In addition to Bayesian methodologies, like this week’s astro-ph, studies on characterizing empirical spatial distributions of voids and galaxies frequently appear, which I believe can be enriched further with the ideas from stochastic geometry and spatial statistics. Click for what was appeared in arXiv this week.

• [astro-ph:0805.0156]R. D’Abrusco, G. Longo, N. A. Walton
  Quasar candidates selection in the Virtual Observatory era

• [astro-ph:0805.0201] S. Vegetti& L.V.E. Koopmans
  Bayesian Strong Gravitational-Lens Modelling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies (many like to see this paper: nest sampling implemented, discusses penalty function and tessllation)

• [astro-ph:0805.0238] J. A. Carter et al.
  Analytic Approximations for Transit Light Curve Observables, Uncertainties, and Covariances

• [astro-ph:0805.0269] S.M.Leach et al.
  Component separation methods for the Planck mission

• [astro-ph:0805.0276] M. Grossi et al.
  The mass density field in simulated non-Gaussian scenarios

• [astro-ph:0805.0790] Ceccarelli, Padilla, & Lambas
  Large-scale modulation of star formation in void walls
  [astro-ph:0805.0797] Ceccarelli et al.
  Voids in the 2dFGRS and LCDM simulations: spatial and dynamical properties

• [astro-ph:0805.0875] S. Basilakos and L. Perivolaropoulos
  Testing GRBs as Standard Candles

• [astro-ph:0805.0968] A. A. Stanislavsky et al.
  Statistical Modeling of Solar Flare Activity from Empirical Time Series of Soft X-ray Solar Emission

AstroGrid is an interface to Virtual Observatory.

XEphem : http://www.clearskyinstitute.com/xephem/
Celestia : http://www.shatters.net/celestia/
Stellarium : http://www.stellarium.org/

There are many more, of varying degrees of usefulness, user friendliness, and price. Your mileage will vary. But for sheer wow factor, hard to beat Celestia.

[Update 10/01]: The e-Astronomer considers how Google Sky could become more useful. Some interesting tie-ins to Virtual Observatory concepts.

Quantifying redshifts is one of the key astronomical measures to identify the type of objects as well as to provide their distance. Typically, measuring redshifts requires spectral data, which are quite expensive in many aspects compared to photometric data. Let me explain a little what are spectral data and photometric data to enhance understandings for non astronomers.

Collecting photometric data starts from taking pictures with different filters. Through blue, yellow, red optical filters, or infrared, ultra-violet, X-ray filters, objects look different (or have different light intensity) and various astronomical objects can be identify via investigating pictures of many filter combinations. On the other hand, collecting spectral data starts from dispersing light through a specially designed prism. Because of this light dispersion, it takes longer to collect lights from a object and the smaller number of objects are recorded in a picture plate compared to collecting photometric data. A nice feature of this expensive spectral data is providing the physical condition of the object directly: first, the distance by the relative spectral line shifts of spectral lines; second, abundance (the metallic composition of the object), temperature, type of the object also from spectral lines. Therefore, utilizing photometric data to infer measures normally available from spectral data is a very attractive topic in astronomy.

However, there are many challenges. The massive volume of data and sampling bias*, like Malmquist bias (wiki) and Lutz-Kelker bias, hinder traditional regression techniques, where numerous statistical and machine learning methods have been introduced to make most of these photometric data to infer distances economically and quickly.

*((For a reference regarding these biases and astronomical distances, please check Distance Estimation in Cosmology by
Hendry, M. A. and Simmons, J. F. L., Vistas in Astronomy, vol. 39, Issue 3, pp.297-314.))

This paper introduces a novel advanced visualization tool VisIVO,^[1] its advantages from combining a protocol called PLASTIC (Platform for Astronomy Tool Interconnection) for displaying and extracting information from astrophysical data, its enhanced connection to VO (Virtual Observatory), and its usage in several scientific cases.

Data visualization has never been emphasized more than these days in all fields. Each field has its own peculiarity of their data patterns and experiencing fast growth in their size. Tools specifically designed for astrophysical data well deserve a welcome.

1. Available at http://visivo.cineca.it

Spectroscopic Surveys: Present by Yip. C. overviews recent spectroscopic sky surveys and spectral analysis techniques toward Virtual Observatories (VO). In addition that spectroscopic redshift measures increase like Moore’s law, the surveys tend to go deeper and aim completeness. Mainly elliptical galaxy formation has been studied due to more abundance compared to spirals and the galactic bimodality in color-color or color-magnitude diagrams is the result of the gas-rich mergers by blue mergers forming the red sequence. Principal component analysis has incorporated ratios of emission line-strengths for classifying Type-II AGN and star forming galaxies. Lyα identifies high z quasars and other spectral patterns over z reveal the history of the early universe and the characteristics of quasars. Also, the recent discovery of 10 satellites to the Milky Way is mentioned.

Spectral analyses take two approaches: one is the model based approach taking theoretical templates, known for its flaws but straightforward extractions of physical parameters, and the other is the empirical approach, useful for making discoveries but difficult in the analysis interpretation. Neither of them has substantial advantage to the other. When it comes to fitting, Chi-square minimization has been dominant but new methodologies are under developing. For spectral classification problems, principal component analysis (Karlhunen-Loeve transformation), artificial neural network, and other machine learning techniques have been applied.

In the end, the author reports statistical and astrophysical challenges in massive spectroscopic data of present days: 1. modeling galaxies, 2. parameterizing star formation history, 3. modeling quasars, 4. multi-catalog based calibration (separating systematic and statistics errors), 5. estimating parameters, which would be beneficial to VO, of which objective is the unification of data access.