The AstroStat Slog » lensing

[ArXiv] 3rd week, June 2008

hlee — Sun, 22 Jun 2008 03:10:21 +0000

This is my last [ArXiv] series.

[astro-ph:0806.2228] T.J. Cornwell
Multi-Scale CLEAN deconvolution of radio synthesis images
[astro-ph:0806.2575] C.A.L. Bailer-Jones
What will Gaia tell us about the Galactic disk?
[astro-ph:0806.2823] Williams et al.
Lensed Image Angles: New Statistical Evidence for Substructure (Apart from their K-S tests, personally lensing is considered to be a nice subject from a geostatistics standpoint.)
[astro-ph:0806.3074] Eriksen and Wehus
Marginal distributions for cosmic variance limited CMB polarization data
[astro-ph:0806.2969] W. Boschin et al.
Optical analysis of the poor clusters Abell 610, Abell 725, and Abell 796, containing diffuse radio sources (astronomers call gaussian mixture models by KMM)
[astro-ph:0806.3096] Miller, Shimon, & Keating
CMB Beam Systematics: Impact on Lensing Parameter Estimation (Note Monte Carlo Markov Chain in the abstract, not Markov chain Monte Carlo.)

[ArXiv] 5th week, Apr. 2008

hlee — Mon, 05 May 2008 07:08:42 +0000

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

Wikipedia link: Hubble sequence

[ArXiv] 4th week, Apr. 2008

hlee — Sun, 27 Apr 2008 15:29:48 +0000

The last paper in the list discusses MCMC for time series analysis, applied to sunspot data. There are six additional papers about statistics and data analysis from the week.

[astro-ph:0804.2904]M. Cruz et al.
The CMB cold spot: texture, cluster or void?

[astro-ph:0804.2917] Z. Zhu, M. Sereno
Testing the DGP model with gravitational lensing statistics

[astro-ph:0804.3390] Valkenburg, Krauss, & Hamann
Effects of Prior Assumptions on Bayesian Estimates of Inflation Parameters, and the expected Gravitational Waves Signal from Inflation

[astro-ph:0804.3413] N.Ball et al.
Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX (Another related publication [astro-ph:0804.3417])

[astro-ph:0804.3471] M. Cirasuolo et al.
A new measurement of the evolving near-infrared galaxy luminosity function out to z~4: a continuing challenge to theoretical models of galaxy formation

[astro-ph:0804.3475] A.D. Mackey et al.
Multiple stellar populations in three rich Large Magellanic Cloud star clusters

[stat.ME:0804.3853] C. R\”over , R. Meyer, N. Christensen
Modelling coloured noise (MCMC & sunspot data)

[ArXiv] 3rd week, Mar. 2007

hlee — Fri, 21 Mar 2008 22:20:33 +0000

Markov chain Monte Carlo (MCMC) never misses a week from recently astro-ph. A book titled MCMC in astronomy will be a best seller. There are, in addition, very interesting non MCMC preprints.

[astro-ph:0803.2130] R. Aurich
   A spatial correlation analysis for a toroidal universe (MCMC)

[astro-ph:0803.2120]M. Martinez & M. Errando
   A new method to study energy-dependent arrival delays on photons from astrophysical sources (Likelihood function and goodness-of-fit)

[astro-ph:0803.2234] G. Dobler et.al.
   Lensing Probabilities for Spectroscopically Selected Galaxy-Galaxy Strong Lenses (could it be helpful to lay out GREAT08 challenges statistically?)

[astro-ph:0803.2529] M. Bazot, S. Bourguignon and J. Christensen-Dalsgaard
   Estimation of stellar parameters using Monte Carlo Markov Chains (MCMC)

[stat.AP:0803.2623] F. Dup\’e, J. Fadili, and J. L. Starck
   A proximal iteration for deconvolving Poisson noisy images using sparse representations

I’m used to see Markov Chain Monte Carlo (or lower case c in Chain) from statistical journals but in astronomical journals, Monte Carlo Markov Chains seem to be standard.

[ArXiv] 2nd week, Mar. 2008

hlee — Fri, 14 Mar 2008 19:44:34 +0000

Warning! The list is long this week but diverse. Some are of CHASC’s obvious interest.

[astro-ph:0803.0997] V. Smolcic et.al.
   A new method to separate star forming from AGN galaxies at intermediate redshift: The submillijansky radio population in the VLA-COSMOS survey

[astro-ph:0803.1048] T.A. Carroll and M. Kopf
   Zeeman-Tomography of the Solar Photosphere — 3-Dimensional Surface Structures Retrieved from Hinode Observations

[astro-ph:0803.1066] M. Beasley et.al.
   A 2dF spectroscopic study of globular clusters in NGC 5128: Probing the formation history of the nearest giant Elliptical

[astro-ph:0803.1098] Z. Lorenzo
   A new luminosity function for galaxies as given by the mass-luminosity relationship

[astro-ph:0803.1199] D. Coe et.al.
   LensPerfect: Gravitational Lens Massmap Reconstructions Yielding Exact Reproduction of All Multiple Images (could it be related to GREAT08 Challenge?)

[astro-ph:0803.1213] H.Y.Wang et.al.
   Reconstructing the cosmic density field with the distribution of dark matter halos

[astro-ph:0803.1420] E. Lantz et.al.
   Multi-imaging and Bayesian estimation for photon counting with EMCCD’s

[astro-ph:0803.1491] Wu, Rozo, & Wechsler
   The Effect of Halo Assembly Bias on Self Calibration in Galaxy Cluster Surveys

[astro-ph:0803.1616] P. Mukherjee et.al.
   Planck priors for dark energy surveys (some CHASCians would like to check!)

[astro-ph:0803.1738] P. Mukherjee and A. R. Liddle
   Planck and reionization history: a model selection view

[astro-ph:0803.1814] J. Cardoso et.al.
   Component separation with flexible models. Application to the separation of astrophysical emissions

[astro-ph:0803.1851] A. R. Marble et.al.
    The Flux Auto- and Cross-Correlation of the Lyman-alpha Forest. I. Spectroscopy of QSO Pairs with Arcminute Separations and Similar Redshifts

[astro-ph:0803.1857] R. Marble et.al.
    The Flux Auto- and Cross-Correlation of the Lyman-alpha Forest. II. Modelling Anisotropies with Cosmological Hydrodynamic Simulations

The GREAT08 Challenge

vlk — Fri, 29 Feb 2008 03:46:49 +0000

Grand statistical challenges seem to be all the rage nowadays. Following on the heels of the Banff Challenge (which dealt with figuring out how to set the bounds for the signal intensity that would result from the Higgs boson) comes the GREAT08 Challenge (arxiv/0802.1214) to deal with one of the major issues in observational Cosmology, the effect of dark matter. As Douglas Applegate puts it:

We are organizing a competition specifically targeting the statistics and computer science communities. The challenge is to measure cosmic shear at a level sufficient for future surveys such as the Large Synaptic Survey Telescope. Right now, we’ve stripped out most of complex observational issues leaving a pure statistical inference problem. The competition kicks off this summer, but we want to give possible participants a chance to prepare.

The website www.great08challenge.info will provide continual updates on the competition.

[ArXiv] 2nd week, Feb. 2008

hlee — Mon, 18 Feb 2008 03:09:05 +0000

Another week went by with astro-ph papers of statistical flavors.

[astro-ph:0802.1069] E. S. Rykoff et.al.
   The L_X–M relation of Clusters of Galaxies (MCMC)

[astro-ph:0802.1239] L.R. Levenson & E.L. Wright
   Probing the 3.6 Micron CIRB with Spitzer in 3 DIRBE Dark Spots (MCMC)

[astro-ph:0802.1273] D.M. Bramich
    A New Algorithm For Difference Image Analysis

[astro-ph:0802.1528] T. D. Kitching et.al.
    Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -II. Application to Simulations

[astro-ph:0802.1890] M. Rowan-Robinson et.al.
    Photometric redshifts in the SWIRE Survey

[ArXiv] GRB host galaxies, Aug. 10, 2007

hlee — Tue, 14 Aug 2007 16:47:04 +0000

From arxiv/astro-ph:0708.1510v1
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.”

The authors added some statistical issues on this matching GRBs and host galaxies but current knowledge and techniques seem short to tackle the problem. Yet, to prevent false discovery, the authors proposed strategic studies for the followings:

Gamma-ray luminosity indicators

Detection (or non-detection ) SNe (Supernova) for long-duration bursts

Classification of associated galaxy : long-duration and short-duration bursts are associated with late-type and early-type galaxies, respectively

Optical afterglow spectral absorption features

Visual detection of true host galaxy as happened with GRB 060912a

X-ray afterglow spectral emission lines, and

Strong lensing of x-ray afterglows

As multi-wavelength studies become popular nowadays, this source matching issue across bands continuously arises where statistics can contribute the validity of source matching methods. So far, those methods are incomprehensible to statisticians.