# LARGE GAPS BETWEEN CONSECUTIVE PRIME NUMBERS

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1 LARGE GAPS BETWEEN CONSECUTIVE PRIME NUMBERS KEVIN FORD, BEN GREEN, SERGEI KONYAGIN, AND TERENCE TAO ABSTRACT. Let GX) denote the size of the largest gap between consecutive primes below X. Answering a question of Erdős, we show that logx loglogx loglogloglogx GX) fx), logloglogx) 2 where fx) is a function tending to infinity with X. Our proof combines eisting arguments with a random construction covering a set of primes by arithmetic progressions. As such, we rely on recent work on the eistence and distribution of long arithmetic progressions consisting entirely of primes. CONTENTS 1. Introduction 1 2. On arithmetic progressions consisting of primes 5 3. Main construction 9 4. Probability estimates Linear equations in primes with large shifts Proof of Lemma 2.1i) Proof of Lemma 2.1ii) Further comments and speculations 26 Appendi A. Linear equations in primes 26 References INTRODUCTION Write GX) for the maimum gap between consecutive primes less than X. It is clear from the prime number theorem that GX) 1+o1))logX, as the average gap between the prime numbers which are X is logx. In 1931, Westzynthius [32] proved that infinitely often, the gap between consecutive prime numbers can be an arbitrarily large multiple of the average gap, that is, GX)/logX as X. Moreover, he proved the qualitative bound 1 GX) logxlog 3X log 4 X. 1 As usual in the subject, log2 = log, log 3 = loglog, and so on. The conventions for asymptotic notation such as and o) will be defined in Section

2 2 KEVIN FORD, BEN GREEN, SERGEI KONYAGIN, AND TERENCE TAO In 1935 Erdős [9] improved this to GX) logxlog 2X log 3 X) 2 and in 1938 Rankin [27] made a subsequent improvement GX) c+o1)) logxlog 2Xlog 4 X log 3 X) 2 with c = 1 3. The constant c was subsequently improved several times: to 1 2 eγ by Schönhage [29], then to c = e γ by Rankin [28], c = e γ by Maier and Pomerance [23] and, most recently, c = 2e γ by Pintz [26]. Our aim in this paper is to show that c can be taken arbitrarily large. Theorem 1. Let R > 0. Then for any sufficiently largex, there are at least R logxlog 2Xlog 4 X log 3 X) 2 consecutive composite natural numbers not eceeding X. In other words, we have GX) fx) logxlog 2Xlog 4 X log 3 X) 2 for some functionfx) that goes to infinity asx. Theorem 1 settles in the affirmative a long-standing conjecture of Erdős [10]. Theorem 1 has been simultaneously and independently established by Maynard [25] by a different method relying on the sieve-theoretic techniques related to those used recently in [24] to obtain bounded gaps between primes, rather than results on linear equations between primes). It appears likely that the techniques of this paper and of that in [25] may be combined to establish further results on large prime gaps; we hope to address this in a subsequent work. Based on a probabilistic model of primes, Cramér [6] conjectured that GX) lim sup X log 2 X = 1, and Granville [13], using a refinement of Cramér s model, has conjectured that the limsup above is in fact at least 2e γ = These conjectures are well beyond the reach of our methods. Cramér s model also predicts that the normalized prime gaps p n+1 p n logp n should have eponential distribution, that is, p n+1 p n Clogp n for about e C πx) primes X. Numerical evidence from prime calculations up to [30] matches this prediction quite closely, with the eception of values of C close to logx, in which there is very little data available. In fact, ma X GX)/log 2 X , slightly below the predictions of Cramér and Granville. Unconditional upper bounds forgx) are far from the conjectured truth, the best beinggx) X and due to Baker, Harman and Pintz [2]. Even the Riemann Hypothesis only 2 furnishes the boundgx) X 1/2 logx [5]. All works on lower bounds for GX) have followed a similar overall plan of attack: show that there are at leastgx) consecutive integers inx/2,x], each of which has a very small prime factor. To describe the results, we make the following definition. 2 Some slight improvements are available if one also assumes some form of the pair correlation conjecture; see [20].

3 LARGE GAPS BETWEEN CONSECUTIVE PRIME NUMBERS 3 Definition 1. Let be a positive integer. Define Y) to be the largest integer y for which one may select residue classes a p mod p), one for each prime p, which together sieve out cover) the whole interval [y] = {1,...,y}. The relation between this function Y and gaps between primes is encoded in the following simple lemma. Lemma 1.1. Write P) for the product of the primes less than or equal to. Then we have GP) + Y)+) Y) for all. Proof. Set y = Y), and select residue classes a p mod p), one for each prime p, which cover [y]. By the Chinese remainder theorem there is some m, < m + P), with m a p mod p) for all primes p. We claim that all of the numbers m + 1,...,m + y are composite, which means that there is a gap of length y amongst the primes less than m+y, thereby concluding the proof of the lemma. To prove the claim, suppose that 1 t y. Then there is some p such that t a p mod p), and hence m+t a p +a p 0 mod p), and thus p divides m+t. Since m+t > m > p, m+t is indeed composite. By the prime number theorem we have P) = e 1+o1)). It turns out see below) that Y) has size O1). Thus the bound of Lemma 1.1 implies that GX) Y 1+o1))logX ) as X. Theorem 1 follows from this and the following bound for Y, the proof of which is the main business of the paper. Theorem 2. For any R > 0 and for sufficiently largewe have 1.1) Y) R log 3 log 2 ) 2. The function Y is intimately related to Jacobsthal s function j. If n is a positive integer then jn) is defined to be the maimal gap between integers coprime to n. In particular jp)) is the maimal gap between numbers free of prime factors, or equivalently1plus the longest string of consecutive integers, each divisible by some primep. The construction given in the proof of Lemma 1.1 in fact proves that jp)) Y 1+o1))logP) ) = Y 1+o1)) ). This observation, together with results in the literature, gives upper bounds for Y. The best upper bound known isy) 2, which comes from Iwaniec s work [22] on Jacobsthal s function. It is conjectured by Maier and Pomerance that in fact Y) ) 2+o1). This places a serious albeit conjectural) upper bound on how large gaps between primes we can hope to find via lower bounds for Y): a bound in the region of GX) logxloglogx) 2+o1), far from Cramér s conjecture, appears to be the absolute limit of such an approach. We turn now to a discussion of the proof of Theorem 2. Recall that our task is to find y, as large as possible, so that the whole interval [y] may be sieved using congruences a p mod p), one for each prime p. Prior authors divided the sieving into different steps, a key to all of them being to take a common value ofa p for large p, saya p = 0 forz < p < δ, whereδ > 0 is a small constant andz = clog 3 /log 2 for some constant c > 0. The numbers in [y] surviving this first sieving either have all of their prime factors z i.e., they are z-smooth ) or are of the form pm with p prime and m y/δ. One then appeals to bounds for smooth numbers, e.g. [3], to see that there are very few numbers of the first kind, say O/log 2 ). By the prime number theorem there are ylog 2 / unsieved numbers of the second

4 4 KEVIN FORD, BEN GREEN, SERGEI KONYAGIN, AND TERENCE TAO kind. By contrast, if one were to take a random choice for a p for z < p < δ, then with high probability, the number of unsifted integers in [y] would be considerably larger, about ylogz/. One then performs a second sieving, choosing a p for small p z. Using a greedy algorithm, for instance, one can easily sieve out all but ylog 2 p z 1 1 ) e γ ylog 2 p logz of the remaining numbers. There are alternative approaches using eplicit choices for a p ; we will choose our a p at random. The set V of numbers surviving this second sieving has about the same size in each case.) If V π) πδ), the number of very large primes, then we perform a rather trivial) third sieving as follows: each v V can be matched with one of these primes p, and one may simply take a p = v. This is the route followed by all authors up to and including Rankin [28]; improvements to G) up to this point depended on improved bounds for counts of smooth numbers. The new idea introduced by Maier and Pomerance [23] was to make the third sieving more efficient and less trivial!) by using many p δ,] to sift not one but two elements of V. To do this they established a kind of twin primes on average result implying that for mostp δ,], there are many pairs of elements ofv that are congruent modulop. Then the authors proved a crucial combinatorial result that disjoint sets V p eist, each of two elements congruent modulo p, for a large proportion of these primes p; that is, for a large proportion of p, a p mod p will sift out two elements of V, and the sifted elements are disjoint. Pintz [26] proved a best possible version of the combinatorial result, that in fact one can achieve a nearly perfect matching, that is, disjoint setsv p for almost all primesp δ,], and this led to the heretofore best lower bound forgx). Heuristically, much more along these lines should be possible. With y comparable to the right-hand side of 1.1), the setv turns out have epected cardinality comparable to a large multiple of/. Assuming that V is a random subset of [y], for every prime p δ,] there should in fact be a residue class a mod p) containing /log 2 ) O1) elements of V. Roughly, the heuristic predicts that the sizes of the sets V a mod p)) are Poisson distributed with parameter V /p.) Whilst we cannot establish anything close to this, we are able to use almost all primesp /2,] to siever elements ofv, for any fied r. Where Maier and Pomerance appealed to in fact proved) a result about pairs of primes on average, we use results about arithmetic progressions of primes of length r, established in work of the second and fourth authors [16], [15] and of these authors and Ziegler [18]. Specifically, we need results about progressions q,q+r!p,q+2r!p,...,q+r 1)r!p; if one ignores the technical factorr!, these are progressions of primes with prime common difference. By taking a p = q, the congruence a p mod p) allows us to sift out all r elements of such a progression, and it is here that we proceed more efficiently than prior authors. Ensuring that many of these r-element sifted sets are disjoint or at least have small intersections) is a rather difficult problem, however. Rather than dealing with these intersections directly, we utilize the random choice of a p in the second step to prove that with high probability, V has a certain regularity with respect to intersections with progressions of the form q, q + r!p, q + 2r!p,..., q +r 1)r!p. We then prove that most elements of V survive the third sieving with uniformly small probability Acknowledgments. The research of SK was partially performed while he was visiting KF at the University of Illinois at Urbana Champaign. Research of KF and SK was also carried out in part at the University of Chicago. KF and SK are thankful to Prof. Wilhelm Schlag for hosting these visits. KF also thanks the hospitality of the Institute of Mathematics and Informatics of the Bulgarian Academy of Sciences.

5 LARGE GAPS BETWEEN CONSECUTIVE PRIME NUMBERS 5 Also, the research of the SK and TT was partially performed while SK was visiting the Institute for Pure and Applied Mathematics IPAM) at UCLA, which is supported by the National Science Foundation. KF was supported by NSF grant DMS BG was supported by ERC Starting Grant , Approimate algebraic structure. TT was supported by a Simons Investigator grant, the James and Carol Collins Chair, the Mathematical Analysis & Application Research Fund Endowment, and by NSF grant DMS Finally, all four of us wish to thank Andrew Granville and James Maynard for helpful discussions Notational conventions. We use f = Og) and f g to denote the claim that there is a constant C > 0 such that f ) Cg ) for all in the domain of f. We adopt the convention that C is independent of any parameter unless such dependence is indicated by subscript such as u, ecept that C may depend on the parameter r which we consider to be fied) in Sections 2 4 and 6 7. In Sections 2 4 and 6 7, the symbol o1) will stand for a function which tends to 0 as, uniform in all parameters ecept r unless otherwise indicated. The same convention applies to the asymptotic notationf) g), which meansf) = 1+o1))g). In Sections 5 and the Appendi,ogN)) refers to some functionhn) satisfyinglim N hn)/gn) = 0. The symbolsp,q will always denote prime numbers. In Sections 2 4 and 6 7,sdenotes a prime, whereas in Sections 5 and the Appendi, s is a positive integer which measures the compleity of a system of linear forms. Finally, we will be using the probabilistic method and will thus be working with finite probability spaces. Generically we write P for probability, and E for epectation. If a finite set A is equipped with the uniform probability measure, we write P a A and E a A for the associated probability and epectation. Variables in boldface will denote random real-valued scalars, while arrowed boldface symbols denote random vectors, e.g. a. We also use #A to denote the cardinality of A, and for any positive real z, we let [z] := {n N : 1 n z} denote the set of natural numbers up to z. 2. ON ARITHMETIC PROGRESSIONS CONSISTING OF PRIMES A key tool in the proof of Theorem 2 is an asymptotic formula for counts of arithmetic progressions of primes. In fact, we shall be interested in progressions of primes of length r whose common difference is r! times a prime 3, for positive integer values of r. The key technical result we shall need is Lemma 2.4 below. This is a relatively straightforward consequence of Lemma 2.1 below, which relies on the work on linear equations in primes of the second and fourth authors and Ziegler. We turn to the details. Let y be a sufficiently large quantity which goes to infinity for the purposes of asymptotic notation), and let be a quantity that goes to infinity at a slightly slower rate than y; for sake of concreteness we will impose the hypotheses 2.1) y. In fact the analysis in this section would apply under the slightly weaker hypotheses ylog O1) y oy), but we will stick with 2.1) for sake of concreteness since this condition will certainly be satisfied when applying the results of this section to prove Theorem 2. From 2.1) we see in particular that logy, 3 One could replacer! here if desired by the slightly smaller primorialpr); as observed long ago by Lagrange and Waring [8], this primorial must divide the spacing of any sufficiently large arithmetic progression of primes of length r. However, replacing r! bypr) would lead to only a negligible savings in the estimates here.

6 6 KEVIN FORD, BEN GREEN, SERGEI KONYAGIN, AND TERENCE TAO so we will use and logy more or less interchangeably in what follows. Let P denote the set of all primes in the interval /2,], and Q denote the set of all primes in the interval /4,y]; thus from the prime number theorem we have 2.2) #P 2 ; #Q y. In other words,p andqboth have density 1 inside/2,] and /4,y] respectively. Let r 1 be a fied natural number. We define a relation between P and Q as follows: if p P and q Q, we write p q if the entire arithmetic progression {q,q + r!p,...,q + r 1)r!p} is contained inside Q. One may think of the r relations p q ir!p for i = 0,...,r 1 as defining r different but closely related) bipartite graphs between P and Q. Note that if p q, then the residue class q mod p) is guaranteed to contain at least r primes from Q, which is the main reason why we are interested in these relations particularly for somewhat large values of r). For our main argument, we will be interested in the typical degrees of the bipartite graphs associated to the relationsp q ir!p. Specifically, we are interested 4 in the following questions for a given0 i r 1: i) For a typical p P, how many q Q are there such that p q ir!p? Note that the answer to this question does not depend on i.) ii) For a typical q Q, how manyp P are there such that p q ir!p? If P and Q were distributed randomly inside the intervals /2, ] and /4, y] respectively, with cardinalities given by 2.2), then standard probabilistic arguments using for instance the Chernoff inequality) would suggest that the answer to question i) is y log r, while the answer to question ii) is 2log r. As it turns out, the local structure of the primes for instance, the fact that all the elements of P and Q are coprime tor!) will bias the answers to each of these two questions; however as one may epect from double counting considerations), they will be biased by eactly the same factorα r defined in 2.3) below), and the net effect of this bias will cancel itself out at the end of the proof of Theorem 2. One can predict the answers to Questions i) and ii) using the Hardy-Littlewood prime tuples conjecture [21]. If we apply this conjecture and ignore any issues as to how uniform the error term in that conjecture is with respect to various parameters), one soon arrives 5 at the prediction that the answer to Question i) y should be α r log r for all p P, and similarly the answer to Question ii) should be α r2log r for all q Q, where for the rest of the paper α r will denote the singular series 2.3) α r := ) p r 1 p r)p r 1 p 1 p 1) r. p r p>r The eact form ofα r is not important for our argument, so long as it is finite, positive, and does not depend onory; but these claims are clear from 2.3) note that the second factor p r)pr 1 p 1) is non-zero and behaves r asymptotically as 1+O1/p 2 )). As mentioned previously, this quantity will appear in two separate places in the proof of Theorem 2, but these two occurrences will eventually cancel each other out. The Hardy-Littlewood conjecture is still out of reach of current technology. Note that even the much weaker question as to whether the relation p q is satisfied for at least one pair of p and q for any given r is at least as hard as establishing that the primes contain arbitrarily long arithmetic progressions, which was 4 Actually, for technical reasons we will eventually replace the relation by slightly smaller relation, which will in turn be randomly refined to an even smaller relation a ; see below. 5 See also Sections 6, 7 for some closely related computations.

7 LARGE GAPS BETWEEN CONSECUTIVE PRIME NUMBERS 7 only established by the second and fourth authors in [14]. However, for the argument used to prove Theorem 2, it will suffice to be able to answer Question i) for almost all p P rather than all p P, and similarly for Question ii). In other words, we only need a special case of) the Hardy-Littlewood prime conjecture on average. This is easier to establish; for instance, Balog [1] was able to use the circle method or linear Fourier analysis ) to establish the prime tuples conjecture for most tuples in some sense. The results in [1] are not strong enough for our applications, because of our need to consider arbitrarily long arithmetic progressions which are well-known to not be amenable to linear Fourier-analytic methods for r 4, see [12]) rather than arbitrary prime tuples. Instead we will use a modification of) the more recent work of the second and fourth authors [16]. More precisely, we claim the following bounds. Lemma 2.1. Let,y,r,P,Q, and be as above. Let0 i r 1. i) For all but o/) of thep P, we have the estimate y #{q Q : p q ir!p} α r log r. ii) For all but oy/) of theq Q, we have #{p P : p q ir!p} α r 2log r. iii) For all p P, we have the upper bounds #{q Q : p q ir!p} y log r. iv) For all q Q, we have the upper bounds #{p P : p q ir!p} 2log r. Parts iii) and iv) follow from standard sieve-theoretic methods e.g. the Selberg sieve); we omit the proof here, referring the reader instead 6 to [19] or [11]. The more interesting bounds are i) and ii). As stated above, these two claims are almost relatively straightforward consequences of the main result of the paper [16] of the second and fourth authors. However, some modifications of that work are required to deal with the fact that and y are of somewhat different sizes. In Section 5 below we state the modified version of the main result of [16] that we need, Theorem 7. The deductions of parts i) and ii) of Lemmas 2.1 are rather similar to one another, and are given in Sections 6 and 7 respectively. Finally, a proof of Theorem 7 can be obtained by modifying the arguments of [16] in quite a straightforward manner, but in a large number of places. We record these modifications in Appendi A. As presently defined, it is possible for the bipartite graphs given by the p q ir!p to overlap, thus it may happen thatp q ir!p andp q jr!p for somep P,q Q, and0 i < j r 1. For instance, this situation will occur if Q has an arithmetic progression q,q +r!p,...,q +r r!p of length r +1 with p P. For technical reasons, such overlaps are undesirable for our applications. However, these overlaps are rather rare and can be easily removed by the following simple device. We define the modified relation between P and Q by declaring p q if the progression{q, q + r!p,..., q +r 1)r!p}is contained inside Q, butq+r r!p does not lie in Q. From construction we have the following basic fact: Lemma 2.2. For anyp P andq Q that there is at most one0 i r 1 such that p q ir!p. We can then modify Lemma 2.1 slightly by replacing the relation with its slightly perturbed version : 6 One could also deduce these bounds from Proposition 6.4 in Appendi A.

8 8 KEVIN FORD, BEN GREEN, SERGEI KONYAGIN, AND TERENCE TAO Lemma 2.3. Let,y,r,P,Q, and be as above. Let0 i r 1. i) For all but o/) of thep P, we have the estimate y #{q Q : p q ir!p} α r log r. ii) For all but oy/) of theq Q, we have #{p P : p q ir!p} α r 2log r. iii) For all p P, we have the upper bounds #{q Q : p q ir!p} y log r. iv) For all q Q, we have the upper bounds #{p P : p q ir!p} 2log r. Proof. Parts iii) and iv) are immediate from their counterparts in Lemma 2.1, since is a subrelation of. To prove i), we simply observe from Lemma 2.1iii) withr replaced byr+1) that #{q Q : p q ir!p butp q ir!p} y log r+1, and the claim then follows from Lemma 2.1i) and the triangle inequality. The claim ii) is proven similarly. For technical reasons, it will be convenient to reformulate the main results of Lemma 2.3 as follows. Lemma 2.4. Let,y,r,P,Q, and be as above. Then there eist subsetsp 0,Q 0 ofp,q respectively with 2.4) #P 0 2 ; #Q 0 y, such that y 2.5) #{q Q : p q ir!p} α r log r for all p P 0 and0 i r 1, and similarly that 2.6) #{p P 0 : p q ir!p} α r 2log r for all q Q 0 and 0 i r 1. Proof. From Lemma 2.3i) we may already find a subset P 0 of the desired cardinality obeying 2.5). If the P 0 in 2.6) were replaced by P, then a similar argument using Lemma 2.3ii) and taking the union bound for the eceptional sets for each 0 i r 1) would give the remainder of the lemma. To deal with the presence of P 0 in 2.6), it thus suffices to show that ) #{p P\P 0 : p q ir!p} = o log r for all but oy/) of theq Q. By Markov s inequality, it suffices to show that #{p,q) P\P 0 ) Q : p q ir!p} = o log r y ).

9 LARGE GAPS BETWEEN CONSECUTIVE PRIME NUMBERS 9 But this follows by summing Lemma 2.3iii) for allp P\P 0, since the setp\p 0 has cardinalityo/). 3. MAIN CONSTRUCTION We now begin the proof of Theorem 2. It suffices to establish the following claim: Theorem 3 First reduction). Let r 13 be an integer. Take to be sufficiently large depending on r and going to infinity for the purposes of asymptotic notation), and then define y by the formula 3.1) y := r 6logr log 3 log 2 ) 2. Then there eists a residue class a s mod s) for each prime s, such that the union of these classes contains every positive integer less than or equal to y. The numerical values of13 and6in the above theorem are only of minor significance, and can be ignored for a first reading. Observe that,y obey the condition 2.1) from the previous section. If Theorem 3 holds, then in terms of the quantity Y) defined in the introduction, we have Y) y which by 3.1) will imply Theorem 2 by takingr sufficiently large depending onr. It remains to prove Theorem 3. Set 3.2) z := log 3 /3log 2 ), and partition the primes less than or equal to into the four disjoint classes S 1 := {s prime : s or z < s /4} S 2 := {s prime : < s z} S 3 := P = {s prime : /2 < s } S 4 := {s prime : /4 < s /2}. We are going to sieve [y] in four stages by removing at most one congruence class a s mod s) for each prime s S i, i = 1,2,3,4. If we can do this in such a way that nothing is left at the end, we shall have achieved our goal. We first dispose of the final sieving process involving S 4 ), as it is rather trivial. Namely, we reduce Theorem 3 to Theorem 4 Second reduction). Let r,,y be as in Theorem 3, and let S 1,S 2,S 3 be as above. Then there eists a residue class a s mod s) for each s S 1 S 2 S 3, such that the union of these classes contains all but at most 1 5 +o1)) of the positive integers less than or equal to y. Indeed, if thea s mod s) fors S 1 S 2 S 3 are as in Theorem 4, then from the prime number theorem, the number of integers less than y that have not already been covered by a residue class is smaller than the number of primes in S 4. Thus, we may eliminate each of these surviving integers using a residue class a s mod s) from a different elementsfroms 4 and selecting residue classes arbitrarily for anys S 4 that are left over), and Theorem 3 follows. It remains to prove Theorem 4. For this, we perform the first sieving process using up the primes from S 1 ) and reduce to

10 10 KEVIN FORD, BEN GREEN, SERGEI KONYAGIN, AND TERENCE TAO Theorem 5 Third reduction). Let r,,y,s 2,S 3 be as in Theorem 4, and as in the previous section) let Q denote the primes in the range/4,y]. Then there eists a residue classa s mod s) for eachs S 2 S 3, such that the union of these classes contains all but at most 1 5 +o1)) of the elements of Q. Proof of Theorem 4 assuming Theorem 5. We take a s := 0 for all s S 1. Write R [y] for the residual set of elements which survive this first sieving, that is to say R consists of all numbers in [y] that are not divisible by any primesins 1. Taking into account that /4) > y from 3.1), we conclude that R = Q R err, where R err contains only z-smooth numbers, that is to say numbers in [y] all of whose prime factors are at mostz. Let u denote the quantity u := logy logz, so from 3.2) one hasu 3 log 2 log 3. By standard counts for smooth numbers e.g. de Bruijn s theorem [3]), #R err ye ulogu+ouloglogu+2)) y = log 3+o1) = log 2+o1) = o/). Thus the contribution of R err may be absorbed into the eceptional set in Theorem 4, and this theorem is now immediate from Theorem 5. Remark 1. One can replace the appeal to de Bruijn s theorem here by the simpler bounds of Rankin [27, Lemma II], if one makes the very minor change of increasing the 3 in the denominator of 3.2) to 4, and to similarly increase the6in 3.1) to8. It remains to establish Theorem 5. Recall from 2.2) that Q has cardinality y/. This is significantly larger than the error term of o1)) permitted in Theorem 5; our sieving process has to reduce the size of Q by a factor comparable to y/. The purpose of the second sieving, by congruences a s mod s) with s S 2, is to achieve almost all of this size reduction. Our choice of the a s for s S 2 will be completely random which is why we are using the boldface font here): that is, for each prime s S 2 we selecta s uniformly at random from{0,1,...,s 1}, and these choices are independent for different values of s. Write a for the random vector a s ) s S2. Observe that if n is any integer not depending on a), then the probability that n lies outside of all of the a s mod s) is eactly equal to γ := s S ). s This quantity will be an important normalizing factor in the arguments that follow. From Mertens theorem and 3.1), 3.2) we see that 3.3) γ log 2 logz 3log 2) 2 log 3 r 2logr y.

11 LARGE GAPS BETWEEN CONSECUTIVE PRIME NUMBERS 11 Set Description Epected cardinality P Primes in/2,] 2 P 0 Primes inp connected to the epected # of primes in Q 2 P 1 a) Primes inp 0 connected to the epected # of primes in Q a) 2 P 1 a,q;i) Primes inp 1 a) i-connected to a given primeq Q 1 a) γ r 1 α r 2log r Q Primes in/4,y] y Q 0 Primes inqconnected to the epected # of primes in P 0 y Q a) Randomly refined subset ofq r 2logr Q a,p) Primes inq a) connected to a given primep P 1 a) γ r y α r Q 0 a) Intersection ofq a) withq 0 r 2logr Q 1 a) Primes inq 0 a) connected to the epected # of primes in P 1 a) r 2logr Q 1 a, q) Randomly refined subset ofq 1 a) 1 2logr log r TABLE 1. A brief description of the various P and Q-type sets used in the construction, and their epected size. Roughly speaking, the congruence classes from S 1 are used to cut down [y] to approimately Q, the congruence classes from S 2 are used to cut Q down to approimately Q 0 a), the congruence classes from S 3 = P are used to cut Q 0 a) down to approimately Q 1 a, q), and the congruence classes in S 4 are used to cover all surviving elements from previous sieving. WriteQ a) for the random) residual set of primesq inqthat do not lie in any of the congruence classes a s mod s) fors S 2. We will in fact focus primarily on the slightly smaller set Q 0 a) := Q a) Q 0 whereq 0 is the subset of Q constructed in Lemma 2.4. From linearity of epectation we see that 3.4) E#Q a) = γ#q and thus from 3.3), 2.2) 3.5) E#Q a) r 2logr. Similarly, from Lemma 2.4 we have ) y #Q\Q 0 ) = o and thus from linearity of epectation and 3.3) we have E#Q a)\q 0 a)) = o γ y ) ) = o. In particular, from Markov s inequality we have 3.6) #Q a)\q 0 a)) = o with probability 1 o1). We have an analogous concentration bound for #Q a): γ y ) ) = o

12 12 KEVIN FORD, BEN GREEN, SERGEI KONYAGIN, AND TERENCE TAO Lemma 3.1. With probability1 o1), we have In particular, from 3.6) we also have with probability 1 o1). #Q a) r 2logr γ y. #Q 0 a) r 2logr γ y This lemma is proven by a routine application of the second moment method; we defer that proof to Section 4. It will now suffice to show Theorem 6 Fourth reduction). Let,y,r, a,p 0,Q 0 a) be as above, and let ε > 0 be a quantity going to zero arbitrarily slowly as, thus ε = o1). Then with probability at least ε in the random choice of a, we may find a length r arithmetic progression {q p + ir!p : 0 i r 1} for each p P 0, such that the union of these progressions contains all but at most 1 5 +o1)) of the elements ofq 0 a). Theo1) decay in the conclusion may depend on ε.) Indeed, from this theorem and taking ε going to zero sufficiently slowly) we may find a such that the conclusions of this theorem hold simultaneously with 3.6), and by combining the residue classes from a with the residue classes q p mod p) for p P 0 from Theorem 6 and selecting residue classes arbitrarily forp P\P 0 ), we obtain Theorem 5. It remains to establish Theorem 6. Note now from Lemma 3.1) that we only need to refine the surviving r logr set Q 0 a) by a constant factor comparable to. Recall from the previous section that we had the relation ), rather than by a factor like y/ that goes to infinity as betweenp andq. We now refine this relation to a random) relation between P 0 and Q a) as follows. If p P 0 and q Q a), we write p a q if p q and if the arithmetic progression {q,q + r!p,...,q + r 1)r!p} is contained in Q a) i.e. the entire progression survives the second sieving process). Intuitively, if p P 0 and q Q are such that p q, we epect p a q to occur with probability close to γ r. The following lemma makes this intuition precise compare with Lemma 2.4): Lemma 3.2. Let ε > 0 be a quantity going to zero arbitrarily slowly as. Then with probability at least ε, we can find random) subsetsp 1 a) of P 0 andq 1 a) of Q 0 a) obeying the cardinality bounds 3.7) #P 1 a) 2 ; #Q 1 a) #Q 0 a) r 2logr, such that for all p P 1 a) and0 i r 1, and such that #{q Q a) : p a q ir!p} γ r y α r log r #{p P 1 a) : p a q ir!p} γ r 1 α r 2log r for all q Q 1 a) and0 i r 1. The impliedo1) errors in the notation may depend onε.)

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