., Garcia, J. E., Montesinos-Navarro, A., Sanchez-Martin, R., Sanz, V., & Verdu, M. (2022). A deep Generative Artificial Intelligence system to predict species coexistence patterns. Methods Ecol. Evol., 13, 1052–1061.
Abstract: Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.
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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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