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A groundbreaking study has revealed that red dwarf stars can produce stellar flares that carry far-ultraviolet (far-UV) radiation levels much higher than previously believed.

Ancient plants, seeds and insects preserved beneath Greenland’s ice sheet reveal that it once melted completely, raising concerns about sea level rise if it happens again

Modern astronomy would struggle without AI and machine learning (ML), which have become indispensable tools. They alone have the capability to manage and work with the vast amounts of data that modern telescopes generate. ML can sift through large datasets, seeking specified patterns that would take humans far longer to find.

The search for biosignatures on Earth-like exoplanets is a critical part of contemporary astronomy, and ML can play a big role in it.

Since exoplanets are so distant, astronomers pay close attention to the ones that allow transmission spectroscopy. When starlight passes through a planet’s atmosphere, spectroscopy can split the light into different wavelengths. Astronomers then examine the light for the telltale signs of particular molecules. However, chemical biosignatures in exoplanet atmospheres are tricky because natural abiogenic processes can generate some of the same signatures.

This is a model JWST transmission spectrum for an Earth-like planet. It shows the wavelengths of sunlight that molecules like ozone (O3), water (H2O), carbon dioxide (CO2), and methane (CH4) absorb. The y-axis shows the amount of light blocked by Earth’s atmosphere rather than the brightness of sunlight that travels through the atmosphere. The brightness decreases from bottom to top. An understanding of Earth’s spectrum helps scientists interpret spectra from exoplanets. Image Credit: NASA, ESA, Leah Hustak (STScI)

Though the method is powerful, it faces some challenges. Stellar activity like starspots and flares can pollute the signal, and the light from the atmosphere can be very weak compared to the star’s light. If there are clouds or haze in the exoplanet’s atmosphere, that can make it difficult to detect molecular absorption lines in the spectroscopic data. Rayleigh scattering adds to the challenge, and there can also be multiple different interpretations of the same spectroscopic signal. The more of these types of ‘noise’ there is in the signal, the worse the signal-to-noise ratio (SNR) is. Noisy data—data with a low SNR—is a pronounced problem.

We’re still discovering different types of exoplanets and planetary atmospheres, and our models and analysis techniques aren’t complete. When combined with the low SNR problem, the pair comprise a major hurdle.

But machine learning can help, according to new research. “Machine-assisted classification of potential biosignatures in earth-like exoplanets using low signal-to-noise ratio transmission spectra” is a paper under review by the Monthly Notices of the Royal Astronomical Society. The lead author is David S. Duque-Castaño from the Computational Physics and Astrophysics Group at the Universidad de Antioquia in Medellin, Colombia.

The JWST is our most powerful transmission spectroscopy tool, and it’s delivered impressive results. But there’s a problem: observing time. Some observing efforts take an enormous amount of time. It can take a prohibitively high number of transits to detect things like ozone. If we had unlimited amounts of observing time, it wouldn’t matter so much.

One study showed that in the case of TRAPPIST-1e, it can take up to 200 transits to obtain statistically significant detections. The transit number becomes more reasonable if the search is restricted to methane and water vapour. “Studies have demonstrated that using a reasonable number of transits, the presence of these atmospheric species, which are typically associated with a global biosphere, can be retrieved,” the authors write. Unfortunately, methane isn’t as robust a biosignature as ozone.

Given the time required to detect some of these potential biomarkers, the researchers say that it might be better to use the JWST to conduct signal-to-noise ratio (SNR) surveys. “Although this may not allow for statistically significant retrievals, it would at least enable planning for future follow-up observations of interesting targets with current and future more powerful telescopes (e.g., ELT, LUVOIR, HabEx, Roman, ARIEL),” the authors write, invoking the names of telescopes that are in the building or planning stages.

The researchers have developed a machine-learning tool to help with this problem. They say it can fast-track the search for habitable worlds by leveraging the power of AI. “In this work, we developed and tested a machine-learning general methodology intended to classify transmission spectra with low Signal-to-Noise Ratio according to their potential to contain biosignatures,” they write.

Since much of our exoplanet atmosphere spectroscopy data is noise, the ML tool is designed to process it, figure out how noisy it is, and classify atmospheres that may contain methane, ozone, and/or water or as interesting enough for follow-up observations.

The team generated one million synthetic atmospheric spectra based on the well-known TRAPPIST-1 e planet and then trained their ML models on them. TRAPPIST-1e is similar in size to Earth and is a rocky planet in the habitable zone of its star. “The TRAPPIST-1 system has gained significant scientific attention
in recent years, especially in planetary sciences and astrobiology, owing to its exceptional features,” the paper states.

Artist’s impression of TRAPPIST-1e, a rocky exoplanet similar in size to Earth. Credit: NASA/JPL-Caltech

The TRAPPIST-1 star is known for hosting the highest number of rocky planets of any system we’ve discovered. For the researchers, it’s an ideal candidate for training and testing their ML models because astronomers can get favourable SNR readings in reasonable amounts of time. The TRAPPIST-1e planet is likely to have a compact atmosphere like Earth’s. The resulting models were successful and correctly identified transmission spectra with suitable SNR levels.

The researchers also tested their models on realistic synthetic atmospheric spectra of modern Earth. Their system successfully identified synthetic atmospheres that contained methane and/or ozone in ratios similar to those of the Proterozoic Earth. During the Proterozoic, the atmosphere underwent fundamental changes because of the Great Oxygenation Event (GOE).

The GOE changed everything. It allowed the ozone layer to form, created conditions for complex life to flourish and even led to the creation of vast iron ore deposits that we mine today. If other exoplanets developed photosynthetic life, their atmospheres should be similar to the Proterozoic Earth’s, so it’s a relevant marker for biological life. (The recent discovery of dark oxygen has serious implications for our understanding of oxygen as a biomarker in exoplanet atmospheres.)

In their paper, the authors describe the detection of oxygen or ozone as the ‘Crown Jewel’ of exoplanet spectroscopy signatures. But there are abiotic sources as well, and whether or not oxygen or ozone are biotic can depend on what else is in the signature. “To distinguish between biotic and abiotic O2, one can look for specific spectral fingerprints,” they write.

To evaluate the performance of their model, they need to know more than which exoplanet atmospheres are correctly identified (True) and which exoplanet atmospheres are falsely identified (False.)

The results also need to be categorized as either True Positives (TP) or True Negatives (TN), which are related to accuracy, or False Positives (FP) or False Negatives (FN), which are errors. To organize their data they created a classification system they call a Confusion Matrix.

“In the diagram, we introduce the category interesting to distinguish planets that deserve follow-up observations or in-depth analysis,” the authors explain. “We should recall again that is the focus of this work: we do not aim at detecting biosignatures using ML but at labelling planets that are interesting or not.”

The Confusion Matrix has four classifications.

One of the models was successful in identifying likely biosignatures in Proterozoic Earth spectra after only a single transit. Based on their testing, they explain that the JWST would successfully detect most “inhabited terrestrial planets observed with the JWST/NIRSpec PRISM around M-dwarfs located at distances similar or smaller than that of TRAPPIST-1 e.” If they exist, that is.

These results can refine the JWST’s future efforts. The researchers write that “machine-assisted strategies similar to those presented here could significantly optimize the usage of JWST resources for biosignature searching.” They can streamline the process and maximize the chances that follow-up observations can discover promising candidates. The telescope is already two years and seven months into its planned five-and-a-half-year primary mission. (Though the telescope could last for up to 20 years overall.) Anything that can optimize the space telescope’s precious observing time is a win.

All in all, the study presents a machine-learning model that can save time and resources. It quickly sifts through the atmospheric spectra of potentially habitable exoplanets. While it doesn’t identify which ones contain biomarkers, it can identify the best candidates for follow-up after only 1 to 5 transits, depending on the type of atmosphere. Some types would require more transits, but the model still saves time.

“Identifying a planet as interesting will only make the allocation of observing time of valuable resources such as JWST more efficient, which is an important goal in modern astronomy,” they write.

The post Fast-Tracking the Search for Habitable Worlds appeared first on Universe Today.

A research team developed artificial intelligence technology that could find ways to improve researchers' understanding of the chemical reactions involved in ammonia production and other complex chemical reactions.

A research team developed artificial intelligence technology that could find ways to improve researchers' understanding of the chemical reactions involved in ammonia production and other complex chemical reactions.

Using state of the art techniques, researchers use vibrations from traffic to measure underground soil moisture.

Understanding neutrino interactions is crucial for obtaining a complete picture of particle physics and the universe. To date, neutrino interaction cross sections have not been measured at high energy above some hundred gigaelectronvolts at particle colliders. Now, researchers have obtained the first direct observation of electron and muon neutrino interactions in the Teraelectronvolt range at CERN's Large Hadron Collider, using the FASER detector. This study marks a significant step for particle physics research.

With GROVER, a new large language model trained on human DNA, researchers could now attempt to decode the complex information hidden in our genome. GROVER treats human DNA as a text, learning its rules and context to draw functional information about the DNA sequences.

A growing body of AI tools screen how people talk, searching for subtle changes that could indicate mental health concerns like depression or anxiety. A study finds that these tools don't perform consistently across people from different genders and races.

New findings on how sharks achieve drag reduction could inspire the design of riblets for more efficient aircraft and boats. In their investigation of great white shark denticles, researchers found that ridge height and spacing play crucial roles in drag reduction at different swimming speeds. Higher middle ridges aid sharks in efficient cruising at slower speeds, while the lower side ridges become more critical for drag reduction during high-speed hunting bursts. The analysis also suggests that the speeds of an extinct giant shark, megalodon, may not much differ from those of the white shark.

Researchers send entangled photons and laser pulses of the same color over a single optical fiber for the first time.

A team developed a new microscopy technique that uses electrical pulses to track the nanosecond dynamics within a material that is known to form charge density waves. Controlling these waves may lead to faster and more energy-efficient electronics.

A team developed a new microscopy technique that uses electrical pulses to track the nanosecond dynamics within a material that is known to form charge density waves. Controlling these waves may lead to faster and more energy-efficient electronics.

A low-carbohydrate ketogenic diet is linked to greater fat loss than a low-sugar diet, but also higher cholesterol levels

Over 5,000 exoplanets have been discovered around distant star systems. Protoplanetary disks have been discovered too and it’s these, out of which all planetary systems form. Such disks have recently been found in two binary star systems. The stellar components in one have a separation of 14 astronomical units (the average distance between the Earth and Sun is one astronomical unit) and the other system has a separation of 22 astronomical units. Studying systems like these allow us to see how the stars of a binary system interact and how they can distort protoplanetary disks.

The discovery of planetary systems around other stars has changed our view of the universe and how stellar systems evolve. The first confirmed detection occurred in 1995 and since then, advanced telescopes and detection techniques have enabled the detection of thousands of exoplanets. Space missions like Kepler and TESS have helped to categorise the planets and have identified large gas planets to Earth-sized rocky worlds, some in their star’s habitable zone. Protoplanetary disks have also been studied and so our understanding of the formation of planetary systems has improved markedly since. 

Artist’s impression of a young star surrounded by a protoplanetary disc made of gas and dust. According to new research, ring-shaped, turbulent disturbances (substructures) in the disk lead to the rapid formation of several gas and ice giants. Credit: LMU / Thomas Zankl, crushed eyes media

The disks of gas and dust around stars have been known to be the precursor to planetary systems for some years. What has been uncertain is the conditions that allow the disk to remain long enough for planets to form and what can lead to their early dissipation. 

An announcement of protoplanetary disks around young binary stars was announced at a meeting of the American Astronomical Society by a team of researchers from National Radio Observatory. They used the Atacama Large Millimetre/sub-millimetre Array otherwise known as ALMA and the near-infrared capability of the Keck II 10 metre telescope. ALMA has been setup in the Atacama Desert at an altitude of 5,000 metres so that the air is clear and dry. It’s composed of 66 antennae all working together as an interferometer to study the coldest and most distant objects in the universe.

Two of the Atacama Large Millimeter/submillimeter Array (ALMA) 12-metre antennas (Credit : Iztok Bon?ina/ESO)

The disks around pre-main sequence binary stars as announced provides an excellent opportunity to study the disks and try to find answers to the questions. The team explored the disk sizes, structure and inclination in relation to the star’s rotation speed and magnetic field strength to try and understand the complex processes at play. Binary and multiple star systems like the DF Tau and FO Tau binary system that were the target of the study are common making an excellent case study. 

DF Tau is a binary quasi-twin star system with component separation of 14 astronomical units with elongated orbits. ALMA detected two circumstellar cool dust disks with one locked magnetically to the star and accreting material onto it. The inner disk seems to have largely eroded and decoupled from the rapidly rotating star. This suggests a link between the rotation of the star, magnetic disk locking and the resultant early disk dissipation. Misalignment of the orbits and disks could impact the evolution of the disk. FO Tau is slightly different where the disks are aligned with the binary orbit, the stars have modest rotation speeds and seem to be magnetically locked to their disks.

ALMA has afforded high resolution images revealing fine levels of disk sub-structures which include spiral patterns, gaps and ring formations around single stars and wide binary companions. It cannot yet resolve detail in the disks of DF Tau and FO Tau systems, to be able to determine disk properties in close binary systems marks a step forward in our understanding of planetary formation.

Source : ALMA Observations Reveal New Insights into Planet Formation in Binary Star Systems

The post Astronomers See Planets Forming Around Binary Stars appeared first on Universe Today.

There are secrets aplenty in a pot of honey – from information about bees' "micro-bee-ota" to DNA from the environment – that can help us fight food fraud and even monitor shifts in climate

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Ever since COVID-19 first emerged in 2020, evidence-free claims that it had arisen due to a "lab leak" have proliferated. A recent paper argues that this conspiracy theory has been very harmful to science. I argue that it's more than just lab leak that is harmful.

The post How conspiracy theories like COVID-19 “lab leak” harm science and public health first appeared on Science-Based Medicine.

Trees in Beijing lit by streetlights at night seem to have tougher leaves and less damage from insects, raising concerns about possible effects on biodiversity