166 research outputs found
Statistical techniques for the detection and analysis of solar explosive events
Solar explosive events are commonly explained as small scale magnetic
reconnection events, although unambiguous confirmation of this scenario remains
elusive due to the lack of spatial resolution and of the statistical analysis
of large enough samples of this type of events. In this work, we propose a
sound statistical treatment of data cubes consisting of a temporal sequence of
long slit spectra of the solar atmosphere. The analysis comprises all the
stages from the explosive event detection to its characterization and the
subsequent sample study. We have designed two complementary approaches based on
the combination of standard statistical techniques (Robust Principal Component
Analysis in one approach and wavelet decomposition and Independent Component
Analysis in the second) in order to obtain least biased samples. These
techniques are implemented in the spirit of letting the data speak for
themselves. The analysis is carried out for two spectral lines: the C IV line
at 1548.2 angstroms and the Ne VIII line at 770.4 angstroms. We find
significant differences between the characteristics of the line profiles
emitted in the proximities of two active regions, and in the quiet Sun, most
visible in the relative importance of a separate population of red shifted
profiles. We also find a higher frequency of explosive events near the active
regions, and in the C IV line. The distribution of the explosive events
characteristics is interpreted in the light of recent numerical simulations.
Finally, we point out several regions of the parameter space where the
reconnection model has to be refined in order to explain the observations.Comment: Accepted for publication in Astronomy and Astrophysics (in Section 9.
The Sun) on 18/01/2011. 17 pages, 22 Figure
Properties of ultra-cool dwarfs with Gaia. An assessment of the accuracy for the temperature determination
We aimed to assess the accuracy of the Gaia teff and logg estimates as
derived with current models and observations. We assessed the validity of
several inference techniques for deriving the physical parameters of ultra-cool
dwarf stars. We used synthetic spectra derived from ultra-cool dwarf models to
construct (train) the regression models. We derived the intrinsic uncertainties
of the best inference models and assessed their validity by comparing the
estimated parameters with the values derived in the bibliography for a sample
of ultra-cool dwarf stars observed from the ground. We estimated the total
number of ultra-cool dwarfs per spectral subtype, and obtained values that can
be summarised (in orders of magnitude) as 400000 objects in the M5-L0 range,
600 objects between L0 and L5, 30 objects between L5 and T0, and 10 objects
between T0 and T8. A bright ultra-cool dwarf (with teff=2500 K and \logg=3.5
will be detected by Gaia out to approximately 220 pc, while for teff=1500 K
(spectral type L5) and the same surface gravity, this maximum distance reduces
to 10-20 pc. The RMSE of the prediction deduced from ground-based spectra of
ultra-cool dwarfs simulated at the Gaia spectral range and resolution, and for
a Gaia magnitude G=20 is 213 K and 266 K for the models based on k-nearest
neighbours and Gaussian process regression, respectively. These are total
errors in the sense that they include the internal and external errors, with
the latter caused by the inability of the synthetic spectral models (used for
the construction of the regression models) to exactly reproduce the observed
spectra, and by the large uncertainties in the current calibrations of spectral
types and effective temperatures.Comment: 18 pages, 17 figures, accepted by Astronomy & Astrophysic
Long-term variations in global sea level extremes
Decadal to multidecadal variations in sea level extremes unrelated to mean sea level changes have been investigated using long tide gauge records distributed worldwide. A state space approach has been applied that provides robust solutions and uncertainties of the time evolving characteristics of extremes, allowing for data gaps and uneven sampling, both common features of historical sea level time series. Two different models have been formulated for the intensity and for the occurrence of extreme sea level events and have been applied independently to each tide gauge record. Our results reveal two key findings: first, the intensity and the frequency of occurrence of extreme sea levels unrelated to mean sea level vary coherently on decadal scales in most of the sites examined (63 out of 77) and, second, extreme sea level changes are regionally consistent, thus pointing toward a common large-scale forcing. This variability of extremes associated with climate drivers should be considered in the framework of climate change studies
Long-term variations in global sea level extremes
Decadal to multidecadal variations in sea level extremes unrelated to mean sea level changes have been investigated using long tide gauge records distributed worldwide. A state space approach has been applied that provides robust solutions and uncertainties of the time evolving characteristics of extremes, allowing for data gaps and uneven sampling, both common features of historical sea level time series. Two different models have been formulated for the intensity and for the occurrence of extreme sea level events and have been applied independently to each tide gauge record. Our results reveal two key findings: first, the intensity and the frequency of occurrence of extreme sea levels unrelated to mean sea level vary coherently on decadal scales in most of the sites examined (63 out of 77) and, second, extreme sea level changes are regionally consistent, thus pointing toward a common large-scale forcing. This variability of extremes associated with climate drivers should be considered in the framework of climate change studies.M. Marcos acknowledges a “Ramon y Cajal” contract funded by the Spanish Ministry of Economy. This work was supported by the research project CLIMPACT (CGL2014-54246-C2-1-R) funded by the Spanish Ministry of EconomyPeer Reviewe
Cluster membership probabilities from proper motions and multiwavelength photometric catalogues: I. Method and application to the Pleiades cluster
We present a new technique designed to take full advantage of the high
dimensionality (photometric, astrometric, temporal) of the DANCe survey to
derive self-consistent and robust membership probabilities of the Pleiades
cluster. We aim at developing a methodology to infer membership probabilities
to the Pleiades cluster from the DANCe multidimensional astro-photometric data
set in a consistent way throughout the entire derivation. The determination of
the membership probabilities has to be applicable to censored data and must
incorporate the measurement uncertainties into the inference procedure.
We use Bayes' theorem and a curvilinear forward model for the likelihood of
the measurements of cluster members in the colour-magnitude space, to infer
posterior membership probabilities. The distribution of the cluster members
proper motions and the distribution of contaminants in the full
multidimensional astro-photometric space is modelled with a
mixture-of-Gaussians likelihood. We analyse several representation spaces
composed of the proper motions plus a subset of the available magnitudes and
colour indices. We select two prominent representation spaces composed of
variables selected using feature relevance determination techniques based in
Random Forests, and analyse the resulting samples of high probability
candidates. We consistently find lists of high probability (p > 0.9975)
candidates with 1000 sources, 4 to 5 times more than obtained in the
most recent astro-photometric studies of the cluster.
The methodology presented here is ready for application in data sets that
include more dimensions, such as radial and/or rotational velocities, spectral
indices and variability.Comment: 14 pages, 4 figures, accepted by A&
The Seven Sisters DANCe III: Projected spatial distribution
Methods. We compute Bayesian evidences and Bayes Factors for a set of
variations of the classical radial models by King (1962), Elson et al. (1987)
and Lauer et al. (1995). The variations incorporate different degrees of model
freedom and complexity, amongst which we include biaxial (elliptical) symmetry,
and luminosity segregation. As a by-product of the model comparison, we obtain
posterior distributions and maximum a posteriori estimates for each set of
model parameters. Results. We find that the model comparison results depend on
the spatial extent of the region used for the analysis. For a circle of 11.5
parsecs around the cluster centre (the most homogeneous and complete region),
we find no compelling reason to abandon Kings model, although the Generalised
King model, introduced in this work, has slightly better fitting properties.
Furthermore, we find strong evidence against radially symmetric models when
compared to the elliptic extensions. Finally, we find that including mass
segregation in the form of luminosity segregation in the J band, is strongly
supported in all our models. Conclusions. We have put the question of the
projected spatial distribution of the Pleiades cluster on a solid probabilistic
framework, and inferred its properties using the most exhaustive and least
contaminated list of Pleiades candidate members available to date. Our results
suggest however that this sample may still lack about 20% of the expected
number of cluster members. Therefore, this study should be revised when the
completeness and homogeneity of the data can be extended beyond the 11.5
parsecs limit. Such study will allow a more precise determination of the
Pleiades spatial distribution, its tidal radius, ellipticity, number of objects
and total mass.Comment: 39 pages, 31 figure
The Seven Sisters DANCe. I. Empirical isochrones, Luminosity and Mass Functions of the Pleiades cluster
The DANCe survey provides photometric and astrometric (position and proper
motion) measurements for approximately 2 millions unique sources in a region
encompassing 80deg centered around the Pleiades cluster.
We aim at deriving a complete census of the Pleiades, and measure the mass
and luminosity function of the cluster. Using the probabilistic selection
method described in Sarro+2014, we identify high probability members in the
DANCe (14mag) and Tycho-2 (12mag) catalogues, and study the
properties of the cluster over the corresponding luminosity range. We find a
total of 2109 high probability members, of which 812 are new, making it the
most extensive and complete census of the cluster to date. The luminosity and
mass functions of the cluster are computed from the most massive members down
to 0.025M. The size, sensitivity and quality of the sample
result in the most precise luminosity and mass functions observed to date for a
cluster. Our census supersedes previous studies of the Pleiades cluster
populations, both in terms of sensitivity and accuracy.Comment: Language Edition Done. Final version to be published in A&A. Tables
will be published at CDS. Meanwhile, they can be requested to H. Bouy (hbouy
-at- cab . inta - csic . es
Corona-Australis DANCe I. Revisiting the census of stars with Gaia-DR2 data
Context. Corona-Australis is one of the nearest regions to the Sun with recent and ongoing star formation, but the current picture of its stellar (and substellar) content is not complete yet.
Aims. We take advantage of the second data release of the Gaia space mission to revisit the stellar census and search for additional members of the young stellar association in Corona-Australis.
Methods. We applied a probabilistic method to infer membership probabilities based on a multidimensional astrometric and photometric data set over a field of 128 deg(2) around the dark clouds of the region.
Results. We identify 313 high-probability candidate members to the Corona-Australis association, 262 of which had never been reported as members before. Our sample of members covers the magnitude range between G greater than or similar to 5 mag and G less than or similar to 20 mag, and it reveals the existence of two kinematically and spatially distinct subgroups. There is a distributed "off-cloud" population of stars located in the north of the dark clouds that is twice as numerous as the historically known "on-cloud" population that is concentrated around the densest cores. By comparing the location of the stars in the HR-diagram with evolutionary models, we show that these two populations are younger than 10 Myr. Based on their infrared excess emission, we identify 28 Class II and 215 Class III stars among the sources with available infrared photometry, and we conclude that the frequency of Class II stars (i.e. "disc-bearing" stars) in the on-cloud region is twice as large as compared to the off-cloud population. The distance derived for the Corona-Australis region based on this updated census is d = 149.4(-0.4)(+0.4) pc, which exceeds previous estimates by about 20 pc.
Conclusions. In this paper we provide the most complete census of stars in Corona-Australis available to date that can be confirmed with Gaia data. Furthermore, we report on the discovery of an extended and more evolved population of young stars beyond the region of the dark clouds, which was extensively surveyed in the past
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