ProtoDUNE Performance and Analysis Utilities Leigh Whitehead DUNE - - PowerPoint PPT Presentation

ProtoDUNE Performance and Analysis Utilities

Leigh Whitehead

DUNE UK Meeting 11/12/19

Leigh Whitehead

Overview

• This talk will be in two main sections
• Pandora performance in ProtoDUNE
• A lot of this was Steve’s work
• Analysis-level utilities for accessing the outputs of the

reconstruction

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Leigh Whitehead

Pandora in ProtoDUNE

• Pandora is the only particle reconstruction algorithm used

in ProtoDUNE-SP

• Generally, it has been working very well!
• All of the future improvements that will be discussed today

will improve the performance

• I’ll show a few slides presented by Steve Green at the last

DUNE collaboration meeting

• Represents the current status as of the second data processing this

summer

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Leigh Whitehead

Pandora in ProtoDUNE

• Reconstructed data event in the upcoming ProtoDUNE-SP

performance paper

• Looks like a nice pion


charge exchange event

• See beam particles amongst


large cosmic background

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w, wire position x, drift position

Beam Interaction Cosmic muons

Leigh Whitehead

Pandora in ProtoDUNE

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Things look very similar for protons

Leigh Whitehead

Pandora in ProtoDUNE

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Leigh Whitehead

Pandora in ProtoDUNE

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Leigh Whitehead

Challenges for ProtoDUNE

• Most of the analysis challenges are sub-leading effects
• Identification of short tracks emerging from the beam

particle interactions

• Detection of small angle kinks for cross-section

measurements (from elastic scattering)

• Beam interaction vertex finding
• The current activities underway for the DUNE FD will also

have a strong impact on these issues

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Leigh Whitehead

Pandora for DUNE FD

• We have to ensure that Pandora remains at the forefront of

the DUNE FD event reconstruction

• Very well represented in the DUNE TDR
• Hence, we must:
• Present updates regularly so that people know it exists and is under

active development

• Help people to use it, and its outputs, correctly and easily
• We are in a good position to do the former:
• The UK has two of the DUNE FD Sim/Reco convenors and the

ProtoDUNE-SP reco convenor

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Leigh Whitehead

Analysis Utilities

• The aim is to provide easy access to the reconstructed

information

• This also allows us to ensure the analysers access the information in
• ur recommended way
• Provide intuitive access to the particle hierarchy and break the “look

at tracks and blobs” mentality we keep with us from our older experiments

• I also try to hide some of the annoying things about LArSoft
• I wrote similar utilities for ProtoDUNE and they are widely

used by the analysers

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Leigh Whitehead

Recommended Workflow

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Event PFParticle Cluster Track Shower Hits … … … …

Recommended 
 Available but discouraged

Reconstructed objects should be accessed using the hierarchy Always start with the PFParticles Following each arrow is a few lines of code in LArSoft

Leigh Whitehead

Recommended Workflow

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Event PFParticle Cluster Track Shower Hits … … … …

Recommended 
 Available but discouraged

Reconstructed objects should be accessed using the hierarchy Always start with the PFParticles Access other products via the PFParticles Following each arrow is a few lines of code in LArSoft

Leigh Whitehead

Recommended Workflow

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Event PFParticle Cluster Track Shower Hits … … … …

Recommended 
 Available but discouraged

Provide simple functions to access these products in the recommended way Following each arrow is a few lines of code in LArSoft

Leigh Whitehead

Recommended Workflow

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Event PFParticle Cluster Track Shower Hits … … … …

Recommended 
 Available but discouraged

I will provide functions to skip these steps for high level users, but provide warnings that it is not the standard method Following each arrow is a few lines of code in LArSoft

Leigh Whitehead

The Utilities

• I have written a number of these utilities
• Here’s a quick look at the PFParticle one
• Easy access to the products that make up the PFParticle

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larpandora/LArPandoraAnalysisUtils/LArPandoraPFParticleUtils.h

Leigh Whitehead

The Utilities

• There are utilities for accessing information from:
• Events
• PFParticles
• Tracks
• Showers
• Clusters
• Slices
• SpacePoints
• Code is easily maintainable since there are only three

underlying functions that do the work

• Extendable to any (current or future) LArSoft products via

templated functions

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Leigh Whitehead

The Utilities

• Allows for quick and simple code
• Two lines to get the reconstructed neutrino and interaction products
• We can then get the interaction vertex
• Look for track-like child particles

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art::Ptr<recob::PFParticle> nu = LArPandoraEventUtils::GetNeutrino(event,”pandora”); std::vector<art::Ptr<recob::PFParticle>> children = LArPandoraPFParticleUtils::GetChildParticles(nu,event,”pandora”); art::Ptr<recob::Vertex> nuVertex = LArPandoraPFParticleUtils::GetVertex(nu,event,”pandora”); for(art::Ptr<recob::PFParticle> &child : children)
 { if (LArPandoraPFParticleUtils::IsTrack(child,event,”pandora”,”pandoraTrack”)) { <insert user code here> } }

Leigh Whitehead

Summary

• Pandora is performing well for ProtoDUNE, but there is

always room for improvement!

• Moving forwards we have to ensure Pandora is the primary

reconstruction tool

• We all need to present update regularly
• Analysis-level tools will ensure that users can access the

information from Pandora and using it in the best way

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Bonus: Track PID

• I take the final 100 hits of pandora tracks as 1D arrays
• Extracted from LArSoft using the analysis utilities
• If a track has 30 < n_hits < 100 then pad the start of the

track as follows:

• Calculate the mean and sigma of dE/dx for hits 10 to 20
• Pad the array to length 100 using random values: Gauss(mean,sigma)
• Store some additional variables
• Mean and sigma of dE/dx for hits 10 to 20
• Sigma of the distribution of angles between successive track points

(how wobbly it is)

• Number of track-like and shower-like child particles

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Leigh Whitehead

Deep Learning in 1D

• There are a few standard techniques for processing 1D

information… lots of examples from speech recognition etc

• I’ve investigated the use of 1D convolutions so far

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100 element dE/dx array # Track-like Children # Shower-like Children Mean dE/dx (10 - 20) Sigma dE/dx (10 - 20) Wobble 1D Convolutions 1D Convolutions 1D Convolutions 1D Convolutions Fully Connected Layer Fully Connected Layer Fully Connected Layer Outputs Fully Connected Layer

Leigh Whitehead

Training Sample

• Use the MCC11 DUNE “workspace” files
• In future might be better to use isotropic particle guns with uniform

energy distributions

• Extract all track-like objects reconstructed from Pandora
• Need to label my tracks with truth information. Currently matching

by finding the true particle that contributes the most hits to the track (if there are more than one)

• 0 = muon, 1 = charged pion, 2 = proton, (3 = kaon when I have more

stats)

• Total of 59128 tracks for training, and for 9522 testing
• Trained for 5 epochs (only a few minutes on my Mac’s CPU)

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Leigh Whitehead

(Very) Preliminary Results

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1 2 3 4 5 6 7 8 9 10 Muon Energy (GeV) 50 100 150 200 250 300 350 400 450 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Pion Energy (GeV) 100 200 300 400 500 600 700 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Proton Energy (GeV) 200 400 600 800 1000 1 2 3 4 5 6 7 8 9 10 Muon Energy (GeV) 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

— All
 — Correct Prediction — All
 — Correct Prediction — All
 — Correct Prediction Muon
 Pion
 Proton

Predicted Muon Predicted Pion Predicted Proton True Muon 85.1% 14.0% 0.9% True Pion 18.3% 75.9% 5.8% True Proton 2.3% 10.6% 87.1%