171,775 research outputs found
Automated supervised classification of variable stars I. Methodology
The fast classification of new variable stars is an important step in making
them available for further research. Selection of science targets from large
databases is much more efficient if they have been classified first. Defining
the classes in terms of physical parameters is also important to get an
unbiased statistical view on the variability mechanisms and the borders of
instability strips. Our goal is twofold: provide an overview of the stellar
variability classes that are presently known, in terms of some relevant stellar
parameters; use the class descriptions obtained as the basis for an automated
`supervised classification' of large databases. Such automated classification
will compare and assign new objects to a set of pre-defined variability
training classes. For every variability class, a literature search was
performed to find as many well-known member stars as possible, or a
considerable subset if too many were present. Next, we searched on-line and
private databases for their light curves in the visible band and performed
period analysis and harmonic fitting. The derived light curve parameters are
used to describe the classes and define the training classifiers. We compared
the performance of different classifiers in terms of percentage of correct
identification, of confusion among classes and of computation time. We describe
how well the classes can be separated using the proposed set of parameters and
how future improvements can be made, based on new large databases such as the
light curves to be assembled by the CoRoT and Kepler space missions.Comment: This paper has been accepted for publication in Astronomy and
Astrophysics (reference AA/2007/7638) Number of pages: 27 Number of figures:
1
Semi-supervised Learning for Photometric Supernova Classification
We present a semi-supervised method for photometric supernova typing. Our
approach is to first use the nonlinear dimension reduction technique diffusion
map to detect structure in a database of supernova light curves and
subsequently employ random forest classification on a spectroscopically
confirmed training set to learn a model that can predict the type of each newly
observed supernova. We demonstrate that this is an effective method for
supernova typing. As supernova numbers increase, our semi-supervised method
efficiently utilizes this information to improve classification, a property not
enjoyed by template based methods. Applied to supernova data simulated by
Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods
achieve (cross-validated) 95% Type Ia purity and 87% Type Ia efficiency on the
spectroscopic sample, but only 50% Type Ia purity and 50% efficiency on the
photometric sample due to their spectroscopic follow-up strategy. To improve
the performance on the photometric sample, we search for better spectroscopic
follow-up procedures by studying the sensitivity of our machine learned
supernova classification on the specific strategy used to obtain training sets.
With a fixed amount of spectroscopic follow-up time, we find that deeper
magnitude-limited spectroscopic surveys are better for producing training sets.
For supernova Ia (II-P) typing, we obtain a 44% (1%) increase in purity to 72%
(87%) and 30% (162%) increase in efficiency to 65% (84%) of the sample using a
25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic
sample used in the original simulations. When redshift information is
available, we incorporate it into our analysis using a novel method of altering
the diffusion map representation of the supernovae. Incorporating host
redshifts leads to a 5% improvement in Type Ia purity and 13% improvement in
Type Ia efficiency.Comment: 16 pages, 11 figures, accepted for publication in MNRA
Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
We present observations of 10 type Ia supernovae (SNe Ia) between 0.16 < z <
0.62. With previous data from our High-Z Supernova Search Team, this expanded
set of 16 high-redshift supernovae and 34 nearby supernovae are used to place
constraints on the Hubble constant (H_0), the mass density (Omega_M), the
cosmological constant (Omega_Lambda), the deceleration parameter (q_0), and the
dynamical age of the Universe (t_0). The distances of the high-redshift SNe Ia
are, on average, 10% to 15% farther than expected in a low mass density
(Omega_M=0.2) Universe without a cosmological constant. Different light curve
fitting methods, SN Ia subsamples, and prior constraints unanimously favor
eternally expanding models with positive cosmological constant (i.e.,
Omega_Lambda > 0) and a current acceleration of the expansion (i.e., q_0 < 0).
With no prior constraint on mass density other than Omega_M > 0, the
spectroscopically confirmed SNe Ia are consistent with q_0 <0 at the 2.8 sigma
and 3.9 sigma confidence levels, and with Omega_Lambda >0 at the 3.0 sigma and
4.0 sigma confidence levels, for two fitting methods respectively. Fixing a
``minimal'' mass density, Omega_M=0.2, results in the weakest detection,
Omega_Lambda>0 at the 3.0 sigma confidence level. For a flat-Universe prior
(Omega_M+Omega_Lambda=1), the spectroscopically confirmed SNe Ia require
Omega_Lambda >0 at 7 sigma and 9 sigma level for the two fitting methods. A
Universe closed by ordinary matter (i.e., Omega_M=1) is ruled out at the 7
sigma to 8 sigma level. We estimate the size of systematic errors, including
evolution, extinction, sample selection bias, local flows, gravitational
lensing, and sample contamination. Presently, none of these effects reconciles
the data with Omega_Lambda=0 and q_0 > 0.Comment: 36 pages, 13 figures, 3 table files Accepted to the Astronomical
Journa
The Mass-Loss Return From Evolved Stars to The Large Magellanic Cloud VI: Luminosities and Mass-Loss Rates on Population Scales
We present results from the first application of the Grid of Red Supergiant
and Asymptotic Giant Branch ModelS (GRAMS) model grid to the entire evolved
stellar population of the Large Magellanic Cloud (LMC). GRAMS is a pre-computed
grid of 80,843 radiative transfer (RT) models of evolved stars and
circumstellar dust shells composed of either silicate or carbonaceous dust. We
fit GRAMS models to ~30,000 Asymptotic Giant Branch (AGB) and Red Supergiant
(RSG) stars in the LMC, using 12 bands of photometry from the optical to the
mid-infrared. Our published dataset consists of thousands of evolved stars with
individually determined evolutionary parameters such as luminosity and
mass-loss rate. The GRAMS grid has a greater than 80% accuracy rate
discriminating between Oxygen- and Carbon-rich chemistry. The global dust
injection rate to the interstellar medium (ISM) of the LMC from RSGs and AGB
stars is on the order of 1.5x10^(-5) solar masses/yr, equivalent to a total
mass injection rate (including the gas) into the ISM of ~5x10^(-3) solar
masses/yr. Carbon stars inject two and a half times as much dust into the ISM
as do O-rich AGB stars, but the same amount of mass. We determine a bolometric
correction factor for C-rich AGB stars in the K band as a function of J - K
color, BC(K) = -0.40(J-K)^2 + 1.83(J-K) + 1.29. We determine several IR color
proxies for the dust mass-loss rate (MLR) from C-rich AGB stars, such as log
(MLR) = (-18.90)/((K-[8.0])+3.37)-5.93. We find that a larger fraction of AGB
stars exhibiting the `long-secondary period' phenomenon are O-rich than stars
dominated by radial pulsations, and AGB stars without detectable mass-loss do
not appear on either the first-overtone or fundamental-mode pulsation
sequences.Comment: 19 pages, 19 figure
Gaia Eclipsing Binary and Multiple Systems. A study of detectability and classification of eclipsing binaries with Gaia
In the new era of large-scale astronomical surveys, automated methods of
analysis and classification of bulk data are a fundamental tool for fast and
efficient production of deliverables. This becomes ever more imminent as we
enter the Gaia era. We investigate the potential detectability of eclipsing
binaries with Gaia using a data set of all Kepler eclipsing binaries sampled
with Gaia cadence and folded with the Kepler period. The performance of fitting
methods is evaluated with comparison to real Kepler data parameters and a
classification scheme is proposed for the potentially detectable sources based
on the geometry of the light curve fits. The polynomial chain (polyfit) and
two-Gaussian models are used for light curve fitting of the data set.
Classification is performed with a combination of the t-SNE (t-distrubuted
Stochastic Neighbor Embedding) and DBSCAN (Density-Based Spatial Clustering of
Applications with Noise) algorithms. We find that approximately 68% of Kepler
Eclipsing Binary sources are potentially detectable by Gaia when folded with
the Kepler period and propose a classification scheme of the detectable sources
based on the morphological type indicative of the light curve, with subclasses
that reflect the properties of the fitted model (presence and visibility of
eclipses, their width, depth, etc.).Comment: 9 pages, 18 figures, accepted for publication in Astronomy &
Astrophysic
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