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تسجيل مستخدم جديدProgress with the LOFAR Imaging Pipeline
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George Heald
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One of the science drivers of the new Low Frequency Array (LOFAR) is large-area surveys of the low-frequency radio sky. Realizing this goal requires automated processing of the interferometric data, such that fully calibrated images are produced by the system during survey operations. The LOFAR Imaging Pipeline is the tool intended for this purpose, and is now undergoing significant commissioning work. The pipeline is now functional as an automated processing chain. Here we present several recent LOFAR images that have been produced during the still ongoing commissioning period. These early LOFAR images are representative of some of the science goals of the commissioning team members.
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[abridged] The International LOFAR Telescope is an interferometer with stations spread across Europe. With baselines of up to ~2,000 km, LOFAR has the unique capability of achieving sub-arcsecond resolution at frequencies below 200 MHz, although this
is technically and logistically challenging. Here we present a calibration strategy that builds on previous high-resolution work with LOFAR. We give an overview of the calibration strategy and discuss the special challenges inherent to enacting high-resolution imaging with LOFAR, and describe the pipeline, which is publicly available, in detail. We demonstrate the calibration strategy by using the pipeline on P205+55, a typical LOFAR Two-metre Sky Survey (LoTSS) pointing. We perform in-field delay calibration, solution referencing to other calibrators, self-calibration, and imaging of example directions of interest in the field. For this specific field and these ionospheric conditions, dispersive delay solutions can be transferred between calibrators up to ~1.5 degrees away, while phase solution transferral works well over 1 degree. We demonstrate a check of the astrometry and flux density scale. Imaging in 17 directions, the restoring beam is typically 0.3 x 0.2 although this varies slightly over the entire 5 square degree field of view. We achieve ~80 to 300 $mu$Jy/bm image rms noise, which is dependent on the distance from the phase centre; typical values are ~90 $mu$Jy/bm for the 8 hour observation with 48 MHz of bandwidth. Seventy percent of processed sources are detected, and from this we estimate that we should be able to image ~900 sources per LoTSS pointing. This equates to ~3 million sources in the northern sky, which LoTSS will entirely cover in the next several years. Future optimisation of the calibration strategy for efficient post-processing of LoTSS at high resolution (LoTSS-HR) makes this estimate a lower limit.
Transient radio phenomena and pulsars are one of six LOFAR Key Science Projects (KSPs). As part of the Transients KSP, the Pulsar Working Group (PWG) has been developing the LOFAR Pulsar Data Pipelines to both study known pulsars as well as search fo
r new ones. The pipelines are being developed for the Blue Gene/P (BG/P) supercomputer and a large Linux cluster in order to utilize enormous amounts of computational capabilities (50Tflops) to process data streams of up to 23TB/hour. The LOFAR pipeline output will be using the Hierarchical Data Format 5 (HDF5) to efficiently store large amounts of numerical data, and to manage complex data encompassing a variety of data types, across distributed storage and processing architectures. We present the LOFAR Known Pulsar Data Pipeline overview, the pulsar beam-formed data format, the status of the pipeline processing as well as our future plans for developing the LOFAR Pulsar Search Pipeline. These LOFAR pipelines and software tools are being developed as the next generation toolset for pulsar processing in Radio Astronomy.
We present the first fully automated pipeline for making images from the interferometric data obtained from the upgraded Giant Metrewave Radio Telescope (uGMRT) called CAsa Pipeline-cum-Toolkit for Upgraded Giant Metrewave Radio Telescope data REduct
ion - CAPTURE. It is a python program that uses tasks from the NRAO Common Astronomy Software Applications (CASA) to perform the steps of flagging of bad data, calibration, imaging and self-calibration. The salient features of the pipeline are: i) a fully automatic mode to go from the raw data to a self-calibrated continuum image, ii) specialized flagging strategies for short and long baselines that ensure minimal loss of extended structure, iii) flagging of persistent narrow band radio frequency interference (RFI), iv) flexibility for the user to configure the pipeline for step-by-step analysis or special cases and v) analysis of data from the legacy GMRT. CAPTURE is available publicly on github (https://github.com/ruta-k/uGMRT-pipeline, release v1.0.0). The primary beam correction for the uGMRT images produced with CAPTURE is made separately available at https://github.com/ruta-k/uGMRTprimarybeam. We show examples of using CAPTURE on uGMRT and legacy GMRT data. In principle, CAPTURE can be tailored for use with radio interferometric data from other telescopes.
We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the SEEDS s
urvey. We implement several new algorithms, including a method to register saturated images, a trimmed mean for combining an image sequence that reduces noise by up to ~20%, and a robust and computationally fast method to compute the sensitivity of a high-contrast observation everywhere on the field-of-view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in python. It is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI requires minimal modification to reduce data from instruments other than HiCIAO. It is freely available for download at www.github.com/t-brandt/acorns-adi under a BSD license.
We describe the difference imaging pipeline (DiffImg) used to detect transients in deep images from the Dark Energy Survey Supernova program (DES-SN) in its first observing season from Aug 2013 through Feb 2014. DES-SN is a search for transients in w
hich ten 3-deg^2 fields are repeatedly observed in the g,r,i,z passbands with a cadence of about 1 week. The observing strategy has been optimized to measure high-quality light curves and redshifts for thousands of Type Ia supernova (SN Ia) with the goal of measuring dark energy parameters. The essential DiffImg functions are to align each search image to a deep reference image, do a pixel-by-pixel subtraction, and then examine the subtracted image for significant positive detections of point-source objects. The vast majority of detections are subtraction artifacts, but after selection requirements and image filtering with an automated scanning program, there are 130 detections per deg^2 per observation in each band, of which only 25% are artifacts. Of the 7500 transients discovered by DES-SN in its first observing season, each requiring a detection on at least 2 separate nights, Monte Carlo simulations predict that 27% are expected to be supernova. Another 30% of the transients are artifacts, and most of the remaining transients are AGN and variable stars. Fake SNe Ia are overlaid onto the images to rigorously evaluate detection efficiencies, and to understand the DiffImg performance. The DiffImg efficiency measured with fake SNe agrees well with expectations from a Monte Carlo simulation that uses analytical calculations of the fluxes and their uncertainties. In our 8 shallow fields with single-epoch 50% completeness depth 23.5, the SN Ia efficiency falls to 1/2 at redshift z 0.7, in our 2 deep fields with mag-depth 24.5, the efficiency falls to 1/2 at z 1.1.
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