• [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)

First, I’d like to point out that my initial concerns from [astro-ph:0707.1611] Probabilistic Cross-Identification of Astronomical Sources are explained in sections 2, 3 and 6 about parameter space, its dimensionality, and priors in Bayesian model selection.

Second, I’d rather concisely list the contents of the paper as follows: (1) priors, various types but rooms were left for further investigations in future; (2) templates (such as point spread function, I guess), crucial for defining sources, and gaussian random field for noise; (3) optimization strategies for fast computation (source detection implies finding maxima and integration for evidence); (4) comparison with other works; (5) upper bound, tuning the threshold for acceptance/rejection to minimize the symmetric loss; (6) challenges of dealing likelihoods in Fourier space from incorporating colored noise (opposite to white noise); (7) decision theory from computing false negatives (undetected objects) and false positives (spurious objects). Many issues in computing Bayesian evidence, priors, tunning parameter relevant posteriors, and the peaks of maximum likelihoods; and approximating templates and backgrounds are carefully presented. The conclusion summarizes their PowellSnakes algorithm pictorially.

Thirdly, although my understanding of object detection and linking it to Bayesian techniques is very superficial, my reading this paper tells me that they propose some clever ways of searching full 4 dimensional space via Powell minimization (It seems to be related with profile likelihoods for a fast computation but it was not explicitly mentioned) and the detail could direct statisticians’ attentions for the improvement of computing efficiency and acceleration.

Fourth, I’d like to talk about my new knowledge that I acquired from this paper about errors in astronomy. Statisticians usually surprise at astronomical catalogs that in general come with errors next to single measurements. These errors are not measurement errors (errors calculated from repeated observations) but obtained from Fisher information owing to Cramer-Rao Lower Bound. The template likelihood function leads this uncertainty measure on each observation.

Lastly, in astronomy, there are many empirical rules, effects, and laws that bear uncommon names. Generally these are sophisticated rules of thumb or approximations of some phenomenon (for instance, Hubble’s law, though it’s well known) but they have been the driving away factors when statisticians reading astronomy papers. On the other hand, despite overwhelming names, when it gets to the point, the objective of mentioning such names is very statistical like regression (fitting), estimating parameters and their uncertainty, goodness-of-fit, truncated data, fast optimization algorithms, machine learning, etc. This paper mentions Sunyaev-Zel’dovich effect, which name scared me but I’d like to emphasize that this kind nomenclature may hinder from understanding details but could not block any collaborations.

Wavelet papers:

• [astro-ph:0802.2377] J. M. Lilly & S. C. Olhede
     On the Design of Optimal Analytic Wavelets

• [math.ST:0802.2424] Autin, Le Pennec & Tribouley
     Thresholding methods to estimate the copula density

A statistics paper and astro-ph papers adopted statistical tools:

• [stat.ME:0802.2155] Guellil & Kernane
     A New Approach of Point Estimation and its Application to Truncated Data Situations

• [astro-ph:0802.2105] N. Padmanabhan et.al.
     The real-space clustering of luminous red galaxies around z<0.6 quasars in the Sloan Digital Sky Survey

• [astro-ph:0802.2446] Banerjee & Ghosh
     Evolution of Compact-Binary Populations in Globular Clusters: A Boltzmann Study II. Introducing Stochasticity

• [astro-ph:0802.2944] E. W. Rosolowsky et.al.
     Structural Analysis of Molecular Clouds: Dendrograms

• [astro-ph:0802.3185] G. Efstathiou
     Limitations of Bayesian Evidence Applied to Cosmology

• [astro-ph:0802.3199] A. A. Mahabal et. al.
     Automated Probabilistic Classification of Transients and Variables

To find the paper or other interesting ones, click

• [astro-ph:0801.0638]
  AIC, BIC, Bayesian evidence and a notion on simplicity of cosmological model M Szydlowski & A. Kurek

• [astro-ph:0801.0642]
  Correlation of CMB with large-scale structure: I. ISW Tomography and Cosmological Implications S. Ho et.al.

• [astro-ph:0801.0780]
  The Distance of GRB is Independent from the Redshift F. Song

• [astro-ph:0801.1081]
  A robust statistical estimation of the basic parameters of single stellar populations. I. Method X. Hernandez and D. Valls–Gabaud

• [astro-ph:0801.1106]
  A Catalog of Local E+A(post-starburst) Galaxies selected from the Sloan Digital Sky Survey Data Release 5 T. Goto (Carefully built catalogs are wonderful sources for classification/supervised learning, or semi-supervised learning)

• [astro-ph:0801.1358]
  A test of the Poincare dodecahedral space topology hypothesis with the WMAP CMB data B.S. Lew & B.F. Roukema

In cosmology, a few candidate models to be chosen, are generally nested. A larger model usually is with extra terms than smaller ones. How to define the penalty for the extra terms will lead to a different choice of model selection criteria. However, astronomy papers in general never discuss the consistency or statistical optimality of these selection criteria; most likely Monte Carlo simulations and extensive comparison across those criteria. Nonetheless, my personal thought is that the field of model selection should be encouraged to astronomers to prevent fallacies of blindly fitting models which might be irrelevant to the information that the data set contains. Physics tells a correct model but data do the same.