The AstroStat Slog » power spectrum http://hea-www.harvard.edu/AstroStat/slog Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders Fri, 09 Sep 2011 17:05:33 +0000 en-US hourly 1 http://wordpress.org/?v=3.4 [ArXiv] 2nd week, June 2008 http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-2nd-week-june-2008/ http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-2nd-week-june-2008/#comments Mon, 16 Jun 2008 14:47:42 +0000 hlee http://hea-www.harvard.edu/AstroStat/slog/?p=335 As Prof. Speed said, PCA is prevalent in astronomy, particularly this week. Furthermore, a paper explicitly discusses R, a popular statistics package.

  • [astro-ph:0806.1140] N.Bonhomme, H.M.Courtois, R.B.Tully
        Derivation of Distances with the Tully-Fisher Relation: The Antlia Cluster
    (Tully Fisher relation is well known and one of many occasions statistics could help. On the contrary, astronomical biases as well as measurement errors hinder from the collaboration).
  • [astro-ph:0806.1222] S. Dye
        Star formation histories from multi-band photometry: A new approach (Bayesian evidence)
  • [astro-ph:0806.1232] M. Cara and M. Lister
        Avoiding spurious breaks in binned luminosity functions
    (I think that binning is not always necessary and overdosed, while there are alternatives.)
  • [astro-ph:0806.1326] J.C. Ramirez Velez, A. Lopez Ariste and M. Semel
        Strength distribution of solar magnetic fields in photospheric quiet Sun regions (PCA was utilized)
  • [astro-ph:0806.1487] M.D.Schneider et al.
        Simulations and cosmological inference: A statistical model for power spectra means and covariances
    (They used R and its package Latin hypercube samples, lhs.)
  • [astro-ph:0806.1558] Ivan L. Andronov et al.
        Idling Magnetic White Dwarf in the Synchronizing Polar BY Cam. The Noah-2 Project (PCA is applied)
  • [astro-ph:0806.1880] R. G. Arendt et al.
        Comparison of 3.6 – 8.0 Micron Spitzer/IRAC Galactic Center Survey Point Sources with Chandra X-Ray Point Sources in the Central 40×40 Parsecs (K-S test)
]]> http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-2nd-week-june-2008/feed/ 0 [ArXiv] 5th week, Apr. 2008 http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-5th-week-apr-2008/ http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-5th-week-apr-2008/#comments Mon, 05 May 2008 07:08:42 +0000 hlee http://hea-www.harvard.edu/AstroStat/slog/?p=281 Since I learned Hubble’s tuning fork[1] for the first time, I wanted to do classification (semi-supervised learning seems more suitable) galaxies based on their features (colors and spectra), instead of labor intensive human eye classification. Ironically, at that time I didn’t know there is a field of computer science called machine learning nor statistics which do such studies. Upon switching to statistics with a hope of understanding statistical packages implemented in IRAF and IDL, and learning better the contents of Numerical Recipes and Bevington’s book, the ignorance was not the enemy, but the accessibility of data was.

I’m glad to see this week presented a paper that I had dreamed of many years ago in addition to other interesting papers. Nowadays, I’m more and more realizing that astronomical machine learning is not simple as what we see from machine learning and statistical computation literature, which typically adopted data sets from the data repository whose characteristics are well known over the many years (for example, the famous iris data; there are toy data sets and mock catalogs, no shortage of data sets of public characteristics). As the long list of authors indicates, machine learning on astronomical massive data sets are never meant to be a little girl’s dream. With a bit of my sentiment, I offer the list of this week:

  • [astro-ph:0804.4068] S. Pires et al.
    FASTLens (FAst STatistics for weak Lensing) : Fast method for Weak Lensing Statistics and map making
  • [astro-ph:0804.4142] M.Kowalski et al.
    Improved Cosmological Constraints from New, Old and Combined Supernova Datasets
  • [astro-ph:0804.4219] M. Bazarghan and R. Gupta
    Automated Classification of Sloan Digital Sky Survey (SDSS) Stellar Spectra using Artificial Neural Networks
  • [gr-qc:0804.4144]E. L. Robinson, J. D. Romano, A. Vecchio
    Search for a stochastic gravitational-wave signal in the second round of the Mock LISA Data challenges
  • [astro-ph:0804.4483]C. Lintott et al.
    Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
  • [astro-ph:0804.4692] M. J. Martinez Gonzalez et al.
    PCA detection and denoising of Zeeman signatures in stellar polarised spectra
  • [astro-ph:0805.0101] J. Ireland et al.
    Multiresolution analysis of active region magnetic structure and its correlation with the Mt. Wilson classification and flaring activity

A relevant post related machine learning on galaxy morphology from the slog is found at svm and galaxy morphological classification

< Added: 3rd week May 2008>[astro-ph:0805.2612] S. P. Bamford et al.
Galaxy Zoo: the independence of morphology and colour

  1. Wikipedia link: Hubble sequence
]]> http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-5th-week-apr-2008/feed/ 0 [ArXiv] 1st week, Jan. 2008 http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-1st-week-jan-2008/ http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-1st-week-jan-2008/#comments Fri, 04 Jan 2008 16:49:57 +0000 hlee http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-1st-week-jan-2008/ It’s a rather short list, this week and I hope I can maintain this conciseness afterwards. Happy new year to everyone.

  • [astro-ph:0801.0336] Astronomical Image Subtraction by Cross-Convolution F. Yuan & C. W. Akerlof
  • [math.TH:0801.0158] Frequency estimation based on the cumulated Lomb-Scargle periodogram C. L\’evy-Leduc, E. Moulines, & F. Roueff
  • [astro-ph:0801.0451] A cgi synthetic CMD calculator for the YY Isochrones P. Demarque et. al.
  • [astro-ph:0801.0554] Likelihood Analysis of CMB Temperature and Polarization Power Spectra S. Hamimeche & A. Lewis
]]> http://hea-www.harvard.edu/AstroStat/slog/2008/arxiv-1st-week-jan-2008/feed/ 3 [ArXiv] CMB statistics, Sept. 7, 2007 http://hea-www.harvard.edu/AstroStat/slog/2007/arxiv-cmb-statistics-mve/ http://hea-www.harvard.edu/AstroStat/slog/2007/arxiv-cmb-statistics-mve/#comments Tue, 11 Sep 2007 05:36:50 +0000 hlee http://hea-www.harvard.edu/AstroStat/slog/2007/arxiv-cmb-statistics-mve/ From arxiv/astro-ph:0709.1144v1: Cosmic Microwave Background Statistics for a Direction-Dependent Primordial Power Spectrum by A. R. Pullen and M. Kamionkowski

The authors developed cosmic microwave background statistics for a primordial power spectrum, motivated from the needs of testing the cosmological common assumption, i.e. the statistical isotropy of primordial perturbations. This statistics is for a primordial power spectrum, depending on the direction and the magnitude of the Fourier wavevector. Statistically speaking, the most interesting part is their construction of the minimum-variance estimators for the coefficients of a spherical-harmonic expansion of the direction-dependence of the primordial power spectrum.

]]> From arxiv/astro-ph:0709.1144v1:
Cosmic Microwave Background Statistics for a Direction-Dependent Primordial Power Spectrum by A. R. Pullen and M. Kamionkowski

The authors developed cosmic microwave background statistics for a primordial power spectrum, motivated from the needs of testing the cosmological common assumption, i.e. the statistical isotropy of primordial perturbations. This statistics is for a primordial power spectrum, depending on the direction and the magnitude of the Fourier wavevector. Statistically speaking, the most interesting part is their construction of the minimum-variance estimators for the coefficients of a spherical-harmonic expansion of the direction-dependence of the primordial power spectrum.

]]> http://hea-www.harvard.edu/AstroStat/slog/2007/arxiv-cmb-statistics-mve/feed/ 0