Database applications in Starlink

Davenhall, Clive

doi:10.1007/978-94-011-3250-3_18

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…The DFOT, which was installed in 2010, has two charge-coupled device ("CCD") cameras that capture images of the sky. A CCD camera is a very small microchip that contains a grid of elements that sense light (each of which is called a "pixel") [Obste,Dea01]. One of the DFOT's CCD cameras captures images of 2048 x 2048 pixels, and the other captures images of 512 x 512 pixels.…”

Section: Devasthal Fast Optical Telescope (Dfot)mentioning

confidence: 99%

Image Classification of Stars and Galaxies Using Different Machine Learning Models

Leifer

2023

Preprint

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The correct classification of astronomical objects -such as stars and galaxies -is essential to the field of astronomy. Today, however, with the advent of powerful next generation telescopes, the quantity of images being collected far exceeds the amount that can be catalogued by astronomers through their own observations and analyses alone. As just one example, the Large Synoptic Survey Telescope ("LSST"), opening in August 2024, will catalogue around 40 billion images of stars and galaxies. Certain machine learning models have proven efficient and accurate in classifying astronomical images. In this paper, we tested the ability of four different machine learning models to classify images of stars and galaxies accurately without inputs of additional measurements of the brightness, size, or shape of stars or galaxies: a convolutional neural network ("CNN") model, a logistic regression model, a random forest classifier, and a small neural network. We discuss and compare the architecture and performance of each model. We found that our neural network model trained on a data set preprocessed using a data preprocessing technique known as Principal Component Analysis ("PCA"), performed the best achieving an accuracy of 84 percent out of sample. We thus demonstrate that using such machine learning models can be an effective way to classify images of stars and galaxies, substantially reducing the time required to catalogue them.

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Section: Devasthal Fast Optical Telescope (Dfot)mentioning

confidence: 99%

Image Classification of Stars and Galaxies Using Different Machine Learning Models

Leifer

2023

Preprint

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“…The NSSDC On-Line Service (see Sec. 3.1.1) maintains a list of CD-ROMs of astronomical interest, and other compilations have been given by Davenhall (1993b) and within the MediaTheque project of CDS (Heck 1994a).…”

Section: Cd-rom Catalogs and Databasesmentioning

confidence: 99%

“…The present report arose from documentation written separately by Hanisch (1992), Andernach (1993), and. Some general references on the subject include Jaschek and Heintz (1981), Jaschek (1989), Murtagh and Heck (1988), Albrecht and Egret (1991), Heck and Murtagh (1992), Davenhall (1993bDavenhall ( , 1993c, Heck and Murtagh (1993), and Crabtree et al (1994a). While further information on many of the services presented here may be found in these texts, we have made extensive use of information presented in "gray" literature like newsletters, observatory reports, and electronic circulars.…”

Section: Introductionmentioning

confidence: 99%

Network resources for astronomers

Andernach

Hanisch

Murtagh

1994

PASP

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The amount of data produced by large observational facilities and space missions has led to the archiving and on-line accessibility of much of this data, available to the entire astronomical community. This allows a much wider multi-frequency approach to astronomical research than previously possible. Here we provide an overview of these services, and give a basic description of their contents and possibilities for accessing them. Apart from services providing observational data, many of those providing general information, e.g. on addresses, bibliographies, software etc. are also described. The eld is rapidly growing with improved network technology, and our attempt to keep the report as complete and up-to-date as possible will inevitably be outdated shortly. We will endeavor to maintain an updated version of this document on-line.

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Information-Sifting Front Ends to Databases

Adorf¹

1993

Intelligent Information Retrieval: The Case of Astronomy and Related Space Sciences

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Database applications in Starlink

Cited by 3 publications

References 1 publication

Image Classification of Stars and Galaxies Using Different Machine Learning Models

Image Classification of Stars and Galaxies Using Different Machine Learning Models

Network resources for astronomers

Information-Sifting Front Ends to Databases

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