R. B. Menezes — Page 3

arXiv2009

Mapping low and high density clouds in astrophysical nebulae by imaging forbidden line emission

Emission line ratios have been essential for determining physical parameters such as gas temperature and density in astrophysical gaseous nebulae. With the advent of panoramic spectroscopic devices, images of regions with emission lines related to these physical parameters can, in principle, also be produced. We show that, with observations from modern instruments, it is possible to transform images taken from density sensitive forbidden lines into images of emission from high and low-density clouds by applying a transformation matrix. In order to achieve this, images of the pairs of density sensitive lines as well as the adjacent continuum have to be observed and combined. We have computed the critical densities for a series of pairs of lines in the infrared, optical, ultraviolet and X-rays bands, and calculated the pair line intensity ratios in the high and low-density limit using a 4 and 5 level atom approximation. In order to illustrate the method we applied it to GMOS-IFU data of two galactic nuclei. We conclude that this method provides new information of astrophysical interest, especially for mapping low and high-density clouds; for this reason we call it "the ld/hd imaging method".

astro-ph.IM↗

arXiv2009

PCA Tomography: how to extract information from datacubes

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies it is possible to obtain datacubes in which one combines both techniques simultaneously, producing 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 present a method of analysis of datacube (data from single field observations, containing two spatial and one spectral dimension) that uses PCA (Principal Component Analysis) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data onto these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal and this information is fundamental for their handling and interpretation. When the datacube shows objects that present uncorrelated physical phenomena, the eigenvector's orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the 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.

astro-ph.IM↗