Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders
Archive for the ‘Fitting’ Category.
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’ »
Irrelevant to astrostatistics but interesting for baseball lovers.
[stat.AP:0802.4317] Jensen, Shirley, & Wyner
Bayesball: A Bayesian Hierarchical Model for Evaluating Fielding in Major League Baseball
With the 5th year WMAP data release, there were many WMAP related papers and among them, most statistical papers are listed. Continue reading ‘[ArXiv] 1st week, Mar. 2008’ »
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’ »
Sherpa is a fitting environment in which Chandra data (and really, X-ray data from any observatory) can be analyzed. It has just undergone a major update and now runs on python. Or allows python to run. Something like that. It is a very powerful tool, but I can never remember how to use it, and I have an amazing knack for not finding what I need in the documentation. So here is a little cheat sheet (which I will keep updating as and when if I learn more): Continue reading ‘Everybody needs crampons’ »
Astronomers have developed their ways of processing signals almost independent to but sometimes collaboratively with engineers, although the fundamental of signal processing is same: extracting information. Doubtlessly, these two parallel roads of astronomers’ and engineers’ have been pointing opposite directions: one toward the sky and the other to the earth. Nevertheless, without an intensive argument, we could say that somewhat statistics has played the medium of signal processing for both scientists and engineers. This particular issue of IEEE signal processing magazine may shed lights for astronomers interested in signal processing and statistics outside the astronomical society.
IEEE Signal Processing Magazine Jul. 2007 Vol 24 Issue 4: Bootstrap methods in signal processing
This link will show the table of contents and provide links to articles; however, the access to papers requires IEEE Xplore subscription via libraries or individual IEEE memberships). Here, I’d like to attempt to introduce some articles and tutorials.
Continue reading ‘Signal Processing and Bootstrap’ »
The chi2 bias can affect the results of the X-ray spectral fitting and it
can be demonstrated in a simple way. The described simulations can be done
in Sherpa or XSPEC, the two software packages that allow for simulating the X-ray
spectra using a function called “fakeit”.
Here I assume an absorbed power law model with the sets of 3 parameters
(absorption column, photon index, and normalization) to simulate Chandra X-ray
spectrum given the instrument calibration files (RMF/ARF) and the Poisson noise.
The resulting simulated X-ray spectrum contains the model predicted counts with
the Poisson noise. This spectrum is then fit with the absorbed power law model to get
the best fit parameter values for NH, photon index and normalization.
I simulate 1000 spectra and fit each of them using different statistics: chi2 data variance,
chi2 model variance and Cash/C-statistics.
The next step is to plot the simulated distributions of the parameters and compare them
to the assumed values for the simulations. The figure shows the distribution of the photon
index parameter obtain from the fit of the spectra generated for the assumed simulated value
of 1.267. The chi2 bias is evident in this analysis, while the
CSTAT and Cash statistics based on the likelihood behave well. chi2 model variance
underestimates the simulated value, chi2 data variance overestimates this parameter.
From arxiv/astro-ph:0709.1073v1
On the Metallicity-Color Relations and Bimodal Color Distributions in Extragalactic Globular Cluster Systems by M. Cantiello and J. P. Blakeslee
Many observations on globular cluster systems (GCS) show bimodal distributions in color and metallicity space. The authors discussed the complication of non-linear metalicity and color relations and presented their careful study to suggest the optimal color(s) for revealing the presence of real bimodal GC metallicity distributions. Based on their simulation study, (V-H) and (V-K) are confirmed to be good colors for revealing unbiased bimodal metallicity distributions in GCS.
Continue reading ‘[ArXiv] Bimodal Color Distribution in GCS, Sept. 7, 2007’ »
From arxiv/astro-ph:0708.1208v1:
The measurement errors in the Swift-UVOT and XMM-OM by N.P.M. Kuin and S.R. Rosen
The probability distribution of photon counts from the Optical Monitor on XMM Newton satellite (XMM-OM) and the UVOT on the Swift satellite follows a binomial distribution due to detector characteristics. Incident count rate was derived as a function of the measured count rate, which was shown to follow a binomial distribution.
Continue reading ‘[ArXiv] Swift and XMM measurement errors, Sep. 8, 2007’ »
From arxiv/math.st: 0708.0499v1
Inference for mixtures of symmetric distributions by Hunter, Wang, and Hettmansperger, Annals of Statistics, 2007, Vol.35(1), pp.224-251.
Continue reading ‘[ArXiv] Identifiability and mixtures of distributions, Aug. 3, 2007’ »
From arxiv/astro-ph:0708.3758v1
Numerical Color-Magnitude Diagram Analysis of SDSS Data and Application to the New Milky Way Satellites by J. T. A. de Jong et. al.
The authors applied MATCH (Dolphin, 2002[1] -note that the year is corrected) to M13, M15, M92, NGC2419, NGC6229, and Pal14 (well known globular clusters), and BooI, BooII, CvnI, CVnII, Com, Her, LeoIV, LeoT, Segu1, UMaI, UMaII and Wil1 (newly discovered Milky Way satellites) from Sloan Digital Sky Survey (SDSS) to fit Color Magnitude diagrams (CMDs) of these stellar clusters and find the properties of these satellites.
Continue reading ‘[ArXiv] Numerical CMD analysis, Aug. 28th, 2007’ »
From arxiv/astro-ph:0708.1204v3
An Isochrone Database and a Rapid Model for Stellar Population Synthesis by Li and Han
This paper emphasize the binary population: CMD fitting with the binary population synthetic model outperformed to the single population model. They used Hurley code (Hurley, Tout, and Pols (2002). Evolution of binary stars and the effect of tides on binary populations, MNRAS, 329(4), p.897-928). They mentioned that two color-color grids can disentangle the age-metallicity degeneracy via binary stellar populations. They fitted their isochrone database to M67 and NGC 1868 with the gT-grid and concluded that the database of binary stellar populations fitted the color magnitude diagrams better.
Continue reading ‘[ArXiv] Isochrone database, Aug. 20, 2007’ »
From arxiv/math.st:0708.0169v1
Data-Driven Goodness-of-Fit Tests by L. Mikhail
Goodness-of-Fit tests have been essential in astronomy to validate the chosen physical model to observed data whereas the limits of these tests have not been taken into consideration carefully when observed data were put into the model for estimating the model parameters. Therefore, I thought this paper would be helpful to have a thought on the different point of views between the astronomers’ practice of goodness-of-fit tests and the statisticians’ constructing tests. (Warning: the paper is abstract and theoretical.)
Continue reading ‘[ArXiv] Data-Driven Goodness-of-Fit Tests, Aug. 1, 2007’ »
During the International X-ray Summer School, as a project presentation, I tried to explain the inadequate practice of χ^2 statistics in astronomy. If your best fit is biased (any misidentification of a model easily causes such bias), do not use χ^2 statistics to get 1σ error for the 68% chance of capturing the true parameter.
Later, I decided to do further investigation on that subject and this paper came along: Astrostatistics: Goodness-of-Fit and All That! by Babu and Feigelson.
Continue reading ‘Astrostatistics: Goodness-of-Fit and All That!’ »
From IMS Bulletin Vol. 36(3), p.11, Terence’s Stuff: Model skeptics
[Once I quoted an article by Prof. Terry Speed in IMS Bulletin: Data-Doctors. Reading his columns in the IMS Bulletin provides me an opportunity to reflect who I am as a statistician and some guidance for treating data. Although his ideas were not from astronomy or astronomical data analysis, I often find his thoughts and words can be shared with astronomers.]
Continue reading ‘[Quote] Model Skeptics’ »
From arxiv/astro-ph:0707.1891v1
The Geneva-Copenhagen Survey of the Solar neighborhood II. New uvby calibrations and rediscussion of stellar ages, the G dwarf problem, age-metalicity diagram, and heating mechanisms of the disk by Holmberg, Nordstrom, and Andersen
Researchers, including scientists from CHASC, working on color magnitude diagrams to infer ages, metalicities, temperatures, and other physical quantities of stars and stellar clusters may find this paper useful.
Continue reading ‘[ArXiv] Geneva-Copenhagen Survey, July 13, 2007’ »