However, if you are interested in massive multivariate data or if you want to have a subsample from a gigantic survey project, impossible all to be documented in contrast to those individual small catalogs, one might like to learn a little about Structured Query Language (SQL). With nice examples and explanation, some Tera byte data are available from SDSS. Instead of images in fits format, one can get ascii/table data sets (variables of million objects are magnitudes and their errors; positions and their errors; classes like stars, galaxies, AGNs; types or subclasses like elliptical galaxies, spiral galaxies, type I AGN, type Ia, Ib, Ic, and II SNe, various spectral types, etc; estimated variables like photo-z, which is my keen interest; and more). Furthermore, thousands of papers related to SDSS are available to satisfy your scientific cravings. (Here are Slog postings under SDSS tag).

If you don’t want to limit yourself with ascii tables, you may like to check the quick guide/tutorial of Gator, which aggregated archives of various missions: 2MASS (Two Micron All-Sky Survey), IRAS (Infrared Astronomical Satellite), Spitzer Space Telescope Legacy Science Programs, MSX (Midcourse Space Experiment), COSMOS (Cosmic Evolution Survey), DENIS (Deep Near Infrared Survey of the Southern Sky), and USNO-B (United States Naval Observatory B1 Catalog). Probably, you also want to check NED or NASA/IPAC Extragalactic Database. As of today, the website said, 163 million objects, 170 million multiwavelength object cross-IDs, 188 thousand associations (candidate cross-IDs), 1.4 million redshifts, and 1.7 billion photometric measurements are accessible, which seem more than enough for data mining, exploring/summarizing data, and developing streaming/massive data analysis tools.

Probably, astronomers might wonder why I’m not advertising Chandra Data Archive (CDA) and its project oriented catalog/database. All I can say is that it’s not independent statistician friendly. It is very likely that I am the only statistician who tried to use data from CDA directly and bother to understand the contents. I can assure you that without astronomers’ help, the archive is just a hot potato. You don’t want to touch it. I’ve been there. Regardless of how painful it is, I’ve kept trying to touch it since It’s hard to resist after knowing what’s in there. Fortunately, there are other data scientist friendly archives that are quite less suffering compared to CDA. There are plethora things statisticians can do to improve astronomers’ a few decade old data analysis algorithms based on Gaussian distribution, iid assumption, or L₂ norm; and to reflect the true nature of data and more relaxed assumptions for robust analysis strategies than for traditionally pursued parametric distribution with specific models (a distribution free method is more robust than Gaussian distribution but the latter is more efficient) not just with CDA but with other astronomical data archives. The latter like vizieR or SDSS provides data sets which are less painful to explore with without astronomical software/package familiarity.

Computer scientists are well aware of UCI machine learning archive, with which they can validate their new methods with previous ones and empirically prove how superior their methods are. Statisticians are used to handle well trimmed data; otherwise we suggest strategies how to collect data for statistical inference. Although tons of data collecting and sampling protocols exist, most of them do not match with data formats, types, natures, and the way how data are collected from observing the sky via complexly structured instruments. Some archives might be extensively exclusive to the funded researchers and their beneficiaries. Some archives might be super hot potatoes with which no statistician wants to involve even though they are free of charges. I’d like to warn you overall not to expect the well tabulated simplicity of text book data sets found in exploratory data analysis and machine learning books.

Some one will raise another question why I do not speculate VOs (virtual observatories, click for slog postings) and Google Sky (click for slog postings), which I praised in the slog many times as good resources to explore the sky and to learn astronomy. Unfortunately, for the purpose of direct statistical applications, either VOs or Google sky may not be fancied as much as their names’ sake. It is very likely spending hours exploring these facilities and later you end up with one of archives or web interfaces that I mentioned above. It would be easier talking to your nearest astronomer who hopefully is aware of the importance of statistics and could offer you a statistically challenging data set without worries about how to process and clean raw data sets and how to build statistically suitable catalogs/databases. Every astronomer of survey projects builds his/her catalog and finds common factors/summary statistics of the catalog from the perspective of understanding/summarizing data, the primary goal of executing statistical analyses.

I believe some astronomers want to advertise their archives and show off how public friendly they are. Such advertising comments are very welcome because I intentionally left room for those instead of listing more archives I heard of without hands-on experience. My only wish is that more statisticians can use astronomical data from these archives so that the application section of their papers is filled with data from these archives. As if with sunspots, I wish that more astronomical data sets can be used to validate methodologies, algorithms, and eventually theories. I sincerely wish that this shall happen in a short time before I become adrift from astrostatistics and before I cannot preach about the benefits of astronomical data and their archives anymore to make ends meet.

There is no single well known data repository in astronomy like UCI machine learning archive. Nevertheless, I can assure you that the nature of astronomical data and catalogs bear various statistical problems and many of those problems have never been formulated properly towards various statistical inference problems. There are so many statistical challenges residing in them. Not enough statisticians bother to look these data because of the gigantic demands for statisticians from uncountably many data oriented scientific disciplines and the persistent shortage in supplies.

• [astro-ph:0711.4356]
  Transformations between 2MASS, SDSS and BVRI photometric systems: bridging the near infrared and optical S. Bilir et.al.
• [astro-ph:0711.4369]
  SED modeling of Young Massive Stars T. P. Robitaille
• [astro-ph:0711.4387]
  SkyMouse: A smart interface for astronomical on-line resources and services C.-Z. Cui et. al.
• [stat.AP:0711.3765]
  MCMC Inference for a Model with Sampling Bias: An Illustration using SAGE data R. Zaretzki et. al.
• [astro-ph:0711.3640]
  Large-Scale Anisotropic Correlation Function of SDSS Luminous Red Galaxies T. Okumura et.al.
• [astro-ph:0711.4598]
  Dynamical Evolution of Globular Clusters in Hierarchical Cosmology O.Y. Gnedin and J. L. Prieto
• [astro-ph:0711.4795]
  Globular Clusters and Dwarf Spheroidal Galaxies S. van den Bergh
• [astro-ph:0711.3897]
  Optical Monitoring of 3C 390.3 from 1995 to 2004 and Possible Periodicities in the Historical Light Curve
  strong assumption on a Gaussian distribution. What would it be if the fitting is performed based on functional data analysis or Bayesian posterior draws? What if we relax strong gaussian assumption and apply robust estimation methods? It seems that modeling and estimating light curves seek more statistical touch!!!
• [astro-ph:0711.3937]
  Sequential Analysis Techniques for Correlation Studies in Particle Astronomy S.Y. BenZvi, B.M. Connolly, and S. Westerhoff
• [astro-ph:0711.4027]
  CCD Photometry of the globular cluster NGC 5466. RR Lyrae light curve decomposition and the distance scale A. A. Ferro et.al.
• [astro-ph:0711.4045]
  Fiducial Stellar Population Sequences for the u’g'r’i'z’ System J. L. Clem, D.A. VandenBerg, and P.B. Stetson
• [astro-ph:0704.0646]
  The Mathematical Universe Max Tegmark
• [stat.ME:0711.3857]
  Periodic Chandrasekhar recursions A. Aknouche and F. Hamdi
• [math.ST:0711.3834]
  On the Analytic Wavelet Transform J. M. Lilly and S. C. Olhede
• [cs.IT:0709.1211]
  Likelihood ratios and Bayesian inference for Poisson channels A. Reveillac
• [astro-ph:0711.4194]
  The Palomar Testbed Interferometer Calibrator Catalog G. T. van Belle et.al.
• [astro-ph:0711.4305]
  2MTF I. The Tully-Fisher Relation in the 2MASS J, H and K Bands Masters, Springob, and Huchra
  Standard candle problems were realizations of various regression problems.
• [astro-ph:0711.4256]
  Observational Window Functions in Planet Transit Searches K. von Braun, and D. R. Ciardi
• [astro-ph:0711.4510]
  The benefits of the orthogonal LSM models Z. Mikulasek

This paper presented an observational study of a globular cluster, named NGC 6397, enhanced and more informative compared to previous observations in a sense that 1) a truncation in the white dwarf cooling sequence occurs at 28 magnitude, 2) the cluster main sequence seems to terminate approximately at the hydrogen-burning limit predicted by two independent stellar evolution models, and 3) luminosity functions (LFs) or mass functions (MFs) are well defined. Nothing statistical, but the idea of defining color magnitude diagrams (CMDs) and LFs described in the paper, will assist developing suitable statistics on CMD and LF fitting problems in addition to the improved measurements (ACS imaging) of stars in NGC 6397.

Instead of adding details of data properties and calibration process including the instrument characteristics, I like to add a few things for statisticians: First, ACS stands for Advance Camera of Surveys and its information can be found at this link. Second, NGC is an abbreviation of New General Catalogue, one of astronomers’ cataloging systems (click for its wiki). Third, CMDs and LFs are results of data processing, described in the paper, but can be considered as scatter plots and kernel density plots (histograms) to be analyzed for inferencing physical parameters. This data processing, or calibration requires multi-level transformations, which cause error propagation. Finally, the chi-square method is incorporated to fit LFs and MFs. Among numerous fitting methods, in astronomy, only the chi-square is ubiquitously used (link to a discussion on the chi-square). Could we develop more robust statistics for fitting astronomical (empirical) functions?

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.

A traditional CMD fitting begins with building synthetic CMDs: Completeness of SDSS Data Release 5, Hess diagram (a bivariate histogram from a CMD), and features in MATCH for CMD synthesis were taken into account. The synthetic CMDs of these well known globular clusters were utilized with the observations from SDSS and compared to previous discoveries to validate their modified MATCH for the SDSS data sets. Afterwards, their method was applied to the newly discovered Milky Way satellites and discussion on their findings of these satellites was presented.

The paper provides plots that enhance the understanding of age, metalicity, and other physical parameter distributions of stellar clusters after they were fit with synthetic CMDs. The paper also describes steps and tricks (to a statistician, the process of simulating stars looks very technical without a mathematical/probabilistic justification) to acquire proper synthetic CMDs that match observations. The paper adopted Padova database of stellar evolutionary tracks and isochrones (there are other databases beyond Padova).

At last, I’d like to add a sentence from their paper, which supports my idea that a priori knowledge in choosing a proper isochrone database is necessary.

In the case of M15, this is due to the blue horizontal branch (BHB) stars that are not properly reproduced by the theoretical isochrones, causing the code to fit them as a younger turn-off.

1. Numerical methods of star formation history measurement and applications to seven dwarf spheroidals,Dolphin (2002), MNRAS, 332, p. 91