An anonymous reader quotes a report from Space.com: NASA's Chandra X-ray Observatory has found a brand new type of an otherwise familiar object -- compact binary systems that steal gas from other stars, but which do so while emitting very low-energy X-rays but copious amounts of ultraviolet. Chandra spotted 84 suspicious object emitting very low-energy X-rays in six galaxies -- four evolved ellipticals and two spirals, namely our near-neighbor the Andromeda Galaxy (M31) and the Pinwheel Galaxy (M101). "We've never encountered a group of objects that act like this," said Mustafa Muhibullah of the University of Alabama in a statement. Compact objects, in the form of black holes, neutron stars and white dwarfs, are the remains of stars that have died. Their small size and powerful gravity means that they're adept at getting close to orbiting companion stars and ripping the stars' outer layers of gas away. The gas swirls around the compact object, falling onto its surface, and this accretionary process usually produces torrents of high-energy X-rays because the stolen gas becomes superheated by the compact object's gravitational vice-like grip. Because of their weak X-rays, Muhibullah's team refer to them as 'hypersoft X-ray sources'. Since low-energy X-rays and ultraviolet light are next to each other on the electromagnetic spectrum, Muhibullah and his team suspect that the low-energy X-rays are spillover from objects emitting prodigious amounts of ultraviolet. Moreover, they think that these objects are lower-level compact binaries. They're hard to find though, because the interstellar medium of hydrogen and helium gas between the stars absorbs ultraviolet light. This could be why we have yet to find any hypersoft X-ray sources in our own galaxy, because we have to look through the interstellar medium through the plane of the Milky Way. Consequently, there could be a large population of hypersoft X-ray sources that remain undiscovered and they could have an important effect on the universe around them. The findings have been published in the journal Nature Astronomy.

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A misconfigured Advance Passenger Information System (APIS) database linked to Vietnam exposed more than 220 million passenger and crew travel records spanning 2017 to 2026, including names, passport numbers, nationalities, flight details, seat assignments, and baggage references. Researchers said the database was reachable through a chain of security mistakes and default credentials. It was later secured after the disclosure, but it's unclear whether the data had already been copied or abused. BleepingComputer reports: Kinryu Labs discovered the Elasticsearch cluster on June 3 while surveying exposed databases as part of research into ransomware activity. The cluster, named 'pax-info', contained 29 indices and roughly 107 GB of data. Its two principal indices held 210,318,069 passenger records and 10,465,631 crew records, for a combined 220,783,700 entries. According to Kinryu Labs, the cluster was hosted in Viettel-assigned IP space in Hanoi. BleepingComputer could not confirm which Vietnamese organization operated the system. The exposed information included passengers' and crew members' names, dates of birth, sex, nationalities, passport or travel-document numbers, document expiration dates, and issuing countries. Associated travel data included flight numbers and dates, airlines, departure, destination and transit airports, seat assignments, baggage references, and scheduled, estimated, and actual flight times, information typically carried by APIS and related airline systems. Sample records reviewed by BleepingComputer included travelers of Korean, Chinese, Canadian, and New Zealand nationality, among others. While the researchers could not provide a complete breakdown by nationality, the data covered numerous international airlines across Asia-Pacific, Europe, and the Middle East. As a result, the exposed records could relate to people from virtually anywhere who visited or transited through Vietnam over the nine-year period. Kinryu Labs expects to publish additional details on its blog later this week.

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NASA and IBM have released an open-source AI model trained on a large collection of lunar observations to help scientists analyze the Moon at scale. "The NASA-IBM Lunar Foundation Model gives scientists a foundation to explore the Moon at scale, connecting observations across instruments, revealing patterns that are difficult to see in isolation, and providing an open platform the global research community can build on," said IBM director of research for Europe, Juan Bernabe-Moreno. The Register reports: It is claimed as the first AI model to integrate observations captured in a range of modalities (data formats), and at different viewing angles and spatial scales. Instead of sifting through maps and images by hand or using low resolution machine learning models, scientists can use this to analyze geographic features, the pair say. In particular, NASA and IBM hope researchers will be able to discover previously unidentified lunar ice deposits, analyze volcanic features called Irregular Mare Patches, and identify and classify craters. Lunar ice indicates the presence of water and oxygen, which may be useful for future manned missions. It is found in permanently shadowed regions, which are among the most difficult areas to observe. The NASA-IBM model combines multimodal and multi-resolution observations to better predict where ice may be present on the lunar surface. Alongside the model, IBM and NASA scientists compiled an open-source lunar dataset from over 30 spatially-aligned layers, using data from nine instruments across four missions. It combines tens of thousands of images and maps showing various geophysical properties of the lunar surface.

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