Dr Heloise Stevance

Abstract:

Abstract We present ATLAS100 – a sample of 1729 supernovae and other explosive optical transients within ~100 Mpc observed by the ATLAS survey over a span of 5.75 years from 2017 September 21 to 2023 June 21. The volume-limited sample includes transients associated with galaxies with a spectroscopic redshift of z ≤ 0.025, and spectroscopically classified transients within this redshift threshold where a host redshift was not available in existing catalogues. Our host galaxy list is constructed from aggregating all available galaxy redshift and distance catalogues. We carefully select all transients within a projected radius of 50 kpc of these hosts. The ATLAS100 transient sample has a host galaxy redshift completeness fraction of 83 per cent, consistent with expectations for the redshift completeness of local galaxy catalogues. Within this volume, the spectroscopic classifications are 87 per cent complete and we reclassify many ambiguous transients with joint light curve and spectroscopic considerations. Here, we release the catalogue together with compiled, binned and cleaned ATLAS photometry for all transients. We fit the light curve data to derive peak luminosities and characteristic timescales. We explore the sample characteristics, demographics and discuss the completeness and purity of the sample. This is the first in a series of papers that will explore the rates and physical parameters of a complete and large sample of nearby supernovae and transients brighter than M ≲ −16.

Abstract:

The volume and complexity of scientific literature are expanding rapidly, making it increasingly difficult to extract and synthesise information across studies. This challenge is particularly acute in the biological sciences, where evidence spans multiple levels of organisation and heterogeneous experimental designs. Large Language Model (LLM) pipelines offer a scalable route to evidence synthesis, but many existing approaches lack transparency, modularity, and effective mechanisms for human oversight. We present MetaBeeAI, an open-source, modular pipeline that integrates established LLM techniques into a coherent, auditable workflow for structured data extraction in biology. MetaBeeAI combines modular prompting, multi-pass extraction, and expert-in-the-loop validation within an interface that presents model outputs alongside source text, enabling inspection, correction, and iterative refinement. The pipeline produces machine-readable records of prompts, configurations, and expert annotations, supporting reproducibility and continuous improvement. We apply MetaBeeAI to 924 research papers on bees and pesticides, extracting structured information on species, compounds, exposure designs, and experimental context. Evaluation demonstrates improved consistency, convergence with expert judgement, and robustness across heterogeneous biological studies, highlighting the value of expert-guided refinement. MetaBeeAI provides a transparent and extensible framework for scalable evidence synthesis, supporting reliable integration of LLMs into biological research workflows.

Abstract:

The Asteroid Terrestrial-impact Last Alert System (ATLAS) is an all-sky optical sky survey with a cadence of 24 to 48 hours (Tonry et al., 2018), and the ATLAS Transient Server (Smith et al., 2020) processes the alert stream to enable the discovery and follow-up of extra-galactic transients. The data from the ATLAS server can be accessed through a REST API, which has allowed the development of bots that need direct access to the data to help rank alerts and trigger follow-up observations of promising targets. Here we present the Python client we have developed for the ATLAS API to help connect bots and scientists to our data.