Search Results for author:
Found 8 papers, 4 papers with code
Scaling Laws for Galaxy Images
We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images.
AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets
We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training.
AstroLLaMA: Towards Specialized Foundation Models in Astronomy
Large language models excel in many human-language tasks but often falter in highly specialized domains like scholarly astronomy.
Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy
This study investigates the application of Large Language Models (LLMs), specifically GPT-4, within Astronomy.
Practical Galaxy Morphology Tools from Deep Supervised Representation Learning
Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch.
Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314, 000 galaxies.
An old stellar population or diffuse nebular continuum emission discovered in green pea galaxies
We use new HST images of nine Green Pea Galaxies (GPGs) to study their resolved structure and color.
Astrophysics of Galaxies
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies.
