1. Intelligent trigger for Hyper-K
Akitaka Ariga
University of Bern, Switzerland

2. Recent changes in design
Conventional design
10 compartments
Noise rate in each of them is about SK scale
Recently coming back to SK style
For cost optimization
1 (or a few) large detector
Longer gate width
Larger number of PMT per detector
Large noise rate to cope

3. Noise rate in Hyper-K
SK -> HK : Smaller signal and larger background
Detector size -> larger -> gate width longer
200ns ->500ns
# of sensors -> larger N
12k -> 20k ~ 80k
Noise rate -> larger N
4kHz -> 10kHz
Photo coverage -> smaller  smaller S
40% -> 15% ~ 20%
SK: 200ns x 12,000PMTs x 4kHz = 10 hits/gate (SK threshold = 33 hits)
HK:500ns x 20,000PMTs x 10kHz = 100 hits/gate
Direct impact on low energy neutrino physics, supernova and partially on proton decay

4. Signal / background Signal: 6 hits/MeV (SK,40%), 3 hits/MeV (HK,20%)
Noise level: expected number of hits in a gate
SK: 200ns x 12,000PMTs x 4kHz = 10 hits/gate
HK:500ns x 20,000PMTs x 10kHz = 100 hits/gate
Noise hits will be dominant at low energy (E<30MeV)
Solar neutrino
Signal in SK (40%)
Supernova
Signal in HK (20%)
Noise level in HK
Noise level in SK

5. Detectable energy
Detectable : Signal+Noise > Noise + noise fluctuation
Noise issue is essential to access low energy physics below 20 MeV, where most of supernova, solar neutrino, some of proton decay signals exist.
Signal + noise in SK
Solar neutrino
Supernova
detectable
Signal + noise in HK
Noise + 5s fluctuation
= realistic threshold

6. Need to improve trigger quality
Be intelligent!
Use of 4D information hits, (x,y,z,t)
Many ideas
Exploit TOF information to narrow gate width  next page
Vertex calculation: 2 hits can make a hyperbolic surface, 3 or more hits could make unique identification of vertex position  high-order hough transform like method – may not work with high BG
Ring pattern fitting C
Hyperbolic by B, C A B
Hyperbolic by A, B

7. One of many ideas: Sub-volume triggering
The largest factor of noise increase is gate width due to large detector  Let’s make it small.
Sub-volume triggering
Divide detector into several sub-volumes
In each sub-volume, perform inversion of hit-time using distance from hit-positions
 smaller gate width, canceling detector size increase
Large computing power required
triggering in O(100) sub-volumes
projected params A’
center of
sub-volume V A t t’

8. Intelligent trigger with GPUs
To profit of 4D data, need more computing power
GPU is an ideal solution: Expertise in LHEP-Bern
GPU: Graphic Processing Unit
Parallel processing with O(1000) processing cores
Triggering code can be highly parallelized

9. Parallel processing
GPU allow you a parallel processing with thousands of processing cores.
Serial process
CPU
Parallel process
GPU
task 1
task 2 .

10. High computing power = 8 TFLOPS = 5-10 TFLOPS NVIDIA Geforce Titan Z
1 full tower of CPU based computing cluster
= 5-10 TFLOPS
FLOPS = floating-point operations per second

11. Experience of LHEP-Bern 1: High speed emulsion reconstruction
Custom-made real-time scanning microscope
CMOS camera
0.5 – 2.4 Gbyte/s
(Real time) 3D track reconstruction with GPUs
x90 faster
Geforece GTX TITAN x 3
2688 cores, 6GB memory, 4.5 TFLOPs in each
JINST 9 P04002 (2014), GTC2014, GPU in high energy physics (2014)

12. Experience of LHEP-Bern 2: Reconstruction of LAr-TPC
LAr detector (ArgonTube at LHEP-Bern)
Hough transform with GPU
x 50 faster processing achieved
x 50 faster

13. Possible hardware for HK
Data will be distributed to several nodes equipped with GPUs
O(100) processes run with O(100,000) GPU cores
Processing machine
CPU
CPU
GPU
Processing machine
CPU
2.5 Gbyte/s
CPU
GPU
4U processing server
2 CPU x 10 cores
8 GPUs (24,000 cores)
Processing machine
CPU
CPU
GPU

14. Status and outlook Discussing with the relevant people
electron generated at center of detector with WCSim
Discussing with the relevant people
Started using WCSim package
Developing algorithm, checking
performance (efficiency, S/N) on CPU
Algorithm to be implemented in GPUs
signal
background

15. Summary
Noise rate is a crucial issue for low energy neutrino, supernova and proton decay
We are investigating an intelligent trigger by exploiting 4D data from detector
Larger computing power of O(100) could be necessary  An use of GPUs is a promising solution
More result will come soon

17. Trigger for Hyper-K
In SK, the triggering has been done by software trigger.
All hits are distributed to 6 PCs (48 processes)
BG level kept low, so that SK performs low energy neutrino physics
In HK, several issues (detector size, dark hits, NPMT) substantially increase BG

18. Trigger rate – Np.e. threshold
acceptable level
BG mean for HK
BG mean for SK

22. Emulsion data processing