1. TREND DAQ BASIC PRINCIPLES … Antennas DAQ ADC Trigger condition: Max > N.σ ??? First level trigger: Nant > 3 antennas ? -By antenna : - 1 data file (Nevent * 1024o ) - 1 time file (Nevent * 4 numbers) - 2 background files (1 Mo every 20 minutes) - 2 PSD files (256o every 20 minutes) + 1 log file (1 line by second, ~ Mo) Data size of 1 run : - depends of noise condition - depends of acquisition time From 5 Go to more than 100 Go (!!) In the future : - DAQ limitation on total size (Fabio) - Second level trigger (data size reduction: 90%) Storage

2. DATA PREPROCESSING OVERVIEW Written integrally in MATLAB language : - preprocessing results given in MATLAB file (.mat) - data analysis (on individual PC) performed with MATLAB 3 different stages : - Coincidence identification - Filtering of main noise source + signal characteristics - Data preprocessing (arrival direction, lateral profile, calibration…) At the end: 3 MATLAB files by run (<100 Mo each)

3. FIRST STAGE Unexpected bug in DAQ: looking for empty signals… Requires access to all signals on each antenna !! In MATLAB: 1 fopen by antenna + 1 fseek, fread by event Looking for coincidences between antennas… Just needs a time table of all the event… But memory issues with MATLAB if table too large (>50 ^ 6 events?): LIMITATION ON RUN SIZE! 1 MATLAB file with coincidence characteristics (+basic infos on run)

4. SECOND STAGE Filtering of the main noise source: 90% of rejected coincidences !! Use only the infos from first stage, no need for data access Signal waveform analysis on remaining coincidences (data access needed) « Boxes » for interesting pulses Number of boxes different for each signal: save in cell array… Problem? 1 MATLAB file with filtered coincidence + signal characteristics

5. THIRD STAGE Filtering on waveform analysis: ~ 30% of rejected coincidences Data preprocessing on remaining candidates : Signal cross-correlation (data access): very slow!! Arrival direction reconstruction: very slow!! Performed externally by a C++ routine (Valentin Niess) Signal calibration: access to PSD files 1 final MATLAB file with pre-analyzed candidates

6. CC IMPLEMENTATION TREND data stocked on irods (Fabio) Big runs implies a very slow analysis + available space on CC for jobs : Each run is divided in 1 Go “metarun” (C routine, Valentin Niess) Data preprocessing launched in parallel on metaruns… Problem? Uses SPS disk Data access problem? 3 MATLAB files generated by metarun Fusion of metarun MATLAB files in 1 final MATLAB file for each run Process can be extremly slow: MATLAB cell array problem?