In order to understand the appropriate field of view (FOV) size of celestial automatic navigation systems for surface ships, we investigate the variations of measurement accuracy of star position and probability of successful star identification with respect to FOV, focusing on the decreasing number of observable star magnitudes and the presence of physically covered stars in marine environments. The results revealed that, although a larger FOV reduces the measurement accuracy of star positions, it increases the number of observable objects and thus improves the probability of star identification using subgraph isomorphism-based methods. It was also found that, although at least four objects need to be observed for accurate identification, four objects may not be sufficient for wider FOVs. On the other hand, from the point of view of celestial navigation systems, a decrease in the measurement accuracy leads to a decrease in positioning accuracy. Therefore, it was found that maximizing the FOV is required for celestial automatic navigation systems as long as the desired positioning accuracy can be ensured. Furthermore, it was found that algorithms incorporating more than four observed celestial objects are required to achieve highly accurate star identification over a wider FOV.

The automatic identification of planetary feature names in astronomy publications presents numerous challenges. These features include craters, defined as roughly circular depressions resulting from impact or volcanic activity; dorsas, which are elongate raised structures or wrinkle ridges; and lacus, small irregular patches of dark, smooth material on the Moon, referred to as "lake" (Planetary Names Working Group, n.d.). Many feature names overlap with places or people's names that they are named after, for example, Syria, Tempe, Einstein, and Sagan, to name a few (U.S. Geological Survey, n.d.). Some feature names have been used in many contexts, for instance, Apollo, which can refer to mission, program, sample, astronaut, seismic, seismometers, core, era, data, collection, instrument, and station, in addition to the crater on the Moon. Some feature names can appear in the text as adjectives, like the lunar craters Black, Green, and White. Some feature names in other contexts serve as directions, like craters West and South on the Moon. Additionally, some features share identical names across different celestial bodies, requiring disambiguation, such as the Adams crater, which exists on both the Moon and Mars. We present a multi-step pipeline combining rule-based filtering, statistical relevance analysis, part-of-speech (POS) tagging, named entity recognition (NER) model, hybrid keyword harvesting, knowledge graph (KG) matching, and inference with a locally installed large language model (LLM) to reliably identify planetary names despite these challenges. When evaluated on a dataset of astronomy papers from the Astrophysics Data System (ADS), this methodology achieves an F1-score over 0.97 in disambiguating planetary feature names.

The Chinese Space Station Telescope (abbreviated as CSST) is a future advanced space telescope. Real-time identification of galaxy and nebula/star cluster (abbreviated as NSC) images is of great value during CSST survey. While recent research on celestial object recognition has progressed, the rapid and efficient identification of high-resolution local celestial images remains challenging. In this study, we conducted galaxy and NSC image classification research using deep learning methods based on data from the Hubble Space Telescope. We built a Local Celestial Image Dataset and designed a deep learning model named HR-CelestialNet for classifying images of the galaxy and NSC. HR-CelestialNet achieved an accuracy of 89.09% on the testing set, outperforming models such as AlexNet, VGGNet and ResNet, while demonstrating faster recognition speeds. Furthermore, we investigated the factors influencing CSST image quality and evaluated the generalization ability of HR-CelestialNet on the blurry image dataset, demonstrating its robustness to low image quality. The proposed method can enable real-time identification of celestial images during CSST survey mission.

As human access to space expands, the influx of new actors promises to forever alter the dynamics of space. The head-to-head U.S.–Soviet rivalry that once dominated the Space Race will evolve into something more inclusive--but also messier. Aspiring space nations, such as Luxembourg, India, and China, together with new categories of nonstate actors, including large industrial players, startups, and universities, raise questions about how we should regulate space. Explosive commercialization is particularly challenging for existing space law, whose foundations were set in the 1960s and designed with national governments in mind. This rapidly changing environment is dramatized in "Little Assistance," a new Future Tense Fiction story from Stephen Harrison.

This work presents a large-scale simulation study investigating the deployment and operation of distributed swarms of CubeSats for interplanetary missions to small celestial bodies. Utilizing Taylor numerical integration and advanced collision detection techniques, we explore the potential of large CubeSat swarms in capturing gravity signals and reconstructing the internal mass distribution of a small celestial body while minimizing risks and Delta V budget. Our results offer insight into the applicability of this approach for future deep space exploration missions. Introduction In the last decade CubeSats have emerged as an innovative and cost-effective platform for testing new satellite technologies, with applications ranging from Earth observation to deep space missions. For instance, the HERA interplanetary mission that will explore the Didymos binary system in 2025 will embark two CubeSats to perform a detailed exploration of the system.

Researchers have discovered another 168 geoglyphs made in the soil of Peru's Nazca Desert, known as the Nazca lines. The newly-discovered drawings – identified by a team at Yamagata University in Japan – depict humans, camelids, birds, killer whales, felines and snakes. One of the human drawings looks like Homer Simpson, with big cartoon eyes and a patch of what looks like stubble around the mouth. These 168 newly-found geoglyphs are thought to date between 100 BC and AD 300, according to experts, but other Nazca lines may go back even further to 400 BC. The Nazca lines are a group of geoglyphs made in the soil of the Nazca Desert in southern Peru.

As artificial intelligence travels through the solar system and gets to explore the heliosphere (enclosing the planets), it will adapt by making decisions that enable it to do its job. Many people in the field of astrobiology are in favour of the so-called post-biological cosmos vision. Is it because of the desire to conquer space that we humans are sowing the seeds of our own destruction in favour of artificial intelligence? Or are we unconsciously following some sort of master plan in which flesh and blood beings are destined to become extinct and be hybridised by silicon and synthetic materials? As for the mind, memory, consciousness, could there also be a place for humans in a robot's brain?

Building accurate and predictive models of the underlying mechanisms of celestial motion has inspired fundamental developments in theoretical physics. Candidate theories seek to explain observations and predict future positions of planets, stars, and other astronomical bodies as faithfully as possible. We use a data-driven learning approach, extending that developed in Lu et al. ($2019$) and extended in Zhong et al. ($2020$), to a derive stable and accurate model for the motion of celestial bodies in our Solar System. Our model is based on a collective dynamics framework, and is learned from the NASA Jet Propulsion Lab's development ephemerides. By modeling the major astronomical bodies in the Solar System as pairwise interacting agents, our learned model generate extremely accurate dynamics that preserve not only intrinsic geometric properties of the orbits, but also highly sensitive features of the dynamics, such as perihelion precession rates. Our learned model can provide a unified explanation to the observation data, especially in terms of reproducing the perihelion precession of Mars, Mercury, and the Moon. Moreover, Our model outperforms Newton's Law of Universal Gravitation in all cases and performs similarly to, and exceeds on the Moon, the Einstein-Infeld-Hoffman equations derived from Einstein's theory of general relativity.

I saw "Apollo 11" in the Los Angeles suburb of Alhambra, sitting in an IMAX theatre with ten or so other freelancers and retirees who could see a documentary about NASA in the middle of a Thursday. The director and editor, Todd Douglas Miller, tells the story of the moon launch using archival footage, including a trove of 70-mm. The film has no voice-over narration. Instead its story is relayed by the newscasts of Walter Cronkite and the radio transmissions of Edwin (Buzz) Aldrin, Neil Armstrong, Michael Collins, and their interlocutors on Earth. The result is a visual museum about America in July, 1969, in which Aldrin's famous 16-mm.

Today, all you have to do to find your way around a new city is fire up Google Maps, Waze, or one of the many other GPS-based navigation apps available. Follow the steps, and, bam, you get where you need to go. We don't have that same luxury in space. But now, researchers from NASA's Frontier Development Lab (FDL) and Intel are working to change that. They've found a way to make it just as easy to navigate a new planet -- they just needed a little help from artificial intelligence (AI) to do it.