Related papers: AXS: A framework for fast astronomical data proces…
We present astroplan - an open source, open development, Astropy affiliated package for ground-based observation planning and scheduling in Python. astroplan is designed to provide efficient access to common observational quantities such as…
HPC and Cloud have evolved independently, specializing their innovations into performance or productivity. Acceleration as a Service (XaaS) is a recipe to empower both fields with a shared execution platform that provides transparent access…
Experimental Particle Physics has been at the forefront of analyzing the world's largest datasets for decades. The HEP community was among the first to develop suitable software and computing tools for this task. In recent times, new…
In the big data era of observational oceanography, passive acoustics datasets are becoming too high volume to be processed on local computers due to their processor and memory limitations. As a result there is a current need for our…
We present BAX, Base de Donnees Amas de Galaxies X (http://webast.ast.obs-mip.fr/bax), a multi-wavelength database dedicated to X-ray clusters and groups of galaxies allowing detailed information retrieval. BAX is designed to support…
The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large datasets. Gaussian Processes are a popular class of models used for this purpose…
AXIS is a probe-class concept under study for submission to the 2020 Decadal Survey. AXIS will extend and enhance the science of high angular resolution X-ray imaging and spectroscopy in the next decade with $\sim 0.4''$ resolution over a…
Edge AI deployments are becoming increasingly complex, necessitating energy-efficient solutions for resource-constrained embedded systems. Approximate computing, which allows for controlled inaccuracies in computations, is emerging as a…
AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data…
ASTRA is a Python package that provides a modular, instrument-independent interface for working with high-resolution stellar spectra. Designed to support data from multiple spectrographs, including ESPRESSO (Pepe et al., 2021), HARPS (Mayor…
XMM-Newton is a cornerstone mission of the European Space Agency (ESA) for X-ray astronomy, providing high-quality X-ray data for astrophysical research since the start of the century. Its Science Analysis System (SAS) has been a reliable…
Four years after the last LISA meeting, the NASA Astrophysics Data System (ADS) finds itself in the middle of major changes to the infrastructure and contents of its database. In this paper we highlight a number of features of great…
The methods and techniques for the slitless spectroscopy software aXe, which was designed to reduce data from the various slitless spectroscopy modes of Hubble Space Telescope instruments, are described. aXe can treat slitless spectra from…
With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random…
Scientific exploitation of the ever increasing volumes of astronomical data requires efficient and practical methods for data access, visualisation, and analysis. Hierarchical sky tessellation techniques enable a multi-resolution approach…
The increasing availability of high-quality optical and near-infrared spectroscopic data, as well as advances in modelling techniques, have greatly expanded the scientific potential of spectroscopic studies. However, the software tools…
This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing…
Retrieval-Augmented Generation (RAG) is crucial for improving the quality of large language models by injecting proper contexts extracted from external sources. RAG requires high-throughput, low-latency Approximate Nearest Neighbor Search…
Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations remain the most widely used, real-space DFT provides…
The recent explosion of recorded digital data and its processed derivatives threatens to overwhelm researchers when analysing their experimental data or when looking up data items in archives and file systems. While current hardware…