41 research outputs found
Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation
With the availability of the huge amounts of data produced by current and
future large multi-band photometric surveys, photometric redshifts have become
a crucial tool for extragalactic astronomy and cosmology. In this paper we
present a novel method, called Weak Gated Experts (WGE), which allows to derive
photometric redshifts through a combination of data mining techniques.
\noindent The WGE, like many other machine learning techniques, is based on the
exploitation of a spectroscopic knowledge base composed by sources for which a
spectroscopic value of the redshift is available. This method achieves a
variance \sigma^2(\Delta z)=2.3x10^{-4} (\sigma^2(\Delta z) =0.08), where
\Delta z = z_{phot} - z_{spec}) for the reconstruction of the photometric
redshifts for the optical galaxies from the SDSS and for the optical quasars
respectively, while the Root Mean Square (RMS) of the \Delta z variable
distributions for the two experiments is respectively equal to 0.021 and 0.35.
The WGE provides also a mechanism for the estimation of the accuracy of each
photometric redshift. We also present and discuss the catalogs obtained for the
optical SDSS galaxies, for the optical candidate quasars extracted from the DR7
SDSS photometric dataset {The sample of SDSS sources on which the accuracy of
the reconstruction has been assessed is composed of bright sources, for a
subset of which spectroscopic redshifts have been measured.}, and for optical
SDSS candidate quasars observed by GALEX in the UV range. The WGE method
exploits the new technological paradigm provided by the Virtual Observatory and
the emerging field of Astroinformatics.Comment: 36 pages, 22 figures and 8 table
Photometric classification of emission line galaxies with machine-learning methods
In this paper, we discuss an application of machine-learning-based methods to the identification of candidate active galactic nucleus (AGN) from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine-learning algorithms, namely the Multi Layer Perceptron, trained, respectively, with the Conjugate Gradient, the Scaled Conjugate Gradient, the Quasi Newton learning rules and the Support Vector Machines, to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs versus non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features, we discuss also the behaviour of the classifiers on finer AGN classification tasks, namely Seyfert I versus Seyfert II, and Seyfert versus LINER. Furthermore, we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self-adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations
Photometric classification of emission line galaxies with Machine Learning methods
In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine learning algorithms, namely the Multi Layer Perceptron (MLP), trained respectively with the Conjugate Gradient, Scaled Conjugate Gradient and Quasi Newton learning rules, and the Support Vector Machines (SVM), to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs vs non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features we discuss also the behavior of the classifiers on finer AGN classification tasks, namely Seyfert I vs Seyfert II and Seyfert vs LINER. Furthermore we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations
Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources
The automatic classification of X-ray detections is a necessary step in
extracting astrophysical information from compiled catalogs of astrophysical
sources. Classification is useful for the study of individual objects,
statistics for population studies, as well as for anomaly detection, i.e., the
identification of new unexplored phenomena, including transients and spectrally
extreme sources. Despite the importance of this task, classification remains
challenging in X-ray astronomy due to the lack of optical counterparts and
representative training sets. We develop an alternative methodology that
employs an unsupervised machine learning approach to provide probabilistic
classes to Chandra Source Catalog sources with a limited number of labeled
sources, and without ancillary information from optical and infrared catalogs.
We provide a catalog of probabilistic classes for 8,756 sources, comprising a
total of 14,507 detections, and demonstrate the success of the method at
identifying emission from young stellar objects, as well as distinguishing
between small-scale and large-scale compact accretors with a significant level
of confidence. We investigate the consistency between the distribution of
features among classified objects and well-established astrophysical hypotheses
such as the unified AGN model. This provides interpretability to the
probabilistic classifier. Code and tables are available publicly through
GitHub. We provide a web playground for readers to explore our final
classification at https://umlcaxs-playground.streamlit.app.Comment: 21 pages, 11 figures. Accepted in MNRA
The use of neural networks to probe the structure of the nearby universe
In the framework of the European VO-Tech project, we are implementing new machine learning methods specifically tailored to match the needs of astronomical data mining. In this paper, we shortly present the methods and discuss an application to the Sloan Digital Sky Survey public data set. In particular, we discuss some preliminary results on the 3-D taxonomy of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for ca. 30 million galaxies distributed over 8,000 sq. deg. We also use unsupervised clustering tools to investigate whether it is possible to characterize in broad morphological bins the nature of each object and produce a reliable list of candidate AGNs and QSOs
Chandra Early-Type Galaxy Atlas
The hot ISM in early type galaxies (ETGs) plays a crucial role in
understanding their formation and evolution. The structural features of the hot
gas identified by Chandra observations point to key evolutionary mechanisms,
(e.g., AGN and stellar feedback, merging history). In our Chandra Galaxy Atlas
(CGA) project, taking full advantage of the Chandra capabilities, we
systematically analyzed the archival Chandra data of 70 ETGs and produced
uniform data products for the hot gas properties. The primary data products are
spatially resolved 2D spectral maps of the hot gas from individual galaxies. We
emphasize that new features can be identified in the spectral maps which are
not readily visible in the surface brightness maps. The high-level images can
be viewed at the dedicated CGA website, and the CGA data products can be
downloaded to compare with data at other wavelengths and to perform further
analyses. Utilizing our data products, we address a few focused science topics.Comment: 52 pages, 9 figures, accepted in ApJ Supp
Two new catalogs of blazar candidates in the WISE infrared sky
We present two catalogs of radio-loud candidate blazars whose WISE
mid-infrared colors are selected to be consistent with the colors of confirmed
gamma-ray emitting blazars. The first catalog is the improved and expanded
release of the WIBRaLS catalog presented by D'Abrusco et al. (2014): it
includes sources detected in all four WISE filters, spatially cross-matched
with radio source in one of three radio surveys and radio-loud based on their
q22 spectral parameter. WIBRaLS2 includes 9541 sources classified as BL Lacs,
FSRQs or mixed candidates based on their WISE colors. The second catalog,
called KDEBLLACS, based on a new selection technique, contains 5579 candidate
BL Lacs extracted from the population of WISE sources detected in the first
three WISE passbands ([3.4], [4.6] and [12]) only, whose mid-infrared colors
are similar to those of confirmed, gamma-ray BL Lacs. KDBLLACS members area
also required to have a radio counterpart and be radio-loud based on the
parameter q12, defined similarly to q22 used for the WIBRaLS2. We describe the
properties of these catalogs and compare them with the largest samples of
confirmed and candidate blazars in the literature. We crossmatch the two new
catalogs with the most recent catalogs of gamma-ray sources detected by Fermi
LAT instrument. Since spectroscopic observations of candidate blazars from the
first WIBRaLS catalog within the uncertainty regions of gamma-ray unassociated
sources confirmed that ~90% of these candidates are blazars, we anticipate that
these new catalogs will play again an important role in the identification of
the gamma-ray sky.Comment: 20 pages, 7 figures. Accepted for publication in The Astrophysical
Journal Supplement Serie
Iris: an Extensible Application for Building and Analyzing Spectral Energy Distributions
Iris is an extensible application that provides astronomers with a
user-friendly interface capable of ingesting broad-band data from many
different sources in order to build, explore, and model spectral energy
distributions (SEDs). Iris takes advantage of the standards defined by the
International Virtual Observatory Alliance, but hides the technicalities of
such standards by implementing different layers of abstraction on top of them.
Such intermediate layers provide hooks that users and developers can exploit in
order to extend the capabilities provided by Iris. For instance, custom Python
models can be combined in arbitrary ways with the Iris built-in models or with
other custom functions. As such, Iris offers a platform for the development and
integration of SED data, services, and applications, either from the user's
system or from the web. In this paper we describe the built-in features
provided by Iris for building and analyzing SEDs. We also explore in some
detail the Iris framework and software development kit, showing how astronomers
and software developers can plug their code into an integrated SED analysis
environment.Comment: 18 pages, 8 figures, accepted for publication in Astronomy &
Computin