OpHit Slicing Dan Pershey Feb 11, 2019 Overview Implemented an - - PowerPoint PPT Presentation

OpHit Slicing

Dan Pershey Feb 11, 2019

Overview

❑Implemented an OpHit clusterer, based on DBScan, but slightly modified ❑Radiological OpHit’s are infrequent enough in our PDS that algorithm measures

delayed scintillation light in LAr

• Exciting moving forward looking at this in the context of calorimetry, PID

❑Current algorithm, simplest procedure you could write down that works:

• Sort OpHit’s by time
• Scan in time and calculate total PE in each time window
• If total PE > some threshold, read out the whole PDS for the time window (up to 0.5μs)

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Helpful Numbers – how much radiological bkg do we expect

❑At a 43 cm2 ARAPUCA SiPM design, our simulation predicts about 4000 distinct

interactions from the radio sim that make some blip in the PDS per drift window

• At a drift window of 4.4 ms, that’s right around 1 radiological / μs
• Almost all decays are from 39Ar, recording 1-2 PE’s / decay

❑A rough cut of 400(300) cm around a reconstructed flash center is visible to

about 20(12)% of the detector

• 0.7 x π x (400)2 / 1400 / 1200 – where 0.7 is an edge effect fudge factor

❑So, a hard cut of 400 cm around a reco’d vertex will slice in light from 0.2

radiological decays / μs of scanning time

❑Prompt light from a typical SNB neutrino’s OpFlash is spread over 0.25 μs – we

expect 0.25μs x 2PE/decay x 0.2decay/μs = 0.1 PE contamination per event

❑At 0.5 PE/MeV, that’s a 2% contribution to a 10 MeV neutrino ❑Low enough, we can hope to include delayed scintillation light

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Example Op Flash Evolution

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Neutrino Light Radiologicals

Eν = 12.75 MeV 0 < t < 1 μs

Z (cm) Z (cm) Y (cm) Y (cm) Y (cm) Y (cm)

Example Op Flash Evolution

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Neutrino Light Z (cm) Z (cm) Y (cm) Y (cm)

Eν = 12.75 MeV 1 < t < 2 μs

Radiologicals

Example Op Flash Evolution

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Neutrino Light Z (cm) Z (cm) Y (cm) Y (cm)

Eν = 12.75 MeV 2 < t < 3 μs

Radiologicals

Example Op Flash Evolution

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Neutrino Light Z (cm) Z (cm) Y (cm) Y (cm)

Eν = 12.75 MeV 3 < t < 4 μs

Radiologicals

Example Op Flash Evolution

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Neutrino Light Z (cm) Z (cm) Y (cm) Y (cm)

Eν = 12.75 MeV 4 < t < 5 μs

Radiologicals

Classifying Sources of Background Hits

❑Almost all unwanted OpHits have

either 1 or 2 PE’s

❑39Ar is the largest single contributor ❑There’s a large portion of the hits

that are not associated with any physics

• These are from the noise simulation
• Noise hits are often correlated with physics
• Cross-talk is simulated, so that neutrinos

induce statistical noise in our PD’s

❑So, I take it that cross-talk is in-separable from our neutrino OpHits, but they

count against OpHit cluster purity

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Clustering Algorithm – DBSCan

❑Relies on a metric to calculate the distance

between each pair of events in your space

• For us, this is a function of Δt, Δz, and Δy

❑Cycle through each point in your space,

• Find how many other pts are in the

neighborhood within 1 unit of test pt

• If there are at least N0 pts in neighborhood,

this pt belongs to a slice, and is labeled a “core” pt

• Add all neighborhood pts to the cluster
• If a point is in the neighborhood of a core pt, but does not have N0 pts in its

neighborhood, it is a peripheral event

• Added to the cluster, but any extra pts in its neighborhood are not

❑N0 technically a free parameter, but fix it to 3

• Similar to putting a 3PE threshold on OpFlashes, going lower would increase our data rate

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Look up DBScan on Wikipedia

Modifying DBScan

❑DBScan works great in the context it’s designed for, but doesn’t do great at

slicing in the context of a space with multiple “slice densities”

❑Fails miserably with accurately reconstructed both prompt and delayed hits into

• ne cluster
• Could not massage parameters to accept all prompt+delayed light in same clusters at

multiple energies without including a bunch of radiological decays

❑Perform DBScan to cluster the prompt light – with restrictions

• While iterating through core DBScan hits, also

calculate a cluster centroid – the hit with the highest density of points surrounding it

• Do not include extra core hits if its distance is

more than 2 units away from the core centroid

❑Then, slice in the delayed light by accepting

hits within 5 μs and Rscale cm away from the centroid

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Slicer Performance

❑We have a well-defined FOM for how well a slicing algorithm does

• For each event, calculate purity x completeness
• Purity = fraction of clustered light that came from neutrino
• Completeness = fraction of light deposited by the neutrino captured by the slice
• FOM = the mean of the distribution of this product for a set of MC

❑Tested performance on a range of algorithm parameters (next slide)

• Chose length scale = 400 cm and time scale = 0.15 μs

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Parameter Tuning

❑Two parameters to tune – the length scales for distance and time that go into

the metric

❑No clear winner, distributions are relatively flat, independent of Eν

• Seems to be a small peak in R scale curves, set scale = 400 cm
• Set T scale = 0.15 μs, roughly half of the prompt time constant

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❑Most events have prompt(delayed) purity > 90(80)% ❑Between the two time windows, we can usually capture all the light released

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Eν = 15.25 MeV

Prompt and Delayed Purity

❑Both shapes are similar, and tied to PE ❑Delayed purity is lower – seems to be from increased cross-talk OpHit’s that

light up slightly delayed from the prompt flash

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Eν = 15.25 MeV

Completeness Also Tied to Total PE Observed

❑Generally speaking, we capture almost all of the light deposited for neutrinos

that deposited > 200 PE in the detector

• At a mean 20 PE/cm, that’s a 10 MeV neutrino

❑Though, second population at total PE deposited > 400 and completeness < 0.8 ❑Don’t currently understand what these are, but would be interesting

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Eν = 15.25 MeV

Delayed Scintillation Light + Calorimetry

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❑Compare the fraction of

neutrino light reco’d from the prompt window and prompt+delayed windows

• Prompt window: find the

time with highest density

• f PE in the flash, and go

+- 250 ns from that time

• Similar to current OpFlash

reco, which looks for an up to 500 ns window with activity

❑Clearly there is more

variance if you only consider prompt light

Summary

❑Implemented a slicing algorithm for OpHits ❑Algorithm tailored to best fit out fast and slow time components – which will

have different density of hits at different neutrino energies

• Potentially very interesting to propagate this to simulation and analysis
• There’s a lot of information in the prompt + delayed total and ratio
• Can help with calorimetry, PID with rejecting radiologicals

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