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Pena-Garay, C., Verde, L., & Jimenez, R. (2017). Neutrino footprint in large scale structure. Phys. Dark Universe, 15, 31–34.
Abstract: Recent constrains on the sum of neutrino masses inferred by analyzing cosmological data, show that detecting a non-zero neutrino mass is within reach of forthcoming cosmological surveys. Such a measurement will imply a direct determination of the absolute neutrino mass scale. Physically, the measurement relies on constraining the shape of the matter power spectrum below the neutrino free streaming scale: massive neutrinos erase power at these scales. However, detection of a lack of small-scale power from cosmological data could also be due to a host of other effects. It is therefore of paramount importance to validate neutrinos as the source of power suppression at small scales. We show that, independent on hierarchy, neutrinos always show a footprint on large, linear scales; the exact location and properties are fully specified by the measured power suppression (an astrophysical measurement) and atmospheric neutrinos mass splitting (a neutrino oscillation experiment measurement). This feature cannot be easily mimicked by systematic uncertainties in the cosmological data analysis or modifications in the cosmological model. Therefore the measurement of such a feature, up to 1% relative change in the power spectrum for extreme differences in the mass eigenstates mass ratios, is a smoking gun for confirming the determination of the absolute neutrino mass scale from cosmological observations. It also demonstrates the synergy between astrophysics and particle physics experiments.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2020). Event reconstruction for KM3NeT/ORCA using convolutional neural networks. J. Instrum., 15(10), P10005–39pp.
Abstract: The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.
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Hernandez, P. (2012). CP violation in the neutrino sector: The new frontier. C. R. Phys., 13(2), 186–192.
Abstract: The discovery of neutrino masses has revealed a new flavour sector in the Standard Model. Just like the quark flavour sector, it contains a seed of CP violation, resulting in an asymmetric behaviour of matter and antimatter. It is argued that this new source of leptonic CP violation may be discovered in more precise neutrino oscillation experiments involving neutrino beams with energies in the GeV range that will be sent to distances of a few thousand kilometres.
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Double Chooz collaboration(Abrahao, T. et al), & Novella, P. (2018). Novel event classification based on spectral analysis of scintillation waveforms in Double Chooz. J. Instrum., 13, P01031–26pp.
Abstract: Liquid scintillators are a common choice for neutrino physics experiments, but their capabilities to perform background rejection by scintillation pulse shape discrimination is generally limited in large detectors. This paper describes a novel approach for a pulse shape based event classification developed in the context of the Double Chooz reactor antineutrino experiment. Unlike previous implementations, this method uses the Fourier power spectra of the scintillation pulse shapes to obtain event-wise information. A classification variable built from spectral information was able to achieve an unprecedented performance, despite the lack of optimization at the detector design level. Several examples of event classification are provided, ranging from differentiation between the detector volumes and an efficient rejection of instrumental light noise, to some sensitivity to the particle type, such as stopping muons, ortho-positronium formation, alpha particles as well as electrons and positrons. In combination with other techniques the method is expected to allow for a versatile and more efficient background rejection in the future, especially if detector optimization is taken into account at the design level.
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KM3NeT Collaboration(Aiello, S. et al), Barrios-Marti, J., Calvo, D., Coleiro, A., Colomer, M., Gozzini, S. R., et al. (2018). Characterisation of the Hamamatsu photomultipliers for the KM3NeT Neutrino Telescope. J. Instrum., 13, P05035–17pp.
Abstract: The Hamamatsu R12199-023-inch photomultiplier tube is the photodetector chosen for the first phase of the KM3NeT neutrino telescope. About 7000 photomultipliers have been characterised for dark count rate, timing spread and spurious pulses. The quantum efficiency, the gain and the peak-to-valley ratio have also been measured for a sub-sample in order to determine parameter values needed as input to numerical simulations of the detector.
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