Related papers: Unsupervised Classification of Galaxies. I. ICA fe…
The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS)…
We consider the problem of recovering a common latent source with independent components from multiple views. This applies to settings in which a variable is measured with multiple experimental modalities, and where the goal is to…
Finding overcomplete latent representations of data has applications in data analysis, signal processing, machine learning, theoretical neuroscience and many other fields. In an overcomplete representation, the number of latent features…
A general method is described for detecting and analysing galaxy systems. The multivariate geometrical structure of the sample is studied by using an extension of the method which we introduced in a previous paper. The method is based on an…
Independent Component Analysis (ICA) offers interpretable semantic components of embeddings. While ICA theory assumes that embeddings can be linearly decomposed into independent components, real-world data often do not satisfy this…
In this paper, we apply a new statistical analysis technique, Mean Field approach to Bayesian Independent Component Analysis (MF-ICA), on galaxy spectral analysis. This algorithm can compress the stellar spectral library into a few…
We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and…
To reveal small galaxy groups in the Local Supercluster, a new approach is suggested which allows for individual properties of galaxies. The criterion is based on the assumption of closed motions of companions around the dominating group…
To separate stars and galaxies in the far infrared AKARI All-Sky Survey data, we have selected a sample with the complete color information available in the low extinction regions of the sky and constructed color-color plots for these data.…
We have selected a sample of 41 extremely isolated galaxies (EIGs) from the local universe using both optical and HI ALFALFA redshifts (Spector & Brosch 2016). Narrow band H$_\alpha$ and wide band imaging along with public data were used to…
We present a catalog of true edge-on disk galaxies automatically selected from the Seventh Data Release (DR7) of the Sloan Digital Sky Survey. A visual inspection of the $g$, $r$ and $i$ images of about 15000 galaxies allowed us to split…
We present a quantitative method to classify galaxies, based on multi-wavelength data and elaborated from the properties of nearby galaxies. Our objective is to define an evolutionary method that can be used for low and high redshift…
(Abridged) We determine the quantitative morphology and star formation properties of galaxies in six nearby X-ray detected, poor groups using multi-object spectroscopy and wide-field R imaging. We measure structural parameters for each…
To ascertain whether photometric decompositions of galaxies into bulges and disks are astrophysically meaningful, we have developed a new technique to decompose spectral data cubes into separate bulge and disk components, subject only to…
The fundamental plane of early-type galaxies is a rather tight three-parameter correlation discovered more than twenty years ago. It has resisted a both global and precise physical interpretation despite a consequent number of works,…
(abridged) Hard X-ray surveys performed by the INTEGRAL satellite have discovered a conspicuous fraction (up to 30%) of unidentified objects among the detected sources. Here we continue our identification program by selecting probable…
Galaxy morphologies provide valuable insights into their formation processes, tracing the spatial distribution of ongoing star formation and encoding signatures of dynamical interactions. While such information has been extensively…
Independent component analysis (ICA) aims at decomposing an observed random vector into statistically independent variables. Deflation-based implementations, such as the popular one-unit FastICA algorithm and its variants, extract the…
We consider a multi-view learning problem known as group independent component analysis (group ICA), where the goal is to recover shared independent sources from many views. The statistical modeling of this problem requires to take noise…
Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis is used for dimension reduction prior to ICA…