1,893 research outputs found
A Blended Artificial Intelligence Approach for Spectral Classification of Stars in Massive Astronomical Surveys
[Abstract]
This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the MorganâKeenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of todayâs major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.This work was supported by Ministry of Science, Innovation and Universities (FEDER RTI2018-095076-B-C22) and Xunta de Galicia (ED431B 2018/42)Xunta de Galicia; ED431B 2018/4
Automatic Detection of Expanding HI Shells Using Artificial Neural Networks
The identification of expanding HI shells is difficult because of their
variable morphological characteristics. The detection of HI bubbles on a global
scale therefore never has been attempted. In this paper, an automatic detector
for expanding HI shells is presented. The detection is based on the more stable
dynamical characteristics of expanding shells and is performed in two stages.
The first one is the recognition of the dynamical signature of an expanding
bubble in the velocity spectra, based on the classification of an artificial
neural network. The pixels associated with these recognized spectra are
identified on each velocity channel. The second stage consists in looking for
concentrations of those pixels that were firstly pointed out, and to decide if
they are potential detections by morphological and 21-cm emission variation
considerations. Two test bubbles are correctly detected and a potentially new
case of shell that is visually very convincing is discovered. About 0.6% of the
surveyed pixels are identified as part of a bubble. These may be false
detections, but still constitute regions of space with high probability of
finding an expanding shell. The subsequent search field is thus significantly
reduced. We intend to conduct in the near future a large scale HI shells
detection over the Perseus Arm using our detector.Comment: 39 pages, 11 figures, accepted by PAS
Classification of Stellar Spectra with LLE
We investigate the use of dimensionality reduction techniques for the
classification of stellar spectra selected from the SDSS. Using local linear
embedding (LLE), a technique that preserves the local (and possibly non-linear)
structure within high dimensional data sets, we show that the majority of
stellar spectra can be represented as a one dimensional sequence within a three
dimensional space. The position along this sequence is highly correlated with
spectral temperature. Deviations from this "stellar locus" are indicative of
spectra with strong emission lines (including misclassified galaxies) or broad
absorption lines (e.g. Carbon stars). Based on this analysis, we propose a
hierarchical classification scheme using LLE that progressively identifies and
classifies stellar spectra in a manner that requires no feature extraction and
that can reproduce the classic MK classifications to an accuracy of one type.Comment: 15 pages, 13 figures; accepted for publication in The Astronomical
Journa
Three-Dimensional Spectral Classification of Low-Metallicity Stars Using Artificial Neural Networks
We explore the application of artificial neural networks (ANNs) for the
estimation of atmospheric parameters (Teff, logg, and [Fe/H]) for Galactic F-
and G-type stars. The ANNs are fed with medium-resolution (~ 1-2 A) non
flux-calibrated spectroscopic observations. From a sample of 279 stars with
previous high-resolution determinations of metallicity, and a set of (external)
estimates of temperature and surface gravity, our ANNs are able to predict Teff
with an accuracy of ~ 135-150 K over the range 4250 <= Teff <= 6500 K, logg
with an accuracy of ~ 0.25-0.30 dex over the range 1.0 <= logg <= 5.0 dex, and
[Fe/H] with an accuracy ~ 0.15-0.20 dex over the range -4.0 <= [Fe/H] <= +0.3.
Such accuracies are competitive with the results obtained by fine analysis of
high-resolution spectra. It is noteworthy that the ANNs are able to obtain
these results without consideration of photometric information for these stars.
We have also explored the impact of the signal-to-noise ratio (S/N) on the
behavior of ANNs, and conclude that, when analyzed with ANNs trained on spectra
of commensurate S/N, it is possible to extract physical parameter estimates of
similar accuracy with stellar spectra having S/N as low as 13. Taken together,
these results indicate that the ANN approach should be of primary importance
for use in present and future large-scale spectroscopic surveys.Comment: 51 pages, 11 eps figures, uses aastex; to appear in Ap
ASPECT: A spectra clustering tool for exploration of large spectral surveys
We present the novel, semi-automated clustering tool ASPECT for analysing
voluminous archives of spectra. The heart of the program is a neural network in
form of Kohonen's self-organizing map. The resulting map is designed as an icon
map suitable for the inspection by eye. The visual analysis is supported by the
option to blend in individual object properties such as redshift, apparent
magnitude, or signal-to-noise ratio. In addition, the package provides several
tools for the selection of special spectral types, e.g. local difference maps
which reflect the deviations of all spectra from one given input spectrum (real
or artificial). ASPECT is able to produce a two-dimensional topological map of
a huge number of spectra. The software package enables the user to browse and
navigate through a huge data pool and helps him to gain an insight into
underlying relationships between the spectra and other physical properties and
to get the big picture of the entire data set. We demonstrate the capability of
ASPECT by clustering the entire data pool of 0.6 million spectra from the Data
Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results
regarding quality and completeness we track objects from existing catalogues of
quasars and carbon stars, respectively, and connect the SDSS spectra with
morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and
Astrophysic
- âŠ