Download - KNO Detector Simulation

KNO Detector Simulation

SEO Ji-Woong

Sungkyunkwan Univ.

(On behalf of KNO software working group)

KNO 6th Workshop

2021.08.20

• Status

• KNO detector

• Neutrino event generator

• Detector simulation : WCSim• Event display

• High Energy study• Vertex resolution• Angular resolution• Energy resolution• PID

• Low Energy study• Observable energy threshold• Energy resolution

• Plan

• Summary

Contents

2021-08-20 KNO 6th workshop 2

Status

• Software working group• Meeting on every Thursday 17:00

• Studied neutrino event generator

• Understanding water Cherenkov detector using MC

• Developing event reconstruction tools

• Manpower• 4 regular contributors

• Computing resource• SKKU : 8-cores CPU with Quadro P7000

• Sejong : 120-cores CPU with 2 RTX3090


KNO detector

• Detector design• Cylindrical shape

• Height : 54.8 m

• Diameter : 100.0 m

• Total water mass : 503 kt• Active water mass : 384 kt

• 63,000 20-inch PMTs (40% coverage)

• ~1.5 deg. off axis angle (mt. Bisul)

• ~1,000 m overburden


• GEANT4 doesn’t simulate neutrino interaction

• Two publicly available neutrino event generators were examined• GENIE

• NuWro

• Both neutrino event generators provide various neutrino interactions• Can input neutrino beam energy profile

• Provide various output file formats (text, nuance, etc.)

Neutrino event generator


• Publicly available GEANT4 based water Cherenkov detector simulation program

• Relies on GEANT4 and ROOT

• SK & HK geometry and basic PMT information are available

• Provides simulated PMT hit information• Hit time, charge, hit position…

• Trigger, dark rate…

• Use output file of an event generator (nuance format) as input

Detector simulation : WCSim


• GUI event display is prepared

• Can draw event display easily

• Use WCSim output file

• Implemented simple functions

Event display


• KNO fit• Step by step fitter using maximum goodness method

• Mainly use hit time information• Time residual = PMT hit time – Time of flight from vertex

High Energy Software


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

• Vertex resolution



• KNOfit tries to fit vertex using ring information

• Shows improvements in Δ𝑅 between pre-fitter and precise-fitter

• Current best Δ𝑅• Peak ~45 cm

• 65% ~90 cm

Current best

65%

• Angular resolution• Δ𝜃 of electron is slightly larger than Δ𝜃 of muon



𝝁 𝒆−

65% 65%

• Energy resolution• Obtain lepton energy using conversion function

• Current best resolution : 1 GeV Muon ~5.7 %, 1 GeV Electron ~8.5 %



1 GeV 𝝁 1 GeV 𝒆−

• Particle Identification (PID)• Muon and electron can be identified using ring pattern

• A muon typically produces a sharp-edged ring

• And an electron will produce a much fuzzier ring due to EM shower

• ~99% accuracy



𝝁 𝒆−

• Machine Learning PID• Two leptons produce

different ring patterns

• Identify lepton type using machine learning

• python 3.7 with pytorch

• Train and test using image of event display only



• Improved event reconstruction tool

• KNOfit is a “step by step” goodness fitter• vertex → lepton direction → momentum

• Developing new likelihood fitter based on the experience accumulated while developing KNOfit

• Simultaneous fitter - vertex (x,y,z,t), lepton direction (θ,Ф) and momentum (p)

• Expect better performance than KNOfit



Low Energy Software


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

• KNO low energy threshold• KNOfit (also likelihood fitter) can not be used for low E (~O(10) MeV) events due to

small number of PMT hits

• Need to check observable energy thresholds for KNO geometry

• Expected energy resolution using low energy fitter

Low Energy Software


• Develop likelihood fitter• Developing as a one ring fitter : ~50% progress• PID will not be included

• Develop low energy fitter• Developing event vertex and lepton direction fitting• Packaging for distribution

• Study of ML PID• Developing ML PID tool using various information of WCSim• To be integrated into likelihood fitter

Plan


• High energy event reconstruction

Super-K reconstruction performance


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

• KNO detector simulation• Use WCSim simulation program based on GEANT4

• Event reconstruction• KNOfit (step by step maximum goodness fitter) is available

• without PID process

• energy resolution : ~5.7% (muon) and ~8.5% (electron)

• Low energy threshold of KNO is about 7~11 MeV (current design)

• Advanced high energy fitter is being developed

• Work on machine learning PID is in progress

Summary


backup


nuWro Genie-MC

exported ‘nuance’ file

from genie-mc : exist ‘-2’ tag particles

“-2 = intermediate state”

from nuWro : exist ‘info’ line2021-08-20 KNO 6th workshop 21

• final muon (tag ‘0’) energy distribution

(using ‘nuance’ file)

• both generator shows 9.x GeV peak


nuWro Genie-MC

𝑛

𝜌

𝑛

𝜌

• Final particles (tag ‘0’) (using ‘nuance’ file)

• Similar outputs


nuWro Genie-MC

𝜇

𝜋0 𝜋+

𝜇

𝛾𝜋0 𝜋+

• genie-mc shows ‘gamma’


Cross-section

25

Carbon scattered cross-section

Looks very similar

2021-08-20 KNO 6th workshop

26

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

Charged Current cross section


27

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

Neutral Current cross section


Vertex Reconstruction using simple method

• Concept• Assume all photons from one point

• Set a virtual vertex in the Detector• can calculate distance from hit PMT

• obtain “Time of Flight (in the water)” with all hit PMTs

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

• In Super-K, already use similar concept vertex simple fitter

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Real Vertex

(unknown)

Virtual Vertex

for calculation

Hit PMT

distance


Vertex Reconstruction using simple method

• 𝑇𝑖𝑚𝑒 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝑡𝑖 = 𝑡𝑖0 −

𝑃𝑖−𝑂

𝑐• 𝑡𝑖

0 : digitized hit time of ith hit PMT

• 𝑃𝑖 : position of ith hit PMT

• 𝑂 : position of virtual vertex

• 𝑐 : light speed in water (refractive index = 1.33)

• 𝐺𝑜𝑜𝑑𝑛𝑒𝑠𝑠 𝐺 = σ𝑖 exp(−𝑡𝑖−𝑡0

2

2 1.5×𝜎 2)

• 𝑡0 : event time (free parameter)

• 𝜎 : typical time resolution (using 2.5 ns from SK)

29

Function of Vertex (x,y,z) and 𝑡0Search minimum “–Goodness” combination


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


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• Particle direction

𝑑𝑝 =

𝑖

𝑞𝑖𝑃𝑖 − 𝑂

𝑃𝑖 − 𝑂

• 𝑞𝑖 ∶ 𝑐ℎ𝑎𝑟𝑔𝑒 𝑖𝑛 𝑖 − 𝑡ℎ 𝑃𝑀𝑇

• 𝑃𝑖 ∶ 𝑖 − 𝑡ℎ 𝑃𝑀𝑇 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛

• 𝑂 ∶ 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑 𝑣𝑒𝑟𝑡𝑒𝑥

• Edge finder

𝑄 𝜃𝑒𝑑𝑔𝑒 =𝑜𝜃𝑒𝑑𝑔𝑒 𝑃𝐸 𝜃 𝑑𝜃

sin 𝜃𝑒𝑑𝑔𝑒ቤ

𝑑𝑃𝐸(𝜃)

𝑑𝜃𝜃𝑒𝑑𝑔𝑒

2

𝑒𝑥𝑝 −(𝜃𝑒𝑑𝑔𝑒 − 𝜃𝑒𝑥𝑝)

2𝜎𝜃2

• 𝜃𝑒𝑥𝑝 ∶ 𝑒𝑥𝑝𝑒𝑐𝑡𝑑 𝐶ℎ𝑒𝑟𝑒𝑛𝑘𝑜𝑣 𝑎𝑛𝑔𝑙𝑒 ( ~ 42°)• 𝜎𝜃 ∶ 𝑎𝑛𝑔𝑢𝑙𝑎𝑟 𝑟𝑒𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 (~3° ? )


Re-fit vertex using Ring information

• Concept• Assume all photons from one point (pre-fitter)

→ add track, In & Out ring information

• From pre-fitted vertex• Get the brightest ring and particle direction using

pre-fitted vertex

• Add track information, more precise vertex fitting

• Outside of ring, PMT hits are considered by scattered light

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Pre-fitted

Vertex

Cherenkov angle

Hit PMT

track

𝜃𝑐 𝜃𝑐


• Super-K TDC fit formula

• Goodness – “out ring”

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𝑅𝑐 = fraction of total charge which inside the Cherenkov ring

𝜎𝑠𝑐𝑎𝑡𝑡 = 60 ns


Neutrino energy reconstruction

• 재진 found more precise formula in T2K doctoral thesis

• Calculate neutrino energy using same sample

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

34

성현’s formula


35

True Energy

Reco Energy

True Energy

Reco Energy

성현’s formula result T2K formula result

T2K formula shows better result


36

성현’s formula result T2K formula result

Decrease difference

T2K formula shows better

reconstruction

performance

energy vs difference has

no correlation


37

True vs Reco distribution shows similar distribution between

성현‘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 성현’s energy resolution 4.5%

(gaussian smearing)

Not bad result, too


Likelihood fitter study

• SK&HK event reconstruction algorithm• fiTQun : maximum likelihood with a particle event hypothesis x

• Using observed information, construct the likelihood function

38

Main challenge is calculating these “mu”


39

Likelihood fitter study
