A lower bound on the mean value of the Erdős-Hooley delta function
What's new 2023-08-24
Kevin Ford, Dimitris Koukoulopoulos and I have just uploaded to the arXiv our paper “A lower bound on the mean value of the Erdős-Hooley delta function“. This paper complements the recent paper of Dimitris and myself obtaining the upper bound
on the mean value of the Erdős-Hooley delta function In this paper we obtain a lower bound where is an exponent that arose in previous work of result of Ford, Green, and Koukoulopoulos, who showed that for all outside of a set of density zero. The previous best known lower bound for the mean value was due to Hall and Tenenbaum.The point is the main contributions to the mean value of are driven not by “typical” numbers of some size , but rather of numbers that have a splitting
where is the product of primes between some intermediate threshold and and behaves “typically” (so in particular, it has about prime factors, as per the Hardy-Ramanujan law and the Erdős-Kac law, but is the product of primes up to and has double the number of typical prime factors – , rather than – thus is the type of number that would make a significant contribution to the mean value of the divisor function . Here is such that is an integer in the range for some small constant there are basically different values of give essentially disjoint contributions. From the easy inequalities (the latter coming from the pigeonhole principle) and the fact that has mean about one, one would expect to get the above result provided that one could get a lower bound of the form for most typical with prime factors between and . Unfortunately, due to the lack of small prime factors in , the arguments of Ford, Green, Koukoulopoulos that give (1) for typical do not quite work for the rougher numbers . However, it turns out that one can get around this problem by replacing (2) by the more efficient inequality where is an enlarged version of when . This inequality is easily proven by applying the pigeonhole principle to the factors of of the form , where is one of the factors of , and is one of the factors of in the optimal interval . The extra room provided by the enlargement of the range to turns out to be sufficient to adapt the Ford-Green-Koukoulopoulos argument to the rough setting. In fact we are able to use the main technical estimate from that paper as a “black box”, namely that if one considers a random subset of for some small and sufficiently large with each lying in with an independent probability , then with high probability there should be subset sums of that attain the same value. (Initially, what “high probability” means is just “close to “, but one can reduce the failure probability significantly as by a “tensor power trick” taking advantage of Bennett’s inequality.)