scholarly journals Kinematics and Principal Component Analysis from IFU Spectroscopy of the Post-Starburst Quasar SDSS J0210–0903

2009 ◽  
Vol 5 (S267) ◽  
pp. 138-138
Author(s):  
David Sanmartim ◽  
Thaisa Storchi-Bergmann ◽  
Michael S. Brotherton
Keyword(s):  
Principal Component Analysis ◽  
Emission Line ◽  
Principal Component ◽  
Radial Velocities ◽  
Absorption Lines ◽  
Two Dimensional ◽  
Line Profiles ◽  
Gas Velocity ◽  
Field Unit ◽  
Integral Field

We present 2-D mapping and analysis of the gaseous kinematics of the inner 7″ × 5″ of one of nearest (z = 0.0414) and brightest post-starburst quasars (PSQ) by using spectra obtained with the Integral Field Unit (IFU) of the Gemini Multi-Object Spectrograph on the Gemini North Telescope (Allington-Smith et al. 2002). Such quasars are broad-lined AGNs that also show the Balmer jumps and the high-order Balmer absorption lines from A stars typical of massive post-starburst populations of a few hundred Myrs (Brotherton et al. 2007). From measurements of the emission-line profiles, we constructed two-dimensional maps for the flux distributions, line ratios, radial velocities and gas velocity dispersions for the Hβ and [Oiii] emitting gas, similar to those of previous studies by our group (e.g., Barbosa et al. 2009).

scholarly journals Integral field spectroscopy of Green Peas – I. Disentangling disc-like turbulence and strong outflow kinematics in SDSS J083843.63+385350.5

2019 ◽  
Vol 489 (2) ◽  
pp. 1787-1796 ◽  
Author(s):  
G Bosch ◽  
G F Hägele ◽  
R Amorín ◽  
V Firpo ◽  
M V Cardaci ◽  
...  
Keyword(s):  
Emission Line ◽  
Large Scale ◽  
Formation Rate ◽  
Mixing Layer ◽  
Line Profiles ◽  
Statistical Indicator ◽  
Integral Field Spectroscopy ◽  
Field Spectroscopy ◽  
Field Unit ◽  
Integral Field

ABSTRACT Integral field spectroscopy is well known for providing detailed insight of extended sources thanks to the possibility of handling space resolved spectroscopic information. Simple and straightforward analysis such as single line fitting yields interesting results, although it might miss a more complete picture in many cases. Violent star-forming regions, such as starburst galaxies, display very complex emission line profiles due to multiple kinematic components superposed in the line of sight. We perform a spatially resolved kinematical study of a single Green Pea (GP) galaxy, SDSS J083843.63+385350.5, using a new method for analysing integral field unit observations of emission line spectra. The method considers the presence of multiple components in the emission line profiles and makes use of a statistical indicator to determine the meaningful number of components to fit the observed profiles. We are able to identify three distinct kinematic features throughout the field and discuss their link with a rotating component, a strong outflow, and a turbulent mixing layer. We also derive an updated star formation rate for SDSS J0838 and discuss the link between the observed signatures of a large-scale outflow and of the Lyman continuum leakage detected in GP galaxies.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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