2015
DOI: 10.1088/0004-637x/814/2/92
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SPITZERIRAC PHOTOMETRY FOR TIME SERIES IN CROWDED FIELDS

Abstract: We develop a new photometry algorithm that is optimized for Spitzer time series in crowded fields and that is particularly adapted to faint and/or heavily blended targets. We apply this to the 170 targets from the 2015 Spitzer microlensing campaign and present the results of three variants of this algorithm

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Cited by 61 publications
(79 citation statements)
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“…The event was indeed faint in Spitzer images, reaching = L 15.9 eff , fainter than the assumed sensitivity limit. However, the data were reduced using the new algorithm for Spitzer photometry in crowded fields (Calchi Novati et al 2015b), resulting in the required high precision. It is important to note that while the peak L eff was similar to the estimation based on the ground-based light curve, it occurred at a later time and with lower magnification than predicted.…”
Section: Spitzer Observationsmentioning
confidence: 99%
“…The event was indeed faint in Spitzer images, reaching = L 15.9 eff , fainter than the assumed sensitivity limit. However, the data were reduced using the new algorithm for Spitzer photometry in crowded fields (Calchi Novati et al 2015b), resulting in the required high precision. It is important to note that while the peak L eff was similar to the estimation based on the ground-based light curve, it occurred at a later time and with lower magnification than predicted.…”
Section: Spitzer Observationsmentioning
confidence: 99%
“…Since this requirement was met, these observations were initiated, and were ultimately conducted during the period 7930.5-7966.9 (∼36.4 days), with both dates set essentially by the spacecraft's Sun-angle restrictions. Spitzer images were taken in the 3.6 μm channel of the IRAC camera, and the data were reduced with a specially developed version of point response function photometry (Calchi Novati et al 2015b). …”
Section: Observations and Datamentioning
confidence: 99%
“…The Spitzer data were reduced using the algorithm specialized for Spitzer photometry in crowded fields (Calchi Novati et al 2015b). For the individual data sets, we readjust error bars by…”
Section: Observationmentioning
confidence: 99%