Astronomers suggest radiation, not supernovae, drives superwinds in some galaxies

When astronomers observe superwinds traveling at extremely high speeds from super star clusters, or “starbursts,” they previously assumed the winds were driven by supernovae, the explosions of stars.

This was the case for a starburst called Mrk 71 in a nearby galaxy. Astronomers had observed incredibly fast superwinds—traveling at about 1% of the speed of light—emanating from the cluster, and classic reasoning suggested the blasts from many supernovae drive the gas to such a high rate of speed.

But University of Michigan astronomers think supernovae aren’t the reason: the cluster is too young to have supernovae. They suspect a different mechanism is behind the superwind.

By studying the wind and starburst properties, the astronomers established that ultraviolet radiation from the compact starburst itself drove the superwind. Their findings, published in the journal Astrophysical Journal Letters, may help explain one chapter of the universe’s beginnings.

Just after the Big Bang, the universe was very dense and opaque, says lead author and graduate student Lena Komarova. The universe was so densely packed with particles, no light could pass through them.

“But when the first stars formed in the first galaxies, they produced lots of ultraviolet light. And this essentially evaporates the gas in the universe,” Komarova said. “It’s a process similar to when you have a fog in the morning that you can’t see through—but then the sunshine comes and hits the fog, it starts breaking up into smaller droplets, and you start seeing the light pass.”

In this analogy, neutral hydrogen atoms, which make up 92% of the cosmos, are the “fog” in the universe. But as light started shining out from the first stars forming in the universe, ultraviolet light from these stars began breaking up the hydrogen particles.

“The universe essentially becomes transparent and this happens at so-called cosmic dawn when the first stars appear,” Komarova said. “And so that’s what we’re trying to figure out: How do you get this UV light that’s energetic enough to evaporate the universe, to get out of the galaxies, without it all being absorbed by hydrogen?”

Komarova and Sally Oey, U-M professor of astronomy and senior author of the paper, think the answer lies in the superwinds that they found are generated by the radiation of these compact starburst galaxies. The radiation—ultraviolet light—”evaporates” hydrogen atoms, which are composed of a single proton and a single electron, by stripping off the electrons, ionizing them.

“Only UV light is capable of doing this, because the light has to be above a certain threshold energy,” Oey said. “Once the hydrogen is ionized, it becomes transparent because it can’t capture more UV photons.”

The U-M astronomers came up with the hypothesis of a radiation-driven wind to explain Mrk 71, a starburst region within the galaxy NGC 2366. By examining the spectrum of this region, Komarova and Oey were able to study the gas velocity structure, and found a smooth wind that originated at the brightest Mrk 71 super star cluster.

“We found that even if you had supernovae, there still wouldn’t be enough energy to accelerate the gas to the speeds that we observe,” Komarova said. “We compared the force of stellar light on the gas to the force of gravity, and we found that the radiation is much stronger than gravity—so it can, in fact, push the gas out without gravity bringing it back in. This is what we call a radiation-driven wind.”

The acceleration itself happens when intense light irradiates dense blobs of hydrogen gas from one direction, pushing the gas along, similar to how exploding gas forces a bullet out of a gun. The blobs must be too dense to be evaporated by the ultraviolet radiation. But the light also escapes through spaces between the dense blobs of hydrogen, and blasts blobs farther out from the star cluster.

“The reason why this is linked to the very high velocities is that in order for the blobs to get accelerated to such high speeds, they need to be constantly trapping these UV rays, even at large distances from the star cluster,” Oey said.

What the researchers then propose is this process clears pathways for ultraviolet light to pass between clumps of hydrogen gas.

“So, that UV light can in fact leave its cluster where it was born and go out into the rest of the universe to evaporate it,” Komarova said. “This is putting in another piece of the puzzle of evaporation of the universe and provides a specific physical mechanism of how you do it.”

The researchers drew their conclusion using the Hubble Space Telescope and archive data gathered at the Gemini-North Observatory in Hawaii.

from ScienceBlog.com https://ift.tt/2YcD9JQ

Scientists identify the cause of Alzheimer’s progression in the brain

For the first time, researchers have used human data to quantify the speed of different processes that lead to Alzheimer’s disease and found that it develops in a very different way than previously thought. Their results could have important implications for the development of potential treatments.

This research shows the value of working with human data instead of imperfect animal models

Tuomas Knowles

The international team, led by the University of Cambridge, found that instead of starting from a single point in the brain and initiating a chain reaction which leads to the death of brain cells, Alzheimer’s disease reaches different regions of the brain early. How quickly the disease kills cells in these regions, through the production of toxic protein clusters, limits how quickly the disease progresses overall.

The researchers used post-mortem brain samples from Alzheimer’s patients, as well as PET scans from living patients, who ranged from those with mild cognitive impairment to those with late-stage Alzheimer’s disease, to track the aggregation of tau, one of two key proteins implicated in the condition.

In Alzheimer’s disease, tau and another protein called amyloid-beta build up into tangles and plaques – known collectively as aggregates – causing brain cells to die and the brain to shrink. This results in memory loss, personality changes and difficulty carrying out daily functions.

By combining five different datasets and applying them to the same mathematical model, the researchers observed that the mechanism controlling the rate of progression in Alzheimer’s disease is the replication of aggregates in individual regions of the brain, and not the spread of aggregates from one region to another.

The results, reported in the journal Science Advances, open up new ways of understanding the progress of Alzheimer’s and other neurodegenerative diseases, and new ways that future treatments might be developed.

For many years, the processes within the brain which result in Alzheimer’s disease have been described using terms like ‘cascade’ and ‘chain reaction’. It is a difficult disease to study, since it develops over decades, and a definitive diagnosis can only be given after examining samples of brain tissue after death.

For years, researchers have relied largely on animal models to study the disease. Results from mice suggested that Alzheimer’s disease spreads quickly, as the toxic protein clusters colonise different parts of the brain.

“The thinking had been that Alzheimer’s develops in a way that’s similar to many cancers: the aggregates form in one region and then spread through the brain,” said Dr Georg Meisl from Cambridge’s Yusuf Hamied Department of Chemistry, the paper’s first author. “But instead, we found that when Alzheimer’s starts there are already aggregates in multiple regions of the brain, and so trying to stop the spread between regions will do little to slow the disease.”

This is the first time that human data has been used to track which processes control the development of Alzheimer’s disease over time. It was made possible in part by the chemical kinetics approach developed at Cambridge over the last decade which allows the processes of aggregation and spread in the brain to be modelled, as well as advances in PET scanning and improvements in the sensitivity of other brain measurements.

“This research shows the value of working with human data instead of imperfect animal models,” said co-senior author Professor Tuomas Knowles, also from the Department of Chemistry. “It’s exciting to see the progress in this field – fifteen years ago, the basic molecular mechanisms were determined for simple systems in a test tube by us and others; but now we’re able to study this process at the molecular level in real patients, which is an important step to one day developing treatments.”

The researchers found that the replication of tau aggregates is surprisingly slow – taking up to five years. “Neurons are surprisingly good at stopping aggregates from forming, but we need to find ways to make them even better if we’re going to develop an effective treatment,” said co-senior author Professor Sir David Klenerman, from the UK Dementia Research Institute at the University of Cambridge. “It’s fascinating how biology has evolved to stop the aggregation of proteins.”

The researchers say their methodology could be used to help the development of treatments for Alzheimer’s disease, which affects an estimated 44 million people worldwide, by targeting the most important processes that occur when humans develop the disease. In addition, the methodology could be applied to other neurodegenerative diseases, such as Parkinson’s disease.

“The key discovery is that stopping the replication of aggregates rather than their propagation is going to be more effective at the stages of the disease that we studied,” said Knowles.

The researchers are now planning to look at the earlier processes in the development of the disease, and extend the studies to other diseases such as Frontal temporal dementia, traumatic brain injury and progressive supranuclear palsy where tau aggregates are also formed during disease.

The study is a collaboration between researchers at the UK Dementia Research Institute, the University of Cambridge and Harvard Medical School. Funding is acknowledged from Sidney Sussex College Cambridge, the European Research Council, the Royal Society, JPB Foundation, the Rainwater Foundation, the NIH, and the NIHR Cambridge Biomedical Research Centre which supports the Cambridge Brain Bank.

Reference:
Georg Meisl et al. ‘In vivo rate-determining steps of tau seed accumulation in Alzheimer’s disease.’ Science Advances (2021). DOI: 10.1126/sciadv.abh1448

from ScienceBlog.com https://ift.tt/3pWhYHm

LEDs and smartphone screens could be made from next-generation glass

Cracked and blurry phone screens could someday be a thing of the past, suggests a new study from the University of Cambridge and the University of Queensland, Australia.

This is an example of how fundamental science leads to fantastic discoveries and possible real-life applications

Thomas Bennett

The international team of researchers has developed technology for next-generation composite glass, for use in lighting LEDs, smartphones, TVs and computer screens.

The materials are based on materials called lead-halide perovskites, which can trap light and store energy, like miniature solar panels.

The results, published in the journal Science, could enable the manufacture of glass screens that are less prone to cracking, but also deliver crystal clear image quality.

The results are a step forward in perovskite nanocrystal technology as previously, researchers were only able to produce this technology in the bone-dry atmosphere of a laboratory setting.

“These nanocrystals are extremely sensitive to light, heat, air and water – even water vapour in our air would kill the current devices in a matter of minutes,” said Dr Jingwei Hou from the University of Queensland (UQ), the paper’s first author.

The team of chemical engineers and material scientists has developed a process to wrap or bind the nanocrystals in porous glass. This process is key to stabilising the materials, enhancing their efficiency and preventing the toxic lead ions from leaching out from the materials.

“It was surprising to see the retention of the high temperature functional form in the glass,” said co-senior author Dr Thomas Bennett from Cambridge’s Department of Materials Science and Metallurgy. “This is an example of how fundamental science leads to fantastic discoveries and a possible real-life application of metal-organic framework glasses.”

The researchers say the technology is scalable and opens the door for many potential applications.

“At present QLED or quantum dot light-emitting diode screens are considered the top performer for image display and performance,” said Hou. “This research will enable us to improve on this nanocrystal technology by offering stunning picture quality and strength.”

“Not only can we make these nanocrystals more robust but we can tune their opto-electronic properties with fantastic light emission efficiency and highly desirable white light LEDs.” said co-author Professor Vicki Chen, also from UQ. “This discovery opens up a new generation of nanocrystal-glass composites for energy conversion and catalysis.”

The researchers say that a lot of optimisation work still needs to be carried out before any products based on the material could be made commercially available. “There are a huge amount of different combinations and it’s definitely going to be a big effort to determine which components seem to give the best combinations,” said Bennett.

The research is a collaborative effort from UQ, the University of Leeds, Université Paris-Saclay and the University of Cambridge.

Reference:
Jingwei Hou et al. ‘Liquid-phase sintering of lead halide perovskites and metal-organic framework glasses.’ Science (2021). DOI: 10.1126/science.abf4460

Adapted from a UQ press release.

from ScienceBlog.com https://ift.tt/3GM8EM4

Avoiding shortcut solutions in artificial intelligence

If your Uber driver takes a shortcut, you might get to your destination faster. But if a machine learning model takes a shortcut, it might fail in unexpected ways.

In machine learning, a shortcut solution occurs when the model relies on a simple characteristic of a dataset to make a decision, rather than learning the true essence of the data, which can lead to inaccurate predictions. For example, a model might learn to identify images of cows by focusing on the green grass that appears in the photos, rather than the more complex shapes and patterns of the cows.  

A new study by researchers at MIT explores the problem of shortcuts in a popular machine-learning method and proposes a solution that can prevent shortcuts by forcing the model to use more data in its decision-making.

By removing the simpler characteristics the model is focusing on, the researchers force it to focus on more complex features of the data that it hadn’t been considering. Then, by asking the model to solve the same task two ways — once using those simpler features, and then also using the complex features it has now learned to identify — they reduce the tendency for shortcut solutions and boost the performance of the model.

One potential application of this work is to enhance the effectiveness of machine learning models that are used to identify disease in medical images. Shortcut solutions in this context could lead to false diagnoses and have dangerous implications for patients.

“It is still difficult to tell why deep networks make the decisions that they do, and in particular, which parts of the data these networks choose to focus upon when making a decision. If we can understand how shortcuts work in further detail, we can go even farther to answer some of the fundamental but very practical questions that are really important to people who are trying to deploy these networks,” says Joshua Robinson, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper.

Robinson wrote the paper with his advisors, senior author Suvrit Sra, the Esther and Harold E. Edgerton Career Development Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) and a core member of the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems; and Stefanie Jegelka, the X-Consortium Career Development Associate Professor in EECS and a member of CSAIL and IDSS; as well as University of Pittsburgh assistant professor Kayhan Batmanghelich and PhD students Li Sun and Ke Yu. The research will be presented at the Conference on Neural Information Processing Systems in December. 

The long road to understanding shortcuts

The researchers focused their study on contrastive learning, which is a powerful form of self-supervised machine learning. In self-supervised machine learning, a model is trained using raw data that do not have label descriptions from humans. It can therefore be used successfully for a larger variety of data.

A self-supervised learning model learns useful representations of data, which are used as inputs for different tasks, like image classification. But if the model takes shortcuts and fails to capture important information, these tasks won’t be able to use that information either.

For example, if a self-supervised learning model is trained to classify pneumonia in X-rays from a number of hospitals, but it learns to make predictions based on a tag that identifies the hospital the scan came from (because some hospitals have more pneumonia cases than others), the model won’t perform well when it is given data from a new hospital.     

For contrastive learning models, an encoder algorithm is trained to discriminate between pairs of similar inputs and pairs of dissimilar inputs. This process encodes rich and complex data, like images, in a way that the contrastive learning model can interpret.

The researchers tested contrastive learning encoders with a series of images and found that, during this training procedure, they also fall prey to shortcut solutions. The encoders tend to focus on the simplest features of an image to decide which pairs of inputs are similar and which are dissimilar. Ideally, the encoder should focus on all the useful characteristics of the data when making a decision, Jegelka says.

So, the team made it harder to tell the difference between the similar and dissimilar pairs, and found that this changes which features the encoder will look at to make a decision.

“If you make the task of discriminating between similar and dissimilar items harder and harder, then your system is forced to learn more meaningful information in the data, because without learning that it cannot solve the task,” she says.

But increasing this difficulty resulted in a tradeoff — the encoder got better at focusing on some features of the data but became worse at focusing on others. It almost seemed to forget the simpler features, Robinson says.

To avoid this tradeoff, the researchers asked the encoder to discriminate between the pairs the same way it had originally, using the simpler features, and also after the researchers removed the information it had already learned. Solving the task both ways simultaneously caused the encoder to improve across all features.

Their method, called implicit feature modification, adaptively modifies samples to remove the simpler features the encoder is using to discriminate between the pairs. The technique does not rely on human input, which is important because real-world data sets can have hundreds of different features that could combine in complex ways, Sra explains.

From cars to COPD

The researchers ran one test of this method using images of vehicles. They used implicit feature modification to adjust the color, orientation, and vehicle type to make it harder for the encoder to discriminate between similar and dissimilar pairs of images. The encoder improved its accuracy across all three features — texture, shape, and color — simultaneously.

To see if the method would stand up to more complex data, the researchers also tested it with samples from a medical image database of chronic obstructive pulmonary disease (COPD). Again, the method led to simultaneous improvements across all features they evaluated.

While this work takes some important steps forward in understanding the causes of shortcut solutions and working to solve them, the researchers say that continuing to refine these methods and applying them to other types of self-supervised learning will be key to future advancements.

“This ties into some of the biggest questions about deep learning systems, like ‘Why do they fail?’ and ‘Can we know in advance the situations where your model will fail?’ There is still a lot farther to go if you want to understand shortcut learning in its full generality,” Robinson says.

This research is supported by the National Science Foundation, National Institutes of Health, and the Pennsylvania Department of Health’s SAP SE Commonwealth Universal Research Enhancement (CURE) program.

from ScienceBlog.com https://ift.tt/3mywHWU

NIH researchers identify how two people controlled HIV after stopping treatment

Research led by scientists at the National Institutes of Health has identified two distinct ways that people with HIV can control the virus for an extended period after stopping antiretroviral therapy (ART) under medical supervision. This information could inform efforts to develop new tools to help people with HIV put the virus into remission without taking lifelong medication, which can have long-term side-effects.

The study, published today in the journal Nature Medicine, was led by Tae-Wook Chun, Ph.D., chief of the HIV Immunovirology Section in the Laboratory of Immunoregulation at the National Institute of Allergy and Infectious Diseases (NIAID), part of NIH; and by Anthony S. Fauci, M.D., NIAID director and chief of the Laboratory of Immunoregulation.

The study involved two adults with HIV who began ART soon after acquiring the virus and continued with treatment for more than six years, successfully suppressing HIV. The individuals then joined an HIV clinical trial and stopped taking ART under medical supervision. The study team followed one of these people for four years and the other for more than five years, with study visits roughly every two to three weeks.

The investigators monitored the timing and size of viral rebounds in each participant, that is, times when the amount of HIV in their blood became detectable. One participant suppressed the virus with intermittent rebounds for nearly 3.5 years, at which point he began taking suboptimal ART without telling the study team. The other participant almost completely suppressed HIV for nearly four years, at which point the virus rebounded dramatically because he became infected with a different HIV strain, a phenomenon known as “superinfection.”

In the first participant but not the second, the scientists found high levels of HIV-specific immune cells called CD8+ T cells that can kill virus-infected cells, indicating that different mechanisms of control were at work in each person. The researchers also found that the second participant, who had a weaker CD8+ T cell response against HIV, had a very strong neutralizing antibody response throughout the follow-up period until the sudden viral rebound. According to the scientists, this suggests that neutralizing antibodies may have played a significant role in facilitating near-complete HIV suppression in this individual until he newly acquired a different strain of the virus.

The researchers emphasized that to avoid the emergence of viral resistance and prevent potential misinterpretation of scientific data in studies like this one, it is important to conduct routine antiretroviral drug testing of people with HIV who halt treatment for extended periods. In addition, the researchers identified HIV superinfection as a potential cause of sudden virologic breakthrough in people with HIV who halt treatment, especially when the breakthrough occurs after a prolonged period of virus suppression.

Article

J et al. Distinct mechanisms of long-term virologic control in two HIV-infected individuals after treatment interruption of antiretroviral therapy. Nature Medicine DOI: 10.1038/s41591-021-01503-6 (2021).

from ScienceBlog.com https://ift.tt/2Y4Dv54

New model sheds light on evolution of Earth’s oxygen

Ateam led by Southwest Research Institute has updated its asteroid bombardment model of the Earth with the latest geologic evidence of ancient, large collisions. These models have been used to understand how impacts may have affected oxygen levels in the Earth’s atmosphere in the Archean eon, 2.5 to 4 billion years ago.

When large asteroids or comets struck early Earth, the energy released melted and vaporized rocky materials in the Earth’s crust. The small droplets of molten rock in the impact plume would condense, solidify and fall back to Earth, creating round, globally distributed sand-size particles. Known as impact spherules, these glassy particles populated multiple thin, discrete layers in the Earth’s crust, ranging in age from about 2.4 to 3.5 billion years old. These Archean spherule layers are markers of ancient collisions. “In recent years, a number of new spherule layers have been identified in drill cores and outcrops, increasing the total number of known impact events during the early Earth,” said Dr. Nadja Drabon, a professor at Harvard University and a co-author of the paper.

“Current bombardment models underestimate the number of late Archean spherule layers, suggesting that the impactor flux at that time was up to 10 times higher than previously thought,” said SwRI’s Dr. Simone Marchi, lead author of a paper about this research in Nature Geoscience. “What’s more, we find that the cumulative impactor mass delivered to the early Earth was an important ‘sink’ of oxygen, suggesting that early bombardment could have delayed oxidation of Earth’s atmosphere.”

The abundance of oxygen in Earth’s atmosphere is due to a balance of production and removal processes. These new findings correspond to the geological record, which shows that oxygen levels in the atmosphere varied but stayed relatively low in the early Archean eon. Impacts by bodies larger than six miles (10 km) in diameter may have contributed to its scarcity, as limited oxygen present in the atmosphere of early Earth would have been chemically consumed by impact vapors, further reducing its abundance in the atmosphere.

“Late Archean bombardment by objects over six miles in diameter would have produced enough reactive gases to completely consume low levels of atmospheric oxygen,” said Dr. Laura Schaefer, a professor at Stanford University and a co-author of the paper. “This pattern was consistent with evidence for so-called ‘whiffs’ of oxygen, relatively steep but transient increases in atmospheric oxygen that occurred around 2.5 billion years ago. We think that the whiffs were broken up by impacts that removed the oxygen from the atmosphere. This is consistent with large impacts recorded by spherule layers in Australia’s Bee Gorge and Dales Gorge.”

SwRI’s results indicate that the Earth was subject to substantial numbers of large impacts throughout the late Archean era. Around 2.4 billion years ago, during the tail end of this bombardment, the Earth went through a major shift in surface chemistry triggered by the rise of atmospheric oxygen, dubbed the Great Oxidation Event (GOE), which is attributed to changes in the oxygen production-sink balance. Among the proposed scenarios are a presumed increase in oxygen production and decrease in gases capable of removing oxygen, either from volcanic sources or through their gradual loss to space.

“Impact vapors caused episodic low oxygen levels for large spans of time preceding the GOE,” Marchi said. “As time went on, collisions become progressively less frequent and too small to be able to significantly alter post-GOE oxygen levels. The Earth was on its course to become the current planet.”

from ScienceBlog.com https://ift.tt/2ZFkLtw

Mars: Jezero crater really was a lake

The first published data from the Perseverance rover shows that there was a large, deep lake on Mars 3.6 billion years ago, and that it was swept away by a climate upheaval.

After years of preparation and the nerve-wracking take-off and landing phases, the publication of the very first results of a space mission is always a very special moment. Those of Perseverance, published today in the journal Science,1 are no exception: as we suspected, Jezero crater, the site 35 kilometers in diameter where the rover landed in February 2021, welcomed a real lake several billion years ago.

Finding traces of water and selecting samples

Confirming this hypothesis requires images of vertical surfaces such as cliffs, something that cannot be obtained using orbital observations, however invaluable they may be. Only a rover with on-board cameras can study them directly. NASA’s Mars 2020 mission, supported by an international team, initially set two major objectives for Perseverance.

“The mission’s goal is to use the rover to help us understand the geology and ancient climate of Mars, and to try to detect traces of past life which might still be preserved,” explains Nicolas Mangold, CNRS senior researcher at LPG.2 “To do this, Perseverance has to sample rocks of various types and ages.” The rover is therefore focusing on sedimentary rocks formed in the presence (or not) of water and on older samples of crust, such as those found in the Gale crater, currently being explored by the Curiosity rover.

Perseverance will also have to select some forty samples, which will be the first ever brought back to Earth. This unprecedented return will be carried out by two other US / European collaborative missions, still under development and planned for the early 2030s.

A lake fed by a river

In the meantime, Perseverance is hard at work. Its first scientific results have just been published in the prestigious journal Science, with Mangold as lead author.3 The findings first of all confirm that, around 3.6 billion years ago, the Jezero crater really was a lake fed by a river flowing through a delta. The circular body of water covered an area 35 kilometres in diameter and was several tens of metres deep.

SuperCam superstar

These discoveries were only made possible by studying sedimentary strata on Kodiak Butte, located in the former delta. But although Perseverance landed two kilometres from its initial target, its seven on-board instruments still enabled it to operate remotely. SuperCam, its main instrument, is an impressive camera whose job is to observe and analyse rocks. It is operational even when analysing objects around ten centimetres in size located several kilometres from the rover.

“SuperCam is unusual in that it brings together five different techniques,” explains Sylvestre Maurice, a researcher at IRAP4 and co-Principal Investigator for the camera. “One instrument provides information about the elemental chemical composition of the rocks, two others analyse their mineralogy, a camera takes high-quality remote images and, last but not least, a microphone succeeded in making the first recording of sounds on Mars. We had to juggle the requirements of each component to make them fit into a single instrument that is as innovative as it is complex.”

A rover with 20 cameras

Although SuperCam only provides a very narrow field of view, Perseverance is armed with twenty or so cameras altogether, including Mastcam-Z, which also helped to obtain these first results. All this material has to survive the launch, the journey to Mars, the landing and the conditions on the planet, where the day / night cycles are accompanied by abrupt changes in temperature. The French teams were able to rely on their expertise since they had previously developed a similar although simpler instrument, ChemCam, used on the Curiosity rover, which has now been on Mars for nine years.

In France, around 300 people worked on SuperCam under the supervision of the French space agency CNES.5 “We are continuing France’s strong commitment to Mars surface missions,” Maurice says. “The French scientific community is heavily involved in the Insight mission, for which it provided the seismometer, and in the European ExoMars mission, scheduled for launch in September 2022.”

Objective delta

“Of course, it’s reassuring that we have already found what we were looking for, but this kind of result always raises more questions than it answers,” Mangold points out. “On the basis of these findings, we plan to take Perseverance across the former delta to undertake a detailed analysis of the strata observed, and in particular of the fluvial sediments located at the top, to try to understand the origin of the climate transition and analyse the large boulders that were probably transported from the ancient crust.”

The teams will therefore have to determine a route that will let the rover access all the geological layers in the Jezero crater. This may then reveal the environment in which water entered the delta before flowing into the lake. But for now, Perseverance can take a break. As happens every two years, Mars is on the opposite side of the Sun from the Earth, cutting off all communication for three weeks.

Footnotes

• 1.N. Mangold et al., “Perseverance rover reveals ancient delta-lake system and flood deposits at Jezero crater, Mars”, Science, 7 October 2021. DOI : 10.1126/science.abl4051
• 2.Laboratoire de planétologie et géodynamique (CNRS/Université de Nantes/Université d’Angers).
• 3.In addition to the LPG, the CNRS researchers involved in this work are from the Institute for Research in Astrophysics and Planetology (IRAP, CNRS / CNES / UT3 Paul Sabatier), the Lyon Geology Laboratory: Earth, Planets, Environment (LGL-TPE, CNRS / ENS Lyon / Université Claude Bernard Lyon 1) and the Institute of Mineralogy, Materials Physics and Cosmochemistry (IMPMC, CNRS / MNHN / Sorbonne University).
• 4.Institute for Research in Astrophysics and Planetology (CNRS / Toulouse Paul Sabatier University / French National Centre for Space Studies).
• 5.French National Centre for Space Studies.

from ScienceBlog.com https://ift.tt/3w6VK6H