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The Tianshan Radio Experiment for Neutrino Detection Genesis & status of the TREND project Autonomous radio-detection of air showers.

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Presentation on theme: "The Tianshan Radio Experiment for Neutrino Detection Genesis & status of the TREND project Autonomous radio-detection of air showers."— Presentation transcript:

1 The Tianshan Radio Experiment for Neutrino Detection Genesis & status of the TREND project Autonomous radio-detection of air showers

2 Short waves Ulastai Simulated galactic bckgd Measured bckgd noise The TREND site Ulastai, Tianshan mountains, XinJiang autonomous province (2650m asl) Urumqi Ulastai Beijing

3 The 21cm array East West North South DAQ 4 km 3 km Radiointerferometer for the study of the Epoch of Reionization (Wu XiangPing, NAOC) completed in (See XiangPing’s talk Feb. 9)

4 TREND genesis ( ) Q: is the 21CMA set-up usable for CRs detection? – Nantes (April 2008) – Site survey (July 2008) A: yes!... And we get free access to it + technical support from 21CMA.

5 The TREND « sales strategy » Autonomous EAS radio detection & identification as a key issue in the persepective of a giant radio array for EAS. Radio in R&D phase: need to explore different technological options. TREND as an opportunity: – low elm Ulastai – 21CMA setup to be used for ~free (for France)! – Large radio-setup instrumental to improve our understanding of EAS radio info Long term plans: neutrino telescope

6 The TREND contributors China: CAS – NAOC: Wu XiangPing, Thomas Saugrin ( ), Zhao Meng (computing), Deng JianRong*, Zhang JianLi**, Gu Junhua** – IHEP: Hu HongBo, Gou QuanBu, Feng Zhaoyang**, Zhang Yi** France: CNRS-IN2P3 – LPNHE: OMH, Patrick Nayman***, Jacques David***, David Martin*** – SUBATECH: Pascal Lautridou ( ), Daniel Ardouin ( ), Didier Charrier (radio antennas) – LPC: Valentin Niess – CC: Fabio Hernandez (computing, ) *: after 2010 **: after 2012 ***: after 2014

7 EAS autonomous radiodetection at the Tianshan Radio Experiment for Neutrino detection (January June 2014)

8 TREND DAQ pod optical fiber 84dB MHz filter 21CMA acquisition optical fiber64dB MHz filter TREND acquisition Total chain gain: G= Driving concepts: - use existing elements - allow for high trig rate (200Hz/antenna) DAQ room: 8b 200MS/s ADC +CPU (soft trigger) +disk

9 TREND acquisition chain fiber to the DAQ room (<4km) Coax cable to optical transmitter (<300m) Amplification & filtering

10 The TREND-50 setup 50 monopolar «Butterfly antennas» deployed in summer-automn 2010 over a total surface ~1.5km². Stable operation between January 2011 & June EW orientation in , then NS. TREND-50 ~1.5 km² TREND-15 Ardouin et al., Astropart. Phys 34, 2011

11 TREND DAQ Analog radio signal transfered through optical fiber to DAQ room. On the fly parrallel digitization at computer level (200MS/s, 8bits). T0 soft trigger if antenna amplitude > Nxσ noise (N in 6-10) Online search for time coincidences between antennas: T1 if at least 4 antennas in causal time frame samples (≈5 μs) written to disk for all antennas in T1. For coincident triggers: offline signal direction reconstruction by triangulation Plane wave treatment: direction (Θ, φ) Point source treatment: position (x, y, z) Nxσ noise TREND DAQ driving concept: DAQ designed to accept large trigger rate (up to 200Hz/antenna). Candidate selection performed through offline treatment.  noise antenna level)

12 TREND antenna sensitivity Major radio source: thermal emission from the Galactic plane. Visible in Ulastai sky between 15h & 23h LST. Galactic 408MHz TREND antennas clearly exhibit a ~3dB increased noise level when the Galactic plane is in the sky. Experimental evolution in good agreement with simulated response (following Lamblin et al, ICRC2007). Local sideral time Signal noise level (A. U. ) ~3dB

13 data: 317 DAQ days analyzed triggers recorded coincidences ~10Hz average coinc rate over whole array (~20 EAS/day expected) 10 ms R data TREND antenna Reconstructed source position TREND trigger performances R3577 Antenna 101 Antenna 106 (700m away) Antenna 112 (1400m away) Antenna 120 (2000m away) T0 rate <100Hz for 90% of the time on all antennas. DAQ efficiency ~ 70%. Large trigger rate variations at all time scales on all antennas: «noise bursts» Noise is correlated between antennas: common (physical) origin. Time delay between consecutive events & point reconstruction points dominantly towards HV sources.

14 RADIO PERFORMANCES: DIRECTION RECONSTRUCTION Plane track reconstruction : events in 4 minutes - Θ > 60° - Max multiplicity: 40 Total angular resolution <1.5° on the track (and improves with smaller zenithal angle) Point source recons mult ≥ 22 antennas σ = 0.7° Estimated antenna trigger timing error: ±10ns

15 RADIO PERFORMANCES: CALIBRATION Cross-check with plane track signals Distant radio source (>4km) Signal intensity almost identical on all antennas Good check for amplitude calibration ~15% amplitude resolution (A Cal - )/ Average amplitude Baseline relative calibration: OK if  env >>  elec : then  bline ~K  env => K   bline at time t Antenna   = 0.15

16 TREND issues «You get what you pay for»: system reliability questionnable – Sudden drops in gain [not solved] – Aging (antennas, amplifiers, optical system, computers…) - Significant maintenance effort required - Reduced detection efficiency - Determination of efficiency & absolute calibration (very) challenging [in progress] MHz noise level (dB)

17 TREND-50 EAS search

18 EAS identification: principle EAS (0.2mHz) Background events (10Hz) Discriminating parameters Selected EAS candidates Residual background

19 EAS identification: principle EAS (0.2mHz) Background events (10Hz) Discriminating parameters (optimized with simulated EAS & bckd events) Selected EAS candidates Residual background Simulated EAS Selected simulated EAS

20 EAS simulation 17 & eV with isotropic sky distrib & random core position Shower dvlpmt (CONEX) elm emission (EVA) Antenna response (EZNEC) (if distance<800m) 400 showers/E x 20 core positions x 15 antennas 120’000 voltage computations  240’000h CPU Using DIRAC+VO France-Asia (IHEP, KEK, CC-IN2P3 & LPNHE) Slow!

21 EAS simulation Simulated antenna signal ( ) 200MS/s (o) V simu x G + noise ( ) using experimental (G, noise) Applying TREND trigger condition with th = 8  noise ( ) Shower considered detected if 5+ antennas triggered.

22 Discriminating parameters Simulated EAS: 92% pass Spherical wave recons: point source reconstruction of backgrd sources close to array, EAS more distant. R>3000m Data: 66% killed Signal shape: prompt signal for EAS Data: 45% killed Simu: 100% pass

23 Discriminating parameters Array trigger pattern should be continuous for EAS (E-field linear polarization at 1st order, random for bckgd) Untrigged antennas: hole in trigger pattern Continuous trig zone Data >50% killed Simulated EAS E = eV 85% pass Limited array size + monopolar antennas (+ system unreliability) reduce cut efficiency. Trigged antenna

24 Environment cuts Bckgd events strongly correlated in time & space Consecutive coincs: reject EAS candidate if 1+ coinc with 4+ antennas in common within 30s. Same direction events: reject EAS candidate if 1+ coinc with 2+ antennas in common and |  |<10° within 10 minutes.

25 Cut efficiency CutN coincs final% survival « 50Hz » cut % Pulse duration % Multiplicity > % Valid direction reconstruction % Radius > 3000m %  < 80° % Trigger pattern/Extension Neighbourgs Neighbourgs (direction)465

26 TREND EAS candidates Azimuth angle [deg] Deficit to East & West Excess to North 30° 60° 90° Zenith angle [deg] Normalized EAS candidates zenithal distrib Normalized EAS candidates azimuthal distrib data (EW polar, 317 DAQ days) 465 candidates PRELIMINARY

27 Simulated skymap For given direction (  20 random x core with min dist to array < 800m. For given shower geometry (  x core ): – check if antennas signals are above threshold (8x  noise ) – If OK for 5+ antennas, tag this geometry as ‘trigged’. For each direction ( , compute ratio N triggered /N simulated (N simulated = 20 in principle) Simu voltage x calib + noise

28 Simulated sky maps 30° 60° 90° 30° 60° 90° 30° 60° 90° 30° 60° 90° 30° 60° 90° eV eV eV eV eV 20/20 shower detection 0/20 shower detection in progress

29 Data-Simu comparison dN/d  (Normalized) Zenithal distribution Data Simu Azimuthal distribution dN/d  (Normalized) Event multiplicity SimuData Nb antennas/event Combining & eV simulated data sets. Comparable zenithal, azim and multiplicity distributions (except for very inclined showers: reflexion issues or cuts?) Expected nb of events for threshold = eV: ~6000 in 317 days. 465 observed… Detection efficiency <10% ?!

30 ToDo: full MC simulation Simulate EAS events with proper distributions in flux, direction, core positions & energies. Generate expected antenna response to these EAS events at fixed random times. If 5+ triggers, insert these simulated events in experimental data (after experimental EAS candidates have been removed). Process these data through standard analysis chain. Produce simulated maps & compare to data -> Background rejection performances -> Detection threshold -> Detection efficiency

31 TREND EAS Candidates data (NS polar, 125 DAQ days) 14 candidates -Very low stat BUT distribution differs significantly from EW polar, as expected for EAS. 30° 60° 90° PRELIMINARY Simulated skymap eV

32 TREND Possible causes for much fewer candidates: – Array maintenance degraded (>20% antennas off) – Bckgd noise significantly higher, affected DAQ live time & acceptance (environment cuts) March 2011 Aug 2012Dec 2012 June 2014 ~15Hz ~100Hz : NS polar 2011: 2012: EW polar

33 TREND-50 summary Initial goal reached: autonomous radio detection and identification of EAS with limited bckgd contamination (<~ 20%) thanks to low DAQ dead time. Limitations: – Low detection efficiency (set-up layout & quality) – Environment cuts kill detection efficiency when bckgd rises. – Event-by-event discrimination not possible. – Physics output with these data questionnable. Larger array with more reliable DAQ would surely perform better…

34

35 Trigger pattern cut EAS signal Background Shower axis Radio cone Very different trigger patterns for EAS & bckgd. Larger array would allow to soften environment cuts (as long as dead time is low).

36 To Do Perform full acceptance study: insert simulated EAS in real data and run standard analysis - ‘True’ simulated EAS skymap - Detector efficiency estimation TREND-50 antenna Absolute calibration tests Summer 2013 EAS sample analysis (LDF, spectrum, …)… Requires absolute calibration of the amplitude.

37 TREND early days ( ) 2009: 6 log periodic antennas : reconstruction algorithm development + autonomous trigger proof of principle. 2010: 15 log-periodic antennas + 3 scintillators: independant trigger & analysis of scint data (EAS) & radio data (EAS radio candidates). 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

38 EAS candidate validation Data Simu (no envir cuts + only West hemisphere) Data Simu (no envir cuts + only West hemisphere) Very good match between data and 17 eV: sky distribution of selected candidates follows what is expected for EAS. large zenith angles: environment cuts effect or simulation issues at large zenith angles [to be checked]… More simulated data needed [in progress]


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