A tremendous amount of information is contained within the temporal variations of various measurable quantities, such as the energy distributions of the incident photons, the overall intensity of the source, and the spatial coherence of the variations. While the detection and interpretation of periodic variations is well studied, the same cannot be said for non-periodic behavior in a multi-dimensional domain. Methods to deal with such problems are still primitive, and any attempts at sophisticated analyses are carried out on a case-by-case basis. Some of the issues we seek to focus on are methods to deal with are:
* Stochastic variability
* Chaotic Quasi-periodic variability
* Irregular data gaps/unevenly sampled data
* Multi-dimensional analysis
* Transient classification

Our goal is to present some basic questions that require sophisticated temporal analysis in order for progress to be made. We plan to bring together astronomers and statisticians who are working in many different subfields so that an exchange of ideas can occur to motivate the development of sophisticated and generally applicable algorithms to astronomical time series data. We will review the problems and issues with current methodology from an algorithmic and statistical perspective and then look for improvements or for new methods and techniques.

He also has the answer worked out in detail.

(h/t Doug Burke)

Instead of “confidence interval,” let’s say “uncertainty interval”

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

A student had to figure out the name of a stellar object as part of an assignment. He was given the following information about it:

• apparent [V] magnitude = 5.76
• B-V = 0.02
• E(B-V) = 0.00
• parallax = 0.0478 arcsec
• radial velocity = -18 km/s
• redshift = 0 km/s

He looked in all the stellar databases but was unable to locate it, so he asked the CfA for help.

Just to help you out, here are a couple of places where you can find comprehensive online catalogs:

• Vizier
• W3Browse
• SIMBAD

See if you can find it!

Answer next week month.

Update (2010-aug-02):
The short answer is, I could find no such star in any commonly available catalog. But that is not the end of the story. There does exist a star in the Hipparcos catalog, HIP 103389, that has approximately the right distance (21 pc), radial velocity (-16.1 km/s), and V magnitude (5.70). It doesn’t match exactly, and the B-V is completely off, but that is the moral of the story.

The thing is, catalogs are not perfect. The same objects often have very different numerical entries in different catalogs. This could be due to a variety of reasons, such as different calibrations, different analysers, or even intrinsic variations in the source. And you can bet your bottom dollar that the quoted statistical uncertainties in the quantities do not account for the observed variance. Take the B-V value, for instance. It is 0.5 for HIP 103389, but the initial problem stated that it was 0.02, which makes it an A type star. But if it were an A type star at 21 pc, it should have had a magnitude of V~1.5, much brighter than the required 5.76!

I think this illustrates one of the fundamental tenets of science as it is practiced, versus how it is taught. The first thing that a practicing scientist does (especially one not of the theoretical persuasion) is to try and see where the data might be wrong or misleading. It should only be included in analysis after it passes various consistency checks and is deemed valid. The moral of the story is, don’t trust data blindly just because it is a “number”.

2010-apr-30: Aneta has setup a blogspot site to deal with simple Sherpa techniques and tactics: http://pysherpa.blogspot.com/

On Help:

• In general, to get help, use: ahelp "something" (note the quotes)
• Even more useful, type: ? wildcard to get a list of all commands that include the wildcard
• You can also do a form of autocomplete: type TAB after writing half a command to get a list of all possible completions.

Data I/O:

• To read in your PHA file, use: load_pha()
• Often for Chandra spectra, the background is included in that same file. In any case, to read it in separately, use: load_bkg()
  • Q: should it be loaded in to the same dataID as the source?
  • A: Yes.
  • A: When the background counts are present in the same file, they can be read in separately and assigned to the background via set_bkg('src',get_data('bkg')), so counts from a different file can be assigned as background to the current spectrum.
• To read in the corresponding ARF, use: load_arf()
  • Q: load_bkg_arf() for the background — should it be done before or after load_bkg(), or does it matter?
  • A: does not matter
• To read in the corresponding RMF, use: load_rmf()
  • Q: load_bkg_rmf() for the background, and same question as above
  • A: same answer as above; does not matter.
• To see the structure of the data, type: print(get_data()) and print(get_bkg())
• To select a subset of channels to analyze, use: notice_id()
• To subtract background from source data, use: subtract()
• To not subtract, to undo the subtraction, etc., use: unsubtract()
• To plot useful stuff, use: plot_data(), plot_bkg(), plot_arf(), plot_model(), plot_fit(), etc.
• (Q: how in god’s name does one avoid plotting those damned error bars? I know error bars are necessary, but when I have a spectrum with 8192 bins, I don’t want it washed out with silly 1-sigma Poisson bars. And while we are asking questions, how do I change the units on the y-axis to counts/bin? A: rumors say that plot_data(1,yerr=0) should do the trick, but it appears to be still in the development version.)

Fitting:

• To fit model to the data, command it to: fit()
• To get error bars on the fit parameters, use: projection() (or covar(), but why deliberately use a function that is guaranteed to underestimate your error bars?)
• Defining models appears to be much easier now. You can use syntax like: set_source(ModelName.ModelID+AnotherModel.ModelID2) (where you can distinguish between different instances of the same type of model using the ModelID — e.g., set_source(xsphabs.abs1*powlaw1d.psrc+powlaw1d.pbkg))
• To see what the model parameter values are, type: print(get_model())
• To change statistic, use: set_stat() (options are various chisq types, cstat, and cash)
• To change the optimization method, use: set_method() (options are levmar, moncar, neldermead, simann, simplex)

(via /.)

A postdoctoral position is available at the University of California, Berkeley for an individual who can lead an effort in real-time classification of astronomical time-series data for the purpose of extraction of novel science. The project is sponsored by a new Cyber-enabled Discovery and Innovation (CDI) grant from the National Science Foundation (NSF; http://128.150.4.107/awardsearch/showAward.do?AwardNumber=0941742 ).

The main goal of this project it to produce a framework (including new theoretical/algorithmic constructs) for extracting novel science from large amounts of data in an environment where the computational needs vastly outweigh the available facilities, and intelligent (as well as dynamic) resource allocation is required. This work will draw from current research in statistics, database engineering, computational science, time-domain astronomy, and machine learning and is expected to lead to applications beyond astronomy. The collaboration has access to proprietary astronomical datasets. We hope to build a system eventually capable of ingesting, assimilating, and creating “new knowledge” from massive data streams expected from new projects, such as the Large Synoptic Survey Telescope. The collaboration also has access to large-scale computing facilities through the Center for Information Technology Research in the Interest of Society (CITRIS) at Berkeley, at Lawrence Berkeley National Laboratory (LBNL), and and through cloud computing time donated by industry partners.

This work will be directed by Prof. Joshua Bloom in the Astronomy Department but the position calls for strong interactions with other senior members of the collaboration in other departments (Martin Wainwright, EECS and Statistics; Nourredine El Kouroui, Statistics; John Rice, Statistics; Massoud Nikravesh, CITRIS; Peter Nugent, LBNL; Horst Simon, LBNL). Experience and a demonstrated interest working with graduate students across these disciplines is also encouraged.

Minimum qualifications include a Ph.D. in Computer Science, Electrical Engineering, Statistics, Astronomy or closely related field is required. The strongest candidates will have demonstrated success in conducting original research in statistics and/or machine learning and should have a deep understanding and/or interest in topics of time-domain Astronomy. Work will commence no later than 1 August 2010. The appointment may start on an earlier date, if mutually convenient (funding is already available to start as early as Spring 2010). The initial appointment is for two years, with renewal expected if progress is satisfactory and funds continue to be available. The starting salary will be commensurate with experience, and competitive with other postdoctoral positions. Please e-mail a short research statement, resume, list of publications, and copies of two recent publications (preprints or reprints) so that they arrive by the 1 February 2010 deadline to Prof. Joshua Bloom, at the above address. To receive full consideration, applicants should arrange to have letters of references from three individuals sent to Prof. Bloom by the 1 February 2010 due date (letters may also be emailed directly by the referees). Immigration status of non-citizens should be stated in the resume.

The CCD, of course, is the workhorse of modern Astronomy. I cannot even imagine how things would be without it.

Actually, Boyle & Smith (1970, Bell Systems Technical Journal, 49, 587) and Amelio, Tompsett & Smith (1970, Bell Systems Tech. J., 49, 593), according to the detailed cite by the Nobel committee.