LSST Instrument Concept

Starr, Barry Michael; Claver, Charles F.; Wolff, Sidney C.; Lesser, Michael P.; Daggert, Larry; Dominguez, Ruben; Gomez, R. Richard; Muller, Gary

doi:10.1117/12.457331

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…At the behest of the National Science Foundation (NSF) astronomy division, NOAO set up a national committee in September 2002, with Michael Strauss as chair, to develop the LSST design reference mission ). Plans for a Gigapixel focal plane (Starr et al 2002), as well as initial designs for the telescope-camera-data system (Tyson 2002), were presented in 2002.…”

Section: The History Of the Ideamentioning

confidence: 99%

LSST Science Book, Version 2.0

Abell¹,

Burke²,

Hamuy³

et al. 2009

553

653

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Prepared by the LSST Science Collaborations, with contributions from the LSST Project. PrefaceMajor advances in our understanding of the Universe over the history of astronomy have often arisen from dramatic improvements in our ability to observe the sky to greater depth, in previously unexplored wavebands, with higher precision, or with improved spatial, spectral, or temporal resolution. Aided by rapid progress in information technology, current sky surveys are again changing the way we view and study the Universe, and the next-generation instruments, and the surveys that will be made with them, will maintain this revolutionary progress. Substantial progress in the important scientific problems of the next decade (determining the nature of dark energy and dark matter, studying the evolution of galaxies and the structure of our own Milky Way, opening up the time domain to discover faint variable objects, and mapping both the inner and outer Solar System) all require wide-field repeated deep imaging of the sky in optical bands.The wide-fast-deep science requirement leads to a single wide-field telescope and camera which can repeatedly survey the sky with deep short exposures. The Large Synoptic Survey Telescope (LSST), a dedicated telecope with an effective aperture of 6.7 meters and a field of view of 9.6 deg 2 , will make major contributions to all these scientific areas and more. It will carry out a survey of 20,000 deg 2 of the sky in six broad photometric bands, imaging each region of sky roughly 2000 times (1000 pairs of back-to-back 15-sec exposures) over a ten-year survey lifetime.The LSST project will deliver fully calibrated survey data to the United States scientific community and the public with no proprietary period. Near real-time alerts for transients will also be provided worldwide. A goal is worldwide participation in all data products. The survey will enable comprehensive exploration of the Solar System beyond the Kuiper Belt, new understanding of the structure of our Galaxy and that of the Local Group, and vast opportunities in cosmology and galaxy evolution using data for billions of distant galaxies. Since many of these science programs will involve the use of the world's largest non-proprietary database, a key goal is maximizing the usability of the data. Experience with previous surveys is that often their most exciting scientific results were unanticipated at the time that the survey was designed; we fully expect this to be the case for the LSST as well.The purpose of this Science Book is to examine and document in detail science goals, opportunities, and capabilities that will be provided by the LSST. The book addresses key questions that will be confronted by the LSST survey, and it poses new questions to be addressed by future study. It contains previously available material (including a number of White Papers submitted to the ASTRO2010 Decadal Survey) as well as new results from a year-long campaign of study and evaluation. This book does not attempt to be complete; there are many ...

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Section: The History Of the Ideamentioning

confidence: 99%

LSST Science Book, Version 2.0

Abell¹,

Burke²,

Hamuy³

et al. 2009

553

653

View full text Add to dashboard Cite

show abstract

“…For k-NN, we choose k = 1 following [23], who have published results on the shape data used in this paper. 4 When we use explicit features, we use a linear kernel. When we use cross-correlation or our kernel in addition to explicit features, we simply add the result of the inner product of the explicit features to the value of the cross-correlation or kernel.…”

Section: Methodsmentioning

confidence: 99%

“…For example, microlensing surveys, such as MACHO [1] and OGLE [2] followed millions of stars for a decade taking one observation per night. The next generation panoramic surveys, such as Pan-STARRS [3] and LSST [4], will begin in 2009 and 2013, respectively, and will collect data on the order of hundreds of billions of stars. It is unreasonable to attempt manual analysis of this data, and there is an immediate need for robust, automatic classification methods.…”

Section: Introductionmentioning

confidence: 99%

Kernels for Periodic Time Series Arising in Astronomy

Wachman

Khardon

Protopapas

et al. 2009

Machine Learning and Knowledge Discovery in Databases

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Abstract. We present a method for applying machine learning algorithms to the automatic classification of astronomy star surveys using time series of star brightness. Currently such classification requires a large amount of domain expert time. We show that a combination of phase invariant similarity and explicit features extracted from the time series provide domain expert level classification. To facilitate this application, we investigate the cross-correlation as a general phase invariant similarity function for time series. We establish several theoretical properties of cross-correlation showing that it is intuitively appealing and algorithmically tractable, but not positive semidefinite, and therefore not generally applicable with kernel methods. As a solution we introduce a positive semidefinite similarity function with the same intuitive appeal as cross-correlation. An experimental evaluation in the astronomy domain as well as several other data sets demonstrates the performance of the kernel and related similarity functions.

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“…However, the number of stars in such surveys is increasing dramatically. For example Pan-STARRS (Hodapp et al, 2004) and LSST (Starr et al, 2002) collect data on the order of hundreds of billions of stars. Therefore, it is desirable to apply state-of-art machine learning techniques to enable automatic processing for astrophysics data classification.…”

Section: Classification On Astrophysics Datamentioning

confidence: 99%

Shift-Invariant Grouped Multi-task Learning for Gaussian Processes

Wang

Khardon

Protopapas

2010

Machine Learning and Knowledge Discovery in Databases

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Abstract.Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task is a phase-shifted periodic time series. In particular, we develop a novel Bayesian nonparametric model capturing a mixture of Gaussian processes where each task is a sum of a group-specific function and a component capturing individual variation, in addition to each task being phase shifted. We develop an efficient em algorithm to learn the parameters of the model. As a special case we obtain the Gaussian mixture model and em algorithm for phased-shifted periodic time series. Experiments in regression, classification and class discovery demonstrate the performance of the proposed model using both synthetic data and real-world time series data from astrophysics. Our methods are particularly useful when the time series are sparsely and non-synchronously sampled.

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LSST Instrument Concept

Cited by 14 publications

References 13 publications

LSST Science Book, Version 2.0

LSST Science Book, Version 2.0

Kernels for Periodic Time Series Arising in Astronomy

Shift-Invariant Grouped Multi-task Learning for Gaussian Processes

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