From PanSTARRS Candidates to New RR Lyraes in the K2 Mission
Áron L. Juhász László Molnár


Learning Depth from Single Images with Deep Neural Network Embedding Focal Length
    Lei He, Guanghui Wang (Senior Member, IEEE) and Zhanyi Hu 
L. He and Z. Hu are with University of Chinese Academy of Sciences, Beijing 100049, China and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. Z. Hu is also with CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China. 



E-mail: lei.he@nlpr.ia.ac.cn; huzy@nlpr.ia.ac.cn.
G. Wang is with the Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA. 

E-mail:  ghwang@ku.edu.

    December 30, 2023
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In this paper we report the discovery of 35 new RR Lyrae
variables. These stars were found by a special searching technique. We
crossmatched the catalog of the PanSTARRS (PS) sky survey with K2 space photometry data to the validate candidates. It turns out
that this technique can find eclipsing binaries as well. 




§ INTRODUCTION


The PS 3π survey contains more than 25 million
targets. <cit.> published a paper which supplements the
catalog of the survey. In their study, they improved an existing
method <cit.> to add unique RR Lyrae probabilities to each
target. They tagged nearly 400 000 stars above a 1 %
probability of being an RR Lyrae variable. More than a thousand of
them were measured by the K2 mission of Kepler. This gives
us the opportunity to search formerly unknown RR Lyrae variables using all the archive data of the first 12 fields of K2. 



§ TARGETS AND NEW VARIABLES IN THE K2 FIELD


We divided the potential targets into two categories. We estimated
that 271 stars could be near enough to the main targets to appear on
the target pixel files (TPF) as background stars. From these we were able to identify new RR Lyraes and also
made light curves. We examined 939 main targets from K2 data. These are usually known stars but
some of them were misclassified. Our main goal was to find new
RR Lyraes in the target images of K2.  

First, we examined the 271 background targets. We paired the coordinates 
of the PS targets with the K2 TPFs, and then we 
performed photometry of these stars with the PyKE
software <cit.>. We found 59 RR Lyraes and 21 among of them were not previously cataloged. The results shown on Table <ref>.  

Secondly, based on the directly measured 939 K2 targets, we
found 779 RR Lyraes (Table <ref>). We identified 14 new RR Lyraes
among these formerly known stars. These targets were misclassified as
mostly eclipsing binaries but the K2 data show clearly RR Lyrae
shaped light curves.  





§ FIELD 0 – THE CASE OF THE ANTI-CENTER


RR Lyraes appear in the Galactic disk but mostly in the direction of
the Bulge. Nonetheless, the PS data contain numerous candidates in the direction of the Galactic anti-center. The first observing zone of the K2 mission (Field 0) partially covered this area. The Galactic
plane contains a lot of binaries at all Galactic longitudes. The
majority of K2 observed PS sources in Field 0 also turned out to
be eclipsing binaries, suggesting that the large density of disk
RR Lyrae candidates in the PS catalog may come from confusion between
these two sources. The second iteration of the catalog
<cit.> excluded the Galactic plane with high extinction,
and thus removed the most problematic areas of the survey. 




§ SUMMARY


Using the K2 archive data, we revealed the true nature of 14
previously misclassified RR Lyraes and found 21 brand new ones (19
RRab, 2 RRc). Furthermore, we identified numerous new eclipsing
binaries. We found that among the PS false positive targets, eclipsing
binaries appear frequently. Based on K2 Field 0 the Galactic
disk could be particularly overrepresented with these impostors. It
suggests that the PS catalog also provides the opportunity to find
binaries in a similar way. 

The research have been supported by the the
  ÚNKP-17-3 program of the Ministry of Human Capacities of Hungary,
  and the Hungarian National Research, Development and Innovation
  Office (NKFIH) grants K-115709, PD-116175, and the Lendület
  LP2014-17 grant of the Hungarian Academy of Sciences. LM was
  supported by the János Bolyai Research Scholarship of the
  Hungarian Academy of Sciences. 

ptapap


