V. Chepel, V. Domingos, R. Martins, A. Morozov, F. Neves and LIP-Coimbra 2014 ANTS 2: Simulation and data processing package for Anger camera-type detectors V. Chepel, V. Domingos, R. Martins, A. Morozov, F. Neves and V. Solovov

Anger camera-type detectors Optical photons Electrical signals Array of optical sensors Event position (or track) reconstruction Detection medium Particle Interaction of particle with detection medium Localized emission of light (primary or secondary scintillation)

Motivation LIP: three types of Anger camera-type detectors Medical gamma cameras (our current project) GSPC neutron detectors (FP7-NMI3 collaboration) LXe dark matter detectors (ZEPLIN, LUX, LZ) Simulations are required for: Detector design and optimization Development of new reconstruction algorithms Processing of experimental data

Package highlights Simple tools to define detector geometry (3D) Solid, liquid and gaseous scintillators Primary and secondary scintillation Detailed model of optical sensors Statistical reconstruction of event position (3D); track reconstruction Large set of tools to analyze data and to test reconstruction quality

Package highlights Open source (C++) Multiplatform (Windows, Linux; Mac soon) Graphical interface (fully interactive) + batch mode Extensive use of CERN ROOT (3D navigation, visualization, math libraries) FANN library for artificial neural networks

Fully custom detector geometry CERN ROOT TGeoManager class is used for 3D navigation and visualization

Simulation modes Direct mode: Simulation of an experiment: An event is simulated by generating light emission from one or several point sources Simulation of an experiment: User defines a radiation source (or several ones), and provides its characteristics. Energy deposition due to interaction of primary and secondary particles with detector media lead to light emission.

Event reconstruction algorithms Center of Gravity Statistical reconstruction 3D position reconstruction with one plane of sensors! Possibility to reconstruct double events! Adaptive algorithms (work in progress) Artificial neural networks (work in progress)

Compact gamma camera Optical photon tracks are shown with teal lines

Neutron detectors Neutron “tracks” (red) ending with emission of p and t

Dark matter detectors LZ detector design optimization: 217 or 271 PMTs?

Track reconstruction Blue track: simulated (alpha particle) Red track: reconstructed using time- resolved secondary scintillation PMT2 PMT1 PMT signals vs time Ionization cloud drifts to the region where secondary scintillation is generated Photons have emission time stamp! Signal PMT1 PMT2 Time Time

Gamma camera + pinhole collimator LYSO crystal Pinhole Phantom Lead block with two conical holes Co-57 gamma source is positioned behind the phantom (mask) Red dots are reconstructed positions of interaction of gammas with LYSO scintillator

Experimental data processing Processing of first dataset obtained at the detector prototype. Center of Gravity Statistical: Least Squares Sum signal spectrum Simulated LRFs

Current work and future plans Parameterization of response of optical sensors in 3D 3D reconstruction of events with low number of photons (~5000) Application of adaptive reconstruction algorithms for medical gamma cameras Real-time event reconstruction