Archive for the ‘Objects’ Category.
There is an ambitious project afoot to build a 3D map of a meteor stream during the Perseids on Aug 11-12. I got this missive about it from the organizer, Chris Crawford:
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:
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:
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.
This question came to the CfA Public Affairs office, and I am sharing it with y’all because I think the solution is instructive.
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:
See if you can find it!
Continue reading ‘An Instructive Challenge’ »
An email was forwarded with questions related to the data sets found in “Be an INTEGRAL astronomer”. Among the sets, the following scatter plot is based on the Crab data.
Continue reading ‘different views’ »
Someone emailed me for globular cluster data sets I used in a proceeding paper, which was about how to determine the multi-modality (multiple populations) based on well known and new information criteria without binning the luminosity functions. I spent quite time to understand the data sets with suspicious numbers of globular cluster populations. On the other hand, obtaining globular cluster data sets was easy because of available data archives such as VizieR. Most data sets in charts/tables, I acquire those data from VizieR. In order to understand science behind those data sets, I check ADS. Well, actually it happens the other way around: check scientific background first to assess whether there is room for statistics, then search for available data sets. Continue reading ‘accessing data, easier than before but…’ »
I wonder what Fisher, Neyman, and Pearson would say if they see “Technique” after “Likelihood Ratio” instead of “Test.” A presenter’s saying “Likelihood Ratio Technique” for source identification, I couldn’t resist checking it out not to offend founding fathers of the likelihood principle in statistics since “Technique” sounded derogatory to be attached with “Likelihood” to my ears. I thank, above all, the speaker who kindly gave me the reference about this likelihood ratio technique. Continue reading ‘Likelihood Ratio Technique’ »
Earlier this year, Peter Edmonds showed me a press release that the Chandra folks were, at the time, considering putting out describing the possible identification of a Type Ia Supernova progenitor. What appeared to be an accreting white dwarf binary system could be discerned in 4-year old observations, coincident with the location of a supernova that went off in November 2007 (SN2007on). An amazing discovery, but there is a hitch.
And it is a statistical hitch, and involves two otherwise highly reliable and oft used methods giving contradictory answers at nearly the same significance level! Does this mean that the chances are actually 50-50? Really, we need a bona fide statistician to take a look and point out the errors of our ways.. Continue reading ‘Did they, or didn’t they?’ »
Astrometry.net, a cool website I heard from Harvard Astronomy Professor Doug Finkbeiner’s class (Principles of Astronomical Measurements), does a complex job of matching your images of unknown locations or coordinates to sources in catalogs. By providing your images in various formats, they provide astrometric calibration meta-data and lists of known objects falling inside the field of view. Continue reading ‘Astrometry.net’ »
This is a quite long paper that I separated from [Arvix] 4th week, Feb. 2008:
[astro-ph:0802.3916] P. Carvalho, G. Rocha, & M.P.Hobso
A fast Bayesian approach to discrete object detection in astronomical datasets – PowellSnakes I
As the title suggests, it describes Bayesian source detection and provides me a chance to learn the foundation of source detection in astronomy. Continue reading ‘[ArXiv] A fast Bayesian object detection’ »
Avalanches are a common process, occuring anywhere that a system can store stress temporarily without “snapping”. It can happen on sand dunes and solar flares as easily as on the snow bound Alps.
Melatos, Peralta, & Wyithe (arXiv:0710.1021) have a nice summary of avalanche processes in the context of pulsar glitches. Their primary purpose is to show that the glitches are indeed consistent with an avalanche, and along the way they give a highly readable description of what an avalanche is and what it entails. Briefly, avalanches result in event parameters that are distributed in scale invariant fashion (read: power laws) with exponential waiting time distributions (i.e., Poisson).
Hence the title of this post: the “Avalanche distribution” (indulge me! I’m using stats notation to bury complications!) can be thought to have two parameters, both describing the indices of power-law distributions that control the event sizes, a, and the event durations, b, and where the event separations are distributed as an exponential decay. Is there a canned statistical distribution that describes all this already? (In our work modeling stellar flares, we assumed that b=0 and found that
a>2 a<-2, which has all sorts of nice consequences for coronal heating processes.)
Comparison of decision tree methods for finding active objects by Y. Zhao and Y. Zhang
The authors (astronomers) introduced and summarized various decision three methods (REPTree, Random Tree, Decision Stump, Random Forest, J48, NBTree, and AdTree) to the astronomical community.
Continue reading ‘[ArXiv] Decision Tree, Aug. 31, 2007’ »
Gamma-ray albedo of the moon by Moskalenko and Porter
The title sounds very interesting although the significance of albedo spectra is not recognized by a statistician. This study was performed to utilize GLAST and PAMELA via Monte Carlo simulations (the toolkit for MC was GEANT 8.2) with EGRET data.
Connecting GRBs and galaxies: the probability of chance coincidence by Cobb and Bailyn
Without an optical afterglow, a galaxy within the 2 arc second error region of a GRB x-ray afterglow is identified as a host galaxy; however confusion can rise due to the facts that 1. the edge of a galaxy is diffused, 2. multiple sources could exist within 2 arc second error region, 3.the distance between the galaxy and the x-ray afterglow is measured by projection, and 4. lensing causes increase of brightness and position shifts. In this paper, the authors “investigated the fields of 72 GRBs in order to examine the general issue of associations between GRBs and host galaxies.”
Continue reading ‘[ArXiv] GRB host galaxies, Aug. 10, 2007’ »
Statistical Evidence for Three classes of Gamma-ray Bursts by T. Chattopadhyay et. al.
In general, gamma-ray bursts (GRBs) are classified into two groups: long (>2 sec) and short (<2 sec) duration bursts. Nonetheless, there have been some studies including arxiv/astro-ph:0705.4020v2 that statistically proved the optimal existence of 3 clusters. The pioneer work of GRB clusterings was based on hierarchical clustering methods by Mukerjee et. al.(Three Types of Gamma-Ray Bursts)
Continue reading ‘[ArXiv] Three Classes of GRBs, July 21, 2007’ »
Star Formation via the Little Guy: A Bayesian Study of Ultracool Dwarf Imaging Surveys for Companions by P. R. Allen.
I rather skip all technical details on ultracool dwarfs and binary stars, reviews on star formation studies, like initial mass function (IMF), astronomical survey studies, which Allen gave a fair explanation in arxiv/astro-ph:0707.2064v1 but want to emphasize that based on simple Bayes’ rule and careful set-ups for likelihoods and priors according to data (ultracool dwarfs), quite informative conclusions were drawn:
Continue reading ‘[ArXiv] Bayesian Star Formation Study, July 13, 2007’ »