Data Modeling of deep sky images

James Handley; Holger Jaenisch; Albert Lim; Graeme White; Alex Hons; Miroslav Filipovic; Matthew Edwards

doi:10.1117/12.548791

Data Modeling of deep sky images

James Handley
, Holger Jaenisch
, Albert Lim
, Graeme White
, Alex Hons
, Miroslav Filipovic
, Matthew Edwards

School of Science

James Cook University Queensland
Alabama A and M University

Research output: Contribution to journal › Conference article › peer-review

6 Citations (Scopus)

Abstract

We present a method for simulating CCD focal plane array (FPA) images of extended deep sky objects using Data Modeling. Data Modeling is a process of deriving functional equations from measured data. These tools are used to model FPA fixed pattern noise, shot noise, non-uniformity, and the extended objects themselves. The mathematical model of the extended object is useful for correlation analysis and other image understanding algorithms used in Virtual Observatory Data Mining. We apply these tools to the objects in the Messier list and build a classifier that achieves 100% correct classification.

Original language	English
Article number	50
Pages (from-to)	449-460
Number of pages	12
Journal	Proceedings of SPIE: The International Society for Optical Engineering
Volume	5497
DOIs	https://doi.org/10.1117/12.548791
Publication status	Published - 2004
Event	Modeling and Systems Engineering for Astronomy - Glasgow, United Kingdom Duration: 24 Jun 2004 → 25 Jun 2004

Keywords

Astronomy
Component modeling
Data Modeling
Deep-sky
Functional modeling
Image modeling
Messier classification
Noise modeling
Virtual Observatory

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10.1117/12.548791

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@article{4c9a2624205c43499d36bd041f276d08,

title = "Data Modeling of deep sky images",

abstract = "We present a method for simulating CCD focal plane array (FPA) images of extended deep sky objects using Data Modeling. Data Modeling is a process of deriving functional equations from measured data. These tools are used to model FPA fixed pattern noise, shot noise, non-uniformity, and the extended objects themselves. The mathematical model of the extended object is useful for correlation analysis and other image understanding algorithms used in Virtual Observatory Data Mining. We apply these tools to the objects in the Messier list and build a classifier that achieves 100\% correct classification.",

keywords = "Astronomy, Component modeling, Data Modeling, Deep-sky, Functional modeling, Image modeling, Messier classification, Noise modeling, Virtual Observatory",

author = "James Handley and Holger Jaenisch and Albert Lim and Graeme White and Alex Hons and Miroslav Filipovic and Matthew Edwards",

year = "2004",

doi = "10.1117/12.548791",

language = "English",

volume = "5497",

pages = "449--460",

journal = "Proceedings of SPIE: The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

note = "Modeling and Systems Engineering for Astronomy ; Conference date: 24-06-2004 Through 25-06-2004",

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TY - JOUR

T1 - Data Modeling of deep sky images

AU - Handley, James

AU - Jaenisch, Holger

AU - Lim, Albert

AU - White, Graeme

AU - Hons, Alex

AU - Filipovic, Miroslav

AU - Edwards, Matthew

PY - 2004

Y1 - 2004

N2 - We present a method for simulating CCD focal plane array (FPA) images of extended deep sky objects using Data Modeling. Data Modeling is a process of deriving functional equations from measured data. These tools are used to model FPA fixed pattern noise, shot noise, non-uniformity, and the extended objects themselves. The mathematical model of the extended object is useful for correlation analysis and other image understanding algorithms used in Virtual Observatory Data Mining. We apply these tools to the objects in the Messier list and build a classifier that achieves 100% correct classification.

AB - We present a method for simulating CCD focal plane array (FPA) images of extended deep sky objects using Data Modeling. Data Modeling is a process of deriving functional equations from measured data. These tools are used to model FPA fixed pattern noise, shot noise, non-uniformity, and the extended objects themselves. The mathematical model of the extended object is useful for correlation analysis and other image understanding algorithms used in Virtual Observatory Data Mining. We apply these tools to the objects in the Messier list and build a classifier that achieves 100% correct classification.

KW - Astronomy

KW - Component modeling

KW - Data Modeling

KW - Deep-sky

KW - Functional modeling

KW - Image modeling

KW - Messier classification

KW - Noise modeling

KW - Virtual Observatory

UR - https://www.scopus.com/pages/publications/10444226711

U2 - 10.1117/12.548791

DO - 10.1117/12.548791

M3 - Conference article

AN - SCOPUS:10444226711

SN - 0277-786X

VL - 5497

SP - 449

EP - 460

JO - Proceedings of SPIE: The International Society for Optical Engineering

JF - Proceedings of SPIE: The International Society for Optical Engineering

M1 - 50

T2 - Modeling and Systems Engineering for Astronomy

Y2 - 24 June 2004 through 25 June 2004

ER -

Data Modeling of deep sky images

Abstract

Keywords

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