Astronomy

The JWST keeps one-upping itself. In the telescope’s latest act of outdoing itself, it examined a distant exoplanet to map its weather. The forecast?

An unending, blistering inferno driven by ceaseless supersonic winds.

WASP-43b is a hot Jupiter orbiting a main sequence star about 261 light-years away. It has a slightly larger radius than Jupiter and is about twice as massive. It orbits its star in under 20 hours and is only 1.3 million miles away from it. That means it is tidally locked to the star, with one side facing all the radiation and the other permanently dark.

This is not unusual for exoplanet gas giants. They’re often tight to their stars and don’t rotate.

WASP-43b’s discovery was announced in 2011. Since then, astronomers have studied it extensively. In 2019, researchers captured its spectrum and reported water in its clouds. Conversely, no methane, carbon dioxide, or carbon monoxide were detected. Further research showed that mineral particles dominate its clouds. The Hubble Space Telescope was largely responsible for these results; other telescopes like the Spitzer also contributed.

Scientists knew that when the JWST was launched, it would eventually turn its eye toward WASP-43b. “Having a short orbital period and being tidally locked makes WASP-43b an ideal candidate for JWST observations,” explained the authors of a 2020 paper. “Phase curve observations of an entire orbit will enable the mapping of the atmospheric structure across the planet, with different wavelengths of observation allowing different atmospheric depths to be seen.” Their paper anticipated what the JWST might find and how its observations might be understood.

Now, we’re in the future, and the JWST has taken a look at WASP-43b and captured more detailed observations than ever. The space telescope’s powerful infrared capabilities measured the heat on both sides of the planet and allowed the mapping of the planet’s atmospheric structure, just as the authors of the 2020 paper stated.

“The fact that we can map temperature in this way is a real testament to Webb’s sensitivity and stability.”

Michael Roman, University of Leicester.

A new paper in Nature Astronomy presents the results. It’s titled “Nightside Clouds and Disequilibrium Chemistry on the Hot Jupiter WASP-43b.” The lead author is Taylor Bell, a researcher from the Bay Area Environmental Research Institute.

“With Hubble, we could clearly see that there is water vapour on the dayside. Both Hubble and Spitzer suggested there might be clouds on the nightside,” explained lead author Bell. “But we needed more precise measurements from Webb to really begin mapping the temperature, cloud cover, winds, and more detailed atmospheric composition all the way around the planet.”

Despite its power, the JWST can’t directly see WASP-43b. Instead, it utilizes phase curve spectroscopy. Phase curve spectroscopy measures the light from the planet and the star over time, sensing small changes in the light from both as the planet orbits the star. Since the JWST senses infrared light, which is emitted depending on an object’s heat, the telescope’s varying brightness data expresses the planet’s temperature.

Phase curve spectroscopy allows the JWST to sense the change in brightness as a planet orbits its star. This diagram shows the change in a planet’s phase (the amount of the lit side facing the telescope) as it orbits its star. Image Credit: NASA, ESA, CSA, Dani Player (STScI), Andi James (STScI), Greg Bacon (STScI)

The JWST’s MIRI spectrometer captured WASP-43b’s phase curve. The planet is hottest when it’s on the opposite side of the star and its lit-up side faces the telescope. The telescope sees the cooler dark side when the planet is on this side of the star and transiting in front of it.

This graph shows more than 8,000 measurements of mid-infrared light captured over a single 24-hour observation using the JWST’s low-resolution spectroscopy mode on its MIRI (Mid-Infrared Instrument). By subtracting the amount of light the star contributes, astronomers can calculate the amount coming from the visible side of the planet as it orbits. The telescope’s extreme sensitivity made this possible. Webb detected differences in brightness as small as 0.004% (40 parts per million). Image Credit: NASA, ESA, CSA, Ralf Crawford (STScI)

“By observing over an entire orbit, we were able to calculate the temperature of different sides of the planet as they rotate into view,” explained Bell. “From that, we could construct a rough map of temperature across the planet.”

To put the data into perspective, the researchers compared WASP-43b’s phase curve to General Circulation Model (GCM) simulations. The JWST phase curve data more closely matched a cloudy GCM than a cloudless GCM.

“The cloudy models are able to suppress the nightside emission and better match the data,” the authors explain in their paper.

This figure from the research shows the JWST’s phase curve data for WASP-43b (black dots) and what cloudless and cloudy GCM simulations predict. The data more closely matches a cloudy atmosphere. Image Credit: Bell et al. 2024.

The researchers used the detailed infrared data to construct a temperature map of the exoplanet. The dayside has an average temperature of about 1,250 Celsius (2,300 F), which is almost hot enough to forge iron. But the nightside likely has a thick layer of high-altitude clouds that trap some of the heat. Those clouds make the nightside appear cooler than it is. It’s much cooler at about 600 degrees Celsius (1,100 degrees Fahrenheit) but still hot enough to melt aluminum.

“The fact that we can map temperature in this way is a real testament to Webb’s sensitivity and stability,” said Michael Roman, a co-author from the University of Leicester in the U.K.

This set of maps shows the temperature of the visible side of the hot gas-giant exoplanet WASP-43 b as the planet orbits its star. Image Credits: Illustration: NASA, ESA, CSA, Ralf Crawford (STScI). Science:
Taylor Bell (BAERI), Joanna Barstow (The Open University), Michael Roman (University of Leicester)

The researchers also mapped a hot spot in WASP-43b’s atmosphere, and it helped them gauge the exoplanet’s ferocious winds. The hot spot is east of the point receiving the most starlight. That means that powerful winds are moving the heated gas.

The JWST’s spectrum also allowed the researchers to measure the presence of water vapour (H2O) and methane (CH4.) “Webb has given us an opportunity to figure out exactly which molecules we’re seeing and put some limits on the abundances,” said Joanna Barstow, a co-author from the Open University in the U.K.

Webb found water vapour on the dayside and the nightside, indicating cloud thickness and elevation. However, the telescope detected an absence of methane (CH4), which is unusual. The extreme heat on the dayside means carbon is in carbon monoxide (CO) form. But the cooler nightside should contain stable methane. Why isn’t it there? Powerful winds are responsible.

“The fact that we don’t see methane tells us that WASP-43b must have wind speeds reaching something like 5,000 miles per hour,” explained Barstow. “If winds move gas around from the dayside to the nightside and back again fast enough, there isn’t enough time for the expected chemical reactions to produce detectable amounts of methane on the nightside.”

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Previous observations with the Hubble, Spitzer, and others revealed some aspects of WASP-43b’s atmosphere. But the JWST has taken it a step further. By determining the extremely high wind velocity on the exoplanet, scientists now believe the atmosphere is the same all around the planet.

“Taken together, our results highlight the unique capabilities of JWST/MIRI for exoplanet atmosphere characterization,” the authors write in their paper. They point out that there are still some discrepancies between the phase curve, the GCM simulations, and the chemical equilibrium in the atmosphere.

According to the researchers, more JWST exoplanet observations can help resolve them. “These remaining discrepancies underscore the importance of further exploring the effects of clouds and disequilibrium chemistry in numerical models as JWST continues to place unprecedented observational constraints on smaller and cooler planets,” they conclude.

The post Is the JWST Now an Interplanetary Meteorologist? appeared first on Universe Today.

Scientists suggest lunar astronauts can stay fit by running sideways within a Wheel of Death.

You’ve seen the Sun, but you’ve never seen the Sun like this. This single frame from a video captured by ESA’s Solar Orbiter mission shows the Sun looking very …. fluffy!  You can see feathery, hair-like structures made of plasma following magnetic field lines in the Sun’s lower atmosphere as it transitions into the much hotter outer corona. The video was taken from about a third of the distance between the Earth and the Sun.

See the full video below, which shows unusual features on the Sun, including coronal moss, spicules, and coronal rain.  

Solar Orbiter recorded this video on September 27, 2023 using its Extreme Ultraviolet Imager (EUI) instrument.

ESA said the brightest regions are around one million degrees Celsius, while cooler material looks darker, as it absorbs radiation.

So, just what is coronal moss? It’s what gives the Sun its fluffy appearance here. These peculiar structures on the Sun resemble the moss we find on Earth, in that it appears like fine, lacy features. But on the Sun, they usually can be found around the center of sunspot groups, where magnetic conditions are strong and large coronal loops are forming. The moss is so hot, most instruments can’t detect them. The moss spans two atmospheric layers, the chromosphere and corona.

Features on the Sun’s surface, as seen by Solar Orbiter. Credit: ESA & NASA/Solar Orbiter/EUI Team

Spicules, as their name implies, are tall spires of gas seen on the solar horizon that reach up from the Sun’s chromosphere. These can reach up to a height of 10,000 km (6,000 miles).

At about 0:30 in the video, you’ll see coronal rain. This material is cooler than the rest of the solar surface (probably less than 10,000 °C) versus the one million degrees C of the coronal loops. The rain is made of higher-density clumps of plasma that fall back towards the Sun under the influence of gravity.

Did you see the small eruption in the center of the field of view at about 0:20 seconds in the video? , with cooler material being lifted upwards before mostly falling back down. It’s not small at all — this eruption is bigger than Earth!

Missions like Solar Orbiter, the Parker Solar Probe and the Solar Dynamics Observatory are giving us unprecedented views of the Sun, helping astronomers to learn more about the dynamic ball of gas that powers our entire Solar System.

Further reading: ESA

The post Solar Orbiter Takes a Mind-Boggling Video of the Sun appeared first on Universe Today.

Artificial intelligence and machine learning have become ubiquitous, with applications ranging from data analysis, cybersecurity, pharmaceutical development, music composition, and artistic renderings. In recent years, large language models (LLMs) have also emerged, adding human interaction and writing to the long list of applications. This includes ChatGPT, an LLM that has had a profound impact since it was introduced less than two years ago. This application has sparked considerable debate (and controversy) about AI’s potential uses and implications.

Astronomy has also benefitted immensely, where machine learning is used to sort through massive volumes of data to look for signs of planetary transits, correct for atmospheric interference, and find patterns in the noise. According to an international team of astrophysicists, this may just be the beginning of what AI could do for astronomy. In a recent study, the team fine-tuned a Generative Pre-trained Transformer (GPT) model using observations of astronomical objects. In the process, they successfully demonstrated that GPT models can effectively assist with scientific research.

The study was conducted by the International Center for Relativistic Astrophysics Network (ICRANet), an international consortium made up of researchers from the International Center for Relativistic Astrophysics (ICRA), the National Institute for Astrophysics (INAF), the University of Science and Technology of China, the Chinese Academy of Sciences Institute of High Energy Physics (CAS-IHEP), the University of Padova, the Isfahan University of Technology, and the University of Ferrera. The preprint of their paper, “Test of Fine-Tuning GPT by Astrophysical Data,” recently appeared online.

Illustration of an active quasar. New research shows AI can identify and classify them. Credit: ESO/M. Kornmesser

As mentioned, astronomers rely extensively on machine learning algorithms to sort through the volumes of data obtained by modern telescopes and instruments. This practice began about a decade ago and has since grown by leaps and bounds to the point where AI has been integrated into the entire research process. As ICRA President and the study’s lead author Yu Wang told Universe Today via email:

“Astronomy has always been driven by data and astronomers are some of the first scientists to adopt and employ machine learning. Now, machine learning has been integrated into the entire astronomical research process, from the manufacturing and control of ground-based and space-based telescopes (e.g., optimizing the performance of adaptive optics systems, improving the initiation of specific actions (triggers) of satellites under certain conditions, etc.), to data analysis (e.g., noise reduction, data imputation, classification, simulation, etc.), and the establishment and validation of theoretical models (e.g., testing modified gravity, constraining the equation of state of neutron stars, etc.).”

Data analysis remains the most common among these applications since it is the easiest area where machine learning can be integrated. Traditionally, dozens of researchers and hundreds of citizen scientists would analyze the volumes of data produced by an observation campaign. However, this is not practical in an age where modern telescopes are collecting terabytes of data daily. This includes all-sky surveys like the Very Large Array Sky Survey (VLASS) and the many phases conducted by the Sloan Digital Sky Survey (SDSS).

To date, LLMs have only been applied sporadically to astronomical research, given that they are a relatively recent creation. But according to proponents like Wang, it has had a tremendous societal impact and has a lower-limit potential equivalent to an “Industrial Revolution.” As for the upper limit, Wang predicts that that could range considerably and could perhaps result in humanity’s “enlightenment or destruction.” However, unlike the Industrial Revolution, the pace of change and integration is far more rapid for AI, raising questions about how far its adoption will go.

The Sloan Digital Sky Survey telescope stands out against the breathtaking backdrop of the Sacramento Mountains. Credit: SDSS/Fermilab Visual Media Services

To determine its potential for the field of astronomy, said Wang, he and his colleagues adopted a pre-trained GPT model and fine-tuned it to identify astronomical phenomena:

“OpenAI provides pre-trained models, and what we did is fine-tuning, which involves altering some parameters based on the original model, allowing it to recognize astronomical data and calculate results from this data. This is somewhat like OpenAI providing us with an undergraduate student, whom we then trained to become a graduate student in astronomy. 

“We provided limited data with modest resolution and trained the GPT fewer times compared to normal models. Nevertheless, the outcomes are impressive, achieving an accuracy of about 90%. This high level of accuracy is attributable to the robust foundation of the GPT, which already understands data processing and possesses logical inference capabilities, as well as communication skills.”

To fine-tune their model, the team introduced observations of various astronomical phenomena derived from various catalogs. This included 2000 samples of quasars, galaxies, stars, and broad absorption line (BAL) quasars from the SDSS (500 each). They also integrated observations of short and long gamma-ray bursts (GRBs), galaxies, stars, and black hole simulations. When tested, their model successfully classified different phenomena, distinguished between types of quasars, inferred their distance based on redshift, and measured the spin and inclination of black holes.

“This work at least demonstrates that LLMs are capable of processing astronomical data,” said Wang. “Moreover, the ability of a model to handle various types of astronomical data is a capability not possessed by other specialized models. We hope that LLMs can integrate various kinds of data and then identify common underlying principles to help us understand the world. Of course, this is a challenging task and not one that astronomers can accomplish alone.”

The Vera Rubin Observatory at twilight on April 2021. It’s been a long wait, but the observatory should see first light later this year. Credit: Rubin Obs/NSF/AURA

Of course, the team acknowledges that the dataset they experimented with was very small compared to the data output of modern observatories. This is particularly true of next-generation facilities like the Vera C. Rubin Observatory, which recently received its LSST camera, the largest digital camera in the world! Once Rubin is operational, it will conduct the ten-year Legacy Survey of Space and Time (LSST), which is expected to yield 15 terabytes of data per night! Satisfying the demands of future campaigns, says Wang, will require improvements and collaboration between observatories and professional AI companies.

Nevertheless, it’s a foregone conclusion that there will be more LLM applications for astronomy in the near future. Not only is this a likely development, but a necessary one considering the sheer volumes of data astronomical studies are generating today. And since this is likely to increase exponentially in the near future, AI will likely become indispensable to the field of study.

Further Reading: arXiv

The post What Can AI Learn About the Universe? appeared first on Universe Today.

China's Chang'e 6 sample return mission to the moon's far side is scheduled to launch early Friday morning (May 3), and you can watch the action live.

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The post Meet Alkaid, the Big Dipper’s Handle appeared first on Sky & Telescope.

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Video: 00:02:59

Last week, Ariane 6’s central core – the main body of the rocket – was stood tall at the launch zone and connected to its two solid-fuel boosters. This exciting moment means only one thing: it’s the start of the first launch campaign.

The main stage and upper stage make up the core stage, and they were autonomously driven at 3 km/h from the rocket assembly building to the launch pad, 800 m away. Then lifted by a crane, the Ariane 6 core was stood upright on the launch table.

The two boosters were transported to the launch pad on a specially designed truck and then configured with the rocket body, now holding it upright.

Ariane 6 is due to launch in summer 2024. The heavy-lift rocket will inaugurate a new era of autonomous European space transportation, powering Europe into space to realise its ambitions on the world stage. It will lift off from a modern launch complex at Europe’s Spaceport in French Guiana, carrying with it not just a variety of spacecraft, but also European goals for prosperity and autonomy.

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