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