Clustering of the Diffuse Infrared Light from the COBE DIRBE maps. III. Power spectrum analysis and excess isotropic component of fluctuations

The cosmic infrared background (CIB) radiation is the cosmic repository for energy release throughout the history of the universe. Using the all-sky data from the COBE DIRBE instrument at wavelengths 1.25 - 100 mic we attempt to measure the CIB fluctuations. In the near-IR, foreground emission is dominated by small scale structure due to stars in the Galaxy. There we find a strong correlation between the amplitude of the fluctuations and Galactic latitude after removing bright foreground stars. Using data outside the Galactic plane ($|b| > 20\deg$) and away from the center ($90\deg< l <270\deg$) we extrapolate the amplitude of the fluctuations to cosec$|b|=0$. We find a positive intercept of $\delta F_{\rm rms} = 15.5^{+3.7}_{-7.0},5.9^{+1.6}_{-3.7}, 2.4^{+0.5}_{-0.9}, 2.0^{+0.25}_{-0.5}$ nW/m2/sr at 1.25, 2.2,3.5 and 4.9 mic respectively, where the errors are the range of 92% confidence limits. For color subtracted maps between band 1 and 2 we find the isotropic part of the fluctuations at $7.6^{+1.2}_{-2.4}$ nW/m2/sr. Based on detailed numerical and analytic models, this residual is not likely to originate from the Galaxy, our clipping algorithm, or instrumental noise. We demonstrate that the residuals from the fit used in the extrapolation are distributed isotropically and suggest that this extra variance may result from structure in the CIB. For 2\deg< \theta < 15^\deg, a power-spectrum analysis yields firm upper limits of (\theta/5^\deg) \times\delta F_{\rm rms} (\theta) < 6, 2.5, 0.8, 0.5 nW/m2/sr at 1.25, 2.2, 3.5 and 4.9 mic respectively. From 10-100 mic, the upper limits <1 nW/m2/sr.Comment: Ap.J., in press. 69 pages including 24 fig

Multivariate Approaches to Classification in Extragalactic Astronomy

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono-or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.Comment: Open Access paper. http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\&gt;. \&lt;10.3389/fspas.2015.00003 \&g