Acciarri R, Adams C, An R, Anthony J, Asaadi J, Auger M, Bagby L, Balasubramanian S, Baller B, Barnes C, Barr G, Bass M, Bay F, Bishai M, Blake A, Bolton T, Camilleri L, Caratelli D, Carls B, Castillo Fernandez R, Cavanna F, Chen H, Church E, Cianci D, Cohen E, Collin G H, Conrad J M, Convery M, Crespo-Anadón J I, Del Tutto M, Devitt A, Dytman S, Eberly B, Ereditato A, Escudero Sanchez L, Esquivel J, Fadeeva A A, Fleming B T, Foreman W, Furmanski A P, Garcia-Gamez D, Garvey G T, Genty V, Goeldi D, Gollapinni S, Graf N, Gramellini E, Greenlee H, Grosso R, Guenette R, Hackenburg A, Hamilton P, Hen O, Hewes J, Hill C, Ho J, Horton-Smith G, Hourlier A, Huang E-C, James C, Jan de Vries J, Jen C-M, Jiang L, Johnson R A, Joshi J, Jostlein H, Kaleko D, Karagiorgi G, Ketchum W, Kirby B, Kirby M, Kobilarcik T, Kreslo I, Laube A, Li Y, Lister A, Littlejohn B R, Lockwitz S, Lorca D, Louis W C, Luethi M, Lundberg B, Luo X, Marchionni A, Mariani C, Marshall J, Martinez Caicedo D A, Meddage V, Miceli T, Mills G B, Moon J, Mooney M, Moore C D, Mousseau J, Murrells R, Naples D, Nienaber P, Nowak J, Palamara O, Paolone V, Papavassiliou V, Pate S F, Pavlovic Z, Piasetzky E, Porzio D, Pulliam G, Qian X, Raaf J L, Rafique A, Rochester L, Rudolf von Rohr C, Russell B, Schmitz D W, Schukraft A, Seligman W, Shaevitz M H, Sinclair J, Smith A, Snider E L, Soderberg M, Söldner-Rembold S, Soleti S R, Spentzouris P, Spitz J, St John J, Strauss T, Szelc A M, Tagg N, Terao K, Thomson M, Toups M, Tsai Y-T, Tufanli S, Usher T, Van De Pontseele W, Van de Water R G, Viren B, Weber M, Wickremasinghe D A, Wolbers S, Wongjirad T, Woodruff K, Yang T, Yates L, Zeller G P, Zennamo J, Zhang C
7Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510 USA.
8Harvard University, Cambridge, MA 02138 USA.
Eur Phys J C Part Fields. 2018;78(1):82. doi: 10.1140/epjc/s10052-017-5481-6. Epub 2018 Jan 29.
The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.
为了充分利用中微子物理学中液氩时间投影室的成像能力，其开发和运行催生了对新模式识别方法的需求。虽然人类大脑在识别记录事件中的特征方面表现出色，但开发一种自动化的算法解决方案却是一项重大挑战。潘多拉软件开发工具包提供了有助于模式识别算法设计和实现的功能。它提倡使用多算法方法进行模式识别，其中各个算法分别处理特定拓扑结构中的特定任务。然后，数十种算法精心构建事件图像，并共同提供强大的自动化模式识别解决方案。本文描述了用于重建MicroBooNE探测器中宇宙射线μ子和中微子事件的一百多种潘多拉算法和工具链的详细信息。针对模拟的MicroBooNE事件，使用一系列末态事件拓扑结构，给出了评估当前模式识别性能的指标。
