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Linearization of Spitzer IRS Data Via Minimization of χ^2 With Correlated Errors

Fowler, John W. (2005) Linearization of Spitzer IRS Data Via Minimization of χ^2 With Correlated Errors. In: Astronomical Data Analysis Software and Systems XIV. ASP Conference Series. No.347. Astronomical Society of the Pacific , San Francisco, CA, pp. 449-453. ISBN 1-58381-215-6.

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The Spitzer Infrared Spectrograph (IRS) data are taken via read-without-reset measurements to obtain multiple samples forming a photometric "ramp" for each pixel in an echellogram. Each ramp is linearized via a quadratic model. After linearization, a quality-assurance test is performed to determine how linear each pixel's ramp has become. This is accomplished by fitting a straight line to the ramp via χ^2 minimization. The goodness of fit is of primary importance, since this determines whether the inevitable deviations from linearity are statistically significant given the estimated photometric noise. Because the latter is dominated by photon noise which is summed up the ramp, the χ^2 parameter used to measure goodness of fit must include the effects of correlated errors. This paper describes the construction of the full error covariance matrix and its use in the χ^2 minimization.

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Additional Information:© 2005 Astronomical Society of the Pacific. This work was performed at the Spitzer Science Center as part of a mission/project managed by Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
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Series Name:ASP Conference Series
Issue or Number:347
Record Number:CaltechAUTHORS:20110810-111101327
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:24785
Deposited By: Tony Diaz
Deposited On:15 Sep 2011 20:55
Last Modified:03 Oct 2019 02:59

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