Prototype-based analysis of GAMA galaxy catalogue data

We present a prototype-based machine learning analysis of labeled galaxy catalogue data containing parameters from the Galaxy and Mass Assembly (GAMA) survey. Using both an unsupervised and supervised method, the Self-Organizing Map and Generalized Relevance Matrix Learning Vec- tor Quantization, we find that the data does not fully support the popular visual-inspection-based galaxy classification scheme employed to categorize the galaxies. In particular, only one class, the Little Blue Spheroids, is consistently separable from the other classes. In a proof-of-concept experiment, we present the galaxy parameters that are most discriminative for this class

Galaxy classification: A machine learning analysis of GAMA catalogue data

We present a machine learning analysis of five labelled galaxy catalogues from the Galaxy And Mass Assembly (GAMA): The SersicCatVIKING and SersicCatUKIDSS catalogues containing morphological features, the GaussFitSimplecatalogue containing spectroscopic features, the MagPhys catalogue including physical parameters for galaxies, and the Lambdar catalogue, which contains photometric measurements. Extending work previously presented at the ESANN 2018 conference – in an analysis based on Generalized Relevance Matrix Learning Vector Quantization and Random Forests – we find that neither the data from the individual catalogues nor a combined dataset based on all 5 catalogues fully supports the visual-inspection-based galaxy classification scheme employed to categorise the galaxies. In particular, only one class, the Little Blue Spheroids, is consistently separable from the other classes. To aid further insight into the nature of the employed visual-based classification scheme with respect to physical and morphological features, we present the galaxy parameters that are discriminative for the achieved class distinctions

Galaxy classification: A machine learning analysis of GAMA catalogue data

We present a machine learning analysis of five labelled galaxy catalogues from the Galaxy And Mass Assembly (GAMA): The SersicCatVIKING and SersicCatUKIDSS catalogues containing morphological features, the GaussFitSimple catalogue containing spectroscopic features, the MagPhys catalogue including physical parameters for galaxies, and the Lambdar catalogue, which contains photometric measurements. Extending work previously presented at the ESANN 2018 conference - in an analysis based on Generalized Relevance Matrix Learning Vector Quantization and Random Forests - we find that neither the data from the individual catalogues nor a combined dataset based on all 5 catalogues fully supports the visual-inspection-based galaxy classification scheme employed to categorise the galaxies. In particular, only one class, the Little Blue Spheroids, is consistently separable from the other classes. To aid further insight into the nature of the employed visual-based classification scheme with respect to physical and morphological features, we present the galaxy parameters that are discriminative for the achieved class distinctions.Comment: Accepted for the ESANN 2018 Special Issue of Neurocomputin

Astronomical Surveys and Big Data

Recent all-sky and large-area astronomical surveys and their catalogued data over the whole range of electromagnetic spectrum are reviewed, from Gamma-ray to radio, such as Fermi-GLAST and INTEGRAL in Gamma-ray, ROSAT, XMM and Chandra in X-ray, GALEX in UV, SDSS and several POSS I and II based catalogues (APM, MAPS, USNO, GSC) in optical range, 2MASS in NIR, WISE and AKARI IRC in MIR, IRAS and AKARI FIS in FIR, NVSS and FIRST in radio and many others, as well as most important surveys giving optical images (DSS I and II, SDSS, etc.), proper motions (Tycho, USNO, Gaia), variability (GCVS, NSVS, ASAS, Catalina, Pan-STARRS) and spectroscopic data (FBS, SBS, Case, HQS, HES, SDSS, CALIFA, GAMA). An overall understanding of the coverage along the whole wavelength range and comparisons between various surveys are given: galaxy redshift surveys, QSO/AGN, radio, Galactic structure, and Dark Energy surveys. Astronomy has entered the Big Data era. Astrophysical Virtual Observatories and Computational Astrophysics play an important role in using and analysis of big data for new discoveries.Comment: 14 pages, 6 figures, 3 tables, 51 references. Presented at EAAS XII General Meeting, submitted to Baltic Astronom

P-MaNGA : full spectral fitting and stellar population maps from prototype observations

MC acknowledges support from a Royal Society University Research Fellowship.MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is a 6-yearSDSS-IV survey that will obtain resolved spectroscopy from 3600 Å to10300 Å for a representative sample of over 10,000 nearby galaxies.In this paper, we derive spatially resolved stellar population properties and radial gradients by performing full spectral fitting of observed galaxy spectra from P-MaNGA, a prototype of the MaNGA instrument. These data include spectra for eighteen galaxies, covering a large range of morphological type. We derive age, metallicity, dust and stellar mass maps, and their radial gradients, using high spectral-resolution stellar population models, and assess the impact of varying the stellar library input to the models. We introduce a method to determine dust extinction which is able to give smooth stellar mass maps even in cases of high and spatially non-uniform dust attenuation.With the spectral fitting we produce detailed maps of stellar population properties which allow us to identify galactic features among this diverse sample such as spiral structure, smooth radial profiles with little azimuthal structure in spheroidal galaxies, and spatially distinct galaxy sub-components. In agreement with the literature, we find the gradients for galaxies identified as early-type to be on average flat in age, and negative (- 0.15 dex / Re ) in metallicity,whereas the gradients for late-type galaxies are on average negative in age (- 0.39 dex / Re ) and flat in metallicity. We demonstrate howdifferent levels of data quality change the precision with which radialgradients can be measured. We show how this analysis, extended to thelarge numbers of MaNGA galaxies, will have the potential to shed lighton galaxy structure and evolution.PostprintPeer reviewe

Machine Learning and Data Analysis in Astroinformatics

Astroinformatics is a new discipline at the cross-road of astronomy, advanced statistics and computer science. With next generation sky surveys, space missions and modern instrumentation astronomy will enter the Petascale regime raising the demand for advanced computer science techniques with hard- and software solutions for data management, analysis, efficient automation and knowledge discovery. This tutorial reviews important developments in astroinformatics over the past years and discusses some relevant research questions and concrete problems. The contribution ends with a short review of the special session papers in these proceedings, as well as perspectives and challenges for the near future