1. I.3. The Tianshan Radio Experiment for Neutrino Detection Olivier Martineau-Huynh TREND workshop, April 19, 2013

2. Why TREND? Why Ulastai?

3. TREND-21CMA site Beijing Urumqi Ulastai Ulastai, Tianshan mountains, XinJiang autonomous province Site of the 21CMA radio interferometer (Epoch of Reionization) AM emitters : forbidden zone Electronics noise Antenna measurment Galactic emission model Background electromagnetic level ≡ Galactic emission

4. 21CMA Interferometer of 10000 antennas Built in XinJiang by Wu XiangPing (NAOC) in 2007 Obj: study of the Epoch of ReIonization (1st stars) N 4 km E S W 3 km

5. The 21CMA setup Signals from 127 antennas added after phasing N E S W 1 pod of 127 antennas DAQ room Transfer to acquisition room through optical fiber (2 fibers/ pod) Sampling at 200MSamples/s & online FFT & correlation Setup, infrastructure & support fully available for TREND!

6. TREND setup & DAQ

7. The TREND-50 setup 50 antennas deployed in summer-automn 2010, total surface ~1.5km² (+ 3 scintillators). Stable operation since March 2011. TREND-50 ~1.5 km² East arm: antennas 101-120 West arm: antennas 121-138, 140 South arm: antennas 148-158 Scintillators: 139, 175(@pod S15), 176 (@podS16)

8. TREND acquisition chain Filter 50- 200MHz LNA 24dB 40dB Filter 50- 100MHz Optical transmitter buffer 200MB disk antenna ~100m coax cable <4km fiber Optical recevier ADC 8b 200MS/s Electronic board Pod DAQ room

9. TREND acquisition chain fiber to the DAQ room (<4km)

10. 64dB ampli+ 50-100MHz filter Optical receiver Antenna 101 U183 300G disk Triggered events Data file u101 Time file u101 scans ADC 8bits 200 MS/s Circular buffer 200MB Circular buffer 200MB computer u101 CPU (trigger) 64dB ampli+ 50-100MHz filter Optical receiver Antenna 158 Triggered events Data file u158 Time file u158 scans ADC 8bits 200 MS/s Circular buffer 200MB Circular buffer 200MB computer u158 CPU (trigger) 50 parallel & identical chains Optical fiber 100m<d<4km 1 triggered event = 4 words in time file & 1024 samples in data file DAQ room computer u203 (ADCs init) start signal 50 parallel & identical chains

11. Buffer structure 2 buffers on each machine u101 – u158. 1 buffer is 200MB (ie 2 28 bytes). ADC flow synchronous on all machines thanks to start signal sent simultaneously to all ADCs by machine u001. ADC running at 200MSamples/s (5 ns per sample) Buffer = 1.34 s of data If buffer full, ADC flow to second buffer. When buffer fully analysed (see next slide), buffer cleared. If buffer full before 2 nd one cleared, both are dumped. Buffer 200MB (268 435 456 samples) 1.34 s of data with ADC @ 200MSamples/s subbuffer n°1 (1024 samples) subbuffer n°262 144 subbuffer n°262 143 subbuffer n° 2 (1024 samples) Buffer structure

12. Trigger principle / Buffer analysis T0: On each machine uNANT (NANT in 101-158): – For every new buffer, compute  noise over 1024 samples of 1 st sub-buffer. – If sample i with amplitude A i > N x  noise, trigger of level T0 on this antenna. 6<N<10. T1: If at least 4 antennas have T0 triggers within a causal time window (  t<  L/c), then data is sent to disk on machine u183 for all antennas with T0s – to data file R[NRUN]_A[NANT]_data.bin: 1024 samples centered on sample i – to time file R[NRUN]_A[NANT]_time.bin : UNIX second of sample i Buffer index of sample i Subbuffer index of sample i Position of sample i in subbuffer. 5  noise  x  noise Code in C with MPI for Master/Slaves dialogs. Master program in python. Securities to check computer & ADC card status, buffer status, machines dropping out of DAQ. Monitoring tools to check status.

13. TREND monitoring

14. Log files Process log files to compute live DAQ time, stalled DAQ & dumped buffers (BuildStat.m).

15. Power Spectrum Density PSD  |FFT|² OK Bad environment No gain Monitoring of the DAQ chain & environment… Also used for calibration.

16. Calibration 1: relative calibration Baseline calibration – Hyp:  env >>  elec then  bline ~K  env => K   bline at time t  A/A = 15%

17. Absolute calibration (under development) Use load measurement PSD load : power spectum density with input = 75Ω load. PSD ref with input = antenna right after load. PSD current with input = antenna at time t. G dB (t) = PSD load + PSD current (t) - PSD ref

18. Calib comparison OK but sometimes really off & not as good as relative callibration on plane tracks (~20%). We need to improve our calibration if we want to study shower later profiles.

19. Absolute callibration with calibrator A solution for TREND? (balloon or helicopter) Pierre CHAUVEAU’s internship (?)

20. TREND RADIO ENVIRONNEMENT Local sidereal time Signal noise level Galactic plane @ 408 MHz Major radio source: thermal emission from the Galactic plane. Visible in Ulastai sky between 15h & 23h LST. TREND antennas clearly exhibit an increased noise level when the Galactic plane is in the sky

21. TREND offline data treatment

22. Radio background TREND antenna TREND-50 antennas radio array: 2011-2012 data 220 real days analysed 1.2 10 10 triggers recorded 1.4 10 9 coincidences ~100Hz background event rate over whole array (physical origin) Expected EAS trigger rate: ~2 events/day for E>10 18 eV Background rejection is a key issue for EAS radio-detection (& major goal for TREND phase 1)

23. EAS signal Background Ex: signal pattern: Elm background point sources Distinct features compared to EAS radio signals. → Localized background can be rejected through data processing. Shower axis

24. Background discrimination EAS radio signals features: -Focused signal spot on ground (decreasing when moving away from shower axis) -~ Flat wavefront -Short (<500ns) pulses -Isolated pulses -Random in time -Random in direction -Come from the sky -Polarized (  shower direction &  B geo ) -Frequency signature (?) Background radio signals features -If distant: ~constant amplitude -If close: curved wavefront -Usualy long pulses -Usually repetition -Possible pattern for consecutive triggers (50Hz) -Point sources or trajectories -Mostly from ground -Non polarized -Frequency signature possible… EAS and background have distinct features → data processing written to efficiently select candidates & reject background

25. DATA PRE PROCESSING : STAGE 1 Informations on run set-up (antennas, positions, delays….) Rejection of « empty » events (bug DAQ, corrected 02/2012) Identification of coincidences between antennas Time (s) Coincidence rate (Hz) Example of run: 3577 - 49 antennas - Total duration: ~ 38h30 - Number of triggers: 4.452.938 - Number of coincs: 633.918

26. DATA PRE PROCESSING : STAGE 2 Main source of pollution: power line Typical signature: Δt between 2 events = n.10 ms On run 3577: 67.406 coincidences remaining (89% efficiency) Influence on acceptance? (mainly cross-point events, estimated dead-time ~ 10%) 10 ms Time (s) Coincidence rate (Hz) Could be implemented as an on-site pre-treatment (C program) reduce data volume & ease up offline treatment

27. DATA PRE PROCESSING : STAGE 2 Signal waveform analysis: Create « boxes » around over-threshold parts of the signal - Total ToT - Number of boxes - Boxes ToT - Pre-trigger ToT - Central box (at expected trigger time) - … Informations available for signal rejection in stage 3 Rejection of « bad » signals (if mult<4, coincidences are rejected) For run 3577, 22.244 remaining coincidences (66% efficiency)

28. Event reconstruction Reconstruct wave associated with coincidences of 4+ antennas. Plane (direction  ) & spherical (source x 0, y 0, z 0 ) wave front hypotheses. Antenna trigger times corrected through signal inter-correlation treatment. “Delay plot” to check reconstruction quality. For all reconstructions, parameters saved into DST (Matlab file)

29. RADIO PERFORMANCES Plane track reconstruction : - 3037 events in 4 minutes - Θ > 60° - Max multiplicity: 40 Total angular resolution <1.5° on the track. (and improves with smaller zenithal angle) mult ≥ 22 antennas σ = 0.7° Reconstruction quality should be checked more systematically (in particular single antenna effect  wrong delay correction)

30. DATA PRE PROCESSING : STAGE 3 Track identification (Δθ/Δφ < 10°, Δt < 1m) and rejection For run 3577, 432 remaining coincidences (98% efficiency)

31. Data pre-processing: stage 4 For surviving events: – shower profile reconstruction – Data saved to DST (‘Candidates’ field) Final cuts (local treatment): – More selective cuts on signal shape – More selective cuts on neighbours – Cut on amplitude difference Amax/Amin>1.5 – Cut on pattern (visual) Final list of EAS candidates

32. Conclusion on data treatment Whole process consists in rejecting background. Trading between cut efficiency / candidate survival probability / data treatment power. This layout does not allow for efficient cut on pattern need for strict cuts on neighbours (very unefficient!) EAS selection could certainly be optimized… Effect on EAS radio signals to be studied with MC simulations (see below)

33. TREND results & present activities

34. The TREND status 2009: 6 antennas protype – Test setup for principle validation. – 25 CR candidates detected.

35. TREND-15 setup (2010) 15 log-periodic antennas + 3 particle detectors (same DAQ algorithm). 400 m 800 m Ardouin et al., Astropart. Phys 34, 2011 First EAS identification with autonomous radio array N ants θ radio θ scints ϕ radio ϕ scints 461±367±5359±23±4 452±149±3195±2191±4 542±136±355±456±5 445±149±312±110±5 756±253±4323±2331±5 Some radio EAS candidates are coincident with scintillator coincidences + direction recons match! Selection of radio EAS candidates with dedicated algorithm Radio data (subset) Reconstruction of 3-fold scintillator coincidences  EAS Scintillator data Independant trigger & analysis of scint data (EAS) & radio data (EAS radio candidates).

36. The TREND-50 setup 50 antennas deployed in summer-automn 2010, total surface ~1.5km². TREND-50 ~1.5 km² TREND-15 (2010) TREND-6 (2009) All according to previously presented, except: Antennas 148, 150-152, 157: log periodic (21CMA type) Antennas 149, 153, 155: proto butterfly Antennas 148-155, 158: elec board @ pod Stable operation between Jan 14, 2011 and Dec 6, 2012: 691 days 8 data taking campaigns: 574 days (83%) DAQ running time: 320 days (56%)  Data analyzed so far: 220 days (69%) [100% off-line treatment] Acceptance = 103 km².days  69 days with full array @100% efficiency (31%)  TREND-50 is a BAD & UNRELIABLE setup. Significant effort in the last 5 years on maintenance, with mitigated success… That is certainly the price to pay for a (nearly) cost-free detector!

37. TREND-50 EAS candidates search 2011-2012 dataset: – 140 EAS candidates found (2/ live day) Valid candidate? Statistical response

38. EAS candidate search Observed distribution very different from background one. All events EAS candidates Deficit for  = 90 & 270° All events

39. West 90° Expected distribution Antennas sensitive to East-West polarization only (x axis): E EAS. x skymap West 90° North 0° Experimental distribution 30° 60° 90° 30° 60° 90° Looks promising, but requires better modeling: Full simulation of radio signal & system response to produce expected EAS distribution. [on-going work] South 180° East 270° A  E EAS.x  v  B geo.x

40. EAS detection simulation Shower simulation (CONEX): FORTRAN – All sky: 5°<  <85° (step:10°) & 0<  <360° (step=20°) – At least 2 energies: E=5 10 17 & 10 18 eV. – At least 100 core positions per direction 2x8x18x100 = 28’800 shower simulations 2 modes: – 1D pure longitudinal MC+ODE => fast (2-3 s/shower). – Full 3D MC => very slow @ ultra high energies (~2+ h / shower @ CCIN2P3 with intel compiler)… Probably still room for tuning though). Radio signal simulation ~10 antenna signals per core position -> 288000 signal simulations 2 modes again: – 1D: EVA/FORTRAN or custom Python code. Fast : ~2-3 s/antenna – 3D: EVA/FORTRAN. Very slow (~3-4 h/antenna) BUT 3D is the only accurate method close to shower axis (~100 m) due to strong Tcherenkov effect. Antenna response (NEC2): FORTRAN+Python interface. Fast (2-3 s/antenna).

41. TREND simulation: tools Code in FORTRAN/C++ + Python 2.6 1D option fast: ~10s / antenna 3D option very slow: ~3-4h/ antenna CPU needs: 28800 showers & 288000 antenna signals Massive computing needs! Go for distributed computing. France-Asia Virtual Organisation (IN2P3, IHEP, KEK,KISTI). DIRAC as middleware. Major effort undergoing.

42. EAS candidate search Ultimate test for EAS detection: rotate antennas to N-S Same background distribution (random polar) but EAS expected distribution radicaly different. Antennas rotated December 2012. Background distribution unchanged. EAS candidates search just began. Validation will open the way for a detailed study of EAS radio characteristics. Valuable because first self-triggered sample ever!

43. Conclusion TREND-50 on the way to confirm EAS radio- detection with limited background contamination: 1st objective validation. Poor reliability, stability & callibration may be a major handicap for detailed & reliable study of EAS radio characteristics.