5,745 research outputs found
SPITZER observations of the λ Orionis cluster. II. Disks around solar-type and low-mass stars
We present IRAC/MIPS Spitzer Space Telescope observations of the solar-type and the low-mass stellar population
of the young (~5Myr) λ Orionis cluster. Combining optical and Two Micron All Sky Survey photometry, we identify
436 stars as probable members of the cluster. Given the distance (450 pc) and the age of the cluster, our sample
ranges in mass from 2 M_⊙ to objects below the substellar limit. With the addition of the Spitzer mid-infrared data,
we have identified 49 stars bearing disks in the stellar cluster. Using spectral energy distribution slopes, we place
objects in several classes: non-excess stars (diskless), stars with optically thick disks, stars with “evolved disks”
(with smaller excesses than optically thick disk systems), and “transitional disk” candidates (in which the inner
disk is partially or fully cleared). The disk fraction depends on the stellar mass, ranging from ~6% for K-type stars (R_C − J 4). We confirm the dependence of disk
fraction on stellar mass in this age range found in other studies. Regarding clustering levels, the overall fraction of disks in the λ Orionis cluster is similar to those reported in other stellar groups with ages normally quoted as ~5Myr
Quantitative features of multifractal subtleties in time series
Based on the Multifractal Detrended Fluctuation Analysis (MFDFA) and on the
Wavelet Transform Modulus Maxima (WTMM) methods we investigate the origin of
multifractality in the time series. Series fluctuating according to a qGaussian
distribution, both uncorrelated and correlated in time, are used. For the
uncorrelated series at the border (q=5/3) between the Gaussian and the Levy
basins of attraction asymptotically we find a phase-like transition between
monofractal and bifractal characteristics. This indicates that these may solely
be the specific nonlinear temporal correlations that organize the series into a
genuine multifractal hierarchy. For analyzing various features of
multifractality due to such correlations, we use the model series generated
from the binomial cascade as well as empirical series. Then, within the
temporal ranges of well developed power-law correlations we find a fast
convergence in all multifractal measures. Besides of its practical significance
this fact may reflect another manifestation of a conjectured q-generalized
Central Limit Theorem
Wavelet versus Detrended Fluctuation Analysis of multifractal structures
We perform a comparative study of applicability of the Multifractal Detrended
Fluctuation Analysis (MFDFA) and the Wavelet Transform Modulus Maxima (WTMM)
method in proper detecting of mono- and multifractal character of data. We
quantify the performance of both methods by using different sorts of artificial
signals generated according to a few well-known exactly soluble mathematical
models: monofractal fractional Brownian motion, bifractal Levy flights, and
different sorts of multifractal binomial cascades. Our results show that in
majority of situations in which one does not know a priori the fractal
properties of a process, choosing MFDFA should be recommended. In particular,
WTMM gives biased outcomes for the fractional Brownian motion with different
values of Hurst exponent, indicating spurious multifractality. In some cases
WTMM can also give different results if one applies different wavelets. We do
not exclude using WTMM in real data analysis, but it occurs that while one may
apply MFDFA in a more automatic fashion, WTMM has to be applied with care. In
the second part of our work, we perform an analogous analysis on empirical data
coming from the American and from the German stock market. For this data both
methods detect rich multifractality in terms of broad f(alpha), but MFDFA
suggests that this multifractality is poorer than in the case of WTMM.Comment: substantially extended version, to appear in Phys.Rev.
Spitzer observations of the Orion OB1 association: disk census in the low mass stars
We present new Spitzer Space Telescope observations of two fields in the
Orion OB1 association. We report here IRAC/MIPS observations for 115 confirmed
members and 41 photometric candidates of the ~10 Myr 25 Orionis aggregate in
the OB1a subassociation, and 106 confirmed members and 65 photometric
candidates of the 5 Myr region located in the OB1b subassociation. The 25
Orionis aggregate shows a disk frequency of 6% while the field in the OB1b
subassociation shows a disk frequency of 13%. Combining IRAC, MIPS and 2MASS
photometry we place stars bearing disks in several classes: stars with
optically thick disks (class II systems), stars with an inner transitional
disks (transitional disk candidates) and stars with "evolved disks"; the last
exhibit smaller IRAC/MIPS excesses than class II systems. In all, we identify 1
transitional disk candidate in the 25 Orionis aggregate and 3 in the OB1b
field; this represents ~10% of the disk bearing stars, indicating that the
transitional disk phase can be relatively fast. We find that the frequency of
disks is a function of the stellar mass, suggesting a maximum around stars with
spectral type M0. Comparing the infrared excess in the IRAC bands among several
stellar groups we find that inner disk emission decays with stellar age,
showing a correlation with the respective disk frequencies. The disk emission
at the IRAC and MIPS bands in several stellar groups indicates that disk
dissipation takes place faster in the inner region of the disks. Comparison
with models of irradiated accretion disks, computed with several degrees of
settling, suggests that the decrease in the overall accretion rate observed in
young stellar groups is not sufficient to explain the weak disk emission
observed in the IRAC bands for disk bearing stars with ages 5 Myr or older.Comment: Accepted in the Astrophysical Journa
Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France
The land monitoring service of the European Copernicus programme has
developed a set of satellite-based biogeophysical products, including
surface soil moisture (SSM) and leaf area index (LAI). This study
investigates the impact of joint assimilation of remotely sensed SSM
derived from Advanced Scatterometer (ASCAT) backscatter data and the
Copernicus Global Land GEOV1 satellite-based LAI product
into the the vegetation growth version of the Interactions
between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model
within the the externalised surface model (SURFEX) modelling
platform of Météo-France. The ASCAT data were bias corrected with
respect to the model climatology by using a seasonal-based CDF
(Cumulative Distribution Function) matching technique. A multivariate
multi-scale land data assimilation system (LDAS) based on the extended
Kalman Filter (EKF) is used for monitoring the soil moisture,
terrestrial vegetation, surface carbon and energy fluxes across the
domain of France at a spatial resolution of 8 km. Each model grid
box is divided into a number of land covers, each having its own set of
prognostic variables. The filter algorithm is designed to provide
a distinct analysis for each land cover while using one observation
per grid box. The updated values are aggregated by computing
a weighted average.
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In this study, it is demonstrated that the assimilation scheme works
effectively within the ISBA-A-gs model over a four-year period
(2008–2011). The EKF is able to extract useful information from the
data signal at the grid scale and distribute the root-zone soil
moisture and LAI increments throughout the mosaic structure of the
model. The impact of the assimilation on the vegetation phenology and
on the water and carbon fluxes varies from one season to another. The
spring drought of 2011 is an interesting case study of the
potential of the assimilation to improve drought
monitoring. A comparison between simulated and in situ soil moisture
gathered at the twelve SMOSMANIA (Soil Moisture Observing
System–Meteorological Automatic Network Integrated Application) stations shows improved anomaly
correlations for eight stations
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