The failed star factory is nicknamed'Cloud-9.' This image shows the location of Cloud-9, which is 2000 light-years from Earth. The diffuse magenta is radio data from the ground-based Very Large Array (VLA) showing the presence of the cloud. The dashed circle marks the peak of radio emission, which is where researchers focused their search for stars. Breakthroughs, discoveries, and DIY tips sent every weekday.

Nicknamed Dracula's Chivito, the disk is 1,000 light-years away from Earth. Breakthroughs, discoveries, and DIY tips sent every weekday. Scientists are leaving space fans with one more treat before the year comes to a close. Using the Hubble Space Telescope, astronomers captured a stunning image of the largest protoplanetary disk ever observed, which just happens to be shaped like a giant celestial sandwich. The massive formation of dust and gas, which astronomers call Dracula's Chivito, resides about 1,000 light-years from Earth and spans roughly 400 billion miles.

Astronomers initially thought the dramatic burst of light was a new exoplanet. Breakthroughs, discoveries, and DIY tips sent every weekday. What astronomers initially suspected to be a new exoplanet is actually a never-before-seen, head-on cosmic crash. As detailed in a study published today in the journal, researchers describe the aftermath of two separate collisions between two small, rocky cosmic objects called planetesimals . However, their findings were only made possible by some eagle eye imaging courtesy of the Hubble Space Telescope .

The interstellar object is still soaring through our solar system. Breakthroughs, discoveries, and DIY tips sent every weekday. It's understandable why every space agency and astronomy enthusiast around the world is trying to catch a glimpse of 3I/ATLAS . Not only is it the third-known interstellar object to pass through our solar system,it's also the fastest comet ever recorded . But even as it races at 130,00 miles per hour towards its closest distance from Earth, NASA's Hubble Space Telescope recently caught another stunning glimpse of the icy rock.

The cosmic oddball that's 67 million light-years away has a puzzling shape. Breakthroughs, discoveries, and DIY tips sent every weekday. What does it look like in your mind? Chances are, it's a swirling circle of galactic energy . A galaxy is often described as one of a few broadly defined shapes--elliptical, spiral, or lenticular--as described by the Hubble sequence .

Breakthroughs, discoveries, and DIY tips sent every weekday. You can now own a 1 gold coin celebrating one of America's most revolutionary achievements: the NASA Space Shuttle program. The latest variant in the ongoing American Innovation 1 Coin series is available to order through the United States Mint. Selected to represent the state of Florida, the noncirculating legal tender is the third coin released this year and the 28th coin in the 15-year project first announced in 2018. While the coin's front displays the series' Statue of Liberty image, the back shows the shuttle launching above plumes of exhaust.

We present PAPERCLIP (Proposal Abstracts Provide an Effective Representation for Contrastive Language-Image Pre-training), a method which associates astronomical observations imaged by telescopes with natural language using a neural network model. The model is fine-tuned from a pre-trained Contrastive Language-Image Pre-training (CLIP) model using successful observing proposal abstracts and corresponding downstream observations, with the abstracts optionally summarized via guided generation using large language models (LLMs). Using observations from the Hubble Space Telescope (HST) as an example, we show that the fine-tuned model embodies a meaningful joint representation between observations and natural language through tests targeting image retrieval (i.e., finding the most relevant observations using natural language queries) and description retrieval (i.e., querying for astrophysical object classes and use cases most relevant to a given observation). Our study demonstrates the potential for using generalist foundation models rather than task-specific models for interacting with astronomical data by leveraging text as an interface.

The success of AI assistants based on Language Models (LLMs) hinges on Reinforcement Learning from Human Feedback (RLHF) to comprehend and align with user intentions. However, traditional alignment algorithms, such as PPO, are hampered by complex annotation and training requirements. This reliance limits the applicability of RLHF and hinders the development of professional assistants tailored to diverse human preferences. In this work, we introduce \textit{Linear Alignment}, a novel algorithm that aligns language models with human preferences in one single inference step, eliminating the reliance on data annotation and model training. Linear alignment incorporates a new parameterization for policy optimization under divergence constraints, which enables the extraction of optimal policy in a closed-form manner and facilitates the direct estimation of the aligned response. Extensive experiments on both general and personalized preference datasets demonstrate that linear alignment significantly enhances the performance and efficiency of LLM alignment across diverse scenarios. Our code and dataset will be published on \url{https://github.com/Wizardcoast/Linear_Alignment.git}.

The performance of automatic summarization models has improved dramatically in recent years. Yet, there is still a gap in meeting specific information needs of users in real-world scenarios, particularly when a targeted summary is sought, such as in the useful aspect-based summarization setting targeted in this paper. Previous datasets and studies for this setting have predominantly concentrated on a limited set of pre-defined aspects, focused solely on single document inputs, or relied on synthetic data. To advance research on more realistic scenarios, we introduce OpenAsp, a benchmark for multi-document \textit{open} aspect-based summarization. This benchmark is created using a novel and cost-effective annotation protocol, by which an open aspect dataset is derived from existing generic multi-document summarization datasets. We analyze the properties of OpenAsp showcasing its high-quality content. Further, we show that the realistic open-aspect setting realized in OpenAsp poses a challenge for current state-of-the-art summarization models, as well as for large language models.

The Orion Nebula, which formed more than 4.5 billion years ago, is teeming with different colored gasses, molecular material, dust and scattered starlight and a new image snapped by NASA's James Webb Space Telescope (JWST) is the first to look at the cosmic formation's center, allowing researchers to better understand how massive stars are birthed by the colossal cloud of dust and gas. The image shows an open cluster of young massive stars that shape the cloud of dust and gas with its intense radiation and dense filaments that may play a key role in birthing new stars. The nebula was previously photographed by the Hubble Telescope in 2004, but this device uses visible light and its view was obscured by the large amounts of stardust. JWST, however, detects the infrared light of the cosmos, allowing observers to see through these layers of dust and peer into its cosmic center - a region that has just now been seen by human eyes. The Orion Nebula, which sits 1,350 light years from Earth, is said to be similar to our own solar system, which scientists believe could provide clues to what happened in the first million years of our planetary evolution. NASA's James Webb Space Telescope has captured the most detailed image of the Orion Nebula that sits 1,344 light years from Earth, which experts say could provide clues to the first few million years of our own solar system A nebula is the name given to a giant cloud in space made of dust, which are also areas that birth new stars - the Orion Nebula is believed to have created thousands of new stars.