Automated Extended Aperture Photometry for K2 RR Lyrae stars
Emese Plachy, Pál Szabó, Attila Bódi, László Molnár, Róbert Szabó
AAutomated Extended Aperture Photometry for K2 RR Lyrae stars
Emese Plachy, , Pál Szabó, , , Attila Bódi, , , László Molnár, , andRóbert Szabó , Konkoly Observatory, Research Centre for Astronomy and Earth Sciences,Konkoly Thege Miklós út 15-17, H-1121 Budapest MTA CSFK Lendület Near-Field Cosmology Research Group; [email protected]
Abstract.
Light curves for RR Lyrae stars can be di ffi cult to obtain properly in theK2 mission due to the similarities between the timescales of the observed physical phe-nomena and the instrumental signals appearing in the data. We developed a new photo-metric method called Extended Aperture Photometry (EAP), a key element of which isto extend the aperture to an optimal size to compensate for the motion of the telescopeand to collect all available flux from the star before applying further corrections. Wedetermined the extended apertures for individual stars by hand so far. Now we managedto automate the pipeline that we intend to use for the nearly four thousand RR Lyraetargets observed in the K2 mission. We present the outline of our pipeline and makesome comparisons to other photometric solutions.
1. The autoEAP pipeline
The automated EAP pipeline is based on the EAP method (Plachy et al. 2019) andconsist of four basic steps. First we define initial stellar apertures with the astropy package. Then we find the threshold to the number of times a given pixel is assigned toan aperture so that the highest number of stars are identified on the images. The thirdstep is to create light curves for all stars and identify the RR Lyrae variable from theFourier parameters. Finally we generate a new set of apertures and select a new thresh-old which assigns the largest aperture to the RR Lyrae star whe the highest number ofstars are detected in the image. The other apertures are then discarded. Steps may berepeated until only a single star is selected.After we selected the aperture and generated the light curve, we apply the K2 Sys-tematics Correction, or K2SC method (Aigrain et al. 2015, 2016), which can separatethe pulsation from systematics e ff ectively.
2. Comparison with other pipelines
We compared our results to other pipelines: the SAP and PDCSAP light curves (VanCleve et al. 2016), plus the K2SFF (Vanderburg & Johnson 2014) and EVEREST(Luger et al. 2017) light curves. Three example stars with decreasing luminositiesare presented in Fig. 1 (left to right). The last row shows the autoEAP solutions. In1 a r X i v : . [ a s t r o - ph . I M ] S e p Plachy et al. the high-luminosity case, the quality of the autoEAP light curve and EVEREST rawflux are comparable, though the former is slightly better. By visual inspection of themedium-luminosity star, autoEAP o ff ers the best light curves, but on the other hand, inthe low luminosity case, autoEAP quality is comparable to that of EVEREST again. Itis our general observation that to maximize the likelihood of choosing a good-qualitylight curve, autoEAP o ff ers the best choice.
3. Future plans
We are preparing the autoEAP light curves for the RR Lyrae stars in the K2 missionand will release the open-source autoEAP code as well. The code might be useful fornot only RR Lyrae stars but other high-amplitude variable stars as well.
Figure 1. Comparison of the pipelines. Gray: raw data; colored: corrected data.
Acknowledgments.