The classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep learning techniques has given room for the automation of this process. As such, this paper offers a comparison of deep learning architectures to determine which is best suited for optical galaxy morphology classification. Adapting the model training method proposed by Walmsley et al in 2021, the Zoobot Python library is used to train models to predict Galaxy Zoo DECaLS decision tree responses, made by volunteers, using EfficientNet B0, DenseNet121 and ResNet50 as core model architectures. The predicted results are then used to generate accuracy metrics per decision tree question to determine architecture performance. DenseNet121 was found to produce the best results, in terms of accuracy, with a reasonable training time. In future, further testing with more deep learning architectures could prove beneficial.

In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-source this work will soon be outpaced by the large amount of data generated by modern surveys. This has brought into question the viability of human-based methods for classifying galaxy morphology. While supervised learning methods require datasets with existing labels, unsupervised learning techniques do not. Therefore, this paper implements unsupervised learning techniques to classify the Galaxy Zoo DECaLS dataset. A convolutional autoencoder feature extractor was trained and implemented. The resulting features were then clustered via k-means, fuzzy c-means and agglomerative clustering. These clusters were compared against the true volunteer classifications provided by the Galaxy Zoo DECaLS project. The best results, in general, were produced by the agglomerate clustering method. However, the increase in performance compared to k-means clustering was not significant considering the increase in clustering time. After undergoing the appropriate clustering algorithm optimizations, this approach could prove useful for classifying the better performing questions and could serve as the basis for a novel approach to generating more ”human-like” galaxy morphology classifications from unsupervised techniques.

Nanosatellites are proliferating as low-cost dedicated sensing systems with lean development cycles. Kyushu Institute of Technology (Kyutech) and collaborators have launched a joint venture for a nanosatellite mission, Visible Extragalactic background RadiaTion Exploration by CubeSat (VERTECS). The primary mission is to elucidate the formation history of stars by observing the optical-wavelength cosmic background radiation. The VERTECS satellite will be equipped with a small-aperture telescope and a high-precision attitude control system to capture the astronomical data for analysis on the ground. However, nanosatellites are limited by their onboard memory resources and downlink speed capabilities. Additionally, due to a limited number of ground stations, the satellite mission will face issues meeting the required data budget for mission success. To alleviate this issue, we propose an on-orbit system pipeline to autonomously classify and then compress desirable image data for downlink prioritization and optimization. The system comprises a prototype Camera Controller Board (CCB) which carries a Raspberry Pi Compute Module four which is used for classification and compression. The system uses a lightweight Convolutional Neural Network (CNN) model to classify and determine the desirability of captured image data. The model is designed to be lean and robust to reduce the computational and memory load on the satellite. The model is trained and tested on a novel star field dataset consisting of data captured by the Sloan Digital Sky Survey (SDSS). The dataset is meant to simulate the expected data produced by the 6U satellite. The compression step implements GZip, RICE or HCOMPRESS compression, which are standards for astronomical data. Preliminary testing on the proposed CNN model results in a validation classification accuracy of 100% on the star field dataset, with compression ratios of 3.99, 5.16 and 5.43 for GZip, RICE and HCOMPRESS that were achieved on tested FITS image data.

We describe scientific objective and project status of an astronomical 6U CubeSat mission VERTECS (Visible Extragalactic background RadiaTion Exploration by CubeSat). The scientific goal of VERTECS is to reveal the star-formation history along the evolution of the universe by measuring the extragalactic background light (EBL) in the visible wavelength. Earlier observations have shown that the near-infrared EBL is several times brighter than integrated light of individual galaxies. As candidates for the excess light, first-generation stars in the early universe or low-redshift intra-halo light have been proposed. Since these objects are expected to show different emission spectra in visible wavelengths, multi-color visible observations are crucial to reveal the origin of the excess light. Since detection sensitivity of the EBL depends on the product of the telescope aperture and the field of view, it is possible to observe it with a small but wide-field telescope system that can be mounted on the limited volume of CubeSat. In VERTECS mission, we develop a 6U CubeSat equipped with a 3U-sized telescope optimized for observation of the visible EBL. The bus system composed of onboard computer, electric power system, communication subsystem, and structure is based on heritage of series of CubeSats developed at Kyushu Institute of Technology in combination with high-precision attitude control subsystem and deployable solar array paddle required for the mission. The VERTECS mission was selected for JAXA-Small Satellite Rush Program (JAXA-SMASH Program), a new program that encourages universities, private companies and JAXA to collaborate to realize small satellite missions utilizing commercial small launch opportunities, and to diversify transportation services in Japan. We started the satellite development in December 2022 and plan to launch the satellite in FY2025.

Due to advances in observation and imaging technologies, modern astronomical satellites generate large volumes of data. This necessitates efficient onboard data processing and high-speed data downlink. Reflecting this trend is the Visible Extragalactic background RadiaTion Exploration by CubeSat (VERTECS) 6U Astronomical Nanosatellite. Designed for the observation of Extragalactic Background Light (EBL), this mission is expected to generate a substantial amount of image data, particularly within the confines of CubeSat capabilities. This paper introduces the VERTECS Camera Control Board (CCB), an open-source payload interface board leveraging Commercial Off-The-Shelf (COTS) components, with a Raspberry Pi Compute Module four at its core. The VERTECS CCB hardware and software have been designed from the ground up to serve as the sole interface between the VERTECS bus system and astronomical imaging payload, while providing compute capability not usually seen in nanosatellites of this class. Responsible for mission data processing, it will facilitate high-speed data transfer from the imaging payload via gigabit Ethernet, while also providing a high-bitrate serial connection to the payload x-band transmitter for mission data downlink. Additional interfaces for secondary payloads are provided via USB-C and standard 15-pin camera connectors. The Raspberry Pi embedded within the VERTECS CCB operates on a standard Linux distribution, streamlining the software development process. Beyond addressing the current mission’s payload control and data handling requirements, the CCB sets the stage for future missions with heightened data demands. Furthermore, it supports the adoption of machine learning and other compute-intensive applications in orbit. This paper delves into the development of the VERTECS CCB, offering insights into the design and validation of this next-generation payload interface, to ensure that it can survive the rigors of space flight.

Nanosatellites increasingly produce more data than can be downlinked within a reasonable time due to their limited bandwidth and power. Therefore, an on-board system is required to prioritize scientifically significant data for downlinking, as described by scientists. This paper determines whether natural language processing can be used to prioritize remote sensing images on CubeSats with more flexibility compared to existing methods. Two approaches implementing the same conceptual prioritization pipeline are compared. The first uses YOLOv8 and Llama2 to extract image features and compare them with text descriptions via cosine similarity. The second approach employs CLIP, fine-tuned on remote sensing data, to achieve the same. Both approaches are evaluated on real nanosatellite hardware, the VERTECS Camera Control Board. The CLIP approach, particularly the ResNet50-based model, shows the best performance in prioritizing and sequencing remote sensing images. This paper demonstrates that on-orbit prioritization using natural language descriptions is viable and allows for more flexibility than existing methods.