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Found 12 papers, 3 papers with code
Sparks of Science: Hypothesis Generation Using Structured Paper Data
We demonstrate that framing hypothesis generation as conditional language modelling, with the model fine-tuned on Bit-Flip-Spark and the Chain-of-Reasoning (and where, at inference, we only provide the Bit), leads to improvements in the overall quality of the hypotheses.
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z < 1.4$ in the Hyper Supreme-Cam Wide Survey
We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1. 4$ and $m<23$ in the Hyper Supreme-Cam Wide survey.
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge.
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.
Using Machine Learning to Determine Morphologies of $z<1$ AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey
The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude.
Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $\sim 100,000$ SDSS and $\sim 20,000$ CANDELS Galaxies
This inferred difference in quenching mechanism is in agreement with previous studies that used other morphology classification techniques on much smaller samples at $z\sim0$ and $z\sim1$.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development.
Exploring galaxy evolution with generative models
Methods: By learning a latent space representation of the data, we can use this network to forward model and explore hypotheses in a data-driven way.
Using transfer learning to detect galaxy mergers
We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers.
PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light
We test the method using Sloan Digital Sky Survey (SDSS) r-band images with artificial AGN point sources added which are then removed using the GAN and with parametric methods using GALFIT.
Astrophysics of Galaxies Data Analysis, Statistics and Probability
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data.
