Papers Astronomy
“Astronomy” 태그가 달린 논문 395편 · 필터 해제
Exoplanet Classification through Vision Transformers with Temporal Image Analysis
The classification of exoplanets has been a longstanding challenge in astronomy, requiring significant computational and observational resources. Traditional methods demand substantial effort, time, and cost, highlightin…
AstronomyCan AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena. While machine learning (ML) increasingly automates this process, models often struggle with generalization in la…
AstronomyMorphology classificationobject-detectionObject DetectionCategory-based Galaxy Image Generation via Diffusion Models
Conventional galaxy generation methods rely on semi-analytical models and hydrodynamic simulations, which are highly dependent on physical assumptions and parameter tuning. In contrast, data-driven generative models do n…
AstronomyImage GenerationStatistical Machine Learning for Astronomy -- A Textbook
This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that reveals connections between modern data …
AstronomyBayesian InferenceGaussian ProcessesUncertainty QuantificationEmulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows
Population synthesis simulations of compact binary coalescences~(CBCs) play a crucial role in extracting astrophysical insights from an ensemble of gravitational wave~(GW) observations. However, realistic simulations are…
AstronomyUncertainty QuantificationUnsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite it…
Astronomyscientific discoveryScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows
Large Language Models (LLMs) have extended their impact beyond Natural Language Processing, substantially fostering the development of interdisciplinary research. Recently, various LLM-based agents have been developed to…
Astronomyscientific discoveryAstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy
Large Language Models (LLMs) are being explored for applications in scientific research, including their capabilities to synthesize literature, answer research questions, generate research ideas, and even conduct computa…
AstronomyRGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification
We introduce a novel machine learning dataset tailored for the classification of bent radio active galactic nuclei (AGN) in astronomical observations. Bent radio AGN, distinguished by their curved jet structures, provide…
AstronomyClassificationAnomaly detection in radio galaxy data with trainable COSFIRE filters
Detecting anomalies in radio astronomy is challenging due to the vast amounts of data and the rarity of labeled anomalous examples. Addressing this challenge requires efficient methods capable of identifying unusual radi…
Anomaly DetectionAstronomyAstroMLab 4: Benchmark-Topping Performance in Astronomy Q&A with a 70B-Parameter Domain-Specialized Reasoning Model
General-purpose large language models, despite their broad capabilities, often struggle with specialized domain knowledge, a limitation particularly pronounced in more accessible, lower-parameter versions. This gap hinde…
AstronomyRethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate and practical predictions is challenging …
AstronomyIrregular Time SeriesTime SeriesTime Series ForecastingAnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
Anomaly detection in large datasets is essential in fields such as astronomy and computer vision; however, supervised methods typically require extensive anomaly labelling, which is often impractical. We present AnomalyM…
Active LearningAnomaly DetectionAstronomyBinary Classification+1Radiometer Calibration using Machine Learning
Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical sig…
AstronomyPolarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes
Radio Frequency Interference (RFI) is a known growing challenge for radio astronomy, intensified by increasing observatory sensitivity and prevalence of orbital RFI sources. Spiking Neural Networks (SNNs) offer a promisi…
AstronomySensitivityDetecting streaks in smart telescopes images with Deep Learning
The growing negative impact of the visibility of satellites in the night sky is influencing the practice of astronomy and astrophotography, both at the amateur and professional levels. The presence of these satellites ha…
AstronomyDeep LearningObject DetectionAdvancing RFI-Detection in Radio Astronomy with Liquid State Machines
Radio Frequency Interference (RFI) from anthropogenic radio sources poses significant challenges to current and future radio telescopes. Contemporary approaches to detecting RFI treat the task as a semantic segmentation …
AstronomySemantic SegmentationAstroLLaVA: towards the unification of astronomical data and natural language
We present AstroLLaVA, a vision language model for astronomy that enables interaction with astronomical imagery through natural dialogue. By fine-tuning the LLaVA model on a diverse dataset of $\sim$30k images with capti…
AstronomyImage CaptioningLanguage ModelingLanguage Modelling+2AstroClearNet: Deep image prior for multi-frame astronomical image restoration
Recovering high-fidelity images of the night sky from blurred observations is a fundamental problem in astronomy, where traditional methods typically fall short. In ground-based astronomy, combining multiple exposures to…
AstronomyDeblurringDenoisingImage RestorationWhen Astronomy Meets AI: Manazel For Crescent Visibility Prediction in Morocco
The accurate determination of the beginning of each Hijri month is essential for religious, cultural, and administrative purposes. Manazel (The code and datasets are available at https://github.com/lairgiyassir/manazel) …
ARCAstronomy