Liger is a next-generation near-infrared imager and integral field spectrograph (IFS) for the W.M. Keck Obser- vatory designed to take advantage of the Keck All-Sky Precision Adaptive Optics (KAPA) upgrade. Liger will operate at spectral resolving powers between R~4,000 - 10,000 over a wavelength range of 0.8-2.4µm. Liger takes advantage of a sequential imager and spectrograph design that allows for simultaneous observations between the two channels using the same filter wheel and cold pupil stop. We present the design for the filter wheels and pupil mask and their location and tolerances in the optical design. The filter mechanism is a multi-wheel de- sign drawing from the heritage of the current Keck/OSIRIS imager single wheel design. The Liger multi-wheel configuration is designed to allow future upgrades to the number and range of filters throughout the life of the instrument. The pupil mechanism is designed to be similarly upgradeable with the option to add multiple pupil mask options. A smaller wheel mechanism allows the user to select the desired pupil mask with open slots being designed in for future upgrade capabilities. An ideal pupil would match the shape of the image formed of the primary and would track its rotation. For different pupil shapes without tracking we model the additional exposure time needed to achieve the same signal to noise of an ideal pupil and determine that a set of fixed masks of different shapes provides a mechanically simpler system with little compromise in performance.
Because of bright starlight leakage in coronagraphic raw images, faint astrophysical objects such as exoplanets can only be detected using powerful point spread function (PSF) subtraction algorithms. However, these algorithms have strong effects on faint objects of interest, and often prevent precise spectroscopic analysis and scattering property measurements of circumstellar disks. For this reason, PSF-subtraction effects is currently the main limitations to the precise characterization of exoplanetary dust with scattered-light imaging. Forward modeling techniques have long been developed for point source objects (Pueyo 2016). However, Forward Modeling with disks is complicated by the fact that the disk cannot be simplified using a simple point source convolved by the PSF as the astrophysical model; all hypothetical disk morphologies must be explored to understand the subtle and non-linear effects of the PSF subtraction algorithm on the shape and local geometry of these systems. Because of their complex geometries, the forward-modeling process has to be repeated tens or hundred of thousands of times on disks with slightly different physical properties. All of these geometries are then compared to the PSF-subtracted image of the data, within an MCMC or a Chi-square wrapper. In this paper, we present here DiskFM, a new open-source algorithm included in the PSF subtraction algo- rithms package pyKLIP. This code allows to produce fast forward-modeling for a variety of observation strategies (ADI, SDI, ADI+SDI, RDI). pyKLIP has already been used for SPHERE/IRDIS and GPI data. It is readily available on all instruments supported by pyKLIP (SPHERE/IFS, SCExAO/CHARIS), and can be quickly adapted for other coronagraphic instruments.
Characterization of an instrument’s detector is an essential part of assessing the overall performance and ca- pabilities of an instrument. We present our efforts to characterize the HAWAII-2RG detector on the imager component of the OSIRIS instrument at W. M. Keck Observatory. In particular, we will report the detector’s read noise, dark current, linearity, and persistence. We find a gain of 2.16 ± 0.34, in good agreement with Teledyne’s reported gain of 2.15. The maximum read noise of the detector is 23.4 ± 1.3 e- decreasing with an increasing number of reads. We find an upper limit on the dark current of the detector to be < 0.021 e-/pix/s. The detector is also linear to the 1% level up to 44,500 e- and to the 5% level at 80,000 e-. The maximum well depth is measured to be 119,000 e-.
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multi-year direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the GPIES Data Cruncher, combines multiple data reduction pipelines together to intelligently process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow-up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our data reduction pipelines. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real-time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.
The Gemini Planet Imager has been successfully obtaining images and spectra of exoplanets, brown dwarfs, and debris and protoplanetary circumstellar disks using its integral field spectrograph and polarimeter. GPI observations are transformed from raw data into high-quality astrometrically and photometrically calibrated datacubes using the GPI Data Reduction Pipeline, an open-source software framework continuously developed by our team and available to the community. It uses a flexible system of reduction recipes composed of individual primitive steps, allowing substantial customization of processing depending upon science goals. This paper provides a broad overview of the GPI pipeline, summarizes key lessons learned, and describes improved calibration methods and new capabilities available in the latest version. Enhanced automation better supports observations at the telescope with streamlined and rapid data processing, for instance through real-time assessments of contrast performance and more automated calibration file processing. We have also incorporated the GPI Data Reduction Pipeline as one component in a larger automated data system to support the GPI Exoplanet Survey campaign, while retaining its flexibility and stand-alone capabilities to support the broader GPI observer community. Several accompanying papers describe in more detail specific aspects of the calibration of GPI data in both spectral and polarimetric modes.
The design and performance of astronomical instruments depend critically on the total system throughput as well as the background emission from the sky and instrumental sources. In designing a pupil stop for background- limited imaging, one seeks to balance throughput and background rejection to optimize measurement signal-to-noise ratios. Many sources affect transmission and emission in infrared imaging behind the Keck Observatory’s adaptive optics systems, such as telescope segments, segment gaps, secondary support structure, and AO bench optics. Here we describe an experiment, using the pupil-viewing mode of NIRC2, to image the pupil plane as a function of wavelength. We are developing an empirical model of throughput and background emission as a function of position in the pupil plane. This model will be used in part to inform the optimal design of cold pupils in future instruments, such as the new imaging camera for OSIRIS.
The Gemini Planet Imager (GPI) is a high-contrast instrument specially designed for direct imaging and spectroscopy of exoplanets and debris disks. GPI can also operate as a dual-channel integral field polarimeter. The instrument primarily operates in a coronagraphic mode which poses an obstacle for traditional photometric calibrations since the majority of on-axis starlight is blocked. To enable accurate photometry relative to the occulted central star, a diffractive grid in a pupil plane is used to create a set of faint copies, named satellite spots, of the occulted star at specified locations and relative intensities in the field of view. We describe the method we developed to perform the photometric calibration of coronagraphic observations in polarimetry mode using these fiducial satellite spots. With the currently available data, we constrain the calibration uncertainty to be <13%, but the actual calibration uncertainty is likely to be lower. We develop the associated calibration scripts in the GPI Data Reduction Pipeline, which is available to the public. For testing, we use it to photometrically calibrate the HD 19467 B and β Pic b data sets taken in the H-band polarimetry mode. We measure the calibrated flux of HD 19467 B and β Pic b to be 0:078±0:011 mJy and 4:87±0:73 mJy, both agreeing with other measurements found in the literature. Finally, we explore an alternative method which performs the calibration by scaling the photometry in polarimetry mode to the photometrically calibrated response in spectroscopy mode. By comparing the reduced observations in raw units, we find that observations in polarimetry mode are 1:03 0:01 brighter than those in spectroscopy mode.