AGATA Pulse Shape Analysis Implementation Status Performance Opportunities Dr Andy Boston ajboston@liverpool.ac.uk The AGATA PSA team

AGATA PSA Implementation Architecture Algorithms Implementation ADL Experimental basis Performance

PSA Architecture

Structure of Data Processing FEE RO PP VME+AGAVA GTS Local level processing AGATA Readout Pre-processing PSA Ancillaries Global Level processing Event builder Event merger Tracking Post-Processing Front end electronics Ch. Theisen

Structure of Data Processing Ancillary 1R 1G 1B …. Digitizer or raw data file VME or raw data file CrystalProducer EventBuilder AncillaryProducer PreprocessingFilter EventMerger AncillaryFilter PSAFilter TrackingFilter Consumer Save PSA output Consumer Save tracked output

Preprocesssing Filter Performs Energy calibrations and xTalk (proportional) correction Analysis of traces Calculation of T0 from core (Digital CFD or linear fit of the first samples) Time calibrations and shifts Vertical normalization of traces Define the net-charge segments Reformats the data The calibration files are produced by external programs as part of the calibration procedures

PSA Filter Signal decomposition & diff xtalk Implemented algorithm is the Grid Search As a full grid search As a coarse/fine search (AGS) Reduces size of data by factor 20 Provides the parameters for the correction of neutron damage (can also perform it) Must be expanded to improve timing Takes ~95 % of total CPU time Is the critical point for the processing speed of online and offline analyses

PSA algorithms

Pulse Shape Analysis algorithms Singular Value Decomposition 8 Adaptive Grid Search Artificial Neural Networks 6 Particle Swarm Optimization Genetic algorithm Adaptive Grid Search (with final LS-fit refinements) now Position resolution (mm FWHM) 4 Wavelet method Least square methods 2 Full Grid Search ms s hr Computation Time/event/detector

AGATA PSA Codes Typical PSA scheme consists of 3 components Figure of Merit (FOM) e.g. Σ |event1i – event2i|n Search Routine: optimization of FOM over library Adaptive Grid Search (A. Venturelli, INFN Padova) Particle Swarm Optimization (M. Schlarb, TU Munich) Decomposition strategy for multiple interactions assuming maximum 1 hit per segment segments influenced by multiple hits excluded

AGATA PSA Codes Other PSA schemes Partial PSA Matrix method (A. Olariu, P. Desesquelles, CSNSM Orsay) Partial PSA Recursive Substraction algorithm (Fabio Crespi, INFN Milan ) Gets radial coordinates & # interactions (~ steepest slope)

Practical PSA Challenges A basis calculated on a 1 mm grid contains ~ 400000 points, each one composed by 37 signals each one with > 50 samples (for a 10 ns time step) Direct comparison of the experimental event to such a basis takes too much time for real time operation at kHz rate Events with more than one hit in a segment are common, often difficult to identify and difficult to analyse

PSA Challenges No good theory for mobility of holes -> must be determined experimentally Shape of signals depends on orientation of collection path with respect to the crystal lattice Detectors for a 4pi array have an irregular geometry, which complicates calculation of pulse shape basis Effective segments are defined by electric field and follow geometrical segmentation only roughly Position resolution/sensitivity is not uniform throughout the crystal

PSA IMplementation

PSA Implementation The signal decomposition algorithm (AGS) The quality of the signal basis Physics of the detector Impurity profile Application of the detector response function to the calculated signals The preparation of the data Energy calibration Cross-talk correction (applied to the signals or to the basis!) Time aligment of traces A well working decomposition has additional benefits, e.g. Correction of energy losses due to neutron damage

The Grid Search algorithm Signal decomposition assumes one interaction per segment The decomposition uses the transients and a differentiated version of the net charge pulse Proportional and differential cross-talk are included using the xTalk coefficients of the preprocessing. The minimum energy of the “hit” segments is a parameter in the PreprocessingFilter  10 keV No limit to the number of fired segments (i.e. up to 36)

The Grid Search algorithm The algorithm cycles through the segments in order of decreasing energy; the result of the decomposition is removed from the remaining signal -> subtraction method at detector level Presently using ADL with the neutron-damage correction model Using 2 mm grids -> ~48000 grid points in a crystal; 700-2000 points/segments Speed is ~ 150 events/s/core for the Full Grid Search ~ 1000 events/s/core for the Adaptive Grid

Signal basis generation Simulation: MGS, JASS, ADL Experimental: Coincidence, PSCS AGATA Data Library Geometries for a wide variety of detectors E-field solver, SIMION potential arrays Creates the calculated basis for each detector Bart Bruyneel and Benedikt Birkenbach IKP (Eur. Phys. J. A (2016) 52: 70)

AGATA Data Library dcwc Cross Talk correction

Optimisation: Crystal orientation •400kBq Am source + •Lead Collimator: ∅ 1.5mm X 1cm •Front Scan at ∅ 4.7cm: 300 cts/s •Fitfunction Risetime(θ) = A.[1+R4cos(θ- θ4)].[1+R2cos(θ- θ2)] Risetime [ns] θ [°]

R G B Cryst al orientatio n ATC1 ATC2 ATC4

Optimisation: Origin of Crosstalk Pure Xtalk signal: 400 200 -200 -400 -600 -800 -1000 -1200 -1400 Proportional Xtalk (50µs decay) → Energy Differential Xtalk (only during risetime) → PSA 0 200 400 600 800 1000 t [ns] Cac PA V0,out Zin With Zin = 1/sACfb + (1/sCac) + Rcold Z01 Xtalk ~ Zin / Z01 ~ C01/ACfb + (C01/Cac) + s . Rcold C01 = Proportional + Differential Xtalk PA V1,out

Proportional Xtalk measurement For any 1406keV single event in the detector: Segment labeling: 0.0 Additional contributions from seg. to seg. capacities! -0.5 Core to seg capacity Baseline Shift in A1 (keV) -1.0 A2..A6 -1.5 B1 F1 -2.0 X -2.5 Sectors: A...F Rings: 1...6 -3.0 6 12 18 24 Hit segment number 30 36

Crosstalk correction: Motivation Crosstalk is present in any segmented detector Creates strong energy shifts proportional to fold Tracking needs segment energies ! Sum of segment Energies vs fold Segment sum energies projected on fold 2folds : Core and Segment sum centroids vs hitpattern …All possible 2fold combinations Energy [keV] B. Bruyneel, NIMA 599 (2009) 196–208

Cross talk

Cross talk correction matrix Core - seg Energy split NN -xtalk

Cross talk correction: Results

How to measure derivative Xtalk? (1) B. Bruyneel et al. NIM A 569 (2006) 774-789

Radiation damage from fast neutrons 6 5 4 3 2 1 A B C D E F CC /150 White: April 2010  FWHM(core) ~ 2.3 keV FWHM(segments) ~2.0 keV Green: July 2010  FWHM(core) ~2.4 keV FWHM(segments) ~2.8 keV Damage after 3 high-rate experiments (3 weeks of beam at 30-80 kHz singles) Worsening seen in most of the detectors; more severe on the forward crystals; segments are the most affected, cores almost unchanged (as expected for n-type HPGe)

Crystal C002 April 2010 July 2010  corrected CC r=15mm SG r=15mm April 2010 CC r=15mm SG r=15mm July 2010  corrected The 1332 keV peak as a function of crystal depth (z) for interaction at r = 15mm The charge loss due to neutron damage is proportional to the path length to the electrodes. The position is provided by the PSA, which is barely affected by the amplitude loss. Knowing the path, the charge trapping can be modeled and corrected away -> Lars

PSA performance

Grid search algorithm result

PSA performance analysis 12C(30Si,np)40K LNL commissioning b = 0:048; @ 5.5cm P.-A. Söderström et al. / NIMA 638 (2011) 96–109

AGATA PSA and Data Analysis Schools and WS The PSA and Data Analysis WG organises “regular” schools and WS: Liverpool 2011 (EGAN) GSI 2012 (EGAN) LNL 2013 (EGAN) GANIL 2016 The teams within the WG aim to have (at least) quarterly team meetings.

Summary... Lots of opportunities In beam use AGS algorithm (Narval implemented) Offline have AGS and Particle Swarm (Narval emulator implemented) Continuous improvement of signal basis Push towards experimental basis generation Implementation of multiple segment interaction algorithm Challenges: Availability of AGATA capsules for characterisation Clustering of points distributed inside detectors Continuity of available personnel to implement PSA algorithms Documentation + Howto guide This work is a big effort from a large number of people.. Thanks to all.

AGATA Pulse Shape Analysis Implementation Status Performance Opportunities Dr Andy Boston ajboston@liverpool.ac.uk The AGATA PSA team