水质契仑科夫探测器中的中子识别

1. First Study of Neutron Tagging with a Water Cherenkov Detector 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

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

3. Neutrinos from Space • Confirmed neutrinos from space • Who’s next? Supernova relic neutrino (SRN)? • Solar neutrino • SN 1987A • 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.

4. SK SRN Limits vs. Theoretical Predictions SK-I limit : <1.25 /cm2/s Previous Searches for SRN The result can be significantly improved if SK enhanced with neutron tagging capability.

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. Methods of Tagging Neutron from Inverse βDecay

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

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

10. # of PMT hits time Signal and Background in Forced Trigger Data • Source run (Am/Be+BGO) – for neutron tagging efficiency study –Signal FOG:500 BG events + one 2.2 MeV  • BG run (BGO only) – for cross checking and background estimation –BG FOG: 500 BG events 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. 2.2MeV  # of hits Averaged BG PMT time # of hits n Averaged BG  PMT time Thermal neutron free mean path ~50cm ~200s Data Pre-process • 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.

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

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

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

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.