3,426 research outputs found
Application of Neural Networks to the study of stellar model solutions
Artificial neural networks (ANN) have different applications in Astronomy,
including data reduction and data mining. In this work we propose the use ANNs
in the identification of stellar model solutions. We illustrate this method, by
applying an ANN to the 0.8M star CG Cyg B. Our ANN was trained using
60,000 different 0.8M stellar models. With this approach we identify
the models which reproduce CG Cyg B's position in the HR diagram. We observe a
correlation between the model's initial metal and helium abundance which, in
most cases, does not agree with a helium to metal enrichment ratio
Y/Z=2. Moreover, we identify a correlation between the model's
initial helium/metal abundance and both its age and mixing-length parameter.
Additionally, every model found has a mixing-length parameter below 1.3. This
means that CG Cyg B's mixing-length parameter is clearly smaller than the solar
one. From this study we conclude that ANNs are well suited to deal with the
degeneracy of model solutions of solar type stars.Comment: Accepted for publication in New Astronom
Automated derivation of stellar atmospheric parameters and chemical abundances: the MATISSE algorithm
We present an automated procedure for the derivation of atmospheric
parameters (Teff, log g, [M/H]) and individual chemical abundances from stellar
spectra. The MATrix Inversion for Spectral SythEsis (MATISSE) algorithm
determines a basis, B_\theta(\lambda), allowing to derive a particular stellar
parameter \theta by projection of an observed spectrum. The B_\theta(\lambda)
function is determined from an optimal linear combination of theoretical
spectra and it relates, in a quantitative way, the variations in the spectrum
flux with variations in \theta. An application of this method to the GAIA/RVS
spectral range is described, together with its performances for different types
of stars of various metallicities. Blind tests with synthetic spectra of
randomly selected parameters and observed input spectra are also presented. The
method gives rapid, accurate and stable results and it can be efficiently
applied to the study of stellar populations through the analysis of large
spectral data sets, including moderate to low signal to noise spectra
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
The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression
Revealing hidden patterns in astronomical data is often the path to
fundamental scientific breakthroughs; meanwhile the complexity of scientific
inquiry increases as more subtle relationships are sought. Contemporary data
analysis problems often elude the capabilities of classical statistical
techniques, suggesting the use of cutting edge statistical methods. In this
light, astronomers have overlooked a whole family of statistical techniques for
exploratory data analysis and robust regression, the so-called Generalized
Linear Models (GLMs). In this paper -- the first in a series aimed at
illustrating the power of these methods in astronomical applications -- we
elucidate the potential of a particular class of GLMs for handling
binary/binomial data, the so-called logit and probit regression techniques,
from both a maximum likelihood and a Bayesian perspective. As a case in point,
we present the use of these GLMs to explore the conditions of star formation
activity and metal enrichment in primordial minihaloes from cosmological
hydro-simulations including detailed chemistry, gas physics, and stellar
feedback. We predict that for a dark mini-halo with metallicity , an increase of in the gas
molecular fraction, increases the probability of star formation occurrence by a
factor of 75%. Finally, we highlight the use of receiver operating
characteristic curves as a diagnostic for binary classifiers, and ultimately we
use these to demonstrate the competitive predictive performance of GLMs against
the popular technique of artificial neural networks.Comment: 20 pages, 10 figures, 3 tables, accepted for publication in Astronomy
and Computin
J-PLUS: Identification of low-metallicity stars with artificial neural networks using SPHINX
We present a new methodology for the estimation of stellar atmospheric
parameters from narrow- and intermediate-band photometry of the Javalambre
Photometric Local Universe Survey (J-PLUS), and propose a method for target
pre-selection of low-metallicity stars for follow-up spectroscopic studies.
Photometric metallicity estimates for stars in the globular cluster M15 are
determined using this method. By development of a neural-network-based
photometry pipeline, we aim to produce estimates of effective temperature,
, and metallicity, [Fe/H], for a large subset of stars in the
J-PLUS footprint. The Stellar Photometric Index Network Explorer, SPHINX, is
developed to produce estimates of and [Fe/H], after training on a
combination of J-PLUS photometric inputs and synthetic magnitudes computed for
medium-resolution (R ~ 2000) spectra of the Sloan Digital Sky Survey. This
methodology is applied to J-PLUS photometry of the globular cluster M15.
Effective temperature estimates made with J-PLUS Early Data Release photometry
exhibit low scatter, \sigma() = 91 K, over the temperature range
4500 < (K) < 8500. For stars from the J-PLUS First Data Release
with 4500 < (K) < 6200, 85 3% of stars known to have [Fe/H]
<-2.0 are recovered by SPHINX. A mean metallicity of [Fe/H]=-2.32 0.01,
with a residual spread of 0.3 dex, is determined for M15 using J-PLUS
photometry of 664 likely cluster members. We confirm the performance of SPHINX
within the ranges specified, and verify its utility as a stand-alone tool for
photometric estimation of effective temperature and metallicity, and for
pre-selection of metal-poor spectroscopic targets.Comment: 18 pages, 12 figure
On the use of machine learning algorithms in the measurement of stellar magnetic fields
Regression methods based in Machine Learning Algorithms (MLA) have become an
important tool for data analysis in many different disciplines.
In this work, we use MLA in an astrophysical context; our goal is to measure
the mean longitudinal magnetic field in stars (H_ eff) from polarized spectra
of high resolution, through the inversion of the so-called multi-line profiles.
Using synthetic data, we tested the performance of our technique considering
different noise levels: In an ideal scenario of noise-free multi-line profiles,
the inversion results are excellent; however, the accuracy of the inversions
diminish considerably when noise is taken into account. In consequence, we
propose a data pre-process in order to reduce the noise impact, which consists
in a denoising profile process combined with an iterative inversion
methodology.
Applying this data pre-process, we have found a considerable improvement of
the inversions results, allowing to estimate the errors associated to the
measurements of stellar magnetic fields at different noise levels.
We have successfully applied our data analysis technique to two different
stars, attaining by first time the measurement of H_eff from multi-line
profiles beyond the condition of line autosimilarity assumed by other
techniques.Comment: Accepted for publication in A&
Data Mining and Machine Learning in Astronomy
We review the current state of data mining and machine learning in astronomy.
'Data Mining' can have a somewhat mixed connotation from the point of view of a
researcher in this field. If used correctly, it can be a powerful approach,
holding the potential to fully exploit the exponentially increasing amount of
available data, promising great scientific advance. However, if misused, it can
be little more than the black-box application of complex computing algorithms
that may give little physical insight, and provide questionable results. Here,
we give an overview of the entire data mining process, from data collection
through to the interpretation of results. We cover common machine learning
algorithms, such as artificial neural networks and support vector machines,
applications from a broad range of astronomy, emphasizing those where data
mining techniques directly resulted in improved science, and important current
and future directions, including probability density functions, parallel
algorithms, petascale computing, and the time domain. We conclude that, so long
as one carefully selects an appropriate algorithm, and is guided by the
astronomical problem at hand, data mining can be very much the powerful tool,
and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra
figures, some minor additions to the tex
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