Science
How String Theory Unraveled Math’s Monstrous Moonshine Enigma
An idea originating from theoretical physics provided validation for the unexpected linkage between two entirely disparate branches of mathematics.
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Following the release of the star-studded mystery thriller “The Number 23” in 2007, a significant number of individuals began to perceive occurrences of the titular number in various aspects of their lives. I was a student at the time, and some of my peers would react with unease whenever the number 23 made an appearance. This phenomenon, often referred to as the “frequency illusion” or the Baader-Meinhof phenomenon, intrigued many, as it illustrated how paying closer attention to a specific element, such as a number, could lead to a heightened perception of its presence.
For a considerable period, there was speculation that the late mathematician John McKay experienced a similar phenomenon, fixating on the number 196,884. McKay’s encounter with this number occurred in 1978 while he was exploring a mathematical paper unrelated to his primary field of study. Despite being immersed in geometry, McKay stumbled upon a sequence of numbers within number theory, beginning with 196,884.
This figure sparked familiarity in McKay due to his previous work on a theoretical mathematical entity known as the “monster.” This complex structure aimed to describe the symmetries of a geometric object residing in 196,883 dimensions, only one dimension fewer than the number 196,884. McKay’s discovery of this number seemed serendipitous, as he connected it to the dimensions of the monster structure: 196,883 + 1 = 196,884.
Although McKay’s findings initially garnered little attention from experts who attributed it to coincidental numerical similarities within diverse mathematical contexts, McKay persisted in his belief that geometry and number theory could be intertwined. He even sported T-shirts inscribed with the equation “196,883 + 1 = 196,884” at academic gatherings.
Subsequently, mathematician John Thompson’s exploration supported McKay’s suspicions by establishing a link between higher dimensions of the monster’s symmetries and subsequent numbers in the number theory sequence. Thompson’s calculations revealed a surprising correlation, reinforcing the possibility of a connection between seemingly disparate mathematical domains.
This revelation piqued the interest of the mathematical community, leading to further investigation into the potential link between geometry and number theory. Despite initial skepticism, the concept of “monstrous moonshine,” as termed by mathematicians John Conway and Simon Norton, gained traction as evidence of numerical patterns emerged, suggesting a deeper connection between these mathematical realms.
The theoretical prediction of the monster structure within group theory, resembling a periodic table for finite symmetries, further fueled this inquiry. With nearly all finite groups categorized into 18 classes and 26 outliers, the concept of the monster challenged conventional mathematical understanding, hinting at profound connections yet to be fully comprehended.
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The initial outlier among these groups was the “monster,” foreseen by mathematicians Bernd Fischer and Robert Griess in 1973. The moniker derives from the immense scale of this group, boasting over 8 x 10^53 symmetries. To put this into perspective, consider that the symmetry group of a 20-sided “D20” die, or icosahedron, comprises only 60 symmetries, indicating that there are merely 60 conceivable transformations (rotations or reflections) that can be executed without altering the D20’s orientation.
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Due to its immense scale, the monster posed significant challenges for mathematicians. “Most people thought it was going to be hopeless to construct it since much, much, much smaller groups required computer constructions at that time,” Borcherds explained in his YouTube video. Even powerful computers struggled with a structure consisting of 8 x 10^53 elements.
However, this pessimistic outlook was ultimately proven incorrect. In 1980, Griess successfully constructed the monster, thereby proving its existence—without the aid of computers.
Number theory primarily deals with integers, which may seem straightforward initially. However, to delve into the relationships between them, experts employ intricate concepts like modular forms. These functions, denoted as f(z), exhibit extreme symmetry. Similar to the sine function, understanding a specific segment of a modular form reveals its appearance across the entire function.
Modular forms are something like trigonometric functions, but on steroids,” mathematician Ken Ono told Quanta Magazine.
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However, despite their complexity, modular forms play a pivotal role in mathematics. For instance, Andrew Wiles from the University of Oxford utilized them to prove Fermat’s Last Theorem, while Maryna Viazovska from the Swiss Federal Institute of Technology in Lausanne employed them to determine the densest sphere-packing arrangement in eight spatial dimensions. Due to the intricate nature of modular forms, they are often approximated by infinitely long polynomials, such as:
[ f(q) = \frac{1}{q} + 744 + 19,688q + 21,493,760q^2 + 864,299,970q^3 + \ldots ]
The prefactors preceding the variable ( q ) constitute a number sequence with intriguing properties from a number-theoretical perspective. McKay linked this number sequence with the monster group.
Borcherds first encountered the moonshine conjecture in the 1980s, recalling, “I was just completely blown away by this,” during an interview with YouTuber Curt Jaimungal. Sitting in one of Conway’s lectures at the time, Borcherds learned of the mysterious connection between number theory and group theory, a subject that captivated him thereafter. He embarked on a quest to uncover this suspected connection, eventually publishing his groundbreaking result in 1992, for which he received a Fields Medal six years later. His conclusion suggested that a highly speculative area of physics, string theory, could provide the missing link between the monster group and the number sequence.
String theory aims to unify the four fundamental forces of physics (electromagnetism, strong and weak nuclear forces, and gravity). Unlike conventional theories relying on particles or waves as the universe’s basic constituents, string theory involves one-dimensional structures resembling tiny vibrating threads, generating the familiar particles and interactions observed in the universe.
Borcherds recognized that string theory relied on numerous mathematical principles related to symmetries, including moduli. Closed strings oscillating through spacetime create two-dimensional tubes exhibiting the same symmetry as modular shapes, regardless of their oscillation patterns.
The specific type of string theory Borcherds explored could only be mathematically formulated in 25 spatial dimensions. While our observable universe comprises three visible spatial dimensions, string theorists presume the remaining 22 dimensions are compactified into tiny spheres or toroidal shapes. However, the physics hinges on their precise configuration, known as “compactification.”
Borcherds compactified 24 dimensions into a 24-dimensional toroidal surface and discovered that the associated string theory exhibited the symmetry of the monster group. The fact that only one free spatial dimension remained did not deter him, as he focused on the model’s mathematical properties rather than its applicability to our physical universe.
In this constructed framework, strings oscillate along the 24-dimensional torus, with the monster group dimensions representing the various vibration modes at different energy levels. Thus, at the lowest energy level, there is only one vibration mode, while at higher energy levels, there are 196,883 distinct possibilities, with the resultant trace exhibiting the symmetry of a modular form.
Borcherds successfully demonstrated the connection between the monster group and a modular form. This was not an isolated case; subsequently, mathematicians have linked other finite groups with various modular forms, with string theory serving as the conduit. Hence, even if string theory fails as a description of our universe, it can still unveil entirely new mathematical vistas.
Space
World’s First Wooden Satellite Launched into Space
Aim is to Test Space-Ready Timber for Future Mission.
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Art: KyotoU/Gakuji Tobiyama
In a groundbreaking achievement, scientists have launched the world’s first wooden satellite into space to explore the material’s suitability for orbit. Named LignoSat, derived from the Latin for “wood,” the satellite was sent into space on a SpaceX mission to the International Space Station (ISS) on Monday. It will later be deployed into orbit, where researchers will observe how the wood withstands the extreme conditions of space over a period of six months.
The initiative, a collaboration between Kyoto University and Sumitomo Forestry, began in 2020. In 2022, they conducted exposure tests aboard the ISS for over 240 days, ultimately selecting Hoonoki, a type of Magnolia wood known for its strength, stability, and workability. This wood is traditionally used in Japan for crafting sword sheaths due to its shatter-resistant properties, according to Reuters.
Since space is devoid of water and oxygen, the wood is shielded from fire and decay, Kyoto University scientists explain. Additionally, they will evaluate the wood’s potential to protect semiconductors from space radiation, Reuters reports.
“If our wooden satellite succeeds, we plan to propose it to Elon Musk’s SpaceX,” said Takao Doi, an astronaut and professor at Kyoto University. The team envisions that wooden satellites could reduce space pollution, as they would not emit aluminum oxide when burning up upon re-entry, unlike traditional metal satellites. Over the next 50 years, Doi’s team envisions growing wood for timber-based habitats on the Moon and Mars. “With timber, a material we can cultivate ourselves, we could construct homes and sustain life in space indefinitely,” Doi told Reuters.
Science
Scientists Behind AI Breakthroughs Awarded Nobel Prize
Geoffrey Hinton, professor emeritus at the University of Toronto, and John Hopfield, professor at Princeton University, were honored with the Nobel Prize in Physics for their pioneering contributions that laid the “foundation of today’s powerful machine learning.”
The Royal Swedish Academy of Sciences highlighted their work from the 1980s, which led to the development of artificial neural networks—computer systems inspired by the brain’s structure. These neural networks, which enable AI to “learn by example,” have been instrumental in advances like language processing and image recognition.
Hinton, often called the “Godfather of AI,” expressed his surprise at the award, stating, “I had no expectations of this. I am extremely surprised and I’m honoured.” His key contribution, the development of the Boltzmann machine, a generative model, played a significant role in modern AI.
Despite his monumental achievements, Hinton has raised concerns about the potential misuse of AI. In a 2023 New York Times interview, he expressed regret over his life’s work, noting, “It is hard to see how you can prevent the bad actors from using it for bad things.” He left his position at Google in 2023 to more openly discuss the dangers AI might pose.
The Nobel committee also acknowledged the work of John Hopfield, whose Hopfield network provided early insights into how artificial neural networks can replicate brain patterns. Both scientists’ discoveries have been crucial in shaping today’s AI technologies.
Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.
Hinton’s contributions build on the work of fellow Nobel laureate John Hopfield, who developed the Hopfield network, an artificial neural network designed to recreate patterns and store memory. This type of network, introduced in the 1980s, models how neurons in the brain interact, using a system that can “remember” and retrieve stored information. Hopfield’s work provided early insight into how artificial neural networks could replicate brain-like processes, paving the way for the more advanced machine learning and neural network models that Hinton and others would later develop.
The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with.
Hinton continues to express his concerns about the future of AI, reiterating these in a recent call with reporters. He noted, “We have no experience of what it’s like to have things smarter than us. And it’s going to be wonderful in many respects.” However, he also cautioned about the potential dangers, emphasizing the need to remain vigilant about “a number of possible bad consequences, particularly the threat of these things getting out of control.” Hinton’s remarks reflect his growing unease about the rapid development of AI technologies and their potential misuse.
Science
Moon Drifting Away, Earth Could Have 25-Hour Days: Study
A study reveals that the Moon has been receding from Earth at a rate of approximately 3.8 centimeters per year.
Scientists suggest that a day on Earth might extend to 25 hours in the future due to the Moon’s gradual drift away from our planet.
Research from the University of Wisconsin-Madison indicates that the Moon is receding from Earth at a rate of approximately 3.8 centimeters per year. This phenomenon could result in Earth days lasting 25 hours in 200 million years. Historically, a day on Earth lasted just over 18 hours around 1.4 billion years ago.
Stephen Meyers, a professor in the geoscience department at the University of Wisconsin-Madison, points to the gravitational interactions between Earth and the Moon as a primary cause. “As the Moon moves away, the Earth is like a spinning figure skater who slows down as they stretch their arms out,” explained Meyers.
Meyers and his team are using ‘astrochronology’ to study ancient geological processes. “We want to be able to study rocks that are billions of years old in a way that is comparable to how we study modern geologic processes,” he said.
The concept of the Moon’s recession is not new, but the Wisconsin research delves deeper into its historical and geological context. By examining ancient geological formations and sediment layers, researchers have tracked the Earth-Moon system over billions of years. They found that while the Moon’s current recession rate is relatively stable, it has fluctuated due to various factors such as Earth’s rotational speed and continental drift.
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