Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe Automated Data Extraction, Processing, and Tracking System for CHARIS
67
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
CHARIS is an IFS designed for imaging and spectroscopy of disks and sub-stellar companions. To improve ease of use and efficiency of science production, we present progress on a fully-automated backend for CHARIS. This Automated Data Extraction, Processing, and Tracking System (ADEPTS) will log data files from CHARIS in a searchable database and perform all calibration and data extraction, yielding science-grade data cubes. The extracted data will also be run through a preset array of post-processing routines. With significant parallelization of data processing, ADEPTS will dramatically reduce the time between data acquisition and the availability of science-grade data products.
rate research
Read More
We present the data reduction pipeline for CHARIS, a high-contrast integral-field spectrograph for the Subaru Telescope. The pipeline constructs a ramp from the raw reads using the measured nonlinear pixel response, and reconstructs the data cube using one of three extraction algorithms: aperture photometry, optimal extraction, or $chi^2$ fitting. We measure and apply both a detector flatfield and a lenslet flatfield and reconstruct the wavelength- and position-dependent lenslet point-spread function (PSF) from images taken with a tunable laser. We use these measured PSFs to implement a $chi^2$-based extraction of the data cube, with typical residuals of ~5% due to imperfect models of the undersampled lenslet PSFs. The full two-dimensional residual of the $chi^2$ extraction allows us to model and remove correlated read noise, dramatically improving CHARIS performance. The $chi^2$ extraction produces a data cube that has been deconvolved with the line-spread function, and never performs any interpolations of either the data or the individual lenslet spectra. The extracted data cube also includes uncertainties for each spatial and spectral measurement. CHARIS software is parallelized, written in Python and Cython, and freely available on github with a separate documentation page. Astrometric and spectrophotometric calibrations of the data cubes and PSF subtraction will be treated in a forthcoming paper.
GWAC will have been built an integrated FOV of 5,000 $degree^2$ and have already built 1,800 square $degree^2$. The limit magnitude of a 10-second exposure image in the moonless night is 16R. In each observation night, GWAC produces about 0.7TB of raw data, and the data processing pipeline generates millions of single frame alerts. We describe the GWAC Data Processing and Management System (GPMS), including hardware architecture, database, detection-filtering-validation of transient candidates, data archiving, and user interfaces for the check of transient and the monitor of the system. GPMS combines general technology and software in astronomy and computer field, and use some advanced technologies such as deep learning. Practical results show that GPMS can fully meet the scientific data processing requirement of GWAC. It can online accomplish the detection, filtering and validation of millions of transient candidates, and feedback the final results to the astronomer in real-time. During the observation from October of 2018 to December of 2019, we have already found 102 transients.
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 Data Cruncher, combines multiple data reduction pipelines 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 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 Stratospheric Observatory for Infrared Astronomy (SOFIA) is an airborne astronomical observatory comprised of a 2.5-meter telescope mounted in the aft section of a Boeing 747SP aircraft. During routine operations, several instruments will be available to the astronomical community including cameras and spectrographs in the near- to far-IR. Raw data obtained in-flight require a significant amount of processing to correct for background emission (from both the telescope and atmosphere), remove instrumental artifacts, correct for atmospheric absorption, and apply both wavelength and flux calibration. In general, this processing is highly specific to the instrument and telescope. In order to maximize the scientific output of the observatory, the SOFIA Science Center must provide these post-processed data sets to Guest Investigators in a timely manner. To meet this requirement, we have designed and built the SOFIA Data Processing System (DPS): an in-house set of tools and services that can be used in both automatic (pipeline) and manual modes to process data from a variety of instruments. Here we present an overview of the DPS concepts and architecture, as well as operational results from the first two SOFIA observing cycles (2013--2014).
The Pan-STARRS Data Processing System is responsible for the steps needed to downloaded, archive, and process all images obtained by the Pan-STARRS telescopes, including real-time detection of transient sources such as supernovae and moving objects including potentially hazardous asteroids. With a nightly data volume of up to 4 terabytes and an archive of over 4 petabytes of raw imagery, Pan-STARRS is solidly in the realm of Big Data astronomy. The full data processing system consists of several subsystems covering the wide range of necessary capabilities. This article describes the Image Processing Pipeline and its connections to both the summit data systems and the outward-facing systems downstream. The latter include the Moving Object Processing System (MOPS) & the public database: the Published Science Products Subsystem (PSPS).
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
