Prime numbers have captivated mathematicians for centuries with their unpredictable and seemingly random distribution. In a groundbreaking preprint study, researchers devised a novel method that bolsters our hunt for the cagey values—but also reveals limits to our ability to detect them.
Prime numbers are divisible only by 1 and themselves. They serve as the “atoms” of mathematics, capable of decomposing other numbers into factors (like 12 = 2 × 2 × 3). As numbers increase, identifying primes becomes more and more challenging. If you were asked, “How many primes are there between 1 and 1,000?” where would you begin?
The classical Sieve of Eratosthenes offers a starting point. This ancient technique systematically eliminates multiples of each prime, allowing only the primes themselves to “fall out.” Mathematicians refer to the eliminated multiples as “Type I information,” which can help predict how many primes are in a given range. Yet this information is limited. “Sometimes you have as good Type I information as you can possibly hope for, but you still can’t find any primes,” explains study co-author Kevin Ford, a mathematician at the University of Illinois at Urbana-Champaign.
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Ford and University of Oxford mathematician James Maynard provide a powerful method for studying prime numbers in large ranges by precisely estimating the number of primes that must exist within them. The work combines two complementary perspectives: the Type I information of eliminated numbers (like crossing off all multiples of 2, then 3, and so on) as well as accounting for numbers that get crossed off multiple times (like how 6 appears on the lists of multiples both of 2 and of 3)—called Type II information.
Mathematicians can adjust how they weigh each type of information to get the most accurate possible count of primes in a given range. But in carefully tuning these two knobs, the paper’s authors discovered there are fundamental limits: precise mathematical boundaries where no further adjustment can improve our count’s accuracy, revealing deep truths about how these numbers are distributed across the number line.
The study likens the accuracy of these estimations for a set, or “strength of information,” to changing the size of the mesh in a sieve: too small, and you’ll catch every number; too big, and the primes will slip through. The work “answers precisely and directly what is ‘sufficiently good’ information to detect primes,” says mathematician Kaisa Matomäki, who studies the distribution of primes at the University of Turku in Finland. Understanding the limits when designing a sieve is crucial for developing a complete theory of prime numbers, adds Princeton University mathematician Peter Sarnak, an expert in prime sieve theory: “Uncovering what one cannot achieve is fundamental.”
Ford hopes this method will help researchers attack long-standing open problems. “Primes are distributed in a very, very, mysterious [way], so we’re trying to push our understanding just a little bit.”
