Related papers: The e-MERLIN Data Reduction Pipeline
In recent years, there has been a proliferation of wide-field sky surveys to search for a variety of transient objects. Using relatively short focal lengths, the optics of these systems produce undersampled stellar images often marred by a…
Asteroseismology is well-established in astronomy as the gold standard for determining precise and accurate fundamental stellar properties like masses, radii, and ages. Several tools have been developed for asteroseismic analyses but many…
In this demo paper, we introduce the DARPA D3M program for automatic machine learning (ML) and JPL's MARVIN tool that provides an environment to locate, annotate, and execute machine learning primitives for use in ML pipelines. MARVIN is a…
Python is one of the most commonly used programming languages in industry and education. Its English keywords and built-in functions/modules allow it to come close to pseudo-code in terms of its readability and ease of writing. However,…
Over the past decade, cryogenic electron microscopy (cryo-EM) has emerged as a primary method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. In order to meet increasing demand for cryo-EM,…
The photometric calibration of the Sloan Digital Sky Survey (SDSS) is a multi-step process which involves data from three different telescopes: the 1.0-m telescope at the US Naval Observatory (USNO), Flagstaff Station, Arizona (which was…
Aims: We provide an open-source code allowing an easy, intuitive, and robust normalisation of spectra. Methods: We developed RASSINE, a Python code for normalising merged 1D spectra through the concepts of convex hulls. The code uses six…
SpectraPy is an Astropy affiliated package for spectroscopic data reduction. It collects algorithms and methods for data reduction of astronomical spectra obtained by through-slits spectrographs. It has been created to fill the gap in…
We introduce our publicly available Wide-Field-Imaging reduction pipeline THELI. The procedures applied for the efficient pre-reduction and astrometric calibration are presented. A special emphasis is put on the methods applied to the…
The rapid advancement of image analysis methods in time-domain astronomy, particularly those leveraging AI algorithms, has highlighted efficient image pre-processing as a critical bottleneck affecting algorithm performance. Image…
Over the past two decades, helioseismology has revolutionized our understanding of the interior structure and dynamics of the Sun. Asteroseismology will soon place this knowledge into a broader context by providing structural data for…
We present a numerical code to simulate maps of Galactic emission in intensity and polarization at microwave frequencies, aiding in the design of Cosmic Microwave Background experiments. This Python code builds on existing efforts to…
The analysis of experimental results with Python often requires writing many code scripts which all need access to the same set of functions. In a common field of research, this set will be nearly the same for many users. The qspec Python…
The increasing adoption of low-cost environmental sensors and AI-enabled applications has accelerated the demand for scalable and resilient data infrastructures, particularly in data-scarce and resource-constrained regions. This paper…
Astronomical observations, physical experiments as well as computer simulations often involve discrete data sets supposed to represent a fair sample of an underlying smooth and continuous field. Reconstructing the underlying fields from a…
Autonomous and targeted underwater visual monitoring and exploration using Autonomous Underwater Vehicles (AUVs) can be a challenging task due to both online and offline constraints. The online constraints comprise limited onboard storage…
Modern scripting languages and database tools combined provide a new framework for developing beam-line control and data management software. The CARPS system supports data collection by storing low level beam-line control commands in a…
Systematic reviews, which entail the extraction of data from large numbers of scientific documents, are an ideal avenue for the application of machine learning. They are vital to many fields of science and philanthropy, but are very…
Astronomy is entering in a new era of Extreme Intensive Data Computation and we have identified three major issues the new generation of projects have to face: Resource optimization, Heterogeneous Software Ecosystem and Data Transfer. We…
Real-world machine learning on tabular data relies on complex data preparation pipelines for prediction, data integration, augmentation, and debugging. Designing these pipelines requires substantial domain expertise and engineering effort,…