PCA Tomography and its application to nearby galactic nuclei

TL;DR

This paper introduces PCA tomography, a method for analyzing spectral data cubes from galactic nuclei, demonstrating its effectiveness by revealing a previously unknown active nucleus in galaxy NGC 4736.

Contribution

The paper presents PCA tomography as a novel data analysis technique for spectral data cubes, enabling efficient extraction of information from large astrophysical datasets.

Findings

Identified a type 1 active nucleus in NGC 4736

Discovered the nucleus is displaced from the stellar bulge center

Demonstrated PCA tomography's effectiveness in astrophysical data analysis

Abstract

With the development of modern technologies such as IFUs, it is possible to obtain data cubes in which one produces images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We briefly describe a method of analysis of data cubes (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. We applied the method, for illustration purpose, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.