Computational Intelligence in Astronomy - A Win-Win Situation

Large archives of astronomical data (images, spectra and catalogues of derived parameters) are being assembled worldwide as part of the Virtual Observatory project. In order for such massive heterogeneous data collections to be of use to astronomers, development of Computational Intelligence techniques that would combine modern machine learning with deep domain knowledge is crucial. Both fields - Computer Science and Astronomy - can hugely benefit from such a research program. Astronomers can gain new insights into structures buried deeply in the data collections that would, without the help of Computational Intelligence, stay masked. On the other hand, computer scientists can get inspiration and motivation for development of new techniques driven by the specific characteristics of astronomical data and the need to include domain knowledge in a fundamental way. In this review we present three diverse examples of such successful symbiosis.

[1]  R. Hilditch,et al.  An Introduction to Close Binary Stars: Contents , 2001 .

[2]  G. Meylan,et al.  COSMOGRAIL: the COSmological MOnitoring of GRAvItational Lenses , 2004, Proceedings of the International Astronomical Union.

[3]  Y. Wadadekar.  Estimating Photometric Redshifts Using Support Vector Machines , 2004, astro-ph/0412005.

[4]  Neil D. Lawrence,et al.  Missing Data in Kernel PCA , 2006, ECML.

[5]  D. Walsh,et al.  0957 + 561 A, B: twin quasistellar objects or gravitational lens? , 1979, Nature.

[6]  Jianyong Sun,et al.  A Fast Algorithm for Robust Mixtures in the Presence of Measurement Errors , 2007, IEEE Transactions on Neural Networks.

[7]  A. Kabán,et al.  Young stellar populations in early-type galaxies in the Sloan Digital Sky Survey , 2006, astro-ph/0608623.

[8]  Astronomy,et al.  A data-driven Bayesian approach for finding young stellar populations in early-type galaxies from their ultraviolet-optical spectra , 2005, astro-ph/0511503.

[9]  M. Irwin,et al.  The remnants of galaxy formation from a panoramic survey of the region around M31 , 2009, Nature.

[10]  J.Pelt,et al.  Time delay controversy on QSO 0957+561 not yet decided , 1994, astro-ph/9401013.

[11]  B. Pindor.  Discovering Gravitational Lenses through Measurements of Their Time Delays , 2005, astro-ph/0501518.

[12]  Peter Tiño,et al.  Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application , 2009, Pattern Recognit..

[13]  P. Murdin,et al.  Encyclopedia of Astronomy and Astrophysics , 2002 .

[14]  M. Bartelmann.  Gravitational lensing , 2010, 1010.3829.

[15]  J. Frieman,et al.  THE SLOAN DIGITAL SKY SURVEY QUASAR LENS SEARCH. II. STATISTICAL LENS SAMPLE FROM THE THIRD DATA RELEASE , 2007, 0708.0828.

[16]  Chao He,et al.  Probability Density Estimation from Optimally Condensed Data Samples , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Peter Tiño,et al.  Visualisation of tree-structured data through generative probabilistic modelling , 2007, ESANN.

[18]  Wei Xing Zheng,et al.  Improved Delay-Dependent Asymptotic Stability Criteria for Delayed Neural Networks , 2008, IEEE Transactions on Neural Networks.

[19]  Peter Tiño,et al.  A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses , 2006, ECML.

[20]  James T. Kwok,et al.  Simplifying Mixture Models Through Function Approximation , 2006, IEEE Transactions on Neural Networks.

[21]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[22]  P. Guhathakurta,et al.  Investigating the Andromeda stream — II. Orbital fits and properties of the progenitor , 2006 .

[23]  Peter Tiño,et al.  How accurate are the time delay estimates in gravitational lensing? , 2006, ArXiv.

[24]  J. Pelt,et al.  The light curve and the time delay of QSO 0957+561. , 1995, astro-ph/9501036.

[25]  School of Physics,et al.  COSMOGRAIL: The COSmological MOnitoring of GRAvItational Lenses - I. How to sample the light curves of gravitationally lensed quasars to measure accurate time delays , 2005 .

[26]  C. Lintott,et al.  Galaxy Zoo: reproducing galaxy morphologies via machine learning★ , 2009, 0908.2033.

[27]  D. Long,et al.  A Robust Determination of the Time Delay in 0957+561A, B and a Measurement of the Global Value of Hubble's Constant , 1996, astro-ph/9610162.

[28]  MIT,et al.  The Hubble Constant from Gravitational Lens Time Delays , 2003 .

[29]  Nikolaos Gianniotis,et al.  Visualisation of structured data through generative probabilistic modeling , 2008 .

[30]  Christopher M. Bishop,et al.  GTM: The Generative Topographic Mapping , 1998, Neural Computation.

[31]  Peter Tiño,et al.  Fast parzen window density estimator , 2009, 2009 International Joint Conference on Neural Networks.

[32]  William H. Press,et al.  The Time Delay of Gravitational Lens 0957+561. I. Methodology and Analysis of Optical Photometric Data , 1992 .

[33]  O. Lahav,et al.  Galaxies, Human Eyes, and Artificial Neural Networks , 1994, Science.

[34]  O. Wucknitz.  Gravitational Lensing , 2007, Large-Scale Peculiar Motions.

[35]  J. Ovaldsen,et al.  New aperture photometry of QSO 0957+561; application to time delay and microlensing , 2003, astro-ph/0308397.

[36]  Markus Harva,et al.  Bayesian Estimation of Time Delays Between Unevenly Sampled Signals , 2008, 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing.

[37]  F. Pijpers.  The determination of time delays as an inverse problem - the case of the double quasar 0957+561 , 1997 .

[38]  Pascal Vincent,et al.  Manifold Parzen Windows , 2002, NIPS.

[39]  P. Magain,et al.  A novel approach for extracting time-delays from lightcurves of lensed quasar images , 2001, astro-ph/0110668.

[40]  Peter Tiño,et al.  A generative probabilistic approach to visualizing sets of symbolic sequences , 2004, KDD '04.

[41]  W. Press,et al.  The time delay of gravitational lens 0957+561. II: Analysis of radio data and combined optical-radio analysis , 1992 .

[42]  Aapo Hyvärinen,et al.  Learning Features by Contrasting Natural Images with Noise , 2009, ICANN.

[43]  R. Hilditch.  An Introduction to Close Binary Stars , 2001 .

[44]  Peter Tiño,et al.  Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model , 2009, ICANN.

[45]  A. W. McConnachie,et al.  Investigating the Andromeda stream – III. A young shell system in M31 , 2006 .

[46]  M. Oguri.  Gravitational Lens Time Delays: A Statistical Assessment of Lens Model Dependences and Implications for the Global Hubble Constant , 2006, astro-ph/0609694.

[47]  Edwin L. Turner,et al.  The Sloan Digital Sky Survey Quasar Lens Search. I. Candidate Selection Algorithm , 2006 .

[48]  J. Hjorth,et al.  ESTIMATION OF MULTIPLE TIME DELAYS IN COMPLEX GRAVITATIONAL LENS SYSTEMS , 1998 .

[49]  S. Refsdal,et al.  On the Possibility of Determining the Distances and Masses of Stars from the Gravitational Lens Effect , 1966 .

[50]  E. Guinan,et al.  The Brave New World of Binary Star Studies , 2006 .

[51]  Sheng Chen,et al.  A Forward-Constrained Regression Algorithm for Sparse Kernel Density Estimation , 2008, IEEE Transactions on Neural Networks.