This will be one of the better years for Perseids; the moon, which often interferes with the Perseids, will not be a problem this year. So I’m putting together something that’s never been done before: a spatial analysis of the Perseid meteor stream. We’ve had plenty of temporal analyses, but nobody has ever been able to get data over a wide area — because observations have always been localized to single observers. But what if we had hundreds or thousands of people all over North America and Europe observing Perseids and somebody collected and collated all their observations? This is crowd-sourcing applied to meteor astronomy. I’ve been working for some time on putting together just such a scheme. I’ve got a cute little Java applet that you can use on your laptop to record the times of fall of meteors you see, the spherical trig for analyzing the geometry (oh my aching head!) and a statistical scheme that I *think* will reveal the spatial patterns we’re most likely to see — IF such patterns exist. I’ve also got some web pages describing the whole shebang. They start here:

http://www.erasmatazz.com/page78/page128/PerseidProject/PerseidProject.html

I think I’ve gotten all the technical, scientific, and mathematical problems solved, but there remains the big one: publicizing it. It won’t work unless I get hundreds of observers. That’s where you come in. I’m asking two things of you:

1. Any advice, criticism, or commentary on the project as presented in the web pages.
2. Publicizing it. If we can get that ol’ Web Magic going, we could get thousands of observers and end up with something truly remarkable. So, would you be willing to blog about this project on your blog?
3. I would be especially interested in your comments on the statistical technique I propose to use in analyzing the data. It is sketched out on the website here:

http://www.erasmatazz.com/page78/page128/PerseidProject/Statistics/Statistics.html

Given my primitive understanding of statistical analysis, I expect that your comments will be devastating, but if you’re willing to take the time to write them up, I’m certainly willing to grit my teeth and try hard to understand and implement them.

Thanks for any help you can find time to offer.

Chris Crawford

URL: http://hea-www.harvard.edu/AstroStat/CAS2010

The California-Boston-Smithsonian Astrostatistics Collaboration plans to host a mini-workshop on Computational Astro-statistics. With the advent of new missions like the Solar Dynamic Observatory (SDO), Panoramic Survey and Rapid Response (Pan-STARRS) and Large Synoptic Survey (LSST), astronomical data collection is fast outpacing our capacity to analyze them. Astrostatistical effort has generally focused on principled analysis of individual observations, on one or a few sources at a time. But the new era of data intensive observational astronomy forces us to consider combining multiple datasets and infer parameters that are common to entire populations. Many astronomers really want to use every data point and even non-detections, but this becomes problematic for many statistical techniques.

The goal of the Workshop is to explore new problems in Astronomical data analysis that arise from data complexity. Our focus is on problems that have generally been considered intractable due to insufficient computational power or inefficient algorithms, but are now becoming tractable. Examples of such problems include: accounting for uncertainties in instrument calibration; classification, regression, and density estimations of massive data sets that may be truncated and contaminated with measurement errors and outliers; and designing statistical emulators to efficiently approximate the output from complex astrophysical computer models and simulations, thus making statistical inference on them tractable. We aim to present some issues to the statisticians and clarify difficulties with the currently used methodologies, e.g. MCMC methods. The Workshop will consist of review talks on current Statistical methods by Statisticians, descriptions of data analysis issues by astronomers, and open discussions between Astronomers and Statisticians. We hope to define a path for development of new algorithms that target specific issues, designed to help with applications to SDO, Pan-STARRS, LSST, and other survey data.

We hope you will be able to attend the workshop and present a brief talk on the scope of the data analysis problem that you confront in your project. The workshop will have presentations in the morning sessions, followed by a discussion session in the afternoons of both days.

——————————————————————
First Announcement and Call for Applications
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The “International Max Planck Research School for Astronomy & Cosmic Physics at the University of Heidelberg” (IMPRS-HD)

announces the

— 4th Heidelberg Summer School:

— Statistical Inferences from Astrophysical Data

— August 10-14, 2009

IMPRS Heidelberg invites graduate students and postdocs to its 4th Heidelberg Summer School. This year’s school is centered on how to draw scientific inferences from astrophysical data sets. We will also discuss proper statistical methods that are crucial for testing specific astrophysical models.

The school will present essential statistical concepts and techniques. These concepts will be illustrated through various astrophysical examples. Approaches such as Monte Carlo, maximum likelihood techniques, Bayesian statistics, parametric tests, biases in censored/incomplete data, or time-series analysis will be applied in computer exercises.

The main lecturing program is presented by invited speakers and is accompanied by practical exercises and also science talks on specific topics by local experts.

Invited lecturers are:

— David W. HOGG, New York University

— Ian McHARDY, University of Southampton

— William H. PRESS, University of Texas, Austin

Deadline for application is June 15, 2009.

Please find more information, our poster, and the application
forms under
www.mpia.de/imprs-hd/
www.mpia.de/imprs-hd/SummerSchools/2009/

A limited number of grants are available to partially cover travel expenses of participants.

IMPRS-HD is an independent part of the Heidelberg Graduate School for Fundamental Physics.