Tuesday, April 30, 2024

Particle physics experiment at LHC zeroes in on magnetic monopoles

 




The MoEDAL (Monopole and Exotics Detector) experiment at the Large Hadron Collider (LHC), which is the world’s largest and highest-energy particle collider, has made a significant leap in its quest for magnetic monopoles. Reported in two papers posted on the arXiv preprint server, the collaboration has narrowed the search window for these magnetic charge-bearing particles. The latest searches conducted by the MoEDAL experiment shrink the theoretical arenas in which the hunt for magnetic monopoles can continue, according to the European Council for Nuclear Research (CERN).


No magnetic monopoles or high-electric-charge objects 

The MoEDAL team found no magnetic monopoles or high-electric-charge objects (HECOs) in their latest scanning of the trapping volumes. But it set bounds on the mass and production rate of these particles for different values of particle spin, an intrinsic form of angular momentum. “MoEDAL’s search reach for both monopoles and HECOs allows the collaboration to survey a huge swathe of the theoretical ‘discovery space’ for these hypothetical particles,” said MoEDAL spokesperson James Pinfold. According to CERN, the MoEDAL team in its second latest study concentrated on the search for monopoles produced via the Schwinger mechanism in heavy-ion collision data taken during Run 1 of the LHC.

Scanned beam pipe in search of trapped monopoles

The team scanned a decommissioned section of the CMS experiment beam pipe, instead of the MoEDAL detector’s trapping volumes, in search of trapped monopoles. Once again, the team found no monopoles, but it set the strongest-to-date mass limits on Schwinger monopoles with a charge between 2gD and 45gD, ruling out the existence of monopoles with masses of up to 80 GeV, as per CERN. “The vital importance of the Schwinger mechanism is that the production of composite monopoles is not suppressed compared to that of elementary ones, as is the case with the Drell–Yan and photon-fusion processes,” explains Pinfold. “Thus, if monopoles are composite particles, this and our previous Schwinger-monopole search may have been the first-ever chances to observe them.”



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Saturday, April 27, 2024

Balancing AI and physics: Toward a learnable climate model


 


Artificial intelligence (AI) is bringing notable changes to atmospheric science, particularly with the introduction of large AI weather models like Pangu-Weather and GraphCast. However, alongside these advancements, questions have arisen about the alignment of these models with fundamental physics principles.


Previous studies have demonstrated that Pangu-Weather can accurately replicate certain climate patterns like tropical Gill responses and extra-tropical teleconnections through qualitative analysis. However, quantitative investigations have revealed significant differences in wind components, such as divergent winds and ageostrophic winds, within current AI weather models. Despite these findings, there are still concerns that the importance of physics in climate science is sometimes overlooked. "The qualitative assessment finds AI models could understand and learn spatial patterns in weather and climate data. On the other hand, the quantitative approach highlights a limitation: current AI models struggle to learn certain wind patterns and instead rely solely on total wind speed," explains Professor Gang Huang from the Institute of Atmospheric Physics (IAP) at the Chinese Academy of Sciences. "This underscores the need for comprehensive dynamic diagnostics of AI models. Only through a holistic analysis can we augment our understanding and impose necessary physical constraints." Researchers, including collaborators from the IAP, Seoul National University, and Tongji University, advocate for a collaborative approach between AI and physics in climate modeling, moving beyond the notion of an "either-or" scenario.

Professor Huang says, "While AI excels in capturing spatial relationships within weather and climate data, it struggles with nuanced physical components like divergent winds and ageostrophic winds. This underscores the necessity for rigorous dynamic diagnostics to enforce physical constraints." Published in Advances in Atmospheric Sciences, their perspectives paper illustrates methods to impose both soft and hard physical constraints on AI models, ensuring consistency with known atmospheric dynamics. Moreover, the team advocates for a transition from offline to online parameterization schemes to achieve global optimality in model weights, thereby fostering fully coupled physics-AI balanced climate models. Dr. Ya Wang says, "This integration enables iterative optimization, transforming our models into truly learnable systems." Recognizing the importance of community collaboration, the researchers promote a culture of openness, comparability, and reproducibility (OCR). By embracing principles akin to those in the AI and computer science communities, they believe in cultivating a culture conducive to the development of a truly learnable climate model. In summary, by synthesizing AI's spatial prowess with physics' foundational principles and fostering a collaborative community, researchers aim to realize a climate model that seamlessly blends AI and physics, representing a significant step forward in climate science.


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Saturday, April 20, 2024

US Electron-Ion Collider hits construction milestone

 



Powerful probe The Electron-Ion Collider at Brookhaven National Laboratory will smash together electrons and protons to study the strong nuclear force and the role of gluons in nucleons and nuclei. (CC BY Brookhaven National Laboratory)

The US Department of Energy has given the green light for the next stage of the Electron-Ion Collider (EIC). Known as “critical decision 3A”, the move allows officials to purchase “long-lead procurements” such as equipment, services and materials before assembling the collider can begin. The EIC, costing between $1.7bn and $2.8bn, will be built at Brookhaven National Laboratory in Long Island, New York. This will involve the lab revamping its existing 3.8 km-long Relativistic Heavy Ion Collider accelerator complex that collides heavy nuclei such as gold and copper to produce a quark–gluon plasma. A major part of the upgrade will involve adding an electron ring so that the EIC consists of two intersecting accelerators – one producing an intense beam of electrons and the other a high-energy beam of protons or heavier atomic nuclei. Each high-luminosity beam will be steered into head-on collisions with the particles produced providing clues to the internal nature of protons and their components. “Passing this milestone and getting these procurements under way will help us achieve our ultimate goal of efficiently delivering a unique high-energy, high-luminosity polarized beam electron–ion collider that will be one of the most challenging and exciting accelerator complexes ever built,” notes EIC project director Jim Yeck. Construction is expected to start in 2026 with first experiments beginning in the first half of the next decade. READ MOREAriel view of the Brookhaven National Laboratory campus Brookhaven chosen to host major US nuclear physics facility Meanwhile, the UK has said it will provide £58.4m ($73.8) to develop new detector and accelerator infrastructure for the EIC. The money comes as part of a £473m package of spending by the UK Research and Innovation (UKRI) Infrastructure Fund. This money also includes £125m for a new diffraction and imaging electron microscopy facility at the Science and Technology Facilities Council’s Daresbury Laboratory. The facility, known as Relativistic Ultrafast Electron Diffraction and Imaging, will be the world’s most powerful microscope for imaging dynamics being able to study biological and chemical processes in “real time” at the femtosecond timescale.




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