LAMOST CCD camera-control system based on RTS2

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope（LAMOST） is the largest existing spectroscopic survey telescope, having 32 scientific charge-coupled-device（CCD） cameras for acquiring spectra. Stability and automation of the camera-control software are essential, but cannot be provided by the existing system. The Remote Telescope System 2 nd Version（RTS2） is an open-source and automatic observatory-control system. However, all previous RTS2 applications were developed for small telescopes. This paper focuses on implementation of an RTS2-based camera-control system for the 32 CCDs of LAMOST. A virtual camera module inherited from the RTS2 camera module is built as a device component working on the RTS2 framework. To improve the controllability and robustness, a virtualized layer is designed using the master-slave software paradigm, and the virtual camera module is mapped to the 32 real cameras of LAMOST. The new system is deployed in the actual environment and experimentally tested. Finally, multiple observations are conducted using this new RTS2-frameworkbased control system. The new camera-control system is found to satisfy the requirements for automatic camera control in LAMOST. This is the first time that RTS2 has been applied to a large telescope, and provides a referential solution for full RTS2 introduction to the LAMOST observatory control system.

Fine-grained distributed averaging for large-scale radio interferometric measurement sets

The Square Kilometre Array(SKA)would be the world’s largest radio telescope with eventually over a square kilometre of collecting area.However,there are enormous challenges in its data processing.The use of modern distributed computing techniques to solve the problem of massive data processing in the SKA is one of the most important challenges.In this study,basing on the Dask distribution computational framework,and taking the visibility function integral processing as an example,we adopt a multi-level parallelism method to implement distributed averaging over time and channel.Dask Array was used to implement super large matrix or arrays with supported parallelism.To maximize the usage of memory,we further exploit the data parallelism provided by Dask that intelligently distributes the computational load across a network of computer agents and has a built-in fault tolerance mechanism.The validity of the proposed pattern was also verified by using the Common Astronomy Software Application(CASA),wherein we analyze the smearing effects on images reconstructed from different resolution visibilities.

Enhanced remote astronomical archive system based on the file-level Unlimited Sliding-Window technique

Data archiving is one of the most critical issues for modern astronomical observations.With the development of a new generation of radio telescopes,the transfer and archiving of massive remote data have become urgent problems to be solved.Herein,we present a practical and robust file-level flow-control approach,called the Unlimited Sliding-Window(USW),by referring to the classic flow-control method in the TCP protocol.Based on the USW and the Next Generation Archive System(NGAS)developed for the Murchison Widefield Array telescope,we further implemented an enhanced archive system(ENGAS)using ZeroMQ middleware.The ENGAS substantially improves the transfer performance and ensures the integrity of transferred files.In the tests,the ENGAS is approximately three to twelve times faster than the NGAS and can fully utilize the bandwidth of network links.Thus,for archiving radio observation data,the ENGAS reduces the communication time,improves the bandwidth utilization,and solves the remote synchronous archiving of data from observatories such as Mingantu spectral radioheliograph.It also provides a better reference for the future construction of the Square Kilometer Array(SKA)Science Regional Center.

A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe.However,effectively analyzing this vast amount of data poses a significant challenge.In response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate workloads.To overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and more.Considering the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively.The proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging Surveys.In particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 requested.In addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.