176,911 research outputs found
A method for exploiting domain information in astrophysical parameter estimation
I outline a method for estimating astrophysical parameters (APs) from
multidimensional data. It is a supervised method based on matching observed
data (e.g. a spectrum) to a grid of pre-labelled templates. However, unlike
standard machine learning methods such as ANNs, SVMs or k-nn, this algorithm
explicitly uses domain information to better weight each data dimension in the
estimation. Specifically, it uses the sensitivity of each measured variable to
each AP to perform a local, iterative interpolation of the grid. It avoids both
the non-uniqueness problem of global regression as well as the grid resolution
limitation of nearest neighbours.Comment: Proceedings of ADASS17 (September 2007, London). 4 pages. To appear
in ASP Conf. Pro
Microarcsecond astrometry with Gaia: the solar system, the Galaxy and beyond
Gaia is an all sky, high precision astrometric and photometric satellite of
the European Space Agency (ESA) due for launch in 2010-2011. Its primary
mission is to study the composition, formation and evolution of our Galaxy.
Gaia will measure parallaxes and proper motions of every object in the sky
brighter than V=20, amounting to a billion stars, galaxies, quasars and solar
system objects. It will achieve an astrometric accuracy of 10muas at V=15 -
corresponding to a distance accuracy of 1% at 1kpc. With Gaia, tens of millions
of stars will have their distances measured to a few percent or better. This is
an improvement over Hipparcos by several orders of magnitude in the number of
objects, accuracy and limiting magnitude. Gaia will also measure radial
velocities for source brighter than V~17. To characterize the objects, each
object is observed in 15 medium and broad photometric bands with an onboard CCD
camera. With these capabilities, Gaia will make significant advances in a wide
range of astrophysical topics. These include a detailed kinematical map of
stellar populations, stellar structure and evolution, the discovery and
characterization of thousands of exoplanetary systems and General Relativity on
large scales. I give an overview of the mission, its operating principles and
its expected scientific contributions. For the latter I provide a quick look in
five areas on increasing scale size in the universe: the solar system, exosolar
planets, stellar clusters and associations, Galactic structure and
extragalactic astronomy.Comment: (Errors corrected) Invited paper at IAU Colloquium 196, "Transit of
Venus: New Views of the Solar System and Galaxy". 14 pages, 6 figures.
Version with higher resolution figures available from
http://www.mpia-hd.mpg.de/homes/calj/gaia_venus2004.htm
A Bayesian method for the analysis of deterministic and stochastic time series
I introduce a general, Bayesian method for modelling univariate time series
data assumed to be drawn from a continuous, stochastic process. The method
accommodates arbitrary temporal sampling, and takes into account measurement
uncertainties for arbitrary error models (not just Gaussian) on both the time
and signal variables. Any model for the deterministic component of the
variation of the signal with time is supported, as is any model of the
stochastic component on the signal and time variables. Models illustrated here
are constant and sinusoidal models for the signal mean combined with a Gaussian
stochastic component, as well as a purely stochastic model, the
Ornstein-Uhlenbeck process. The posterior probability distribution over model
parameters is determined via Monte Carlo sampling. Models are compared using
the "cross-validation likelihood", in which the posterior-averaged likelihood
for different partitions of the data are combined. In principle this is more
robust to changes in the prior than is the evidence (the prior-averaged
likelihood). The method is demonstrated by applying it to the light curves of
11 ultra cool dwarf stars, claimed by a previous study to show statistically
significant variability. This is reassessed here by calculating the
cross-validation likelihood for various time series models, including a null
hypothesis of no variability beyond the error bars. 10 of 11 light curves are
confirmed as being significantly variable, and one of these seems to be
periodic, with two plausible periods identified. Another object is best
described by the Ornstein-Uhlenbeck process, a conclusion which is obviously
limited to the set of models actually tested.Comment: Published in A&A as free access article. Software and additional
information available from http://www.mpia.de/~calj/ctsmod.htm
Active Gel Model of Amoeboid Cell Motility
We develop a model of amoeboid cell motility based on active gel theory.
Modeling the motile apparatus of a eukaryotic cell as a confined layer of
finite length of poroelastic active gel permeated by a solvent, we first show
that, due to active stress and gel turnover, an initially static and
homogeneous layer can undergo a contractile-type instability to a polarized
moving state in which the rear is enriched in gel polymer. This agrees
qualitatively with motile cells containing an actomyosin-rich uropod at their
rear. We find that the gel layer settles into a steadily moving, inhomogeneous
state at long times, sustained by a balance between contractility and filament
turnover. In addition, our model predicts an optimal value of the
gel-susbstrate adhesion leading to maximum layer speed, in agreement with cell
motility assays. The model may be relevant to motility of cells translocating
in complex, confining environments that can be mimicked experimentally by cell
migration through microchannels.Comment: To appear in New Journal of Physic
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