9 citations · 11 across the 5 of their papers we have counts for
6 papers
AI Agents as a Research Team: A Human-Supervised Case Study in Stellar Spectroscopy
Marwan Gebran, Ian Bentley
We present a qualitative case study of a human-supervised team of specialized AI agents supporting an astronomy research project. The Bot Spectroscopist team, described in the proj…
Combined Garvey Kelson Relations for Mass Determinations and Machine Learning
I. Bentley, A. Fiorito, M. Gebran +2
Simple Garvey Kelson mass relations applied in two regions are often used as an evaluation metric for machine learning based mass models. These relations have also been used in the…
TheUse of Conditional Variational Autoencoders in Generating Stellar Spectra
Marwan Gebran, Ian Bentley
We present a conditional variational autoencoder (CVAE) that generates stellar spectra covering 4000 $T_{\mathrm{eff}$ 11,000 K, dex, $-1.5 \le…
Further exploration of binding energy residuals using machine learning and the development of a composite ensemble model
I. Bentley, J. Tedder, M. Gebran +1
This paper describes the development of the Four Model Tree Ensemble (FMTE). The FMTE is a composite of machine learning models trained on experimental binding energies from the At…
Deep Learning application for stellar parameters determination: III- Denoising Procedure
Marwan Gebran, Ian Bentley, Rose Brienza +1
In this third paper in a series, we investigate the need of spectra denoising for the derivation of stellar parameters. We have used two distinct datasets for this work. The first…
High Precision Binding Energies from Physics Informed Machine Learning
Ian Bentley, James Tedder, Marwan Gebran +1
Twelve physics-informed machine learning models have been trained to model binding energy residuals. Our approach begins with determining the difference between measured experiment…