2,214 research outputs found
Automated Classification of Stellar Spectra. II: Two-Dimensional Classification with Neural Networks and Principal Components Analysis
We investigate the application of neural networks to the automation of MK
spectral classification. The data set for this project consists of a set of
over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates
from the Michigan Spectral Survey. These spectra, along with their
two-dimensional MK classifications listed in the Michigan Henry Draper
Catalogue, were used to develop supervised neural network classifiers. We show
that neural networks can give accurate spectral type classifications (sig_68 =
0.82 subtypes, sig_rms = 1.09 subtypes) across the full range of spectral types
present in the data set (B2-M7). We show also that the networks yield correct
luminosity classes for over 95% of both dwarfs and giants with a high degree of
confidence.
Stellar spectra generally contain a large amount of redundant information. We
investigate the application of Principal Components Analysis (PCA) to the
optimal compression of spectra. We show that PCA can compress the spectra by a
factor of over 30 while retaining essentially all of the useful information in
the data set. Furthermore, it is shown that this compression optimally removes
noise and can be used to identify unusual spectra.Comment: To appear in MNRAS. 15 pages, 17 figures, 7 tables. 2 large figures
(nos. 4 and 15) are supplied as separate GIF files. The complete paper can be
obtained as a single gziped PS file from
http://wol.ra.phy.cam.ac.uk/calj/p1.htm
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
Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
The volume of data that will be produced by new-generation surveys requires
automatic classification methods to select and analyze sources. Indeed, this is
the case for the search for strong gravitational lenses, where the population
of the detectable lensed sources is only a very small fraction of the full
source population. We apply for the first time a morphological classification
method based on a Convolutional Neural Network (CNN) for recognizing strong
gravitational lenses in square degrees of the Kilo Degree Survey (KiDS),
one of the current-generation optical wide surveys. The CNN is currently
optimized to recognize lenses with Einstein radii arcsec, about
twice the -band seeing in KiDS. In a sample of colour-magnitude
selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN
retrieves 761 strong-lens candidates and correctly classifies two out of three
of the known lenses. The misclassified lens has an Einstein radius below the
range on which the algorithm is trained. We down-select the most reliable 56
candidates by a joint visual inspection. This final sample is presented and
discussed. A conservative estimate based on our results shows that with our
proposed method it should be possible to find massive LRG-galaxy
lenses at z\lsim 0.4 in KiDS when completed. In the most optimistic scenario
this number can grow considerably (to maximally 2400 lenses), when
widening the colour-magnitude selection and training the CNN to recognize
smaller image-separation lens systems.Comment: 24 pages, 17 figures. Published in MNRA
The Gaia satellite: a tool for Emission Line Stars and Hot Stars
The Gaia satellite will be launched at the end of 2011. It will observe at
least 1 billion stars, and among them several million emission line stars and
hot stars. Gaia will provide parallaxes for each star and spectra for stars
till V magnitude equal to 17. After a general description of Gaia, we present
the codes and methods, which are currently developed by our team. They will
provide automatically the astrophysical parameters and spectral classification
for the hot and emission line stars in the Milky Way and other close Local
Group galaxies such as the Magellanic Clouds.Comment: SF2A2008, session GAIA, invited tal
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
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