Neutrinos in IceCube/KM3NeT as probes of Dark Matter Substructures in Galaxy Clusters

Galaxy clusters are one of the most promising candidate sites for dark matter annihilation. We focus on dark matter with mass in the range 10 GeV - 100 TeV annihilating to muon pairs, neutrino pairs, top pairs, or two neutrino pairs, and forecast the expected sensitivity to the annihilation cross section into these channels by observing galaxy clusters at IceCube/KM3NeT. Optimistically, the presence of dark matter substructures in galaxy clusters is predicted to enhance the signal by 2-3 orders of magnitude over the contribution from the smooth component of the dark matter distribution. Optimizing for the angular size of the region of interest for galaxy clusters, the sensitivity to the annihilation cross section of heavy DM with mass in the range 300 GeV - 100 TeV will be of the order of 10^{-24} cm^3 s^{-1}, for full IceCube/KM3NeT live time of 10 years, which is about one order of magnitude better than the best limit that can be obtained by observing the Milky Way halo. We find that neutrinos from cosmic ray interactions in the galaxy cluster, in addition to the atmospheric neutrinos, are a source of background. We show that significant improvement in the experimental sensitivity can be achieved for lower DM masses in the range 10 GeV - 300 GeV if neutrino-induced cascades can be reconstructed to approximately 5 degrees accuracy, as may be possible in KM3NeT. We therefore propose that a low-energy extension "KM3NeT-Core", similar to DeepCore in IceCube, be considered for an extended reach at low DM masses.Comment: v2: 17 pages, 5 figures. Neutrino spectra corrected, dependence on dark matter substructure model included, references added. Results unchanged. Accepted in PR

Improving the analysis of near-infrared spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation

Objective. The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). Approach. This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. Main results. The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. Significance. The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions, and that therefore a data-driven approach such as that provided by the proposed LDA-based method is to be favored.EC/H2020/641858/EU/Understanding and predicting developmental language abilities and disorders in multilingual Europe/PREDICTABL