Statistical Methodology 9 (2012) 1–3

Contents lists available at SciVerse ScienceDirect

Statistical Methodology

journal homepage: www.elsevier.com/locate/stamet

Editorial for the special issue on astrostatistics

Held regularly since 2001, the Astronomical Data Analysis conference series gathers astronomersand information scientists around the topics of astrostatistics and astroinformatics, the emergingdisciplines developing methods and tools for information extraction from astronomical data sets anddata flows. The sixth conference in the series, ADA 6, was held in May 2010 at the Kuriat Palace Hotelon the Skanes coast at Monastir in Tunisia.

This conference series has been characterized by awide range of innovative themesdrivenby front-line open problems and issues in astrophysics and cosmology, including astrophysical applicationsof advanced transforms and sparse representations (e.g., curvelet transforms and compressedsensing for image data), data mining and machine learning techniques for large survey data sets(e.g., classification of stars and galaxies), spatial process modeling and analysis (e.g., characterizationof structures in the cosmic microwave background and in the distributions of stars and galaxies),and challenging parametric and semiparametric modeling of temporal, spectral, and spatial data(e.g., Bayesian estimation and comparison of astrophysical models, and estimation with Poisson-distributed data).

The ADA 6 Conference was held additionally in honor of Albert Bijaoui, a pioneer in multipleareas of astronomical data analysis. Monastir was Bijaoui’s birthplace in 1943. After initial studiesat the Ecole Polytechnique, in 1964 he started research at Paris Observatory on the use of Lallemand’selectronic camera for astrophysical purposes. After his thesis defense in 1971, he moved to NiceObservatory and he created, with collaborators, a national center for astronomical data analysis.This team built a processing system for analyzing astronomical images, specially adapted to largeimages. Bijaoui employed this sytem towork on diverse astronomical problems, including the study ofGalactic structure and observational cosmology. In the 1980s, he introduced, withmany collaborators,the use of the wavelet transform for astronomical data analysis and image processing. This teameventually constructed a multiscale vision model incorporating matching between images, denoisingand restoration, inventory making, and reconstruction of individual components. They applied thismodel to a diverse range of problems, not only in astronomy, but also in remote sensing and biologicalimaging. Bijaoui has published extensively in international journals on electronography, data, signaland image processing, and astrophysics. In 1981 he published the book Image and Information. In 1998he published, with J.L. Starck and F. Murtagh, the book Multiscale Approach to Image Processing andData Analysis. He has taught image and data processing in astronomy at the University of Nice SophiaAntipolis from 1974 up to 2008. In parallel with his research and teaching, Bijaoui has held variousleadership positions. In particular he was head of the OCA/CNRS Cassiopée laboratory in the period2004–2007. Bijaoui is a correspondent member of the French Academy of Sciences since 1997.

Bijaoui’s influence was evident in several sessions at ADA 6. A recent focus of his research hasbeen data analysis issues associated with the upcoming Gaia mission. Due for launch in 2013, thismission will (quoting from a European Space Agency text) create the largest and most precisethree dimensional chart of our Galaxy by providing unprecedented positional and radial velocity

1572-3127/$ – see front matter© 2011 Published by Elsevier B.V.doi:10.1016/j.stamet.2011.08.002

2 Editorial / Statistical Methodology 9 (2012) 1–3

measurements for about one billion stars in our Galaxy and throughout the Local Group. A sessionat ADA 6 was dedicated to the Gaia mission, including a presentation by Bijaoui’s on behalf of hisGaia team. Other sessions included presentations on new developments in wavelet and other imageprocessing techniques that build upon foundations laid in earlierwork byBijaoui andhis collaborators.

The ADA 6 program covered a wide variety of other application areas marked by advanced astro-statistics and astroinformatics research, including asteroseismology, extrasolar planet (exoplanet) de-tection, large scale structures (weak lensing, galaxy catalogs), cosmic microwave background (CMB)data analysis (including source separation and polarization measurement); image restoration (mapmaking, deconvolution, modeling); hyperspectral data analysis; and compressed sensing.

In this Special Issue a range of articles is presented that illustrate the breadth and scope ofADA 6. Topics include fundamental methodology (Bayesian modeling and experimental design,parameter estimation with complex simulation models), image processing (deconvolution, imagefusion), astronomy applications to cosmology and eexoplanet hunting, and other diverse domainsof application including survey analysis, radio interferometry, and hyperspectral data analysis. All thearticles went through peer-reviewing process.

‘‘Deconvolution under Poisson noise using exact data fidelity and synthesis or analysis sparsitypriors’’, by F.X. Dupé, M.J. Fadili and J.L. Starck, develops a Bayesian MAP (maximum a posteriori)estimator for signal deconvolution under Poisson noise. Images are modeled sparsely in terms ofa dictionary of waveforms. The effectiveness of doing this is shown, in addition to the improvedcomputational benefits.

‘‘Astronomical image restorationusing variationalmethods andmodel combination’’, is byM.Vega,J. Mateos, R. Molina and A.K. Katsaggelos. These authors propose an image restoration method basedon a variational framework which uses several priors.

‘‘Processing MUSE hyperspectral data: denoising, deconvolution and detection of astrophysicalsources’’, by S. Bourguignon, D. Mary and E. Slezak, also describes a new restoration algorithm thatis, in the case of their work, based on sparsity, and developed for hyperspectral data sets.

‘‘Optimal Bayesian fusion of large hyperspectral astronomical observations’’, by M. Petremand,A. Jalobeanu and C. Collet, considers hyperspectral data from the perspective of data fusion, usinga Bayesian approach.

‘‘Parameter estimation from a model grid: Application to the Gaia RVS spectra’’, by A. Bijaoui,A.A. Recio-Blanco, P. de Laverny and C. Ordenovic, discusses the fitting of parametric models whenthe model is computed via computationally expensive simulations, requiring nontrivial interpolationover model grids. Such problems are arising with increasing frequency throughout astronomy; theapplication treated here is analysis of spectral data expected from the Gaia mission.

‘‘Spin-spherical harmonics from Hopf fibration: a symplectic view’’, by M. Lachièze-Rey, addressesthe following mathematical modeling that underpins data analysis. In quantum physics from thegeometrical or dynamical point of view, the Hopf fibration, a hypersphere in 4-dimensional space,is considered in terms of the sympletic group. Applications related to spherical harmonics includedata analysis on the celestial sphere such as studies of the CMB.

‘‘Towards a fast, model-independent cosmic microwave background bispectrum estimator’’, byS. Pires, S. Plaszczynski and A. Lavabre, develops a new computational approach to assessingprimordial non-Gaussianity via the impact it leaves on the spatial structure in the CMB. The ideais to characterize spatial structure beyond the (second-order) spatial correlation function by usingthe bispectrum, with calculations enabled by breaking the sky into patches that facilitate use of FFTalgorithms.

‘‘Uncertainty in 2-point correlation function estimators and baryon acoustic oscillations detectionin galaxy surveys’’, by A. Labatie, J.L. Starck, M. Lachièze-Rey and P. Arnalte-Mur, deals with large-scaled structure in the distribution of galaxies. The focus is on baryon acoustic oscillations (BAO), arelic of univers-scale sound waves in the primordial matter distribution that can provide a sort of‘‘standard ruler’’’ for cosmological measurements via features in the spatial correlation function ofthe galaxy distribution. The authors compare a variety of spatial correlation function estimators, usingsimulated data to calibrate their biases and uncertainties.

‘‘Bayesian methods for analysis and adaptive scheduling of exoplanet observations’’, byT.J. Loredo, J.O. Berger, D.F. Chernoff, M.A. Clyde, and Bin Liu, discusses a Bayesian framework for

Editorial / Statistical Methodology 9 (2012) 1–3 3

addressing a variety of statistical issues raised by exoplanet searches, including adaptive schedulingof observations. The very small periodic perturbation in a star’s motion due to the gravitational pullof an orbiting planet is what is analyzed. An overview is given of current research in the area.

As editors of this Special Issueweexpress our gratitude to the Editor-in-Chief and the editorial teamfor their support. We also express our great appreciation to our colleagues in both the astronomicaland information science communities who have served as reviewers.

Appendix. Supplementary data

Supplementary material related to this article can be found online at doi:10.1016/j.stamet.2011.08.002.

Fionn MurtaghTom Loredo

Jean-Luc Starck