●The use of the ML prediction shows a better identiﬁcation on the blobs that are likely to be a photon pair increasing the purity of the ﬁnal selection. Coming soon: ●Diﬀerential cross section for muo, pion and neutrino kinematic variables, also for interaction variables Q2 and W will be reported. ●Comparisons to GENIE-based simulation. ●The eﬀects of pion ﬁnal-state interactions on these cross-sections. ●Neutral pions can be misidentiﬁed as electrons. ●Cross-section for Delta production since Delta production with an absorbed neutral pion would appear as quasielastic-like. ●Resonance production is the most abundant interaction mechanism in DUNE energy. ●Pion kinematics is a background for neutrino oscillations analysis. ●FSI simulation models can be improved by analyzing the CC(𝜋) scattering on diﬀerent nuclei. Barbara Yaeggy for the MINER𝝂A Collaboration - Fermilab Neutral Pion Reconstruction in CH at < E𝝂 > ~ 6 GeV UNIVERSIDAD TECNICA FEDERICO SANTA MARIA MINER𝜈A Experiment Neutral Pion Reconstruction A pair of Electromagnetic Showers as gamma candidates produced via 𝜋0→ 𝛾𝛾 ●Clusters: nested set of hits in a single plane in the same time slice. ●The available clusters are grouped into conical regions about the neutrino interaction vertex (Bayesian Algorithm). ●With the assumption being that they arise from an electromagnetic shower originating at the neutrino interaction vertex. PhotonScore: ●Based on a score Z via log likelihood radio (LLR) for the dE/dx proﬁle for charged pions and neutral pions. ●Events having a score less than 2 are likely to be have a photon, those are selected. Summary References Acknowledgments Selection: ●All the selected events require one and only one neutral pion. ●The invariant mass of the selected gammas is used as a feature for selecting the neutral pion. ●The pair of gammas are most often reconstructed into a 𝜋0 candidate between 60 < m𝛾𝛾 < 200 MeV. This document was prepared by members of the MINERvA Collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Oﬃce of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. These resources included support for the MINERνAconstruction project, and support for construction also was granted by the United States National Science Foundation under Award No. PHY-0619727 and by the University of Rochester. Sup-port for participating scientists was provided by NSF and DOE (USA); by CAPES and CNPq(Brazil); by CoNaCyT (Mexico); by Proyecto Basal FB 0821, CONICYT PIA ACT1413, Fondecyt 3170845 and 11130133 (Chile); by CONCYTEC (Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica), DGI-PUCP (DirecciÛn de Gestión de la Investigación - Pontiﬁcia Universidad Católica del Peru), and VRI-UNI (Vice-Rectorate for Research of National University of Engineering) (Peru); and by the Latin American Center for Physics (CLAF); NCN Opus Grant No.2016/21/B/ST2/01092 (Poland); by Science and Technology Facilities Council (UK). We thank theMINOS Collaboration for use of its near detector data. Finally, we thank the staﬀ of Fermilab for support of the beam line, the detector, and computing infrastructure. [1] O. Altinok et al.[MINERvA], Phys. Rev. D 96, no.7, 072003 (2017).Doi:10.1103/PhysRevD.96.072003 [arXiv:1708.03723 [hep-ex]] [2] G. Perdue et al. [MINERvA], JINST 13, no.11, P11020 (2018) doi:10.1088/1748-0221/13/11/P11020. [arXiv:1808.08332 [physics.data-an]] [3] Jonathan Long and Evan Shelhamer and Trevor Darrell “Fully Convolutional Networks for Semantic Segmentation," CoRR abs/1411.4038 (2014) [4] C. Adams et al. [MicroBooNE], Phys. Rev. D 99 , no.9, 092001 (2019) doi:10.1103/PhysRevD.99.092001 [arXiv:1808.07269 [hep-ex]] [5] C. Adams et al. [MicroBooNE], JINST 15, no.02, P02007 (2020) doi:10.1088/1748-0221/15/02/P02007 [arXiv:1910.02166 [hep-ex]] [6] D. Coplowe et al. [MINERvA], [arXiv:2002.05812 [hep-ex]] Motivations Ba