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

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

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

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

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

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

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’

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

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

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

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)

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

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

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

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

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

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

Emulsion data processing