1. 1 水质契仑科夫探测器中的中子识别 张海兵 清华大学 2008.4.28, 南京 First Study of Neutron Tagging with a Water Cherenkov Detector

2. 2 Neutrino Detection at Super-Kamiokande e Electron (e)  Muon (  ) The products are charged particles. The neutrino is observed by “seeing” the product of its interaction with water. Charged particles with β>1/n emits Cherenkov light

3. 3 -All 6 types of neutrino emitted when supernovae explode but only is most likely to observe. - Detection of is the key step to see SRN at SK. Neutrinos from Space Confirmed neutrinos from space Who’s next? Supernova relic neutrino (SRN)? a) Solar neutrino b) SN 1987A

4. 4 Previous Searches for SRN SK-I limit : <1.25 /cm 2 /s SK SRN Limits vs. Theoretical Predictions The result can be significantly improved if SK enhanced with neutron tagging capability.

5. 5 Why Neutron Tagging? Neutron tagging plays a role in identifying inverse beta decay. A delayed coincidence technique can be used to identify reaction chain.

6. 6 Methods of Tagging Neutron from Inverse βDecay

7. 7 Forced Trigger (FOG) Generate 500 additional “forced triggers” at the interval of 1us after primary trigger by e +. Search 2.2MeV candidates in the 500 us data pack. Threshold

8. 8 Test with a Simulated Signal 5 cm Am/Be Am/Be neutron source embedded in BGO crystal

9. 9 Experimental Setup n 5 cm Am/Be (1)Forced trigger case (2)Gadolinium case

10. 10 Signal and Background in Forced Trigger Data Source run (Am/Be+BGO) BG run (BGO only) – for neutron tagging efficiency study – Signal FOG: 500 BG events + one 2.2 MeV  – for cross checking and background estimation – BG FOG: 500 BG events # of PMT hits time The main difficulty rests with how to extract the weak 2.2 MeV  signal from heavy background, e.g. PMT noise and other low energy events.

11. 11 Data Pre-process 2.2MeV  Because of time-of-flight difference to individual PMT, the PMT timings of 2.2MeV  can not form a peak against BG. Thermal neutron free mean path ~50cm, even smaller than vertex resolution at SK. So the first step is to use e + vertex to do time-of-flight correction to restore timing information. # of hits PMT time Averaged BG n  ~200  s Thermal neutron free mean path ~50cm

12. 12 Distinctive Variables Several distinctive variables introduced, e.g. anisotropy, N10, etc. Anisotropy: average open angle of hits N10: PMT hits in 10ns window Green: signal; Red: background Neural Net method adopted to optimize results.

13. 13 Neural Net Method Event with NN>0.99 is identified as 2.2MeV gamma signal. Signal Efficiency vs. BG probability

14. 14 Measurement of Neutron Capture Time Expected exponential distribution clearly observed in source run (right). Y x A B C

15. 15 Neutron Lifetime & Tagging Efficiency Efficiency from data is in agreement with M.C. *

16. 16 Summary Neutron tagging in large water Cherenkov detector studied for the first time. Two methods tested at SK: Add 0.2% Gd in water: High efficiency but complicated, application delayed for at least 5 years. Tag 2.2MeV γwith forced trigger: Low efficiency(~20%) but simple, approved for SRN detection at SK now.