2009
DOI: 10.1111/j.1365-2966.2009.15098.x
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Difference imaging photometry of blended gravitational microlensing events with a numerical kernel

Abstract: Accepted by MNRAS, 8 pages, 4 figuresInternational audienceThe numerical kernel approach to difference imaging has been implemented and applied to gravitational microlensing events observed by the PLANET collaboration. The effect of an error in the source-star coordinates is explored and a new algorithm is presented for determining the precise coordinates of the microlens in blended events, essential for accurate photometry of difference images. It is shown how the photometric reference flux need not be measur… Show more

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Cited by 205 publications
(153 citation statements)
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“…All reductions for the light-curve analysis were conducted using variants of difference image analysis (DIA; Alard & Lupton 1998), specifically Woźniak (2000) and Albrow et al (2009).…”
Section: Full Kepler Orbits In Microlensingmentioning
confidence: 99%
“…All reductions for the light-curve analysis were conducted using variants of difference image analysis (DIA; Alard & Lupton 1998), specifically Woźniak (2000) and Albrow et al (2009).…”
Section: Full Kepler Orbits In Microlensingmentioning
confidence: 99%
“…OGLE data were reduced using the OGLE Difference Image Analysis software and optimal centroid method (Wozniak 2000;Udalski 2003). The μFUN photometry was produced using a modified version of the PySIS package (Albrow et al 2009). Robonet data were reduced using the DanDIA package (Bramich 2008).…”
Section: Data Reductionmentioning
confidence: 99%
“…RoboNet/LCOGT data were reduced using an automatic image subtraction package, and reduced again off-line. PLANET telescopes also use image subtraction: at telescope an on-line version called WISIS, based on Alard's ISIS package, then off-line version 3.0 of pySIS (Albrow et al 2009), based on a numerical kernel (Bramich 2008). SAAO I photometry obtained with pySIS has been checked independently using a DIA package.…”
Section: Data Sets: Observations and Data Reductionsmentioning
confidence: 99%