Event reconstruction and tau neutrino appearance using CNNs for KM3NeT / ORCA T. Eberl*, M. Moser, S. Hallmann, J. Hofestädt, and S. Reck for the KM3NeT Collaboration Erlangen Centre for Astroparticle Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg KM3NeT / ORCA: a water Cherenkov detector in the deep sea several Mtons instrumented mass 115 strings 18 DOMs/string ~ 64k PMTs ~200 m ~20 – 23 m ~9 m Letter of Intent for KM3NeT 2.0, J.Phys. G43 (2016) no.8, 084001 Simulated sets of KM3NeT data, on the level of individual recorded photosensor signals above a set threshold, are binned in space and time, and then used as inputs for different Convolutional Neural Networks (CNNs). A complete data classification and regression chain using these CNNs is provided, cf. arXiv:2004.08254. The classification tasks consist of the rejection of background events, induced by atmospheric muons and random noise, and the separation of track-like and shower-like event topologies induced by different neutrino flavours and interaction modes. The regression CNN allows for the reconstruction of the neutrino interaction position, the neutrino energy and direction, and of their corresponding uncertainties. This analysis chain reaches a competitive and partly superior performance with respect to classical approaches pursued in KM3NeT. Gains in sensitivity of 10% and more can be reached compared to classical approaches in event reconstruction and classification. The CNN-based reconstruction results are then used to calculate the sensitivity of KM3NeT/ORCA to deviations from the standard-model expectations for the purely oscillation-induced flux of tau neutrinos from the atmosphere. Classification: Background suppression Recorded event data is dominated by atmospheric muons (several tens of Hz trigger rate) from cosmic- ray air-showers reaching the detector from above and pure-noise events due to 40K decays and biological background light. An efficient selection of neutrino events (mHz rate) is necessary for physics analyses. Fraction of atmospheric neutrinos having passed classical analysis pre-selection that survive the anti-muon cut vs. the contamination (fraction of atmospheric muons) in the final sample for the CNN and Random Forest (RF) anti-muon classifier. References: [1] http://github.com/KM3NeT/OrcaNet [2] http://www.tensorflow.org [3] http://keras.io KM3NeT is a distributed neutrino research infrastructure in the abyss of the Mediterranean Sea. One part of KM3NeT is the ORCA (Oscillation Research with Cosmics in the Abyss) detector. It is under construction and has been optimised to study the properties of neutrinos with GeV energies. Neutrinos are detected through the Cherenkov radiation induced by secondary particles generated in neutrino interactions in the water. To this end, several megatons of seawater will be instrumented with a 3D array of 2070 glass spheres (DOMs), each housing 31 3’’ PMTs. Classification: Event topology Neutrino interactions lead to different event topologies: Track-like: !" charged-current (CC) interactions create a final-state # that travels ~4m/GeV leaving a long track-like light signature. Shower-like: e with short radiation length ($eCC), % with short lifetime ($%CC) and neutral current (hadronic cascade only) events are localised around the interaction vertex. Top: Fraction of events classified as track (track score > 0.5) by the CNN as a function of neutrino energy. Bottom: Relative improvement using the CNN compared to the RF classifier in terms of classification distance between $#CC and the respective shower-dominated electron neutrino channels. Regression: Energy reconstruction Reconstructed energy vs. true MC neutrino energy for $eCC. The distribution is normalised to unity in each true energy bin. KM3NeT preliminary Default CNN Tau neutrino sensitivity KM3NeT preliminary CNN Non-appearance (=0) exclusion at 5s level possible within a few months of operation with full ORCA detector. Fit results robu