kno detector simulation

41
KNO Detector Simulation SEO Ji-Woong Sungkyunkwan Univ. (On behalf of KNO software working group) KNO 6 th Workshop 2021.08.20

Upload: others

Post on 16-Oct-2021

10 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: KNO Detector Simulation

KNO Detector Simulation

SEO Ji-Woong

Sungkyunkwan Univ.

(On behalf of KNO software working group)

KNO 6th Workshop

2021.08.20

Page 2: KNO Detector Simulation

β€’ Status

β€’ KNO detector

β€’ Neutrino event generator

β€’ Detector simulation : WCSimβ€’ Event display

β€’ High Energy studyβ€’ Vertex resolutionβ€’ Angular resolutionβ€’ Energy resolutionβ€’ PID

β€’ Low Energy studyβ€’ Observable energy thresholdβ€’ Energy resolution

β€’ Plan

β€’ Summary

Contents

2021-08-20 KNO 6th workshop 2

Page 3: KNO Detector Simulation

Status

β€’ Software working groupβ€’ Meeting on every Thursday 17:00

β€’ Studied neutrino event generator

β€’ Understanding water Cherenkov detector using MC

β€’ Developing event reconstruction tools

β€’ Manpowerβ€’ 4 regular contributors

β€’ Computing resourceβ€’ SKKU : 8-cores CPU with Quadro P7000

β€’ Sejong : 120-cores CPU with 2 RTX3090

2021-08-20 KNO 6th workshop 3

Page 4: KNO Detector Simulation

KNO detector

β€’ Detector designβ€’ Cylindrical shape

β€’ Height : 54.8 m

β€’ Diameter : 100.0 m

β€’ Total water mass : 503 ktβ€’ Active water mass : 384 kt

β€’ 63,000 20-inch PMTs (40% coverage)

β€’ ~1.5 deg. off axis angle (mt. Bisul)

β€’ ~1,000 m overburden

2021-08-20 KNO 6th workshop 4

Page 5: KNO Detector Simulation

β€’ GEANT4 doesn’t simulate neutrino interaction

β€’ Two publicly available neutrino event generators were examinedβ€’ GENIE

β€’ NuWro

β€’ Both neutrino event generators provide various neutrino interactionsβ€’ Can input neutrino beam energy profile

β€’ Provide various output file formats (text, nuance, etc.)

Neutrino event generator

2021-08-20 KNO 6th workshop 5

Page 6: KNO Detector Simulation

β€’ Publicly available GEANT4 based water Cherenkov detector simulation program

β€’ Relies on GEANT4 and ROOT

β€’ SK & HK geometry and basic PMT information are available

β€’ Provides simulated PMT hit informationβ€’ Hit time, charge, hit position…

β€’ Trigger, dark rate…

β€’ Use output file of an event generator (nuance format) as input

Detector simulation : WCSim

2021-08-20 KNO 6th workshop 6

Page 7: KNO Detector Simulation

β€’ GUI event display is prepared

β€’ Can draw event display easily

β€’ Use WCSim output file

β€’ Implemented simple functions

Event display

2021-08-20 KNO 6th workshop 7

Page 8: KNO Detector Simulation

β€’ KNO fitβ€’ Step by step fitter using maximum goodness method

β€’ Mainly use hit time informationβ€’ Time residual = PMT hit time – Time of flight from vertex

High Energy Software

2021-08-20 KNO 6th workshop 8

Pre-fitterLepton

directionPrecise

fitterLepton energy

Obtain vertex (x,y,z,t)

fastly

Use pre-fitter’s

information, find

lepton direction &

ring edge

Use lepton direction

and ring edge,

re-fit vertex (x,y,z,t)

Use vertex, lepton

direction and conversion

function, calculate lepton

energy

Page 9: KNO Detector Simulation

β€’ Vertex resolution

High Energy Software

2021-08-20 KNO 6th workshop 9

β€’ KNOfit tries to fit vertex using ring information

β€’ Shows improvements in Δ𝑅 between pre-fitter and precise-fitter

β€’ Current best Δ𝑅‒ Peak ~45 cm

β€’ 65% ~90 cm

Current best

65%

Page 10: KNO Detector Simulation

β€’ Angular resolutionβ€’ Ξ”πœƒ of electron is slightly larger than Ξ”πœƒ of muon

High Energy Software

2021-08-20 KNO 6th workshop 10

𝝁 π’†βˆ’

65% 65%

Page 11: KNO Detector Simulation

β€’ Energy resolutionβ€’ Obtain lepton energy using conversion function

β€’ Current best resolution : 1 GeV Muon ~5.7 %, 1 GeV Electron ~8.5 %

High Energy Software

2021-08-20 KNO 6th workshop 11

1 GeV 𝝁 1 GeV π’†βˆ’

Page 12: KNO Detector Simulation

β€’ Particle Identification (PID)β€’ Muon and electron can be identified using ring pattern

β€’ A muon typically produces a sharp-edged ring

β€’ And an electron will produce a much fuzzier ring due to EM shower

β€’ ~99% accuracy

High Energy Software

2021-08-20 KNO 6th workshop 12

𝝁 π’†βˆ’

Page 13: KNO Detector Simulation

β€’ Machine Learning PIDβ€’ Two leptons produce

different ring patterns

β€’ Identify lepton type using machine learning

β€’ python 3.7 with pytorch

β€’ Train and test using image of event display only

High Energy Software

2021-08-20 KNO 6th workshop 13

Page 14: KNO Detector Simulation

β€’ Improved event reconstruction tool

β€’ KNOfit is a β€œstep by step” goodness fitterβ€’ vertex β†’ lepton direction β†’ momentum

β€’ Developing new likelihood fitter based on the experience accumulated while developing KNOfit

β€’ Simultaneous fitter - vertex (x,y,z,t), lepton direction (ΞΈ,Π€) and momentum (p)

β€’ Expect better performance than KNOfit

High Energy Software

2021-08-20 KNO 6th workshop 14

Page 15: KNO Detector Simulation

Low Energy Software

2021-08-20 KNO 6th workshop 15

40% 20% 10%

Photo coverage 40 % 20 % 10 %

trigger pass 99 % 11 MeV 17 MeV 21 MeV

trigger pass 65 % 7 MeV 9 MeV 11 MeV

β€’ KNO low energy thresholdβ€’ KNOfit (also likelihood fitter) can not be used for low E (~O(10) MeV) events due to

small number of PMT hits

β€’ Need to check observable energy thresholds for KNO geometry

Page 16: KNO Detector Simulation

β€’ Expected energy resolution using low energy fitter

Low Energy Software

2021-08-20 KNO 6th workshop 16

Page 17: KNO Detector Simulation

β€’ Develop likelihood fitterβ€’ Developing as a one ring fitter : ~50% progressβ€’ PID will not be included

β€’ Develop low energy fitterβ€’ Developing event vertex and lepton direction fittingβ€’ Packaging for distribution

β€’ Study of ML PIDβ€’ Developing ML PID tool using various information of WCSimβ€’ To be integrated into likelihood fitter

Plan

2021-08-20 KNO 6th workshop 17

Page 18: KNO Detector Simulation

β€’ High energy event reconstruction

Super-K reconstruction performance

2021-08-20 KNO 6th workshop 18

Reference : An Analysis of the Oscillation of Atmospheric Neutrinos, doctoral thesis, Shimpei Tobayama, 2010

Page 19: KNO Detector Simulation

β€’ KNO detector simulationβ€’ Use WCSim simulation program based on GEANT4

β€’ Event reconstructionβ€’ KNOfit (step by step maximum goodness fitter) is available

β€’ without PID process

β€’ energy resolution : ~5.7% (muon) and ~8.5% (electron)

β€’ Low energy threshold of KNO is about 7~11 MeV (current design)

β€’ Advanced high energy fitter is being developed

β€’ Work on machine learning PID is in progress

Summary

2021-08-20 KNO 6th workshop 19

Page 20: KNO Detector Simulation

backup

2021-08-20 KNO 6th workshop 20

Page 21: KNO Detector Simulation

nuWro Genie-MC

exported β€˜nuance’ file

from genie-mc : exist β€˜-2’ tag particles

β€œ-2 = intermediate state”

from nuWro : exist β€˜info’ line2021-08-20 KNO 6th workshop 21

Page 22: KNO Detector Simulation

β€’ final muon (tag β€˜0’) energy distribution

(using β€˜nuance’ file)

β€’ both generator shows 9.x GeV peak

2021-08-20 KNO 6th workshop 22

Page 23: KNO Detector Simulation

nuWro Genie-MC

𝑛

𝜌

𝑛

𝜌

β€’ Final particles (tag β€˜0’) (using β€˜nuance’ file)

β€’ Similar outputs

2021-08-20 KNO 6th workshop 23

Page 24: KNO Detector Simulation

nuWro Genie-MC

πœ‡

πœ‹0 πœ‹+

πœ‡

π›Ύπœ‹0 πœ‹+

β€’ genie-mc shows β€˜gamma’

2021-08-20 KNO 6th workshop 24

Page 25: KNO Detector Simulation

Cross-section

25

Carbon scattered cross-section

Looks very similar

2021-08-20 KNO 6th workshop

Page 26: KNO Detector Simulation

26

QE + RES + DIS QE + RES + DIS + MEC

Charged Current cross section

2021-08-20 KNO 6th workshop

Page 27: KNO Detector Simulation

27

QE + RES + DIS QE + RES + DIS + MEC

Neutral Current cross section

2021-08-20 KNO 6th workshop

Page 28: KNO Detector Simulation

Vertex Reconstruction using simple method

β€’ Conceptβ€’ Assume all photons from one point

β€’ Set a virtual vertex in the Detectorβ€’ can calculate distance from hit PMT

β€’ obtain β€œTime of Flight (in the water)” with all hit PMTs

β€’ calculate best vertex (x,y,z) combination using all of TOF information in vertex grid in detector

β€’ In Super-K, already use similar concept vertex simple fitter

28

Real Vertex

(unknown)

Virtual Vertex

for calculation

Hit PMT

distance

2021-08-20 KNO 6th workshop

Page 29: KNO Detector Simulation

Vertex Reconstruction using simple method

β€’ π‘‡π‘–π‘šπ‘’ π‘…π‘’π‘ π‘–π‘‘π‘’π‘Žπ‘™ 𝑑𝑖 = 𝑑𝑖0 βˆ’

π‘ƒπ‘–βˆ’π‘‚

𝑐‒ 𝑑𝑖

0 : digitized hit time of ith hit PMT

β€’ 𝑃𝑖 : position of ith hit PMT

β€’ 𝑂 : position of virtual vertex

β€’ 𝑐 : light speed in water (refractive index = 1.33)

β€’ πΊπ‘œπ‘œπ‘‘π‘›π‘’π‘ π‘  𝐺 = σ𝑖 exp(βˆ’π‘‘π‘–βˆ’π‘‘0

2

2 1.5Γ—πœŽ 2)

β€’ 𝑑0 : event time (free parameter)

β€’ 𝜎 : typical time resolution (using 2.5 ns from SK)

29

Function of Vertex (x,y,z) and 𝑑0Search minimum β€œβ€“Goodness” combination

2021-08-20 KNO 6th workshop

Page 30: KNO Detector Simulation

Concept

30

Search for Proton Decay Using an Improved Event Reconstruction Algorithm in Super-Kamiokande, Yusuke

Suda , PhD Thesis, University of Tokyo , Sep. 2017

1. Calculate particle direction using vertex

2. Calculate angle between particle direction and hit

PMT

3. Draw angle vs. p.e. distribution ( left (i) )

4. Find an angle contained maximum p.e. from 2nd

derivative of the distribution

5. It is the edge of Cherenkov Ring

2021-08-20 KNO 6th workshop

Page 31: KNO Detector Simulation

31

β€’ Particle direction

𝑑𝑝 =

𝑖

π‘žπ‘–π‘ƒπ‘– βˆ’ 𝑂

𝑃𝑖 βˆ’ 𝑂

β€’ π‘žπ‘– ∢ π‘β„Žπ‘Žπ‘Ÿπ‘”π‘’ 𝑖𝑛 𝑖 βˆ’ π‘‘β„Ž 𝑃𝑀𝑇

β€’ 𝑃𝑖 ∢ 𝑖 βˆ’ π‘‘β„Ž 𝑃𝑀𝑇 π‘π‘œπ‘ π‘–π‘‘π‘–π‘œπ‘›

β€’ 𝑂 ∢ π‘π‘Žπ‘™π‘π‘’π‘™π‘Žπ‘‘π‘’π‘‘ π‘£π‘’π‘Ÿπ‘‘π‘’π‘₯

β€’ Edge finder

𝑄 πœƒπ‘’π‘‘π‘”π‘’ =π‘œπœƒπ‘’π‘‘π‘”π‘’ 𝑃𝐸 πœƒ π‘‘πœƒ

sin πœƒπ‘’π‘‘π‘”π‘’α‰€

𝑑𝑃𝐸(πœƒ)

π‘‘πœƒπœƒπ‘’π‘‘π‘”π‘’

2

𝑒π‘₯𝑝 βˆ’(πœƒπ‘’π‘‘π‘”π‘’ βˆ’ πœƒπ‘’π‘₯𝑝)

2πœŽπœƒ2

β€’ πœƒπ‘’π‘₯𝑝 ∢ 𝑒π‘₯𝑝𝑒𝑐𝑑𝑑 πΆβ„Žπ‘’π‘Ÿπ‘’π‘›π‘˜π‘œπ‘£ π‘Žπ‘›π‘”π‘™π‘’ ( ~ 42Β°)β€’ πœŽπœƒ ∢ π‘Žπ‘›π‘”π‘’π‘™π‘Žπ‘Ÿ π‘Ÿπ‘’π‘ π‘œπ‘™π‘’π‘‘π‘–π‘œπ‘› (~3Β° ? )

2021-08-20 KNO 6th workshop

Page 32: KNO Detector Simulation

Re-fit vertex using Ring information

β€’ Conceptβ€’ Assume all photons from one point (pre-fitter)

β†’ add track, In & Out ring information

β€’ From pre-fitted vertexβ€’ Get the brightest ring and particle direction using

pre-fitted vertex

β€’ Add track information, more precise vertex fitting

β€’ Outside of ring, PMT hits are considered by scattered light

32

Pre-fitted

Vertex

Cherenkov angle

Hit PMT

track

πœƒπ‘ πœƒπ‘

2021-08-20 KNO 6th workshop

Page 33: KNO Detector Simulation

β€’ Super-K TDC fit formula

β€’ Goodness – β€œout ring”

33

𝑅𝑐 = fraction of total charge which inside the Cherenkov ring

πœŽπ‘ π‘π‘Žπ‘‘π‘‘ = 60 ns

2021-08-20 KNO 6th workshop

Page 34: KNO Detector Simulation

Neutrino energy reconstruction

β€’ μž¬μ§„ found more precise formula in T2K doctoral thesis

β€’ Calculate neutrino energy using same sample

Reference : Otani, Masashi. "Measurement of Neutrino Oscillation in the T2K Experiment." (2012).

34

μ„±ν˜„β€™s formula

2021-08-20 KNO 6th workshop

Page 35: KNO Detector Simulation

35

True Energy

Reco Energy

True Energy

Reco Energy

μ„±ν˜„β€™s formula result T2K formula result

T2K formula shows better result

2021-08-20 KNO 6th workshop

Page 36: KNO Detector Simulation

36

μ„±ν˜„β€™s formula result T2K formula result

Decrease difference

T2K formula shows better

reconstruction

performance

energy vs difference has

no correlation

2021-08-20 KNO 6th workshop

Page 37: KNO Detector Simulation

37

True vs Reco distribution shows similar distribution between

μ„±ν˜„β€˜s and T2K

Linear correlation

Not bad result

True E : True neutrino E

Reco E : calculated neutrino E using true

lepton E & direction

Linear correlation

Add μ„±ν˜„β€™s energy resolution 4.5%

(gaussian smearing)

Not bad result, too

2021-08-20 KNO 6th workshop

Page 38: KNO Detector Simulation

Likelihood fitter study

β€’ SK&HK event reconstruction algorithmβ€’ fiTQun : maximum likelihood with a particle event hypothesis x

β€’ Using observed information, construct the likelihood function

38

Main challenge is calculating these β€œmu”

2021-08-20 KNO 6th workshop

Page 39: KNO Detector Simulation

39

Likelihood fitter study

2021-08-20 KNO 6th workshop

Page 40: KNO Detector Simulation

2021-08-20 KNO 6th workshop 40

Page 41: KNO Detector Simulation

2021-08-20 KNO 6th workshop 41