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Register a new userorbitize!: A Comprehensive Orbit-fitting Software Package for the High-contrast Imaging Community
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orbitize! is an open-source, object-oriented software package for fitting the orbits of directly-imaged objects. It packages the Orbits for the Impatient (OFTI) algorithm and a parallel-tempered Markov Chain Monte Carlo (MCMC) algorithm into a consistent and intuitive Python API. orbitize! makes it easy to run standard astrometric orbit fits; in less than 10 lines of code, users can read in data, perform one fit using OFTI and another using MCMC, and make two publication-ready figures. Extensive pedagogical tutorials, intended to be navigable by both orbit-fitting novices and seasoned experts, are available on our documentation page. We have designed the orbitize! API to be flexible and easy to use/modify for unique cases. orbitize! was designed by members of the exoplanet imaging community to be a central repository for algorithms, techniques, and know-how developed by this community. We intend for it to continue to expand and change as the field progresses and new techniques are developed, and call for community involvement in this process. Complete and up-to-date documentation is available at orbitize.info.
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We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical high-contrast imaging. Our package relies on the extensive python stack of scientific libraries and aims to provide a flexible framework for high-contrast data and image processing. In this paper, we describe the capabilities of VIP related to processing image sequences acquired using the angular differential imaging (ADI) observing technique. VIP implements functionalities for building high-contrast data processing pipelines, encompass- ing pre- and post-processing algorithms, potential sources position and flux estimation, and sensitivity curves generation. Among the reference point-spread function subtraction techniques for ADI post-processing, VIP includes several flavors of principal component analysis (PCA) based algorithms, such as annular PCA and incremental PCA algorithm capable of processing big datacubes (of several gigabytes) on a computer with limited memory. Also, we present a novel ADI algorithm based on non-negative matrix factorization (NMF), which comes from the same family of low-rank matrix approximations as PCA and provides fairly similar results. We showcase the ADI capabilities of the VIP library using a deep sequence on HR8799 taken with the LBTI/LMIRCam and its recently commissioned L-band vortex coronagraph. Using VIP we investigated the presence of additional companions around HR8799 and did not find any significant additional point source beyond the four known planets. VIP is available at http://github.com/vortex-exoplanet/VIP and is accompanied with Jupyter notebook tutorials illustrating the main functionalities of the library.
The preparation of a space-mission that carries out any kind of imaging to detect high-precision low-amplitude variability of its targets requires a robust model for the expected performance of its instruments. This model cannot be derived from simple addition of noise properties due to the complex interaction between the various noise sources. While it is not feasible to build and test a prototype of the imaging device on-ground, realistic numerical simulations in the form of an end-to-end simulator can be used to model the noise propagation in the observations. These simulations not only allow studying the performance of the instrument, its noise source response and its data quality, but also the instrument design verification for different types of configurations, the observing strategy and the scientific feasibility of an observing proposal. In this way, a complete description and assessment of the objectives to expect from the mission can be derived. We present a high-precision simulation software package, designed to simulate photometric time-series of CCD images by including realistic models of the CCD and its electronics, the telescope optics, the stellar field, the jitter movements of the spacecraft, and all important natural noise sources. This formalism has been implemented in a software tool, dubbed ASTROID Simulator.
We present the first public version (v0.2) of the open-source and community-developed Python package, Astropy. This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as Flexible Image Transport System (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity
We discuss the results of a multi-wavelength differential imaging lab experiment with the High Contrast Imaging Testbed (HCIT) at the Jet Propulsion Laboratory. The HCIT combines a Lyot coronagraph with a Xinetics deformable mirror in a vacuum environment to simulate a space telescope in order to test technologies and algorithms for a future exoplanet coronagraph mission. At present, ground based telescopes have achieved significant attenuation of speckle noise using the technique of spectral differential imaging (SDI). We test whether ground-based SDI can be generalized to a non-simultaneous spectral differential imaging technique (NSDI) for a space mission. In our lab experiment, a series of 5 filter images centered around the O2(A) absorption feature at 0.762 um were acquired at nominal contrast values of 10^-6, 10^-7, 10^-8, and 10^-9. Outside the dark hole, single differences of images improve contrast by a factor of ~6. Inside the dark hole, we found significant speckle chromatism as a function of wavelength offset from the nulling wavelength, leading to a contrast degradation by a factor of 7.2 across the entire ~80 nm bandwidth. This effect likely stems from the chromatic behavior of the current occulter. New, less chromatic occulters are currently in development; we expect that these new occulters will resolve the speckle chromatism issue.
The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed insights into the atmospheres of young low-mass companions. In addition, improvements in data reduction and PSF subtraction algorithms are equally relevant for maximizing the scientific yield, both from new and archival data sets. We aim at developing a generic and modular data reduction pipeline for processing and analysis of high-contrast imaging data obtained with pupil-stabilized observations. The package should be scalable and robust for future implementations and in particular well suitable for the 3-5 micron wavelength range where typically (ten) thousands of frames have to be processed and an accurate subtraction of the thermal background emission is critical. PynPoint is written in Python 2.7 and applies various image processing techniques, as well as statistical tools for analyzing the data, building on open-source Python packages. The current version of PynPoint has evolved from an earlier version that was developed as a PSF subtraction tool based on PCA. The architecture of PynPoint has been redesigned with the core functionalities decoupled from the pipeline modules. Modules have been implemented for dedicated processing and analysis steps, including background subtraction, frame registration, PSF subtraction, photometric and astrometric measurements, and estimation of detection limits. The pipeline package enables end-to-end data reduction of pupil-stabilized data and supports classical dithering and coronagraphic data sets. As an example, we processed archival VLT/NACO L and M data of beta Pic b and reassessed the planets brightness and position with an MCMC analysis, and we provide a derivation of the photometric error budget.
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