The Large Hadron Collider, the particle accelerator that enabled the discovery of the Higgs boson, is back in action after over three years in hiatus. CERN shut the accelerator down for maintenance and upgrade work that was extended due to delays caused by the COVID-19 pandemic. Now, it's ready to smash particles for various research projects throughout its third run that's scheduled to last until 2026. In fact, two beams of protons had already circulated in opposite directions around the 27-kilometer collider as of April 22nd at 12:16 CEST (6:16AM Eastern Time). 

It's just a start, however: The beams contained a relatively small number of protons and circulated at 450 billion electronvolts. The LHC team will ramp up the energy and intensity of the beams until the accelerator can perform collisions at a record energy of 13.6 trillion electronvolts.

Mike Lamont, CERN's Director for Accelerators and Technology, said:

"The machines and facilities underwent major upgrades during the second long shutdown of CERN's accelerator complex. The LHC itself has undergone an extensive consolidation programme and will now operate at an even higher energy and, thanks to major improvements in the injector complex, it will deliver significantly more data to the upgraded LHC experiments."

Research teams using the accelerator for their studies are expecting to be able to perform a lot more collisions — one, in particular, is expecting a 50 times increase — thanks to the upgrade. The more powerful LHC will allow scientists to study the Higgs boson more closely and to resume their hunt for a particle that proves the existence of dark matter with a more capable tool at hand. 

At the moment, dark matter is but a hypothetical form of matter that's believed to be five times more prevalent than its ordinary counterpart. It's invisible, doesn't reflect or emit light, and all attempts at looking for it have so far been unsuccessful. LHC researchers have narrowed down the regions where the particle may be hidden, though, and the upgraded accelerator could bring us closer to its discovery. To note, CERN previously approved plans to build a more powerful $23 billion super-collider that's 100 km in circumference, but its construction isn't expected to begin until 2038. 

A team of UK scientists has analyzed the complete genetic makeup of 12,000 tumors from NHS patients and discovered 58 new mutations that provide clues about their potential causes. The team, comprised of scientists from Cambridge University Hospitals and the University of Cambridge, used data from the 100,000 Genomes Project. That's a British initiative to sequence the whole genomes of patients with cancers and rare diseases. 

Team leader Professor Serena Nik-Zainal said this is the largest study of its kind and that the vast amount of data her team worked with allowed them to detect patterns in the genetic alterations or "mutational signatures" found in the tumors. By comparing their results with other studies, they were able to confirm that 58 of the mutational signatures they found were previously unknown. Some of them are pretty common, while some are rare.

"The reason it is important to identify mutational signatures is because they are like fingerprints at a crime scene — they help to pinpoint cancer culprits," Nik-Zainal explained. Some signatures could show that past exposure to environmental causes such as smoking or UV light had triggered the cancer, while others could have treatment implications. They could, for instance, pinpoint genetic abnormalities that could be targeted by specific drugs. 

Professor Matt Brown, chief scientific officer of Genomics England said: "Mutational signatures are an example of using the full potential of [whole genome sequencing]. We hope to use the mutational clues seen in this study and apply them back into our patient population, with the ultimate aim of improving diagnosis and management of cancer patients."

In addition to conducting DNA analysis and publishing its results in Science, the team also developed an algorithm called FitMS that will give clinicians easy access to the new information they discovered. FitMS looks for both common and rare signatures in the results of a patient's whole genome sequencing test. Doctors can use the algorithm to find out if their patients exhibit any of the newly discovered mutations for a more accurate diagnosis and for personalized treatments. 

Astrobotic has finally offered a good look at the vehicle that will carry scientific payloads to the lunar surface. The company has revealed the finished version of the Peregrine Moon lander ahead of its launch in the fourth quarter of the year. It's an externally simple design that resembles an upside-down pot, but that will be enough to carry 24 missions that include 11 NASA items, a Carnegie Mellon rover, private cargo and even "cultural messages" from Earth.

Peregrine is slightly over 6 feet tall and can hold up to 100kg (about 220lbs on Earth). More importantly for customers, it's relatively cheap— it'll cost $1.2 million per kilogram to ferry payloads to the Moon's surface ($300,000 to orbit). That sounds expensive, but it's a bargain compared to the cost of rocket launches. SpaceX is currently charging $67 million for each Falcon 9 launch, and that 'only' reaches Earth orbit.

The Astrobotic team still has to finish integrating payloads, conduct environmental testing and ship Peregrine to Cape Canaveral, where it will launch aboard a ULA Vulcan Centaur rocket. The payloads are already integrated into the flight decks, however.

The machine should make history if and when it's successful. Peregrine is expected to be the first US spacecraft to (properly) land on the Moon since the Apollo program ended. Past missions like Lunar Prospector, LCROSS, GRAIL and LADEE all ended with deliberate crashes. Astrobotic's effort won't be quite as momentous as the crewed Artemis landing, but it will help mark humanity's renewed interest in a lunar presence.

For humans, identifying items in a scene — whether that’s an avocado or an Aventador, a pile of mashed potatoes or an alien mothership — is as simple as looking at them. But for artificial intelligence and computer vision systems, developing a high-fidelity understanding of their surroundings takes a bit more effort. Well, a lot more effort. Around 800 hours of hand-labeling training images effort, if we’re being specific. To help machines better see the way people do, a team of researchers at MIT CSAIL in collaboration with Cornell University and Microsoft have developed STEGO, an algorithm able to identify images down to the individual pixel.

MIT CSAIL

Normally, creating CV training data involves a human drawing boxes around specific objects within an image — say, a box around the dog sitting in a field of grass — and labeling those boxes with what’s inside (“dog”), so that the AI trained on it will be able to tell the dog from the grass. STEGO (Self-supervised Transformer with Energy-based Graph Optimization), conversely, uses a technique known as semantic segmentation, which applies a class label to each pixel in the image to give the AI a more accurate view of the world around it.

Whereas a labeled box would have the object plus other items in the surrounding pixels within the boxed-in boundary, semantic segmentation labels every pixel in the object, but only the pixels that comprise the object — you get just dog pixels, not dog pixels plus some grass too. It’s the machine learning equivalent of using the Smart Lasso in Photoshop versus the Rectangular Marquee tool.

The problem with this technique is one of scope. Conventional multi-shot supervised systems often demand thousands, if not hundreds of thousands, of labeled images with which to train the algorithm. Multiply that by the 65,536 individual pixels that make up even a single 256x256 image, all of which now need to be individually labeled as well, and the workload required quickly spirals into impossibility.

Instead, “STEGO looks for similar objects that appear throughout a dataset,” the CSAIL team wrote in a press release Thursday. “It then associates these similar objects together to construct a consistent view of the world across all of the images it learns from.”

“If you're looking at oncological scans, the surface of planets, or high-resolution biological images, it’s hard to know what objects to look for without expert knowledge. In emerging domains, sometimes even human experts don't know what the right objects should be,” MIT CSAIL PhD student, Microsoft Software Engineer, and the paper’s lead author Mark Hamilton said. “In these types of situations where you want to design a method to operate at the boundaries of science, you can't rely on humans to figure it out before machines do.”

Trained on a wide variety of image domains — from home interiors to high altitude aerial shots — STEGO doubled the performance of previous semantic segmentation schemes, closely aligning with the image appraisals of the human control. What’s more, “when applied to driverless car datasets, STEGO successfully segmented out roads, people, and street signs with much higher resolution and granularity than previous systems. On images from space, the system broke down every single square foot of the surface of the Earth into roads, vegetation, and buildings,” the MIT CSAIL team wrote.

MIT CSAIL

“In making a general tool for understanding potentially complicated data sets, we hope that this type of an algorithm can automate the scientific process of object discovery from images,” Hamilton said. “There's a lot of different domains where human labeling would be prohibitively expensive, or humans simply don’t even know the specific structure, like in certain biological and astrophysical domains. We hope that future work enables application to a very broad scope of data sets. Since you don't need any human labels, we can now start to apply ML tools more broadly.”

Despite its superior performance to the systems that came before it, STEGO does have limitations. For example, it can identify both pasta and grits as “food-stuffs” but doesn't differentiate between them very well. It also gets confused by nonsensical images, such as a banana sitting on a phone receiver. Is this a food-stuff? Is this a pigeon? STEGO can’t tell. The team hopes to build a bit more flexibility into future iterations, allowing the system to identify objects under multiple classes.

NASA has pickedSpaceX, Amazon and four other American companies to develop the next generation of near-Earth space communication services meant to support its future missions. The agency started looking for partners under the Communication Services Project (CSP) in mid-2021, explaining that the use of commercially provided SATCOM will reduce costs and allow it to focus its efforts on deep space exploration and science missions.

"Adopting commercial SATCOM capabilities will empower missions to leverage private sector investment that far exceeds what government can do," NASA wrote in the official project page. By using technology developed by commercial companies, the agency will have continued access to any innovation they incorporate into the system. At the moment, NASA relies on its Tracking and Data Relay Satellite (TDRS) system for near-Earth space communications. Many of its satellites were launched in the 80's and 90's, though, and it's set to be decommissioned in the coming years. 

The funded agreements under NASA's Communication Services Project has a combined value of $278.5 million, with SpaceX getting the highest cut. NASA expects the companies to match and exceed its contribution during the five-year development period. SpaceX, which proposed a "commercial optical low-Earth orbiting relay network for high-rate SATCOM services," has been awarded $69.95 million. Amazon's Project Kuiper is getting the second-highest cut and has been awarded $67 million, while Viasat Incorporated has been awarded $53.3 million. The other three awardees are Telesat US Services ($30.65 million), SES Government Solutions ($28.96 million) and Inmarsat Government Inc. ($28.6 million).

All the participants are expected to be able to conduct in-space demonstrations by 2025 and show that their technology is capable of "new high-rate and high-capacity two-way communications." NASA will sign multiple long-term contracts with the companies that succeed in developing effective communication technologies for near-Earth operations by 2030.

Researchers at MIT created a 3D-printed device to develop a better understanding of the science behind what happens to the cream filling when you split the two sides of an Oreo cookie. Their device, the Oreometer, uses rubber bands and coins to control the torque applied to each side as a cookie is twisted apart. Adding pennies to one side rotates one of the two chambers and separates the Oreo.

After testing various types of Oreos, the researchers added scientific weight to something that nearly every American over the age of three already knows: the cream filling usually sticks to one side, even with Double and Mega Stuf varieties. Twisting speed mattered, according to the team — if you try to do it quickly, it may take more strain and stress to split a cookie. Curiously, the scientists found that the cream only separated more evenly when testing older boxes of cookies. 

The researchers suspect the Oreo manufacturing process is one reason for the phenomenon. “Videos of the manufacturing process show that they put the first wafer down, then dispense a ball of cream onto that wafer before putting the second wafer on top,” Crystal Owens, an MIT mechanical engineering PhD candidate, said. “Apparently that little time delay may make the cream stick better to the first wafer.”

The team published a paper on their research in the journal Physics of Fluids. As Gizmodo notes, they conducted the experiment as an exercise in rheology, which is the study of how matter flows. 

The researchers determined that, based on how the filling responded to stress, it should be classified as "mushy" instead of brittle, tough or rubbery. They also found that the cream's failure stress — the force per area needed to deform the filling or make it flow — is around the same as mozzarella cheese and double that of peanut butter and cream cheese.

There could be some other practical benefits of the research. “My 3D printing fluids are in the same class of materials as Oreo cream,” Owens said. “So, this new understanding can help me better design ink when I’m trying to print flexible electronics from a slurry of carbon nanotubes, because they deform in almost exactly the same way.”

In addition, Owens suggested that if the inside of each Oreo half had more texture, it might have a better grip on the cream and the filling would be more even when a cookie's twisted apart “As they are now, we found there’s no trick to twisting that would split the cream evenly,” Owens added.

If you'd like to try the experiment yourself, you can download the 3D printer files. Just be sure to eat some of the separated Oreos afterward. For science.

Europa's potential to support life may have increased thanks to geographic observations. Reutersnotes researchers have discovered similarities between double ridges on the Jovian moon's surface and smaller-scale equivalents in Greenland. As the Greenland ridges were formed by subsurface water that refroze, this suggests Europa's counterparts formed the same way. That, in turn, would indicate large volumes of the liquid water necessary to support life similar to that on Earth.

The geographic features are not only common on Europa, but are large enough that the water pockets for these ridges would each be comparable in size to North America's Lake Erie. They'd also be relatively shallow (about 0.6 miles below the surface), putting them near other chemicals that could help form life.

There are still no direct signs of life on Europa, and there might not be for a long time. NASA is launching its Europa Clipper spacecraft in 2024, but it won't reach orbit until 2030. Even so, the Greenland comparison bolsters the case for investigating Jupiter's fourth-largest moon. It suggests that at least some conditions are well-suited to life, even if factors like the extreme cold (a maximum -260F at the equator) limit what's possible.

After multiple attempts to complete a critical fueling test of its next-generation Space Launch System, NASA has decided to finish the rocket’s “wet dress rehearsal” at a later date. On late Saturday evening, the agency announced it would move the SLS off from its launch pad and back to the Kennedy Space Center’s Vehicle Assembly Building to give one of its gaseous nitrogen suppliers time to complete a critical upgrade. Nitrogen supply issues had delayed two previous countdown rehearsals, according to Space News.

NASA will also use the opportunity to replace a faulty helium check valve and repair a minor hydrogen leak technicians found in one of the “umbilical” fuel lines running from the rocket’s mobile launch tower. “During that time, the agency will also review schedules and options to demonstrate propellant loading operations ahead of launch,” NASA said. It promised to share more information about the decision, as well as its plans moving forward, during a press conference scheduled for April 18th.

Since April 1st, NASA has tried three times to complete a “wet dress rehearsal” of the Artemis 1 Moon mission. The test is designed to replicate the countdown procedure the SLS will undergo when the mission hopefully gets underway later this year. NASA most recently attempted to complete a modified version of the test on April 14th, but that trial was cut short after it discovered the aforementioned hydrogen leak in the rocket’s mobile launch tower. Initially, the agency left the door open for another attempt as early as April 21st but then had a change of mind.

The delay may have a domino effect on the timeline for the Artemis 1 Moon mission. NASA has yet to set a date for the flight, and won’t do so until the SLS wet dress rehearsal is complete. Despite all the issues NASA has run into with its next-generation rocket, the agency remains confident it will fly. "There's no doubt in my mind that we will finish this test campaign, and we will listen to the hardware, and the data will lead us to the next step," said Artemis launch director Charlie Blackwell-Thompson on Friday. "And we will take the appropriate steps, and we will launch this vehicle.”

Chinese astronauts — or taikonauts, as the country calls them — Zhai Zhigang, Ye Guangfu and Wang Yaping have returned to Earth after spending 183 days in space. That's the country's longest crewed mission to date so far, with the taikonauts spending those six months aboard Tianhe, the living module of China's Tiangong space station. As Space notes, Wang Yaping was also the first female taikonaut to live aboard Tianhe and the first Chinese woman to go on a spacewalk. 

The taikonauts were part of the Shenzhou-13 mission, which is the second of four crewed missions and the fifth out of the eleven overall missions China intends to launch to finish building its space station by the end of the year. They did two spacewalks and performed 20 science experiments while in orbit. The team also manually controlled the Tianhe module for a docking experiment with an unmanned cargo spacecraft. 

China, which isn't an ISS partner, launched Tianhe to low Earth orbit in April 2021 and quickly followed that up with several more launches in an effort to meet its space station's 2022 construction deadline. The country sent the first crewed mission to its fledgling station in June last year, and the three taikonauts involved spent three months in Tianhe testing systems and conducting spacewalks. In June, China is expected to launch its next crewed mission, the Shenzhou-14, with three taikonauts onboard who'll also spend six months in orbit.

Comets aren't known for being gargantuan, but there are clearly exceptions to that rule. Researchers using the Hubble Space Telescope have spotted the largest known comet to date, C/2014 UN271 (Bernardinelli-Bernstein). With a nucleus 80 miles across, it easily overshadows the 60-mile girth of previous record holder C/2002 VQ94 — it's about 50 times bigger than the typical comet. 

The comet was first discovered in 2010 by its namesake astronomers Pedro Bernardinelli and Gary Bernstein. However, scientists only recently verified the size by comparing Hubble imagery against a computer model of the coma (the 'atmosphere' of the comet as it releases gas) and data from the Atacama Large Millimeter/submillimeter Array. At roughly 2 billion miles away from Earth, C/2014 UN271 is too far away for Hubble to visualize the nucleus.

And before you ask: no, there's no danger of an Earth-shattering collision. C/2014 UN271 is on a 3-million-year-long elliptical orbit that will take it no closer than 1 billion miles from the Sun, or slightly beyond Saturn's distance, in 2031. It appears to have originated from the Oort Cloud (the still-theoretical nest of comets at least 2,000AU from the Sun) and may travel up to half a light-year away. Its -348F temperature may seem frigid, but it's warm enough to produce a carbon monoxide coma.

The size confirmation isn't just about bragging rights. This finding widens humanity's understanding of comet sizes, and adds to the still-small catalog of very distant comets. It might also provide more evidence of the Oort Cloud's existence and, by extension, help explain the cloud's role in Solar System development.