Physics Newsletter December#1
- Bhavya Goel
- Dec 22, 2024
- 4 min read
Physics Pulse: Physics Newsletter
By: Bhavya Goel - Researcher
Geoffrey Hinton Wins Nobel Prize for AI Breakthrough
Geoffrey Hinton, a British-Canadian scientist and professor emeritus at the University of Toronto, recently received the Nobel Prize in Physics for his groundbreaking work in artificial intelligence (AI). Hinton, alongside co-winner John Hopfield, was recognized for developing foundational ideas in machine learning, a branch of AI that helps computers learn like humans.
Hinton’s invention, the Boltzmann machine, dates back to the 1980s when he worked at Carnegie Mellon University. This system allows computers to learn from examples rather than direct instructions, enabling them to identify patterns and make predictions even with unfamiliar data. This work laid the groundwork for today’s powerful neural networks, used in fields like medicine, physics, and everyday technology.
The Nobel Prize committee described Hinton as a pioneer in creating effective AI learning systems, noting their ability to analyze and improve from vast amounts of data. His achievements earned him a gold medal, a diploma, and about $1.4 million CAD, which he plans to share with charities, including one that helps Indigenous communities access clean water.
Now 77, Hinton remains active in the AI community. He advises the Vector Institute in Toronto and advocates for the safe use of AI. While proud of his contributions, he has expressed concern about the potential risks of AI, such as job loss, misinformation, and even threats to humanity. Hinton hopes his work inspires others to prioritize safety as AI continues to evolve.
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https://www.ctvnews.ca/sci-tech/ai-pioneer-geoffrey-hinton-to-receive-nobel-prize-in-physics today-1.7139735
Physicists Find Particle That Only Has Mass When Moving in One Direction
Physicists have discovered a unique quasiparticle, the semi-Dirac fermion, which only has an effective mass—a property describing a particle's resistance to motion—when moving in a specific direction. Unlike regular particles, which exhibit consistent mass regardless of direction, the semi-Dirac fermion is massless in one direction but gains mass when moving perpendicularly. This breakthrough was observed in ZrSiS semi-metal crystals, cooled to-269°C (-452°F), using magneto-optical spectroscopy, a method analyzing light reflections under an intense magnetic field 900,000 times stronger than Earth's.
An analogy helps clarify its behavior: imagine a train on tracks representing the crystal's structure. On a straight "fast track," it moves without resistance, behaving as if massless, but at intersections, switching to a perpendicular track introduces resistance, giving it mass. This directional dependence is what makes the semi-Dirac fermion extraordinary.
First predicted in 2008, this discovery could have significant implications for quantum physics and electronic sensors. However, much remains unexplained, and researchers aim to refine their understanding by isolating single layers of the ZrSiS crystal. As physicist Yinming Shao noted, “The most thrilling part of this experiment is that the data cannot be fully explained yet.” The semi-Dirac fermion opens new possibilities for exploring the universe’s fundamental rules.
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https://www.sciencealert.com/physicists-find-particle-that-only-has-mass-when-moving-in-on e-direction
World’s Most Accurate and Precise Atomic Clock Pushes New Frontiers in Physics
Researchers at JILA, a collaboration between NIST and the University of Colorado Boulder, have developed the world’s most precise atomic clock, surpassing previous designs in accuracy. This breakthrough clock can measure the effects of gravity, as predicted by Einstein’s general relativity has inspired a groundbreaking advancement in timekeeping at the microscopic scale. Unlike traditional atomic clocks that rely on microwaves, this innovative design uses visible light waves with significantly higher frequencies, achieving extraordinary precision—losing only one second every 30 billion years. The clock traps strontium atoms within a delicate web of laser light, known as an optical lattice. This setup minimizes disturbances to the atoms’ quantum states while simultaneously measuring tens of thousands of atoms, enhancing accuracy.
This breakthrough enables the detection of minute changes in time caused by gravity at submillimeter scales, effectively bridging the gap between quantum mechanics and general relativity. It holds immense potential for transformative applications, including precise space navigation and advancements in quantum computing. As physicist Jun Ye explains, "When you measure with this precision, you begin to observe phenomena that were previously only theoretical."
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How can AI help physicists search for new particles?
Physicists from the ATLAS and CMS collaborations at the Large Hadron Collider (LHC) are leveraging advanced machine learning (ML) techniques to search for exotic particle collisions that could reveal new physics. Traditionally, searches for new particles are guided by theoretical predictions, focusing on specific possibilities. However, AI now allows researchers to explore unpredicted phenomena in the vast LHC dataset more efficiently.
The CMS collaboration recently showcased their use of ML to analyze jets—collimated sprays of particles formed by quarks and gluons. Jets are complex and difficult to interpret, but they may conceal evidence of unknown interactions. AI algorithms trained on real collision data can distinguish jets from known particles and identify unusual jet signatures indicative of potential new physics.
Different ML approaches are used across the experiments. Some methods analyze the full collision event to detect anomalies, while others use simulated examples of new signals to guide the AI in recognizing atypical patterns in real data. In 2023, ATLAS demonstrated one of the first applications of unsupervised ML to LHC data, allowing the AI to identify anomalies without prior assumptions.
Recent CMS results highlight the diverse sensitivities of ML models to various potential particles. While no single method excelled universally, AI-driven algorithms consistently outperformed traditional techniques, improving sensitivity to a wide range of particle signatures. These efforts not only limit the production rates of hypothetical particles but also pave the way for broader searches in unexplored areas.
As CMS physicist Oz Amram notes, researchers are already working on refining the algorithms and expanding their application to uncover more possibilities. Machine learning is proving transformative, offering new avenues for uncovering the unknown in particle physics.
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