Astronomy and Computing

Machine and Deep Learning morphological classification for 670,560 galaxies from Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). Classifications are provided for 2 classes problem (0: elliptical; or, 1: spiral galaxy) and 3 classes problem (0: elliptical, 1: non-barred spiral, or 2: barred spiral galaxy). ML2classes classification is obtained by Traditional Machine Learning Approach, using Morphological non-parametric parameters and Decision Tree. Classifications using Deep Learning are obtained using a Convolutional Neural Network (CNN). Morphological non-parametric parameters are provided as well: Concentration (C), Asymmetry (A), Smoothness (S), Gradient Pattern Analysis (G2) parameter and Entropy (H). We also provide the Error from CyMorph processing. All error flags are mapped as follows: Error = 0: success (no errors); Error = 1: many objects of significant brightness inside 2 Rp of the galaxy; Error = 2: not possible to calculate the galaxy's Rp; Error = 3: problem calculating GPA; Error = 4: problem calculating H; Error = 5: problem calculating C; Error = 6: problem calculating A; Error = 7: problem calculating S.

Run times of the LOFAR prefactor pipeline obtained by scaling the Number of CPUs, Data size and skymodel size. The prefactor version used for this data was https://github.com/apmechev/prefactor/commit/da4ac885bce9b24e604c9aac6bf649992065326f

Code and self-downloading data of the paper "Unsupervised Learning of Structure in Spectroscopic Cubes" in Jupyter notebooks format.

MAPLE worksheets. edge.mw: contains the full derivation of the Edgeworth decomposition for the false alarm probability of a sample wavelet transform qij.mw: contains only the final expressions for the coefficients of the normality test