First lead-ion collisions in the LHC at record energy

November 18, 2022. GENEVA, Switzerland.

A test using collisions of lead ions was carried out in the LHC and provided an opportunity for the experiments to validate the new detectors and new data-processing systems ahead of next year’s lead-lead physics run.

Event displays of the first Pb-Pb collision of Run3 taken on 18 November 2022 (Image: CERN)

After the successful start of Run 3 in July this year, which featured proton-proton collisions at the record energy of 13.6 TeV, it was the turn of lead nuclei to circulate in the Large Hadron Collider (LHC) again last Friday after a gap of four years. Lead nuclei comprise 208 nucleons (protons and neutrons) and are used at the LHC to study quark-gluon plasma (QGP), a state of matter in which the elementary constituents, quarks and gluons, are not confined within nucleons but can move and interact over a much larger volume.

Event display of a lead-argon collision in LHCb (Image: CERN)

In the test carried out last Friday, lead nuclei were accelerated and collided at a record energy of 5.36 TeV per nucleon-nucleon collision1. This is an important milestone in preparation for the physics runs with lead-lead collisions that are planned for 2023 and the following years of Run 3 and Run 4.

Event display of a heavy ion collision event recorded in ATLAS on 18 Nov 2022, when stable beams of lead ions colliding at a center-of-mass energy per nucleon pair of 5.36 TeV were delivered to ATLAS by the LHC. (Image: CERN)

The CERN ion injector complex has undergone a series of upgrades in preparation for a doubling of the total intensity of the lead-ion beams for the High-Luminosity LHC. Achieving this goal requires a technique called “momentum slip-stacking” to be used in the Super Proton Synchrotron (SPS), where two batches of four lead-ion bunches separated by 100 nanoseconds “slip” to produce a single batch of 8 lead bunches separated by 50 nanoseconds. This will allow the total number of bunches injected into the LHC to increase from 648 in Run 2 to 1248 in Run 3 and onwards. After all the upgrades have been completed the LHC will provide a ten-fold higher number of heavy ion collisions with respect to the past Runs.

The test was also a crucial milestone for ALICE, the LHC experiment that specialises in the study of lead-ion collisions. The ALICE apparatus was upgraded during the recent shutdown of the LHC and now features several completely new or greatly improved detectors, as well as new hardware and software for data processing. The new detectors provide a higher spatial resolution in the reconstruction of the trajectories and properties of the particles produced in the collisions. In addition, the upgraded apparatus and upgraded processing chain can record the full collision information at a rate two orders of magnitude higher.

Events as seen in the CMS detector from Pb-Pb collisions (Image: CERN)

Other experiments used the test run to commission their upgraded and newly installed subsystems in the new heavy-ion environment of higher energy and 50ns bunch spacing. ATLAS tested upgrades to its selection (trigger) software, which is designed to enhance heavy-ion-physics data taking in Run 3. In particular, physicists tested a new particle-track trigger designed to spot a wider range of “ultra-peripheral collisions”. CMS upgraded several components of its readout, data acquisition, trigger and reconstruction chains to be able to take full advantage of the high-energy lead-lead collisions. The lead-lead fills delivered by the LHC allowed CMS to commission the entire system with beam and spot the areas that could be further optimized for the 2023 heavy-ion runs. LHCb started commissioning its brand-new detector in the challenging conditions of lead-lead collisions characterised by a very large particle multiplicity. In addition to lead-lead collisions, LHCb collected lead-argon collisions in fixed-target mode using the new SMOG2 system, which is unique to the experiment and is designed to inject noble gases into the LHCb collision area.

Even if very short, the 2022 lead-lead programme can be considered a success for the LHC accelerator, the experiments and CERN's heavy-ion injector complex. The four big LHC detectors saw and recorded lead-lead collisions at a new record energy for the first time. Researchers are now looking forward to the heavy-ion physics campaign in 2023 and the following years.

1 In lead-lead collisions, each of the 208 nucleons of one of the lead nuclei can interact with one or several nucleons of the other lead nucleus.

Source Link: https://home.cern/news/news/experiments/first-lead-ion-collisions-lhc-record-energy

Caltech: Using AIA to Navigate Flow

December 9, 2021. ZURICH, Switzerland and HARVARD, US.

Peter Gunnarson describes his work in the Dabiri Lab to use Reinforcement Learning to give robots the ability to navigate flow autonomously.

Youtube: Caltech

More information: https://www.caltech.edu/about/news/scientists-teach-ai-to-navigate-ocean-with-minimal-energy

Engineers Teach AI to Navigate Ocean with Minimal Energy

December 08, 2021.

Research could enable monitoring of our oceans or exploration of alien ocean worlds

Engineers at Caltech, ETH Zurich, and Harvard are developing an artificial intelligence (AI) that will allow autonomous drones to use ocean currents to aid their navigation, rather than fighting their way through them.

"When we want robots to explore the deep ocean, especially in swarms, it's almost impossible to control them with a joystick from 20,000 feet away at the surface. We also can't feed them data about the local ocean currents they need to navigate because we can't detect them from the surface. Instead, at a certain point we need ocean-borne drones to be able to make decisions about how to move for themselves," says John O. Dabiri (MS '03, PhD '05), the Centennial Professor of Aeronautics and Mechanical Engineering and corresponding author of a paper about the research that was published by Nature Communications on December 8.

The AI's performance was tested using computer simulations, but the team behind the effort has also developed a small palm-sized robot that runs the algorithm on a tiny computer chip that could power seaborne drones both on Earth and other planets. The goal would be to create an autonomous system to monitor the condition of the planet's oceans, for example using the algorithm in combination with prosthetics they previously developed to help jellyfish swim faster and on command. Fully mechanical robots running the algorithm could even explore oceans on other worlds, such as Enceladus or Europa.

In either scenario, drones would need to be able to make decisions on their own about where to go and the most efficient way to get there. To do so, they will likely only have data that they can gather themselves—information about the water currents they are currently experiencing.

To tackle this challenge, researchers turned to reinforcement learning (RL) networks. Compared to conventional neural networks, reinforcement learning networks do not train on a static data set but rather train as fast as they can collect experience. This scheme allows them to exist on much smaller computers—for the purposes of this project, the team wrote software that can be installed and run on a Teensy—a 2.4-by-0.7-inch microcontroller that anyone can buy for less than $30 on Amazon and only uses about a half watt of power.

Using a computer simulation in which flow past an obstacle in water created several vortices moving in opposite directions, the team taught the AI to navigate in such a way that it took advantage of low-velocity regions in the wake of the vortices to coast to the target location with minimal power used. To aid its navigation, the simulated swimmer only had access to information about the water currents at its immediate location, yet it soon learned how to exploit the vortices to coast toward the desired target. In a physical robot, the AI would similarly only have access to information that could be gathered from an onboard gyroscope and accelerometer, which are both relatively small and low-cost sensors for a robotic platform.

This kind of navigation is analogous to the way eagles and hawks ride thermals in the air, extracting energy from air currents to maneuver to a desired location with the minimum energy expended. Surprisingly, the researchers discovered that their reinforcement learning algorithm could learn navigation strategies that are even more effective than those thought to be used by real fish in the ocean.

John Dabiri (R) and Peter Gunnarson (L) testing CARL-bot at Caltech

"We were initially just hoping the AI could compete with navigation strategies already found in real swimming animals, so we were surprised to see it learn even more effective methods by exploiting repeated trials on the computer," says Dabiri.

The technology is still in its infancy: currently, the team would like to test the AI on each different type of flow disturbance it would possibly encounter on a mission in the ocean—for example, swirling vortices versus streaming tidal currents—to assess its effectiveness in the wild. However, by incorporating their knowledge of ocean-flow physics within the reinforcement learning strategy, the researchers aim to overcome this limitation. The current research proves the potential effectiveness of RL networks in addressing this challenge—particularly because they can operate on such small devices. To try this in the field, the team is placing the Teensy on a custom-built drone dubbed the "CARL-Bot" (Caltech Autonomous Reinforcement Learning Robot). The CARL-Bot will be dropped into a newly constructed two-story-tall water tank on Caltech's campus and taught to navigate the ocean's currents.

The technology is still in its infancy: currently, the team would like to test the AI on each different type of flow disturbance it would possibly encounter on a mission in the ocean—for example, swirling vortices versus streaming tidal currents—to assess its effectiveness in the wild. However, by incorporating their knowledge of ocean-flow physics within the reinforcement learning strategy, the researchers aim to overcome this limitation. The current research proves the potential effectiveness of RL networks in addressing this challenge—particularly because they can operate on such small devices. To try this in the field, the team is placing the Teensy on a custom-built drone dubbed the "CARL-Bot" (Caltech Autonomous Reinforcement Learning Robot). The CARL-Bot will be dropped into a newly constructed two-story-tall water tank on Caltech's campus and taught to navigate the ocean's currents.

"Not only will the robot be learning, but we'll be learning about ocean currents and how to navigate through them," says Peter Gunnarson, graduate student at Caltech and lead author of the Nature Communications paper.

The paper is titled "Learning efficient navigation in vortical flow fields." Co-authors include Ioannis Mandralis, graduate student at Caltech, Guido Novati of ETH Zurich in Switzerland, and Petros Koumoutsakos (PhD '92) of Harvard University. This research was funded by a National Science Foundation Graduate Fellowship to Gunnarson and by NSF Waterman Award funding to Dabiri.

WRITTEN BY

Robert Perkins

CONTACT

Robert Perkins

(626) 395‑1862

rperkins@caltech.edu

Japanese researchers are developing artificial-gravity buildings for space

July 15, 2022. KYOTO, Japan.

Architects have conjured some odd-shaped space habitats over the years—airtight orbs, geodesic domes, and lantern-shaped structures among them.

The team's artificial gravity living facility. Image: Kajima corporation.

Designed for atmospheric conditions on Mars and the Moon, the team aims to erect a prototype of The Glass on the lunar surface by 2050, the local paper Asahi Shimbum reports.

• Japanese researchers unveil renderings of an 'artificial gravity living facility' dubbed as 'The Glass'.

• 'The Glass' prototype is designed for the atmospheric conditions on Mars and the Moon with a focus on artificial gravity.

• Creating an environment with Earth-like gravity is the key to thriving in space, explain the researchers.

Architects have conjured some odd-shaped space habitats. [Image: Unsplash/NASA]

Architects have conjured some odd-shaped space habitats over the years—airtight orbs, geodesic domes, and lantern-shaped structures among them. Japanese researchers, however, believe that the optimal extraterrestrial architecture is conical.

At a July 5 conference, a team from Kyoto University and the construction firm Kajima Corporation unveiled renderings of an “artificial gravity living facility” whose shape is conducive to approximating living conditions on earth. The 1,300-ft.-tall rotating structure, dubbed “The Glass,” is designed to complete a full rotation every 20 seconds, using centrifugal force to achieve the “normal gravity” humans are used to.

A focus on artificial gravity research as the age of space tourism begins

The Japanese researchers say that creating an environment with Earth-like gravity is the key to thriving in space. “Without gravity, mammals might not be able to reproduce and their babies might not develop well,” the team explains in a press statement. “When a person grows under a zero or low gravity environment, their body would change so they wouldn’t be able to stand up on earth.”

The Kajima-Kyoto University team says Earthlings are clueless about how children adapt to a state of weightlessness, pointing out that NASA’s gravity research has largely been focused on adults. Studies show that traveling across different gravity fields can cause bone loss, back pain, and kidney stones.

As space tourism becomes available to more people, researchers say they want to shed light on the effect of microgravity environments on a diversity of human bodies.

Making other planets hospitable to humans

Beyond the standalone habitats, the researchers say we need to think of designing other artificial-gravity infrastructure to support communities on other celestial bodies. The scope of their research even includes developing a transportation system for interplanetary travel. They envision a “Hexagon Space Track System” that will maintain normal gravity during long-distance journeys.

“A completely original idea from Japan”

“The US and the UAE are proactively proposing the migration to Mars, but I would like to send out a completely original idea from Japan,” said Yosuke Yamashiki, a professor at Kyoto University’s SIC Manned Cosmology Research Center. “The core technologies are not being developed by other countries, and they’re indispensable for realizing human space migration.”

“Developing an artificial gravity residential facility with Kyoto University will be a watershed moment in space research,” echoed Takuya Ohno, an architect and researcher at Kajima. “We will work to make this joint research meaningful for humankind.”

Source Link: https://www.weforum.org/agenda/2022/07/japanese-space-extra-terrestrial-architecture-earth-mars-moon/

This article is published in collaboration with Quartz.

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.