Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRobust Automated Photometry Pipeline for Blurred Images
223
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The primary task of the 1.26-m telescope jointly operated by the National Astronomical Observatory and Guangzhou University is photometric observations of the g, r, and i bands. A data processing pipeline system was set up with mature software packages, such as IRAF, SExtractor, and SCAMP, to process approximately 5 GB of observational data automatically every day. However, the success ratio was significantly reduced when processing blurred images owing to telescope tracking error; this, in turn, significantly constrained the output of the telescope. We propose a robust automated photometric pipeline (RAPP) software that can correctly process blurred images. Two key techniques are presented in detail: blurred star enhancement and robust image matching. A series of tests proved that RAPP not only achieves a photometric success ratio and precision comparable to those of IRAF but also significantly reduces the data processing load and improves the efficiency.
rate research
Read More
The VST Telescope Control Software logs continuously detailed information about the telescope and instrument operations. Commands, telemetries, errors, weather conditions and anything may be relevant for the instrument maintenance and the identification of problem sources is regularly saved. All information are recorded in textual form. These log files are often examined individually by the observatory personnel for specific issues and for tackling problems raised during the night. Thus, only a minimal part of the information is normally used for daily maintenance. Nevertheless, the analysis of the archived information collected over a long time span can be exploited to reveal useful trends and statistics about the telescope, which would otherwise be overlooked. Given the large size of the archive, a manual inspection and handling of the logs is cumbersome. An automated tool with an adequate user interface has been developed to scrape specific entries within the log files, process the data and display it in a comprehensible way. This pipeline has been used to scan the information collected over 5 years of telescope activity.
Scattered light noise affects the sensitivity of gravitational waves detectors. The characterization of such noise is needed to mitigate it. The time-varying filter empirical mode decomposition algorithm is suitable for identifying signals with time-dependent frequency such as scattered light noise (or scattering). We present a fully automated pipeline based on the pytvfemd library, a python implementation of the tvf-EMD algorithm, to identify objects inducing scattering in the gravitational-wave channel with their motion. The pipeline application to LIGO Livingston O3 data shows that most scattering noise is due to the penultimate mass at the end of the X-arm of the detector (EXPUM) and with a motion in the micro-seismic frequency range.
Current time domain facilities are discovering hundreds of new galactic and extra-galactic transients every week. Classifying the ever-increasing number of transients is challenging, yet crucial to further our understanding of their nature, discover new classes, or ensuring sample purity, for instance, for Supernova Ia cosmology. The Zwicky Transient Facility is one example of such a survey. In addition, it has a dedicated very-low resolution spectrograph, the SEDMachine, operating on the Palomar 60-inch telescope. This spectrographs primary aim is object classification. In practice most, if not all, transients of interest brighter than ~19 mag are typed. This corresponds to approximately 10 to 15 targets a night. In this paper, we present a fully automated pipeline for the SEDMachine. This pipeline has been designed to be fast, robust, stable and extremely flexible. pysedm enables the fully automated spectral extraction of a targeted point source object in less than 5 minutes after the end of the exposure. The spectral color calibration is accurate at the few percent level. In the 19 weeks since pysedm entered production in early August of 2018, we have classified, among other objects, about 400 Type Ia supernovae and 140 Type II supernovae. We conclude that low resolution, fully automated spectrographs such as the `SEDMachine with pysedm installed on 2-m class telescopes within the southern hemisphere could allow us to automatically and simultaneously type and obtain a redshift for most (if not all) bright transients detected by LSST within z<0.2, notably potentially all Type Ia Supernovae. In comparison to the current SEDM design, this would require higher spectral resolution (R~1000) and slightly improved throughput. With this perspective in mind, pysedm has been designed to easily be adaptable to any IFU-like spectrograph (see https://github.com/MickaelRigault/pysedm).
Light curves for RR Lyrae stars can be difficult to obtain properly in the K2 mission due to the similarities between the timescales of the observed physical phenomena and the instrumental signals appearing in the data. We developed a new photometric method called Extended Aperture Photometry (EAP), a key element of which is to extend the aperture to an optimal size to compensate for the motion of the telescope and to collect all available flux from the star before applying further corrections. We determined the extended apertures for individual stars by hand so far. Now we managed to automate the pipeline that we intend to use for the nearly four thousand RR Lyrae targets observed in the K2 mission. We present the outline of our pipeline and make some comparisons to other photometric solutions.
A fully autonomous data reduction pipeline has been developed for FRODOSpec, an optical fibre-fed integral field spectrograph currently in use at the Liverpool Telescope. This paper details the process required for the reduction of data taken using an integral field spectrograph and presents an overview of the computational methods implemented to create the pipeline. Analysis of errors and possible future enhancements are also discussed.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
