Suppressing Background Radiation Using Poisson Principal Component Analysis

05/26/2016
by   P. Tandon, et al.
0

Performance of nuclear threat detection systems based on gamma-ray spectrometry often strongly depends on the ability to identify the part of measured signal that can be attributed to background radiation. We have successfully applied a method based on Principal Component Analysis (PCA) to obtain a compact null-space model of background spectra using PCA projection residuals to derive a source detection score. We have shown the method's utility in a threat detection system using mobile spectrometers in urban scenes (Tandon et al 2012). While it is commonly assumed that measured photon counts follow a Poisson process, standard PCA makes a Gaussian assumption about the data distribution, which may be a poor approximation when photon counts are low. This paper studies whether and in what conditions PCA with a Poisson-based loss function (Poisson PCA) can outperform standard Gaussian PCA in modeling background radiation to enable more sensitive and specific nuclear threat detection.

READ FULL TEXT

page 1

page 2

research
11/25/2019

Tropical principal component analysis on the space of ultrametrics

In 2019, Yoshida et al. introduced a notion of tropical principal compon...
research
08/07/2020

Modal Principal Component Analysis

Principal component analysis (PCA) is a widely used method for data proc...
research
12/13/2011

Data Processing For Atomic Resolution EELS

The high beam current and sub-angstrom resolution of aberration-correcte...
research
06/02/2012

Poisson noise reduction with non-local PCA

Photon-limited imaging arises when the number of photons collected by a ...
research
11/26/2022

Utility of PCA and Other Data Transformation Techniques in Exoplanet Research

This paper focuses on the utility of various data transformation techniq...
research
04/27/2022

On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification

Identification of the current and expected future pollution sources to r...
research
04/26/2019

Poisson PCA: Poisson Measurement Error corrected PCA, with Application to Microbiome Data

In this paper, we study the problem of computing a Principal Component A...

Please sign up or login with your details

Forgot password? Click here to reset