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.
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.
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.
We present SoFiA 2, the fully automated 3D source finding pipeline for the WALLABY extragalactic HI survey with the Australian SKA Pathfinder (ASKAP). SoFiA 2 is a reimplementation of parts of the original SoFiA pipeline in the C programming language and makes use of OpenMP for multi-threading of the most time-critical algorithms. In addition, we have developed a parallel framework called SoFiA-X that allows the processing of large data cubes to be split across multiple computing nodes. As a result of these efforts, SoFiA 2 is substantially faster and comes with a much reduced memory footprint compared to its predecessor, thus allowing the large WALLABY data volumes of hundreds of gigabytes of imaging data per epoch to be processed in real-time. The source code has been made publicly available to the entire community under an open-source licence. Performance tests using mock galaxies injected into genuine ASKAP data suggest that in the absence of significant imaging artefacts SoFiA 2 is capable of achieving near-100% completeness and reliability above an integrated signal-to-noise ratio of about 5-6. We also demonstrate that SoFiA 2 generally recovers the location, integrated flux and w20 line width of galaxies with high accuracy. Other parameters, including the peak flux density and w50 line width, are more strongly biased due to the influence of the noise on the measurement. In addition, very faint galaxies below an integrated signal-to-noise ratio of about 10 may get broken up into multiple components, thus requiring a strategy to identify fragmented sources and ensure that they do not affect the integrity of any scientific analysis based on the SoFiA 2 output.
The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and 17 satellites has been shown to be capable of detecting bolides (bright meteors) in Earths atmosphere. Due to its large, continuous field of view and immediate public data availability, GLM provides a unique opportunity to detect a large variety of bolides, including those in the 0.1 to 3 m diameter range and complements current ground-based bolide detection systems, which are typically sensitive to smaller events. We present a machine learning-based bolide detection and light curve generation pipeline being developed at NASA Ames Research Center as part of NASAs Asteroid Threat Assessment Project (ATAP). The ultimate goal is to generate a large catalog of calibrated bolide lightcurves to provide an unprecedented data set which will be used to inform meteor entry models on how incoming bodies interact with the Earths atmosphere and to infer the pre-entry properties of the impacting bodies. The data set will also be useful for other asteroidal studies. This paper reports on the progress of the first part of this ultimate goal, namely, the automated bolide detection pipeline. Development of the training set, ML model training and iterative improvements in detection performance are presented. The pipeline runs in an automated fashion and bolide lightcurves along with other measured properties are promptly published on a NASA hosted publicly accessible website, https://neo-bolide.ndc.nasa.gov.
A methodology of adaptive time series analysis, based on Empirical Mode Decomposition (EMD), and on its time varying version tvf-EMD has been applied to strain data from the gravitational wave interferometer (IFO) Virgo in order to characterise scattered light noise affecting the sensitivity of the IFO in the detection frequency band. Data taken both during hardware injections, when a part of the IFO is put in oscillation for detector characterisation purposes, and during periods of science mode, when the IFO is fully locked and data are used for the detection of gravitational waves, were analysed. The adaptive nature of the EMD and tvf-EMD algorithms allows them to deal with nonlinear non-stationary data and hence they are particularly suited to characterise scattered light noise which is an intrinsically nonlinear and non-stationary noise. Obtained results show that tvf-EMD algorithm allows to obtain more precise results compared to the EMD algorithm, yielding higher cross-correlation values with the auxiliary channels that are the culprits of scattered light noise.
Stefano Bianchi
,Alessandro Longo
,Guillermo Valdes
.
(2021)
.
"Gwadaptive_scattering: an automated pipeline for scattered light noise characterization"
.
Guillermo Valdes Ph.D.
هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا