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RAS Techniques and Instruments is a peer-reviewed journal published by Royal Astronomical Society, covering Stellar, planetary, and galactic studies, Astronomy and Astrophysical Research, Radio Astronomy Observations and Technology. Over its lifetime it has published 246 papers, which have received 824 citations.
| Publisher | Royal Astronomical Society |
|---|---|
| Reference style | Author–year (MNRAS) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', RAS Techniques and Instruments, 12(3), pp. 45–58.
Formats any DOI in the closest standard style — RAS Techniques and Instruments has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Stellar, planetary, and galactic studies Astronomy and Astrophysical Research Radio Astronomy Observations and Technology Galaxies: Formation, Evolution, Phenomena Gamma-ray bursts and supernovae |
| ISSN | 2752-8200 |
| Citation impact (2-yr) | 1.79 |
| h-index | 14 |
| i10-index | 25 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | University College London |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract Study Analysis Group 21 (SAG21) of NASA’s Exoplanet Exploration Program Analysis Group was organized to study the effect of stellar contamination on space-based transmission spectroscopy, a method for studying exoplanetary atmospheres by measuring the wavelength-dependent radius of a planet as it transits its star. Transmission spectroscopy relies on a precise understanding of the spectrum…
Abstract In this work, we apply self-supervised learning with instance differentiation to learn a robust, multipurpose representation for image analysis of resolved extragalactic continuum images. We train a multi-use model which compresses our unlabelled data into a structured, low dimensional representation which can be used for a variety of downstream tasks (e.g. classification, similarity search).…
Abstract Comparison of appropriate models to describe observational data is a fundamental task of science. The Bayesian model evidence, or marginal likelihood, is a computationally challenging, yet crucial, quantity to estimate to perform Bayesian model comparison. We introduce a methodology to compute the Bayesian model evidence in simulation-based inference (SBI) scenarios (often called likelihood-free inference).…
ABSTRACT The goal of this white paper is to provide a snapshot of the data availability and data needs primarily for the Ariel space mission, but also for related atmospheric studies of exoplanets and cool stars. It covers the following data-related topics: molecular and atomic line lists, line profiles, computed cross-sections and opacities, collision-induced absorption…
Abstract Reliable tools to extract patterns from high-dimensionality spaces are becoming more necessary as astronomical data sets increase both in volume and complexity. Contrastive Learning is a self-supervised machine learning algorithm that extracts informative measurements from multidimensional data sets, which has become increasingly popular in the computer vision and Machine Learning communities in recent years.…
