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Aparici, A.; Herrero-Garcia, J.; Rius, N.; Santamaria, A. |
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Title |
Neutrino masses from new generations |
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Journal Article |
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Year |
2011 |
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Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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07 |
Issue |
7 |
Pages |
122 |
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Abstract |
We reconsider the possibility that Majorana masses for the three known neutrinos are generated radiatively by the presence of a fourth generation and one right-handed neutrino with Yukawa couplings and a Majorana mass term. We find that the observed light neutrino mass hierarchy is not compatible with low energy universality bounds in this minimal scenario, but all present data can be accommodated with five generations and two right-handed neutrinos. Within this framework, we explore the parameter space regions which are currently allowed and could lead to observable effects in neutrinoless double beta decay, mu-e conversion in nuclei and μ-> e gamma experiments. We also discuss the detection prospects at LHC. |
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1126-6708 |
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WOS:000293741500058 |
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no |
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yes |
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no |
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IFIC @ elepoucu @ |
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759 |
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Author |
Herrero-Garcia, J.; Patrick, R.; Scaffidi, A. |
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Title |
A semi-supervised approach to dark matter searches in direct detection data with machine learning |
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Journal Article |
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Year |
2022 |
Publication |
Journal of Cosmology and Astroparticle Physics |
Abbreviated Journal |
J. Cosmol. Astropart. Phys. |
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02 |
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Pages |
039 - 19pp |
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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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no |
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yes |
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IFIC @ pastor @ |
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5495 |
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