The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. We demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization.

An Astronomer has released our best and sharpest look to date at Comet Borisov, the second ever-known interstellar object to visit our solar system, using NASA's Hubble Space Telescope to capture the new image. The comet was travelling at around 110,000 miles per hour when University of California Los Angeles astronomer David Jewitt studied it on October 12, 2019, when it was 260 million miles away. The comet -- which is named after the Crimean astronomer who discovered it -- will pass within around 177,000 miles (285,000 kilometres) of the Earth in early December this year. It is trailing behind it a 100,000 mile-long tail of dust, which is released as the comet melts in the Sun's glare. After this, it will head back out towards interstellar space, passing Jupiter around the middle of 2020.

If you haven't heard of chatbots yet -- or your experience is limited to novelty programs like Cleverbot -- chances are you'll be seeing more of them in the coming years. Because companies are slowly starting to leverage chatbots as a way to manage basic communication tasks that used to belong solidly to the realm of human capabilities. In this piece, Hristo Borisov, the Director of Product Management at Progress, helps illuminate what chatbots are, how to build them, and their role in the future of business. In short, chatbots are robots programmed to respond like humans. According to Borisov's definition, "A chatbot is a computer program that is capable of having a human-like conversation with a user by receiving and sending text messages for the purpose of automating a business process."