Preserving Structure in Multi-wavelength Images of Extended Objects

TL;DR

This paper introduces a PCA-based non-parametric smoothing technique for multi-wavelength galaxy images that preserves structural and color information, improving stellar population parameter estimates over traditional methods.

Contribution

The paper presents a novel PCA-based smoothing method that reduces noise while maintaining color structure in multi-wavelength images, outperforming fixed and adaptive radial smoothing techniques.

Findings

Reduces scatter in stellar population parameters.

Outperforms fixed and adaptive radial smoothing methods.

Maintains color structure in noisy data.

Abstract

A non-parametric smoothing method is presented that reduces noise in multi-wavelength imaging data sets. Using Principle Component Analysis (hereafter PCA) to associate pixels according to their $ugriz$-band colors, smoothing is done over pixels with a similar location in PCA space. This method smoothes over pixels with similar color, which reduces the amount of mixing of different colors within the smoothing region. The method is tested using a mock galaxy with signal-to-noise levels and color characteristics of SDSS data. When comparing this method to smoothing methods using a fixed radial profile or an adaptive radial profile, the chi^2-like statistic for the method presented here is smaller. The method shows a small dependence on input parameters. Running this method on SDSS data and fitting theoretical stellar population models to the smoothed data of the mock galaxy and SDSS data, shows that the method reduces scatter in the best-fit stellar population analysis parameters, when compared to cases where no smoothing is done. For an area centered on the star forming region of the mock galaxy, the median and standard deviation of the PCA-smoothed data is 7 Myr (+/- 3 Myr), as compared to 10 Myr (+/- 1 Myr) for a simple radial average, where the noise-free true value is 7.5 Myr (+/- 3.7 Myr).