Surface Preparation and Examination

Connolly, T. F.

doi:10.1007/978-1-4684-6207-4_1

Cited by 2 publications

(5 citation statements)

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“…Similar spectacular speed-ups will be achieved for the 3 and 4-point functions and we will report these results elsewhere (Connolly et al 2001). Furthermore, controlled approximations can further accelerate the computations by several orders of magnitude.…”

Section: Discussionsupporting

confidence: 76%

“…We plan to present a more detailed discussion of the techniques presented here in a forthcoming paper (Connolly et al 2001). That paper will also include a full analysis of the computational speed and overhead of our N -point correlation function algorithm and compare those with existing software for computing the higher-order correlation functions e.g.…”

Section: Discussionmentioning

confidence: 99%

“…A further speed-up can be obtained by simultaneously computing the DualTreeCount(n root , n root , s lo , s hi ) over a series of bins. We will discuss this in further detail in Connolly et al (2001).…”

Section: The Dual Tree Approach To Two-point Computationmentioning

confidence: 99%

“…The algorithm presented here was developed as part of the "Computational AstroStatistics" collaboration (see Nichol et al 2000) and is a member of a family of algorithms for a very general class of statistical computations, including nearest-neighbor methods, kernel density estimation, and clustering. The work presented here was initially presented by Gray & Moore (2001) and will soon be discussed in a more substantial paper by Connolly et al (2001). In this conference proceeding, we provide a brief review of kd-trees (Section 2), a discussion of the use of kd-trees in range searches (Section 3), an overview of the development of a fast N -point correlation function code (Section 4) as well as presenting the concept of controlled approximations in the calculation of the correlation function (Section 5).…”

Section: Introductionmentioning

confidence: 99%

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Fast Algorithms and Efficient Statistics: N-Point Correlation Functions

Moore

Connolly

Genovese

et al.

Eso Astrophysics Symposia

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We present here a new algorithm for the fast computation of N -point correlation functions in large astronomical data sets. The algorithm is based on kdtrees which are decorated with cached sufficient statistics thus allowing for orders of magnitude speed-ups over the naive non-tree-based implementation of correlation functions. We further discuss the use of controlled approximations within the computation which allows for further acceleration. In summary, our algorithm now makes it possible to compute exact, all-pairs, measurements of the 2, 3 and 4-point correlation functions for cosmological data sets like the Sloan Digital Sky Survey (SDSS; York et al. 2000) and the next generation of Cosmic Microwave Background experiments (see .

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Section: The Dual Tree Approach To Two-point Computationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Algorithms and Efficient Statistics: N-Point Correlation Functions

Moore

Connolly

Genovese

et al.

Eso Astrophysics Symposia

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“…The analysis we describe provides an optimal noise suppression that, when combined with the redshift determination, will produce a high signal-to-noise representation of noisy and incomplete data together with an associated error estimate. In a following paper (Connolly and Szalay 1999) we describe the implementation of our techniques for constructing spectral energy distributions and classifying galaxy spectra using redshift survey data.…”

Section: Discussionmentioning

confidence: 99%

A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data

Connolly

Szalay

1999

The Astronomical Journal

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Over the next few years new spectroscopic surveys (from the optical surveys of the Sloan Digital Sky Survey and the 2 degree Field survey through to space-based ultraviolet satellites such as GALEX) will provide the opportunity and challenge of understanding how galaxies of different spectral type evolve with redshift. Techniques have been developed to classify galaxies based on their continuum and line spectra. Some of the most promising of these have used the Karhunen and Loéve transform (or Principal Component Analysis) to separate galaxies into distinct classes. Their limitation has been that they assume that the spectral coverage and quality of the spectra are constant for all galaxies within a given sample. In this paper we develop a general formalism that accounts for the missing data within the observed spectra (such as the removal of sky lines or the effect of sampling different intrinsic rest wavelength ranges due to the redshift of a galaxy). We demonstrate that by correcting for these gaps we can recover an almost redshift independent classification scheme. From this classification we can derive an optimal interpolation that reconstructs the underlying galaxy spectral energy distributions in the regions of missing data.This provides a simple and effective mechanism for building galaxy spectral energy distributions directly from data that may be noisy, incomplete or drawn from a number of different sources.

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Surface Preparation and Examination

Cited by 2 publications

References 0 publications

Fast Algorithms and Efficient Statistics: N-Point Correlation Functions

Fast Algorithms and Efficient Statistics: N-Point Correlation Functions

A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data

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