Domi, A.

Guderian, D.

KM3NeT Collaboration

Reck, S.

Vermariën, G.

arXiv

Leiden University Scholary Publications

Graph neural networks for reconstruction and classification inKM3NeTReck, S.; Guderian, D.; Vermariën, G.; Domi, A.; KM3NeT CollaborationCitationReck, S., Guderian, D., Vermariën, G., & Domi, A. (2021). Graph neural networks forreconstruction and classification in KM3NeT. Journal Of Instrumentation, 16(10).doi:10.1088/1748-0221/16/10/C10011 Version: Not Applicable (or Unknown)License: Leiden University Non-exclusive licenseDownloaded from: https://hdl.handle.net/1887/3270810 Note: To cite this publication please use the final published version (if applicable).Prepared for submission to JINSTVLVNT 2021 - Very Large Volume Neutrino Telescope Workshop18-21 May 2021ValenciaGraph Neural Networks for reconstruction andclassification in KM3NeTS.Reck,𝑎,1 D. Guderian,𝑏 G. Vermariën,𝑐,𝑒 A. Domi𝑑,𝑒 on behalf of the KM3NeT collaboration𝑎Universität Erlangen-Nürnberg, Erwin-Rommel-Str. 1, 91058 Erlangen, GermanyErlangen Centre for Astroparticle Physics (ECAP)𝑏WWU Münster,Wilhelm-Klemm-Straße 9, 48149 Münster, Germany𝑐Leiden Observatory, Leiden University,Niels Bohrweg 2, Leiden, The Netherlands𝑑University of Amsterdam, Institute of Physics, Science Park 904, Amsterdam, The Netherlands𝑒Dutch National Institute for Subatomic Physics (Nikhef), Science Park 105, Amsterdam, The NetherlandsE-mail: stefan.reck@fau.deAbstract: KM3NeT, a neutrino telescope currently under construction in the Mediterranean Sea,consists of a network of large-volume Cherenkov detectors. Its two different sites, ORCA andARCA, are optimised for few GeV and TeV-PeV neutrino energies, respectively. This allows forstudying a wide range of physics topics spanning from the determination of the neutrino masshierarchy to the detection of neutrinos from astrophysical sources.Deep Learning techniques provide promising methods to analyse the signatures induced bycharged particles traversing the detector. This document will cover a Deep Learning based approachusing Graph Convolutional Networks to classify and reconstruct events in both the ORCA and ARCAdetector. Performance studies on simulations as well as applications to real data will be presented,together with comparisons to classical approaches.Keywords: Neutrino detectors, Cherenkov detectors, Data analysis, Pattern recognition, clusterfinding, calibration and fitting methods1Corresponding author.arXiv:2107.13375v1  [astro-ph.IM]  28 Jul 2021Contents1 Introduction 12 Atmospheric muon reconstruction 13 Studies on neutrino selections 21 IntroductionKM3NeT is a network of neutrino telescopes under construction in the Mediterranean Sea [1],consisting of two detectors: ORCA, optimised for oscillation studies with atmospheric neutrinos inthe 1 to 100 GeV range, and ARCA, optimised for cosmic neutrino searches in the TeV to PeV energyrange. Charged particles produced in neutrino interactions with water emit Cherenkov light, whichis detected by 3" photomultiplier tubes (PMTs) hosted in high-pressure glass spheres (DOMs). 31PMTs are stored in a single DOM, and 18 DOMs are fixed to a long vertical detection unit (DU) thatis anchored to the sea floor. Each of the three building blocks of KM3NeT will consist out of 115DUs. The detected light on each PMT (hit) can be used for reconstructing the particles’ properties,such as their energy or direction. A hit holds information about the time and the xyz-position of thePMT that recorded the light, as well as its pointing direction. Together, the hits of an event are theinput to reconstruction and classification algorithms. Since the data recorded by KM3NeT closelyresembles point clouds, Graph Neural networks (GNNs) are a natural choice for the architecture.In the input to the GNN, the hit information of each single hit becomes the node feature [2].The architecture of the GNN used in this work resembles the ParticleNet model proposed by Qu etal. [3]. It consists out of three Edge Convolutional blocks [4], followed by a global pooling layerand two fully connected layers. A custom open-source implementation for this architecture wasdeveloped using the tensorflow [5] back end1.2 Atmospheric muon reconstructionKM3NeT’s primary goal is to study neutrinos. However, the majority of detected events areatmospheric muons produced in cosmic ray induced air showers, which usually pose as a backgroundfor neutrino measurements. But they provide a wide range of applications as well, like measuringthe cosmic ray composition. GNNs can be used to reconstruct the zenith angle and the muonmultiplicity of an incident muon or muon bundle. For this, a set of 25 million atmospheric muonevents was simulated using the software package MUPAGE [6] for the initial four-line set-up ofthe ORCA detector. Figure 1 shows the zenith angle reconstruction performance in comparisonto a classical maximum-likelihood based algorithm for single tracks as described in [1]. Since it1See https://github.com/StefReck/MEdgeConv.– 1 –0.2 0.4 0.6 0.8 1.0true cosine zenith angle10 1100101|reco zenith - true zenith| (deg)KM3NeT Preliminary, ORCA4 simulationsdeep learning reco (median)deep learning reco (68% band)classical reco (median)classical reco (68% band)(a) all events0.2 0.4 0.6 0.8 1.0true cosine zenith angle100101|reco zenith - true zenith| (deg)KM3NeT Preliminary, ORCA4 simulationsdeep learning reco (median)deep learning reco (68% band)classical reco (median)classical reco (68% band)(b) events with two or more muonsFigure 1: Absolute difference between reconstructed and true zenith angle plotted over the truezenith angle for selected atmospheric muons in ORCA4. Shown are the median and the 68% bandfor the classical reco (orange) and the deep learning reco (blue).was trained on the expected distribution, the deep learning reconstruction is biased for true cosinezenith below 0.5, leading to an increase in the error there. GNNs provide a comparable precision toclassical methods on atmospheric muon events and a substantial improvement for the rarer multi-muon events, which make up about a fifth of the dataset and for which the classical methods werenot optimised.The number of atmospheric muons in an event is an important observable for indirectly studyingcosmic rays. This deep learning approach is the first reconstruction of the muon multiplicity inKM3NeT. Even though the detector is still in an early stage of construction, deep learning canalready provide a good estimator for the muon multiplicity (see Figure 2). The muon multiplicitycan be used for the identification of primary particles. Figure 3 shows the result of applying thetrained network on 250,000 atmospheric muon events simulated with Corsika [7] SIBYLL 2.3c[8] using the GST-3 [9] spectrum. As can be seen, the current detector already provides a decentseparation power between iron and proton induced events.3 Studies on neutrino selectionsGNNs can be used to select neutrino events in ORCA4 real data. To this end, different networkshave been trained to perform classification and reconstruction tasks. The cuts are optimized onMC data for the highest possible neutrino yield while keeping a low background contamination.The application to real data shows a reasonable agreement between expectation and observation,proving to be a valuable method to cross-check conventional methods.The ParticleNet used this far is implemented in Tensorflow. An alternative architecture usingPyTorch Geometric [10] has also been tested. The benefit of this framework is that it has manyimplementations of GNN layers included, which allow for easy prototyping of different architectures.As a first benchmark for the framework, ParticleNet is implemented and it is trained for thesignal/background classifier on ORCA4. The accuracy of both implementations can be seen in– 2 –5 10 15 20 25 30 35 40true muon multiplicity510152025303540reco muon multiplicityKM3NeT Preliminary, ORCA4 simulations100101102103events / binMuon multiplicity reco for events between 500 and 600 hitsFigure 2: Reconstructed versus true muonmultiplicity for events with a similar num-ber of hits (500 to 600). Even without useof the correlation between the multiplicityand the number of hits, the reconstructionis meaningful.1 5 10 15 20 25 30 35 40muon multiplicity10 610 510 410 310 210 1100events / bin / sKM3NeT Preliminary, ORCA4 simulationsproton recoproton trueiron recoiron trueFigure 3: Reconstructed (light) and true(dark) muon multiplicity rates for eventsgenerated in proton (blue) and iron (or-ange) induced atmospheric showers.20 40 60 80 100energy (GeV)0.880.900.920.940.960.981.00AccuracyKM3NeT Preliminary ORCA4 Simulations Background ClassificationTensorflowPyTorchFigure 4: A comparison of the accu-racy for background muon classificationbetween PyTorch and Tensorflow for thesame ParticleNet architecture [3].0.0 0.2 0.4 0.6 0.8 1.0neutrino_score10 1100101102Normed countsKM3NeT PreliminaryProbability to be classified as neutrinoGNN - atm. muonsGNN - neutrinosFigure 5: Normed distribution of theneutrino_score for true muon (blue-dashed) and true neutrino (red-continuous)events for the full ARCA detector.Figure 4. The difference between the implementations can be attributed to the use of more advancedlearning rate schedulers in Tensorflow. The shape of the accuracy curve is identical between thetwo implementations. GNNs can also be used for selecting cosmic neutrinos in ARCA over themuon background. This can be achieved by cutting over a GNN neutrino_score, representingthe probability of an event being induced by a neutrino. The selected neutrinos are then furtherclassified into tracks and showers by a subsequent GNN classification step. Figure 5 shows thedistribution of the neutrino_score assigned by the GNN to true neutrinos and true atmosphericmuons, for the full ARCA detector. The GNN performances are now being compared with otherML methods with the goal of finding the most promising method for cosmic neutrino selection.– 3 –References[1] S. Adrián-Martínez et al. (KM3NeT Collaboration), Letter of intent for KM3NeT 2.0, Journal ofPhysics G: Nuclear and Particle Physics, 43 (8), 084001, 2016 [1601.07459v2].[2] J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu and M. Sun, Graph neural networks: A review of methodsand applications, 1812.08434.[3] H. Qu and L. Gouskos, ParticleNet: Jet tagging via particle clouds, Phys. Rev. D 101, 056019 (2020)[1902.08570v3].[4] Y. Wang, Y. Sun, Z. Liu, S.E. Sarma, M.M. Bronstein and J.M. Solomon, Dynamic graph CNN forlearning on point clouds, 1801.07829.[5] M. Abadi et al., TensorFlow: Large-scale machine learning on heterogeneous distributed systems,1603.04467v2.[6] G. Carminati, A. Margiotta and M. Spurio, Atmospheric muons from parametric formulas: a fastgenerator for neutrino telescopes (MUPAGE), Comput.Phys.Commun.179:915-923,2008[0802.0562v2].[7] D. Heck, J. Knapp, J. Capdevielle, G. Schatz and T. Thouw, CORSIKA: A Monte Carlo code tosimulate extensive air showers, FZKA-6019 (1998) .[8] F. Riehn, H.P. Dembinski, R. Engel, A. Fedynitch, T.K. Gaisser and T. Stanev, The hadronicinteraction model SIBYLL 2.3c and feynman scaling, PoS ICRC2017 (2017) 301 [1709.07227v1].[9] T.K. Gaisser, T. Stanev and S. Tilav, Cosmic ray energy spectrum from measurements of air showers,Frontiers of Physics (2013) [1303.3565v1].[10] M. Fey and J.E. Lenssen, Fast graph representation learning with PyTorch Geometric, in ICLRWorkshop on Representation Learning on Graphs and Manifolds, 2019.– 4 –

Graph neural networks for reconstruction and classification in KM3NeT

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