A Robust Principal Component Analysis for Outlier Identification in Messy Microcalorimeter Data

Fowler, Joseph W.; Alpert, Bradley K.; Joe, Young Il; O’Neil, Galen C.; Swetz, Daniel S.; Ullom, Joel N.

doi:10.1007/s10909-019-02248-w

“…In the context of calorimetric signal processing, PCA-based methods have been shown to have resolution power superior to standard optimal filtering techniques under certain circumstances [9,10], with the leading PCA components used to estimate the energy of a pulse. Other studies have demonstrated the ability to perform outlier detection using PCA-based methods without having to rely on labeled training data like in supervised learning [11,12].…”

Section: Principal Component Analysismentioning

confidence: 99%

Pulse shape discrimination in CUPID-Mo using principal component analysis

Huang¹,

Armengaud²,

Augier³

et al. 2021

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CUPID-Mo is a cryogenic detector array designed to search for neutrinoless double-beta decay (0νββ) of 100Mo. It uses 20 scintillating 100Mo-enriched Li2MoO4 bolometers instrumented with Ge light detectors to perform active suppression of α backgrounds, drastically reducing the expected background in the 0νββ signal region. As a result, pileup events and small detector instabilities that mimic normal signals become non-negligible potential backgrounds. These types of events can in principle be eliminated based on their signal shapes, which are different from those of regular bolometric pulses. We show that a purely data-driven principal component analysis based approach is able to filter out these anomalous events, without the aid of detector response simulations.

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“…The main objectives are mitigating nonlinearity of the detector output, reconstructing single events from pile-up events, lowering the threshold for unresolved pile-up, and detecting outliers. Most approaches are based on either (modified) optimal filtering techniques [15,16] or on principal component analysis [17][18][19][20][21]. While these methods show promising results, in this study we focus on arithmetically simple approaches as we aim for a fast online data reduction.…”

Section: Data Reductionmentioning

confidence: 99%

Data reduction for a calorimetrically measured $$^{163}\mathrm {Ho}$$ spectrum of the ECHo-1k experiment

Hammann

¹

,

Barth

²

,

Fleischmann

³

et al. 2021

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The electron capture in $$^{163}\mathrm {Ho}$$ 163 Ho experiment (ECHo) is designed to directly measure the effective electron neutrino mass by analysing the endpoint region of the $$^{163}\mathrm {Ho}$$ 163 Ho electron capture spectrum. We present a data reduction scheme for the analysis of high statistics data acquired with the first phase of the ECHo experiment, ECHo-1k, to reliably infer the energy of $$^{163}\mathrm {Ho}$$ 163 Ho events and discard triggered noise or pile-up events. On a first level, the raw data is filtered purely based on the trigger time information of the acquired signals. On a second level, the time profile of each triggered event is analysed to identify the signals corresponding to a single energy deposition in the detector. We demonstrate that events not belonging to this category are discarded with an efficiency above 99.8%, with a minimal loss of $$^{163}\mathrm {Ho}$$ 163 Ho events of about 0.7%. While the filter using the trigger time information is completely energy independent, a slight energy dependence of the filter based on the time profile is precisely characterised. This data reduction protocol will be important to minimise systematic errors in the analysis of the $$^{163}\mathrm {Ho}$$ 163 Ho spectrum for the determination of the effective electron neutrino mass.

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“…In the context of calorimetric signal processing, PCA-based methods have been shown to have resolution power superior to standard optimal filtering techniques under certain circumstances [9,10], with the leading PCA components used to estimate the energy of a pulse. Other studies have demonstrated the ability to perform outlier detection using PCA-based methods without having to rely on labeled training data like in supervised learning [11,12].…”

Section: Principal Component Analysismentioning

confidence: 99%

Pulse Shape Discrimination in CUPID-Mo using Principal Component Analysis

Huang,

Armengaud,

Augier

et al. 2020

Preprint

1

0

View full text Add to dashboard Cite

Mo is a cryogenic detector array designed to search for neutrinoless double-beta decay (0𝜈𝛽𝛽) of 100 Mo. It uses 20 scintillating 100 Mo-enriched Li 2 MoO 4 bolometers instrumented with Ge light detectors to perform active suppression of 𝛼 backgrounds, drastically reducing the expected background in the 0𝜈𝛽𝛽 signal region. As a result, pileup events and small detector instabilities that mimic normal signals become non-negligible potential backgrounds. These types of events can in principle be eliminated based on their signal shapes, which are different from those of regular bolometric pulses. We show that a purely data-driven principal component analysis based approach is able to filter out these anomalous events, without the aid of detector response simulations.

show abstract

A Robust Principal Component Analysis for Outlier Identification in Messy Microcalorimeter Data

Cited by 6 publications

References 31 publications

Pulse shape discrimination in CUPID-Mo using principal component analysis

Pulse shape discrimination in CUPID-Mo using principal component analysis

Data reduction for a calorimetrically measured $$^{163}\mathrm {Ho}$$ spectrum of the ECHo-1k experiment

Pulse Shape Discrimination in CUPID-Mo using Principal Component Analysis

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