Auto-guiding Performance from IGRINS Test Observations (Immersion GRating INfrared Spectrograph)

  • Lee, Hye-In (School of Space Research, Kyung Hee University) ;
  • Pak, Soojong (School of Space Research, Kyung Hee University) ;
  • Le, Huynh Anh N. (School of Space Research, Kyung Hee University) ;
  • Kang, Wonseok (National Youth Space Center) ;
  • Mace, Gregory (Department of Astronomy, the University of Texas) ;
  • Pavel, Michael (Department of Astronomy, the University of Texas) ;
  • Jaffe, Daniel T. (Department of Astronomy, the University of Texas) ;
  • Lee, Jae-Joon (Korea Astronomy & Space Science institute) ;
  • Kim, Hwihyun (Korea Astronomy & Space Science institute) ;
  • Jeong, Ueejeong (Korea Astronomy & Space Science institute) ;
  • Chun, Moo-Young (Korea Astronomy & Space Science institute) ;
  • Park, Chan (Korea Astronomy & Space Science institute) ;
  • Yuk, In-Soo (Korea Astronomy & Space Science institute) ;
  • Kim, Kangmin (Korea Astronomy & Space Science institute)
  • Published : 2014.10.13

Abstract

In astronomical spectroscopy, stable auto-guiding and accurate target centering capabilities are critical to increase the achievement of high observation efficiency and sensitivity. We developed an instrument control software for the Immersion GRating INfrared Spectrograph (IGRINS), a high spectral resolution near-infrared slit spectrograph with (R=40,000). IGRINS is currently installed on the McDonald 2.7 m telescope in Texas, USA. We had successful commissioning observations in March, May, and July of 2014. The role of the IGRINS slit-viewing camera (SVC) is to move the target onto the slit, and to provide feedback about the tracking offsets for the auto-guiding. For a point source, we guide the telescope with the target on the slit. While for an extended source, we use another a guide star in the field offset from the slit. Since the slit blocks the center of the point spread function, it is challenging to fit the Gaussian function to guide and center the target on slit. We developed several center finding algorithms, e.g., 2D-Gaussian Fitting, 1D-Gaussian Fitting, and Center Balancing methods. In this presentation, we show the results of auto-guiding performances with these algorithms.

Keywords