StarWhisper Telescope: An AI framework for automating end-to-end astronomical observations
- URL: http://arxiv.org/abs/2412.06412v3
- Date: Sun, 19 Oct 2025 00:25:23 GMT
- Title: StarWhisper Telescope: An AI framework for automating end-to-end astronomical observations
- Authors: Cunshi Wang, Yu Zhang, Yuyang Li, Xinjie Hu, Yiming Mao, Xunhao Chen, Pengliang Du, Rui Wang, Ying Wu, Hang Yang, Yansong Li, Beichuan Wang, Haiyang Mu, Zheng Wang, Jianfeng Tian, Liang Ge, Yongna Mao, Shengming Li, Xiaomeng Lu, Jinhang Zou, Yang Huang, Ningchen Sun, Jie Zheng, Min He, Yu Bai, Junjie Jin, Hong Wu, Jifeng Liu,
- Abstract summary: We present the StarWhisper Telescope, an AI agent framework automating end-to-end astronomical observations for surveys like Nearby Galaxy Supernovae Survey.<n>StarWhisper Telescope autonomously generates site-specific observation lists, executes real-time image analysis via pipelines, and dynamically triggers follow-up proposals upon transient detection.<n>The system reduces human intervention through automated observation planning, telescope controlling and data processing, while enabling seamless collaboration between amateur and professional astronomers.
- Score: 20.76043220625063
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The exponential growth of large-scale telescope arrays has boosted time-domain astronomy development but introduced operational bottlenecks, including labor-intensive observation planning, data processing, and real-time decision-making. Here we present the StarWhisper Telescope system, an AI agent framework automating end-to-end astronomical observations for surveys like the Nearby Galaxy Supernovae Survey. By integrating large language models with specialized function calls and modular workflows, StarWhisper Telescope autonomously generates site-specific observation lists, executes real-time image analysis via pipelines, and dynamically triggers follow-up proposals upon transient detection. The system reduces human intervention through automated observation planning, telescope controlling and data processing, while enabling seamless collaboration between amateur and professional astronomers. Deployed across Nearby Galaxy Supernovae Survey's network of 10 amateur telescopes, the StarWhisper Telescope has detected transients with promising response times relative to existing surveys. Furthermore, StarWhisper Telescope's scalable agent architecture provides a blueprint for future facilities like the Global Open Transient Telescope Array, where AI-driven autonomy will be critical for managing 60 telescopes.
Related papers
- AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios [64.51320327698231]
We introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios.<n>We develop an innovative semi-automated collaborative agent-based labeling assistant framework.<n>We also propose HawkEyeTrack, a novel method that collaboratively enhances vision-language representation learning.
arXiv Detail & Related papers (2025-11-26T04:44:27Z) - Robustness analysis of Deep Sky Objects detection models on HPC [0.0]
Astronomical surveys and the growing involvement of amateur astronomers are producing more sky images than ever before.<n> Detecting Deep Sky Objects remains challenging because of their faint signals and complex backgrounds.<n>Computer Vision and Deep Learning now make it possible to improve and automate this process.
arXiv Detail & Related papers (2025-08-13T14:05:48Z) - STAR: A Benchmark for Astronomical Star Fields Super-Resolution [52.895107920663236]
We propose STAR, a large-scale astronomical SR dataset containing 54,738 flux-consistent star field image pairs.<n>We propose a Flux-Invariant Super Resolution (FISR) model that could accurately infer the flux-consistent high-resolution images from input photometry.
arXiv Detail & Related papers (2025-07-22T09:28:28Z) - AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy [59.32718342798908]
We introduce AstroVisBench, the first benchmark for both scientific computing and visualization in the astronomy domain.<n>We present an evaluation of state-of-the-art language models, showing a significant gap in their ability to engage in astronomy research as useful assistants.
arXiv Detail & Related papers (2025-05-26T21:49:18Z) - The Exoplanet Citizen Science Pipeline: Human Factors and Machine Learning [0.0]
We present the progress of work to streamline and simplify the process of exoplanet observation by citizen scientists.
International collaborations such as ExoClock and Exoplanet Watch enable citizen scientists to use small telescopes to carry out transit observations.
Our projects work closely with these communities to streamline their observation pipelines and enable wider participation.
arXiv Detail & Related papers (2025-03-18T15:54:00Z) - Solving Online Resource-Constrained Scheduling for Follow-Up Observation in Astronomy: a Reinforcement Learning Approach [4.027575411413831]
This paper presents ROARS, a reinforcement learning approach for online astronomical resource-constrained scheduling.
To capture the structure of the astronomical observation scheduling, we depict every schedule using a directed acyclic graph (DAG)
Deep reinforcement learning is used to learn a policy that can improve the feasible solution by iteratively local rewriting until convergence.
arXiv Detail & Related papers (2025-02-16T14:01:12Z) - TelescopeML -- I. An End-to-End Python Package for Interpreting Telescope Datasets through Training Machine Learning Models, Generating Statistical Reports, and Visualizing Results [1.3372051498158442]
textttTelescopeML is a Python package developed to perform three main tasks.
Process the synthetic astronomical datasets for training a CNN model and prepare the observational dataset for later use for prediction.
arXiv Detail & Related papers (2024-07-24T00:44:52Z) - Real-time gravitational-wave inference for binary neutron stars using machine learning [71.29593576787549]
We present a machine learning framework that performs complete BNS inference in just one second without making any approximations.
Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $sim30%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses.
arXiv Detail & Related papers (2024-07-12T18:00:02Z) - Self-Driving Telescopes: Autonomous Scheduling of Astronomical
Observation Campaigns with Offline Reinforcement Learning [0.6976905094072174]
We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO)
We show that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set.
This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.
arXiv Detail & Related papers (2023-11-29T21:23:30Z) - AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor
Shower Mapping [0.32622301272834514]
The Cameras for Allsky Meteor Surveillance (CAMS) project aims to map our meteor showers by triangulating meteor trajectories detected in low-light video cameras.
Our research aimed to streamline the data processing by implementing an automated cloud-based AI-enabled pipeline.
To date, CAMS has discovered over 200 new meteor showers and has validated dozens of previously reported showers.
arXiv Detail & Related papers (2023-08-02T18:26:16Z) - Radio astronomical images object detection and segmentation: A benchmark
on deep learning methods [5.058069142315917]
In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical images obtained by radio interferometric instrumentation, to solve the task of automatic source detection.
The goal is to provide an overview of existing techniques, in terms of prediction performance and computational efficiency, to scientists in the astrophysics community who would like to employ machine learning in their research.
arXiv Detail & Related papers (2023-03-08T10:55:24Z) - Applications of AI in Astronomy [0.0]
We provide an overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology.
Over the past decade we have seen an exponential growth of the astronomical literature involving a variety of ML/AI applications.
As the data complexity continues to increase, we anticipate further advances leading towards a collaborative human-AI discovery.
arXiv Detail & Related papers (2022-12-03T00:38:59Z) - Multi-strip observation scheduling problem for ac-tive-imaging agile
earth observation satellites [0.0]
We investigate the multi-strip observation scheduling problem for an active-image agile earth observation satellite (MOSP)
A bi-objective optimization model is presented along with an adaptive bi-objective memetic algorithm which integrates the combined power of an adaptive large neighborhood search algorithm (ALNS) and a nondominated sorting genetic algorithm II (NSGA-II)
Our model is more versatile than existing models and provide enhanced capabilities in applied problem solving.
arXiv Detail & Related papers (2022-07-04T08:35:57Z) - The State of Aerial Surveillance: A Survey [62.198765910573556]
This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective.
The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed.
arXiv Detail & Related papers (2022-01-09T20:13:27Z) - First Full-Event Reconstruction from Imaging Atmospheric Cherenkov
Telescope Real Data with Deep Learning [55.41644538483948]
The Cherenkov Telescope Array is the future of ground-based gamma-ray astronomy.
Its first prototype telescope built on-site, the Large Size Telescope 1, is currently under commissioning and taking its first scientific data.
We present for the first time the development of a full-event reconstruction based on deep convolutional neural networks and its application to real data.
arXiv Detail & Related papers (2021-05-31T12:51:42Z) - DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts
using Deep Learning [70.80563014913676]
We investigate the use of convolutional neural networks (CNNs) for the problem of separating low-surface-brightness galaxies from artifacts in survey images.
We show that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
arXiv Detail & Related papers (2020-11-24T22:51:08Z) - Smart obervation method with wide field small aperture telescopes for
real time transient detection [8.751383520994425]
We propose ARGUS (Astronomical taRGets detection framework for Unified telescopes) for real-time transit detection.
The ARGUS uses a deep learning based astronomical detection algorithm implemented in embedded devices in each WFSATs to detect astronomical targets.
We use simulated data to test the performance of ARGUS and find that ARGUS can increase the performance of WFSATs in transient detection tasks robustly.
arXiv Detail & Related papers (2020-11-20T13:48:32Z) - Assisted Perception: Optimizing Observations to Communicate State [112.40598205054994]
We aim to help users estimate the state of the world in tasks like robotic teleoperation and navigation with visual impairments.
We synthesize new observations that lead to more accurate internal state estimates when processed by the user.
arXiv Detail & Related papers (2020-08-06T19:08:05Z) - Agile Earth observation satellite scheduling over 20 years:
formulations, methods and future directions [69.47531199609593]
Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs)
The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs)
arXiv Detail & Related papers (2020-03-13T09:38:40Z) - A Spatial-Temporal Attentive Network with Spatial Continuity for
Trajectory Prediction [74.00750936752418]
We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC)
First, spatial-temporal attention mechanism is presented to explore the most useful and important information.
Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
arXiv Detail & Related papers (2020-03-13T04:35:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.