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Machine Learning Applications for Reactor Antineutrino Detection at PROSPECT

Andrea Delgado, PhD

On behalf of the PROSPECT collaboration

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Outline

● Overview of PROSPECT

● Antineutrino Event Reconstruction at PROSPECT

● Selected Machine Learning Applications at PROSPECT○ Positron ID through o-Ps tagging

○ Single PMT Event Reconstruction

○ Siamese LSTM for Pulse Pair Matching

○ MVA Analysis for Background Suppression

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Precision Reactor Oscillation and SPECTrum Experiment- Short-baseline reactor neutrino experiment.

Designed to address anomalies in flux and spectrum

- ~6% deficit in measurement of antineutrino.

- “Bump” around 5-7 MeV range

- Possible explanations:- Sterile neutrino hypothesis- Incorrect isotope predictions (235U)

- Challenge: Above-ground deployment - large backgrounds

7m

PROSPECT detector at High Flux Isotope Reactor (HFIR) at ORNL.

44cm

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Detector Design + Results- General description of experiment:

- The PROSPECT Reactor Antineutrino Experiment- Main published results:

- Measurement of the Antineutrino Spectrum from U 235 Fission at HFIR with PROSPECT

- First Search for short-baseline neutrino oscillations at HFIR with PROSPECT

- Most recent results just published.arxiv:2006.11210

Detector Performance- Detector response has been fully characterized.- PROSPECT-Geant4 (PG4): Geant4 - based simulation package for full

detector response simulation (by Michael Mendenhall)

Neutrino 2020 Talks & Posters - Talk: Recent Results from PROSPECT (B. Littlejohn) - June 25- Posters: 158, 516, 408, 527, 540, 556

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Antineutrino Event Reconstruction

Prompt signal: ~1-10 MeV positron energyDelayed signal: ~ 0.5 MeV neutron capture

Background suppression through set of cuts based on IBD topology, PSD, cosmic shower veto, etc...

Particle ID classification in 6Li-doped liquid scintillator

allows to differentiate ionization/nuclear

recoil/quenched n-Li

SIMULATION

Antineutrinos detected through Inverse Beta Decay (IBD) interaction

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Positron ID through o-Ps tagging- Can we ID a subset of positrons though

positronium formation?

- If the distortion in the timing distribution induced by o-Ps is not smeared by optical effects, we can use this feature as an extra handle for particle ID (P-ID).

- Preliminary results from optical simulation indicate that PROSPECT might be sensitive to these effects.

1ns Binning4ns Binning

3ns lifetime

20 ns lifetime

Para Positronium, (t=1, 2.5ns)

Ortho Positronium, (t=1,140ns)

PROSPECTSimulation

PROSPECTSimulation

PROSPECTSimulation

PROSPECTSimulation

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Positron ID through o-Ps tagging- Feed pulses from each detector into a Convolutional Neural Network.- For preliminary testing purposes, we are using 1ns sample widths, 150 samples taken from

simulated PMT pulses (PROSPECT uses 4ns digitizers)- Simulation details:

- 2 PMTs on each cell (11 x 14 cells) - 150 samples per pulse (1ns sample width)- Samples are 16-bit integers

- Map the 2 x 150 samples for each cell to the “channels” of a CNN - Output of CNN is a discriminant parameter (will be extended for generic particle ID soon)CNN Architecture

11x14x300 11x14x37 9x12x37 4x5x19 270 135 32 2

conv 1x1, 300

Stride 1Pad 0

conv 3x3, 32

Stride 1Pad 1

conv 3x3, 16

Stride 2Pad 0

flatten

Dense layers

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Positron ID through o-Ps tagging

CNN Architecture

11x14x300 11x14x37 9x12x37 4x5x19 270 135 32 2

conv 1x1, 300

Stride 1Pad 0

conv 3x3, 32

Stride 1Pad 1

conv 3x3, 16

Stride 2Pad 0

flatten

Dense layers

Model 3ns lifetime accuracy

20ns lifetime accuracy

Training time (CPU seconds)

XGBoost 0.5 0.55 860

CNN 0.5 0.64 9605

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Single PMT Event Reconstruction• Can we maintain PID performance while

using information from single PMT readout?1. Replicate the ML particle ID in double PMTs2. Train the model with single PMT info (from

double PMTs) ■ Compare the model prediction with

TRUE double PMT particle ID

Dense layers

Before bootstrap

After bootstrap

256 256 Output layer:

Label (4)

Input layer:E,dt,PE1,PE2,

PSD

Double-PMT

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Single PMT Event Reconstruction• Can we maintain PID performance while

using information from single PMT readout?1. Replicate the ML particle ID in double PMTs2. Train the model with single PMT info (from

double PMTs) ■ Compare the model prediction with

TRUE double PMT particle ID

Dense layers

Single-PMT

256 256 Output layer:

Label (4)Input layer:

E,dt,PE1, PSD

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Siamese LSTM for Pulse Matching• Cast pulse matching to a

“similarity” problem.• Keras-based implementation of

Siamese neural network.– Two identical subnetwork,

parameter updating is mirrored across both subnetworks

– Accuracy of ~73%.

Input 1:[E1, t1, x1]

Input 2 :[E2,t2,x2]

Prediction[0,1]

LSTM Encoder 1

Cosine distance

LSTM Encoder 2

Same weights, same model

How similar are the input vectors?

Predicted Label

True

Lab

el

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MVA Analysis for Background SuppressionUsed in addition to set of rectangular selection cuts to optimize IBD selection efficiency.

• Keep signal rate, but reduce background contamination.• Use spatial and temporal correlations of prompt and

delayed signals.• Use ROOT’s TMVA package for simplicity + easy to add to

existing analysis framework.

MVA Classifiers

• Multi-Layer Perceptron (MLP)

• Boosted Decision Tree (BDT).

• Support Vector Machine (SVM).

Training/test datasets

• IBD simulation from PG4 for signal

• Reactor-off dataset for background

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• Presented a few ideas on the directions our team is pursuing on ML applications to antineutrino data from PROSPECT experiment/simulation.

• Why Oak Ridge National Laboratory?

– Access to CPU/GPU resources through the Oak Ridge Leadership Computing Facility.

– Large community of machine learning experts.

– Synergy with existing groups working on machine learning + quantum computing (ask me about QC!)

• ML applications in PROSPECT can help enhance PSD and P-ID power and improve reactor antineutrino event reconstruction.

Summary

Questions?

delgadoa@ornl.gov

uribarri@ornl.gov

Thank you!Andrea DelgadoAdriana GhiozziCorey GilbertBlaine HeffronXiaobin (Jeremy) LuDiego Venegas-VargasRosa Luz Zamora-PeinadoAlfredo Galindo-Uribarri

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Quantum Computing Applications for Reactor Antineutrino Event Selection

- Earth mover's distance (EMD) is a measure of the distance between two probability distributions over a region D.

- Cast the minimization problem to an Ising Hamiltonian, which can then be solved in a quantum annealer, such as D-Wave.

The minimum cost of turning one pile of dirt into another