Calculating the transfer function of noise removal by principal component analysis and application to AzTEC observations
Thomas Patrick Downes, David E. Welch, Kimberly Scott, Jason, Austermann, Min S. Yun, Grant W. Wilson

TL;DR
This paper presents a method to accurately calculate the transfer function of PCA-based noise removal in bolometer array instruments, improving flux estimates in AzTEC observations without affecting detection significance.
Contribution
It introduces a general approach for measuring PCA transfer functions on small point sources and applies it to AzTEC data, enhancing photometric accuracy.
Findings
Flux density estimates increase by 10-25% after correction.
Detection significance remains largely unchanged.
Revised technique will be adopted in future AzTEC data releases.
Abstract
Instruments using arrays of many bolometers have become increasingly common in the past decade. The maps produced by such instruments typically include the filtering effects of the instrument as well as those from subsequent steps performed in the reduction of the data. Therefore interpretation of the maps is dependent upon accurately calculating the transfer function of the chosen reduction technique on the signal of interest. Many of these instruments use non-linear and iterative techniques to reduce their data because such methods can offer improved signal-to-noise over those that are purely linear, particularly for signals at scales comparable to that subtended by the array. We discuss a general approach for measuring the transfer function of principal component analysis (PCA) on point sources that are small compared to the spatial extent seen by any single bolometer within the…
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