Related papers: Pipeline Reduction of Binary Light Curves from Lar…
In this paper, we report on the construction of a new and independent pipeline for analyzing the public data from the first observing run of advanced LIGO for mergers of compact binary systems. The pipeline incorporates different techniques…
In the first paper of this series we presented EBAS, a new fully automated algorithm to analyse the lightcurves of eclipsing binaries, based on the EBOP code. Here we apply the new algorithm to the whole sample of 2580 binaries found in the…
In-depth analysis of eclipsing binary (EB) observational data collected for several decades can inform us about a lot of astrophysically interesting processes taking place in the systems. We have developed a wide-ranging method for the…
The production of science-ready data from major solar telescopes requires expertise beyond that of the typical observer. This is a consequence of the increasing complexity of instruments and observing sequences, which require calibrations…
The observation of small bodies in the Space Environment is an ongoing important task in astronomy. While nowadays new objects are mostly detected in larger sky surveys, several follow-up observations are usually needed for each object to…
The primary aim of this research is to evaluate several convolutional neural network-based object detection algorithms for identifying oscillation-like patterns in light curves of eclipsing binaries. This involves creating a robust…
Since October 1, 2006, spectroscopic data from the two FORS instruments have been reduced with a new pipeline, which is based on a bottom-up calibration approach. I give a short description of the pipeline and discuss first experiences with…
Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the…
We describe a new neural-net based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general…
This paper describes a new approach to the optimization of information extraction in multi-wavelength image cubes of cosmological fields. The objective is to create a framework for the automatic identification and tagging of sources…
We present an application of computer vision methods to classify the light curves of eclipsing binaries (EB). We have used pre-trained models based on convolutional neural networks ($\textit{ResNet50}$) and vision transformers…
We present the data reduction pipeline, MEAD, for Arizona Lenslets for Exoplanet Spectroscopy (ALES), the first thermal infrared integral field spectrograph designed for high-contrast imaging. ALES is an upgrade of LMIRCam, the $1-5\,\mu$m…
Large-scale photometric surveys are revolutionizing astronomy by delivering unprecedented amounts of data. The rich data sets from missions such as the NASA Kepler and TESS satellites, and the upcoming ESA PLATO mission, are a treasure…
The VISIONS public survey provides large-scale, multiepoch imaging of five nearby star-forming regions at subarcsecond resolution in the near-infrared. All data collected within the program and provided by the European Southern Observatory…
We demonstrate the eclipsing binary detection performance of the Gaia variability analysis and processing pipeline using Hipparcos data. The automated pipeline classifies 1,067 (0.9%) of the 118,204 Hipparcos sources as eclipsing binary…
As part of an on-going effort to simplify the data analysis path for VLBI experiments, a pipeline procedure has been developed at JIVE to carry out much of the data reduction required for EVN experiments in an automated fashion. This…
The sky-averaged redshifted 21-cm signal from Cosmic Dawn is expected to provide a unique view of the first compact objects. However, its measurement remains daunting. Difficulties are driven by the large dynamic contrast between the…
We present a new Python pipeline for processing data from astronomical long-slit spectroscopy observations recorded with CCD detectors. The pipeline is designed to aim for simplicity, manual execution, transparency and robustness. The goal…
We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine & deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e.g. IBM PAIRS…
We have written an automatic image processing pipeline for the Advanced Camera for Surveys (ACS) Guaranteed Time Observation (GTO) program. The pipeline, known as Apsis, supports the different cameras available on the ACS instrument and is…