Modern astronomy has generated an extensive taxonomy of celestial objects based on their physical characteristics and predicted future state. As theories of the development, expansion, history, and predicted future state of the universe rely on identifying and observing celestial bodies, it is essential to have quick and accurate classification of newly observed objects. Historically, classification was performed manually, but the rapid expansion of modern catalogues of celestial objects - such as the Sloan Digital Sky Survey, which grows at a rate of thousands of entries daily [1] - makes this manual classification impractical. Supervised and semi-supervised machine learning represent the most promising candidates for the desired computational classification. Until recently, the data, hardware, and software required for large-scale training and deployment of these methods were unavailable to the general research community. However, improvements to parallel processing hardware have driven increased success and adoption, resulting in the invention of models capable of equaling or surpassing human-level intelligence in tasks formerly considered intractable to computers. Such improvements have been recognized in facial recognition [2] and combinatorial game theory [3], but despite their meteoric rise in popularity, there is a significant gap in astronomical literature on applying machine learning models to the problem of celestial object classification. In an effort to improve this state, we explore a number of machine learning based models for a simplified celestial object classification problem to assess the performance and potential of these models in the field of astronomy.

Fast moving celestial objects are characterized by velocities across the celestial sphere that significantly differ from the motions of background stars. In observational images, these objects exhibit distinct shapes, contrasting with the typical appearances of stars. Depending on the observational method employed, these celestial entities may be designated as near-Earth objects or asteroids. Historically, fast moving celestial objects have been observed using ground-based telescopes, where the relative stability of stars and Earth facilitated effective image differencing techniques alongside traditional fast moving celestial object detection and classification algorithms. However, the growing prevalence of space-based telescopes, along with their diverse observational modes, produces images with different properties, rendering conventional methods less effective. This paper presents a novel algorithm for detecting fast moving celestial objects within star fields. Our approach enhances state-of-the-art fast moving celestial object detection neural networks by transforming them into physical-inspired neural networks. These neural networks leverage the point spread function of the telescope and the specific observational mode as prior information; they can directly identify moving fast moving celestial objects within star fields without requiring additional training, thereby addressing the limitations of traditional techniques. Additionally, all neural networks are integrated using the mixture of experts technique, forming a comprehensive fast moving celestial object detection algorithm. We have evaluated our algorithm using simulated observational data that mimics various observations carried out by space based telescope scenarios and real observation images. Results demonstrate that our method effectively detects fast moving celestial objects across different observational modes.

The skies overhead could soon be filled with constellations of commercial space stations occupying low earth orbit while human colonists settle the Moon with an eye on Mars, if today's robber barons have their way. But this won't result in the same freewheeling Wild West that we saw in the 19th century, unfortunately, as tomorrow's interplanetary settlers will be bringing their lawyers with them. In their new book, The End of Astronauts: Why Robots Are the Future of Exploration, renowned astrophysicist and science editor, Donald Goldsmith, and Martin Rees, the UK's Astronomer Royal, argue in favor of sending robotic scouts -- with their lack of weighty necessities like life support systems -- out into the void ahead of human explorers. But what happens after these synthetic astronauts discover an exploitable resource or some rich dork declares himself Emperor of Mars? In the excerpt below, Goldsmith and Rees discuss the challenges facing our emerging exoplanetary legal system.

Classifying celestial objects is a long-standing problem. With sources at near unimaginable distances, sometimes it's difficult for researchers to distinguish between objects such as stars, galaxies, quasars or supernovae. Instituto de Astrofísica e Ciências do Espaço's (IA) researchers Pedro Cunha and Andrew Humphrey tried to solve this classical problem by creating SHEEP, a machine-learning algorithm that determines the nature of astronomical sources. Andrew Humphrey (IA & University of Porto, Portugal) comments: "The problem of classifying celestial objects is very challenging, in terms of the numbers and the complexity of the universe, and artificial intelligence is a very promising tool for this type of task." The first author of the article, now published in the journal Astronomy & Astrophysics, Pedro Cunha, a Ph.D. student at IA and in the Dept. of Physics and the University of Porto, says, "This work was born as a side project from my MSc thesis. It combined the lessons learned during that time into a unique project."

As AI models become more computationally demanding, engineers are looking to new types of materials and hardware to speed up the model development process. One category of components with promise is photonic chips, which leverage light to send signals as opposed to the electricity that conventional processors use. In theory, photonic chips could lead to higher performance because light produces less heat than electricity, can travel faster, and is less susceptible to changes in temperature and electromagnetic fields. But photonic chips have drawbacks that must be addressed if the technology is to reach the mainstream. Moreover, photonic architectures still largely rely on electronic control circuits, which can create bottlenecks.

One thousand newly-minted microrobots created in EPFL labs will soon be deployed at two large-scale telescopes in Chile and the United States. These high-precision instruments, capable of positioning optical fibers to within a micron, will vastly increase the quantity of astrophysics data that can be gathered – and expand our understanding of the Universe.

A guest post by Rodrigo Carrasco-Davis & The ALeRCE Collaboration, Millennium Institute of Astrophysics, Chile IntroductionAstronomy is the study of celestial objects, such as stars, galaxies or black holes. Studying celestial objects is a bit like having a natural physics laboratory - where the most extreme processes in nature occur - and most of them cannot be reproduced here on Earth.

Gazing up at the night sky in a rural area, you'll probably see the shining moon surrounded by stars. If you're lucky, you might spot the furthest thing visible with the naked eye – the Andromeda galaxy. When the Department of Energy's (DOE) Legacy Survey of Space and Time (LSST) Camera at the National Science Foundation's Vera Rubin Observatory turns on in 2022, it will take photos of 37 billion galaxies and stars over the course of a decade. The output from this huge telescope will swamp researchers with data. In those 10 years, the LSST Camera will take 2,000 photos for each patch of the Southern Sky it covers.

Gazing up at the night sky in a rural area, you'll probably see the shining moon surrounded by stars. If you're lucky, you might spot the furthest thing visible with the naked eye--the Andromeda galaxy. When the Department of Energy's (DOE) Legacy Survey of Space and Time (LSST) Camera at the National Science Foundation's Vera Rubin Observatory turns on in 2022, it will take photos of 37 billion galaxies and stars over the course of a decade. The output from this huge telescope will swamp researchers with data. In those 10 years, the LSST Camera will take 2,000 photos for each patch of the Southern Sky it covers.