Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector 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

Pileup noise behavior and corrections in the ATLAS EMCal

In this note, the pile-up influence on the energy resolution of the ATLAS LAr EMCal expected until the end of run 2 is explored, with the supercluster algorithm. The pile-up noise is extracted for various η-regions and electron energies, and found to match the expected behavior. An attempt is then made at improving the energy resolution using the ambient energy density and pileup tracks using two methods for finding correlations. The corrections yield no significant improvement to the energy resolution due to small correlations

Environmental Justice Pedagogies and Self-Efficacy for Climate Action

As institutions of knowledge and innovation, colleges and universities have a responsibility to prepare students to lead in a world impacted by climate change. While sustainability and climate change have been increasingly addressed on campuses, several aspects of typical climate change education, such as the use of fear appeals, and crisis narratives, have served to disempower and disengage students from the issue. Evidence suggests that incorporating justice-oriented concepts and pedagogies may help students build the skills and confidence to engage in complex social concerns. This qualitative study sought to understand the ways in which an undergraduate environmental justice course at the University of Michigan might contribute to students’ sense of self-efficacy for climate change action. Findings indicated that teaching from a justice perspective supported students’ understanding of root causes, the need for collective action, and their empathy for others. Self-efficacy for climate action was most apparent when students were (1) confident in a particular skill set and (2) when the scale of the problem matched their ability to address it. This supported prior evidence that environmental justice can serve as a critical pedagogical approach for encouraging engagement and empowerment in climate action