Distribution of primes in smooth moduli

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(Type I estimates)
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but this is inferior to the Level 3 estimates in practice.  Details can be found [https://terrytao.wordpress.com/2013/06/23/the-distribution-of-primes-in-densely-divisible-moduli/#comment-236682 here].
but this is inferior to the Level 3 estimates in practice.  Details can be found [https://terrytao.wordpress.com/2013/06/23/the-distribution-of-primes-in-densely-divisible-moduli/#comment-236682 here].
 +
 +
=== Level 6 ===
 +
 +
Even further improvement in the Type I sums may be possible by rebalancing the final Cauchy-Schwarz: instead of performing Cauchy-Schwarz in <math>n,q_1</math> (leaving <math>h,q_2</math> to be doubled), factor <math>q_2 = r_2 s_2</math> and Cauchy-Schwarz in <math>n,q_1,r_2</math> and only double <math>h,s_2</math>.  The idea is to make the diagonal case <math>h s'_2 = h' s_2</math> do more of the work and the off-diagonal case <math>hs'_2 \neq h' s_2</math> do less of the work.  This idea was first raised [http://terrytao.wordpress.com/2013/06/23/the-distribution-of-primes-in-densely-divisible-moduli/#comment-236673 here].  [http://terrytao.wordpress.com/2013/06/30/bounded-gaps-between-primes-polymath8-a-progress-report/#comment-237087 Preliminary computations] suggest that this allows one to take <math>56 \varpi + 16 \delta + 4 \sigma < 1</math> for the Type I sums in <math>x^\delta</math>-smooth case.  In [http://terrytao.wordpress.com/2013/07/07/the-distribution-of-primes-in-doubly-densely-divisible-moduli/ this post] it is shown that the same bound holds in the densely divisible case, thus <math>Type''_I[\varpi,\delta,\sigma]</math> holds whenever
 +
 +
:<math>56 \varpi + 16 \delta + 4 \sigma < 1</math>.
=== Level 5 ===
=== Level 5 ===
-
Further improvement to the (still sketchy) Level 4 estimate should be obtainable by taking advantage of averaging in auxiliary "h" parameters in order to reduce the contribution of the diagonal terms.
+
(The numbering here is out of order because the Level 5 estimates proved harder to implement than the Level 6 estimates.)
-
=== Level 6 ===
+
Further improvement to these be obtainable by taking advantage of averaging in auxiliary parameters; in particular averaging over the parameter <math>d_1</math> has provisionally (subject to verification of some Deligne-level estimates) shown to establish <math>Type''''_I[\varpi,\delta,\sigma]</math> whenever
-
Even further improvement in the Type I sums may be possible by rebalancing the final Cauchy-Schwarz: instead of performing Cauchy-Schwarz in <math>n,q_1</math> (leaving <math>h,q_2</math> to be doubled), factor <math>q_2 = r_2 s_2</math> and Cauchy-Schwarz in <math>n,q_1,r_2</math> and only double <math>h,s_2</math>.  The idea is to make the diagonal case <math>h s'_2 = h' s_2</math> do more of the work and the off-diagonal case <math>hs'_2 \neq h' s_2</math> do less of the work.  This idea was first raised [http://terrytao.wordpress.com/2013/06/23/the-distribution-of-primes-in-densely-divisible-moduli/#comment-236673 here].  [http://terrytao.wordpress.com/2013/06/30/bounded-gaps-between-primes-polymath8-a-progress-report/#comment-237087 Preliminary computations] suggest that this allows one to take <math>56 \varpi + 16 \delta + 4 \sigma < 1</math> for the Type I sums in <math>x^\delta</math>-smooth case.  In [http://terrytao.wordpress.com/2013/07/07/the-distribution-of-primes-in-doubly-densely-divisible-moduli/ this post] it is shown that the same bound holds in the densely divisible case, thus <math>Type''_I[\varpi,\delta]</math> holds whenever
+
:<math>\frac{160}{3} \varpi + 16 \delta + \frac{34}{9} \sigma < 1</math>;
-
:<math>56 \varpi + 16 \delta + 4 \sigma < 1</math>.
+
see [http://terrytao.wordpress.com/2013/07/07/the-distribution-of-primes-in-doubly-densely-divisible-moduli/#comment-239189 this comment].
== Type II estimates ==
== Type II estimates ==

Revision as of 17:13, 20 July 2013

A key input to Zhang's proof that bounded gaps occur infinitely often is a distribution result on primes in smooth moduli, which we have called MPZ[\varpi,\delta] (and later strengthened to MPZ'[\varpi,\delta]). These estimates are obtained as a combination of three other estimates, which we will call Type_I[\varpi,\delta,\sigma], Type_{II}[\varpi,\delta], and Type_{III}[\varpi,\delta,\sigma].

Contents

Definitions

Asymptotic notation

x is a parameter going off to infinity, and all quantities may depend on x unless explicitly declared to be "fixed". The asymptotic notation O(), o(), \ll is then defined relative to this parameter. A quantity q is said to be of polynomial size if one has q = O(xO(1)), and bounded if q = O(1). We also write X \lessapprox Y for X \ll x^{o(1)} Y, and \displaystyle X \sim Y for X \ll Y \ll X.

Coefficient sequences

We need a fixed quantity A0 > 0.

A coefficient sequence is a finitely supported sequence \alpha: {\mathbf N} \rightarrow {\mathbf R} that obeys the bounds

\displaystyle  |\alpha(n)| \ll \tau^{O(1)}(n) \log^{O(1)}(x)
  • If α is a coefficient sequence and a\ (q) = a \hbox{ mod } q is a primitive residue class, the (signed) discrepancy \Delta(\alpha; a\ (q)) of α in the sequence is defined to be the quantity
\displaystyle  \Delta(\alpha; a \ (q)) := \sum_{n: n = a\ (q)} \alpha(n) - \frac{1}{\phi(q)} \sum_{n: (n,q)=1} \alpha(n).
  • A coefficient sequence α is said to be at scale N for some N \geq 1 if it is supported on an interval of the form [(1-O(\log^{-A_0} x)) N, (1+O(\log^{-A_0} x)) N].
  • A coefficient sequence α at scale N is said to obey the Siegel-Walfisz theorem if one has
 \displaystyle  | \Delta(\alpha 1_{(\cdot,q)=1}; a\ (r)) | \ll \tau(qr)^{O(1)} N \log^{-A} x

for any q,r \geq 1, any fixed A, and any primitive residue class a\ (r).

  • A coefficient sequence α at scale N is said to be smooth if it takes the form α(n) = ψ(n / N) for some smooth function \psi: {\mathbf R} \rightarrow {\mathbf C} supported on [1-O(\log^{-A_0} x), 1+O(\log^{-A_0} x)] obeying the derivative bounds
\displaystyle  \psi^{(j)}(t) = O( \log^{j A_0} x )

for all fixed j \geq 0 (note that the implied constant in the O() notation may depend on j).

Congruence class systems

Let I \subset {\mathbf R}, and let {\mathcal S}_I denote the square-free numbers whose prime factors lie in I.

  • A singleton congruence class system on I is a collection {\mathcal C} = (\{a_q\})_{q \in {\mathcal S}_I} of primitive residue classes a_q \in ({\mathbf Z}/q{\mathbf Z})^\times for each </math>q \in {\mathcal S}_I</math>, obeying the Chinese remainder theorem property
\displaystyle  a_{qr}\ (qr) = (a_q\ (q)) \cap (a_r\ (r))

whenever q,r \in {\mathcal S}_I are coprime. We say that such a system {\mathcal C} has controlled multiplicity if the quantity

\displaystyle  \tau_{\mathcal C}(n) := |\{ q \in {\mathcal S}_I: n = a_q\ (q) \}|

obeys the estimate

\displaystyle  \sum_{C^{-1} x \leq n \leq Cx: n = a\ (r)} \tau_{\mathcal C}(n)^2 \tau(n)^C \ll \frac{x}{r} \tau(r)^{O(1)} \log^{O(1)} x + x^{o(1)}.

for any fixed C > 1 and any congruence class a\ (r) with r \in {\mathcal S}_I. Here τ is the divisor function. [Actually, in the most recent proofs of Type I, II, and III estimates, the controlled multiplicity hypothesis is no longer needed, and so this definition is no longer relevant for the project.]

Smooth and densely divisible numbers

A natural number n is said to be y-smooth if all of its prime factors are less than or equal to y. We say that n is y-densely divisible if, for every 1 \leq R \leq n, one can find a factor of n in the interval [y − 1R,R]. Note that y-smooth numbers are automatically y-densely divisible, but the converse is not true in general. We say that n is doubly y-densely divisible if, for every 1 \leq R \leq n, one can find a factor of n in the interval [y − 1R,R] which is itself y-densely divisible.

We let {\mathcal D}_y denote the space of y-densely divisible numbers, and {\mathcal D}_y^2 the space of doubly densely divisible numbers, thus

{\mathcal S}_{[1,y]} \subset {\mathcal D}^2_y \subset {\mathcal D}_y.

MPZ and variants

Let 0 < \varpi < 1/4 and 0 < \delta < \varpi + 1/4 be fixed. Let Λ denote the von Mangoldt function.

  • We say that the estimate MPZ[\varpi,\delta] holds if one has the estimate
\displaystyle  \sum_{q \in {\mathcal S}_I: q< x^{1/2+2\varpi}} |\Delta(\Lambda 1_{[x,2x]}; a_q)| \ll x \log^{-A} x

for any fixed A > 0, any I \subset [1,x^\delta], and any congruence class system  (\{a_q\})_{q \in {\mathcal S}_I}.

  • We say that the estimate MPZ'[\varpi,\delta] holds if one has the estimate
\displaystyle  \sum_{q \in {\mathcal S}_I \cap {\mathcal D}_{x^\delta}: q< x^{1/2+2\varpi}} |\Delta(\Lambda 1_{[x,2x]}; a_q)| \ll x \log^{-A} x

for any fixed A > 0, any I \subset {\mathbf R}, and any congruence class system  (\{a_q\})_{q \in {\mathcal S}_I}.


  • We say that the estimate MPZ''[\varpi,\delta] holds if one has the estimate
\displaystyle  \sum_{q \in {\mathcal S}_I \cap {\mathcal D}_{x^\delta}^2: q< x^{1/2+2\varpi}} |\Delta(\Lambda 1_{[x,2x]}; a_q)| \ll x \log^{-A} x

for any fixed A > 0, any I \subset {\mathbf R}, and any congruence class system  (\{a_q\})_{q \in {\mathcal S}_I}.

In early arguments an additional "controlled multiplicity" hypothesis was added to these assertions, but this hypothesis is no longer necessary.

Type I, Type II, and Type III

Let 0 < \varpi < 1/4, 0 < \delta < 1/4+\varpi, and 0 < σ < 1 / 2 be fixed.

  • We say that Type_I[\varpi,\delta,\sigma] holds if, whenever M,N are quantities with
\displaystyle MN \sim x

and

\displaystyle x^{1/2-\sigma} \ll N \ll x^{1/2-2\varpi-c}

or equivalently

\displaystyle x^{1/2+2\varpi+c} \ll M \ll x^{1/2+\sigma}

for some fixed c > 0, and α,β are coefficient sequences at scale M,N respectively with β obeying a Siegel-Walfisz theorem, I \subset [1,x^\delta], and (\{a_q\})_{q \in {\mathcal S}_I} is a congruence class system, then one has

\sum_{q \in {\mathcal S}_I: q < x^{1/2+2\varpi}} |\Delta( \alpha * \beta; a_q\ (q))| \leq x \log^{-A} x

for all fixed A > 0.

  • We say that Type_{II}[\varpi,\delta] holds if, whenever M,N are quantities with
\displaystyle MN \sim x

and

\displaystyle x^{1/2-2\varpi-c} \ll N \ll x^{1/2}

or equivalently

\displaystyle x^{1/2} \ll M \ll x^{1/2+2\varpi+c}

for some sufficiently small fixed c > 0, and α,β are coefficient sequences at scale M,N respectively with β obeying a Siegel-Walfisz theorem, I \subset [1,x^\delta], and (\{a_q\})_{q \in {\mathcal S}_I} is a congruence class system, then one has

\sum_{q \in {\mathcal S}_I: q < x^{1/2+2\varpi}} |\Delta( \alpha * \beta; a_q\ (q))| \leq x \log^{-A} x

for all fixed A > 0.

  • We say that Type_{III}[\varpi,\delta,\sigma] holds if, whenever M,N1,N2,N3 are quantities with
\displaystyle MN \sim x
\displaystyle N_1N_2, N_2 N_3, N_1 N_3 \gg x^{1/2 + \sigma}
\displaystyle x^{2\sigma} \ll N_1,N_2,N_3 \ll x^{1/2-\sigma},

α,ψ123 are coefficient sequences at scale M,N1,N2,N3 respectively with ψ123 smooth, I \subset [1,x^\delta], and (\{a_q\})_{q \in {\mathcal S}_I} is a congruence class system, then one has

\sum_{q \in {\mathcal S}_I: q < x^{1/2+2\varpi}} |\Delta( \alpha * \psi_1 * \psi_2 * \psi_3; a_q\ (q))| \leq x \log^{-A} x

for all fixed A > 0.

  • We define Type'_I[\varpi,\delta,\sigma], Type'_{II}[\varpi,\delta], Type_{III}[\varpi,\delta,\sigma] analogously to Type_I[\varpi,\delta,\sigma], Type_{II}[\varpi,\delta], Type_{III}[\varpi,\delta,\sigma] but with the hypothesis I \subset [1,x^\delta] replaced with I \subset \mathbf{R}, and {\mathcal S}_I replaced with {\mathcal S}_I \cap {\mathcal D}_{x^\delta}. These estimates are slightly stronger than their unprimed counterparts.
  • There is also a "double-primed" variant Type''_I[\varpi,\delta,\sigma], Type''_{II}[\varpi,\delta], Type''_{III}[\varpi,\delta,\sigma] of these estimates, intermediate in strength between the primed and unprimed estimates, in which dense divisibility is replaced with "double dense divisibility" hypothesis.

The combinatorial lemma

Combinatorial lemma Let 0 < \varpi < 1/4, 0 < \delta < 1/4 + \varpi, and 1 / 10 < σ < 1 / 2 be fixed.
  • If Type_I[\varpi,\delta,\sigma], Type_{II}[\varpi,\delta], and Type_{III}[\varpi,\delta,\sigma] all hold, then MPZ[\varpi,\delta] holds.
  • Similarly, if Type'_I[\varpi,\delta,\sigma], Type'_{II}[\varpi,\delta], and Type'_{III}[\varpi,\delta,\sigma] all hold, then MPZ'[\varpi,\delta] holds.
  • Similarly, if Type''_I[\varpi,\delta,\sigma], Type''_{II}[\varpi,\delta], and Type''_{III}[\varpi,\delta,\sigma] all hold, then MPZ''[\varpi,\delta] holds.

This lemma is (somewhat implicitly) proven here. It reduces the verification of MPZ[\varpi,\delta] and MPZ'[\varpi,\delta] to a comparison of the best available Type I, Type II, and Type III estimates, as well as the constraint σ > 1 / 10.

Type I estimates

In all of the estimates below, 0 < \varpi < 1/4, 0 < \delta < 1/4 + \varpi, and σ > 0 are fixed.

Level 1

Type I-1 We have Type'_I[\varpi,\delta,\sigma] (and hence Type_I[\varpi,\delta,\sigma]) whenever
\displaystyle 11\varpi +3\delta + 2 \sigma < \frac{1}{4}.

This result is implicitly proven here. (There, only Type_I[\varpi,\delta,\sigma] is proven, but the method extends without difficulty to Type'_I[\varpi,\delta,\sigma].) It uses the method of Zhang, and is ultimately based on exponential sums for incomplete Kloosterman sums on smooth moduli obtained via completion of sums.

Level 2

Type I-2 We have Type'_I[\varpi,\delta,\sigma] (and hence Type_I[\varpi,\delta,\sigma]) whenever
\displaystyle 17\varpi +4\delta + \sigma < \frac{1}{4}
and
\displaystyle 20\varpi +6\delta + 3\sigma < \frac{1}{2}
and
\displaystyle 32\varpi +9\delta + \sigma < \frac{1}{2}.

This estimate is implicitly proven here. It improves upon the Level 1 estimate by using the q-van der Corput A-process in the d2 direction. The final constraint 32\varpi +9\delta + \sigma < \frac{1}{2} was removed in this comment.

Level 3

Type I-3 We have Type'_I[\varpi,\delta,\sigma] (and hence Type_I[\varpi,\delta,\sigma]) whenever
\displaystyle 54\varpi + 15 \delta + 5 \sigma < 1.

This estimate is established here (it was previously tentatively established in this comment with an additional condition 32 \varpi + 9 \delta + \sigma < 1/2, which can now be dropped, thanks to an improved control on a secondary error term in the exponential sum estimates). It improves upon the Level 2 estimate by taking advantage of dense divisibility to optimise the direction of averaging.

Level 4

By iterating the q-van der Corput A-process, it appears that one can obtain Type_I[\varpi,\delta,\sigma] assuming a constraint of the form

\displaystyle \frac{236}{3}\varpi + \frac{64}{3} \delta + 4 \sigma < 1

but this is inferior to the Level 3 estimates in practice. Details can be found here.

Level 6

Even further improvement in the Type I sums may be possible by rebalancing the final Cauchy-Schwarz: instead of performing Cauchy-Schwarz in n,q1 (leaving h,q2 to be doubled), factor q2 = r2s2 and Cauchy-Schwarz in n,q1,r2 and only double h,s2. The idea is to make the diagonal case hs'2 = h's2 do more of the work and the off-diagonal case hs'_2 \neq h' s_2 do less of the work. This idea was first raised here. Preliminary computations suggest that this allows one to take 56 \varpi + 16 \delta + 4 \sigma < 1 for the Type I sums in xδ-smooth case. In this post it is shown that the same bound holds in the densely divisible case, thus Type''_I[\varpi,\delta,\sigma] holds whenever

56 \varpi + 16 \delta + 4 \sigma < 1.

Level 5

(The numbering here is out of order because the Level 5 estimates proved harder to implement than the Level 6 estimates.)

Further improvement to these be obtainable by taking advantage of averaging in auxiliary parameters; in particular averaging over the parameter d1 has provisionally (subject to verification of some Deligne-level estimates) shown to establish Type''''_I[\varpi,\delta,\sigma] whenever

\frac{160}{3} \varpi + 16 \delta + \frac{34}{9} \sigma < 1;

see this comment.

Type II estimates

In all of the estimates below, 0 < \varpi < 1/4 and 0 < \delta < 1/4 + \varpi are fixed.

Level 1

Type II-1 We have Type'_{II}[\varpi,\delta] (and hence Type_{II}[\varpi,\delta]) whenever
\displaystyle 58\varpi + 10\delta < \frac{1}{2}.

This estimate is implicitly proven here. (There, only Type_I[\varpi,\delta,\sigma] is proven, but the method extends without difficulty to Type'_I[\varpi,\delta,\sigma].) It uses the method of Zhang, and is ultimately based on exponential sums for incomplete Kloosterman sums on smooth moduli obtained via completion of sums.

Level 1a

Type II-1a We have Type'_{II}[\varpi,\delta] (and hence Type_{II}[\varpi,\delta]) whenever
\displaystyle 48\varpi + 7\delta < \frac{1}{2}.

This estimate is implicitly proven here. It is a slight refinement of the Level 1 estimate based on a more careful inspection of the error terms in the completion of sums method.

Level 1b

Type II-1b We have Type'_{II}[\varpi,\delta] (and hence Type_{II}[\varpi,\delta]) whenever
\displaystyle 38\varpi + 7\delta < \frac{1}{2}.

This refinement of the Level 1a estimate came from realising that in the Type II case, the R parameter can be selected to lie in the range [x^{1/2-2\varpi-\delta-\varepsilon}, x^{1/2-2\varpi-\varepsilon}] rather than [x^{-2\varpi-\delta-\varepsilon} N, x^{-2\varpi-\varepsilon} N]. See this comment for details.

Level 1c

Type II-1c We have Type'_{II}[\varpi,\delta] (and hence Type_{II}[\varpi,\delta] and Type''_{II}[\varpi,\delta]) whenever
\displaystyle 34\varpi + 7\delta < \frac{1}{2}.

This further refinement of the Level 1b estimate came from realising that R can in fact range in [x^{-\delta-\varepsilon} N, x^{-\varepsilon} N] if one strengthens the controlled multiplicity hypothesis slightly; see this comment or this post for details.

Level 2

In analogy with the Type I-2 estimates, one could hope to improve the Type II estimates by using the q-van der Corput process in the d2 direction. Interestingly, however, it appears that the Type II numerology lies outside of the range in which the van der Corput process is beneficial (at least if one only applies it once), so the Level 2 estimate looks to be inferior to the Level 1b estimate.

Level 3

In analogy with the Type I-3 estimates, one should be able to improve the Type II estimates by using the q-van der Corput process in an optimised direction. As with Level 2 estimates though, it appears that Level 3 estimates are inferior to the Level 1b estimate.

Level 4

In analogy with the Type I-4 estimates, one should be able to improve the Type II estimates by iterating the q-van der Corput A-process.

Level 5

In analogy with the Type I-5 estimates, one should be able to improve the Type II estimates by taking advantage of averaging in the h parameters.

Level 6

Even further improvement in the Type II sums may be possible by rebalancing the final Cauchy-Schwarz: instead of performing Cauchy-Schwarz in n (leaving h,q1,q2 to be doubled), factor q1 = r1s1 and Cauchy-Schwarz in n,r1 and only double h,s1,q2. The idea is to make the diagonal case hs'1q'2 = h's1q2 do more of the work and the off-diagonal case hs'_1 q'_2 \neq h' s_1 q_2 do less of the work. This idea was first raised here.

Type III estimates

In all of the estimates below, 0 < \varpi < 1/4, 0 < \delta < 1/4 + \varpi, and σ > 0 are fixed.

Level 1

Type III-1 We have Type'_{III}[\varpi,\delta,\sigma] (and hence Type_{III}[\varpi,\delta,\sigma]) whenever
\displaystyle \frac{13}{2} (\frac{1}{2} + \sigma) > 8 (\frac{1}{2} + 2 \varpi) + \delta

This estimate is implicitly proven here. (There, only Type_{III}[\varpi,\delta,\sigma] is proven, but the method extends without difficulty to Type'_{III}[\varpi,\delta,\sigma].) It uses the method of Zhang, using Weyl differencing and not exploiting the averaging in the α or q parameters. The constraint can also be written as a lower bound on σ:

\displaystyle \sigma > \frac{3}{26} + \frac{32}{13} \varpi + \frac{2}{13} \delta.

Level 2

Type III-2 We have Type'_{III}[\varpi,\delta,\sigma] (and hence Type_{III}[\varpi,\delta,\sigma]) whenever
\displaystyle 1 + 5 (\frac{1}{2} + \sigma) > 8 (\frac{1}{2} + 2 \varpi) + \delta

This estimate is implicitly proven here. It is a refinement of the Level 1 estimate that takes advantage of the α averaging. The constraint may also be written as a lower bound on σ:

\displaystyle \sigma > \frac{1}{10} + \frac{16}{5} \varpi + \frac{1}{5} \delta.

Level 3

Type III-3 We have Type'_{III}[\varpi,\delta,\sigma] (and hence Type_{III}[\varpi,\delta,\sigma]) whenever
\displaystyle \frac{3}{2} (\frac{1}{2} + \sigma) > \frac{7}{4} (\frac{1}{2} + 2 \varpi) + \frac{3}{8} \delta .

This estimate is proven in this comment. It uses the newer method of Fouvry, Kowalski, Michel, and Nelson that avoids Weyl differencing. The constraint may also be written as a lower bound on σ:

\displaystyle \sigma > \frac{1}{12} + \frac{7}{3} \varpi + \frac{1}{4} \delta.

Level 4

Type III-4 We have Type'_{III}[\varpi,\delta,\sigma] (and hence Type_{III}[\varpi,\delta,\sigma] and Type''_{III}[\varpi,\delta,\sigma]) whenever
\displaystyle \frac{1}{4} + \frac{3}{4} \frac{3}{2} (\frac{1}{2} + \sigma) > \frac{7}{4} (\frac{1}{2} + 2 \varpi) + \frac{1}{4} \delta .

This estimate is proven in this comment and then in this post. It modifies the Level 3 argument by exploiting averaging in the α parameter (this was suggested already by Fouvry, Kowalski, Michel, and Nelson).The constraint may also be written as a lower bound on σ:

\displaystyle \sigma > \frac{1}{18} + \frac{28}{9} \varpi + \frac{2}{9} \delta.

Level 5

One may also hope to improve upon Level 4 estimates by exploiting Ramanujan sum cancellation (as Zhang did in his Level 1 argument).

Level 6

An alternative way to improve upon Level 4 estimates would be to use the q-van der Corput process to bound incomplete Kloosterman correlations.

Combinations

By combining a Type I estimate, a Type II estimate, and a Type III estimate together one can get estimates of the form MPZ[\varpi,\delta] or MPZ[\varpi',\delta'] for \varpi,\delta small enough by using the combinatorial lemma. Here are the combinations that have been arisen so far in the Polymath8 project:

Type I Type II Type III Result Details Where optimum is obtained
Level 1 Level 1 Level 1 828\varpi + 172\delta < 1 details Type I / Type III border
Level 1 Level 1 Level 2 348\varpi + 68\delta < 1 details Type I / Type III border
Level 2 Level 1a Level 1 178\varpi + 52\delta < 1 details Type I / Type III border
Level 2 Level 1a Level 2 148\varpi + 33\delta < 1 details Type I / Type III border
Level 3? Level 1a Level 2 140 \varpi + 32\delta < 1? details Type I / Type III border
Level 2 Level 1a Level 3 116\varpi + 25.5 \delta < 1 details refinement Type I / Type III border
Level 3 Level 1a Level 3 112 \frac{4}{7} \varpi+27 \frac{6}{7} \delta < 1 details Type I / Type III border
Level 3 Level 1c Level 4 108\varpi+30\delta < 1 details Type I / combinatorial border
Level 6 Level 1c Level 4 \frac{280}{3} \varpi + \frac{80}{3} \delta < 1 details Type I / combinatorial border

For simplicity, only the constraint that is relevant for near-maximal values of \varpi is shown.

Here is some Maple code for finding the constraints coming from a certain set of inequalities (e.g. Type I level 6, Type II level 1c, and Type III level 4). To reduce the complexity of the output, one can introduce an artificial cutoff of, say, \varpi > 1/200, in the base constraints to restrict attention to the regime of large values of \varpi.

with(SolveTools[Inequality]);
base := [ sigma > 1/10, sigma < 1/2, varpi > 0, varpi < 1/4, delta > 0, delta < 1/4+varpi ];
typeI_1 := [ 11 * varpi + 3 * delta + 2 * sigma < 1/4 ];
typeI_2 := [ 17 * varpi + 4 * delta + sigma < 1/4, 20 * varpi + 6 * delta + 3 * sigma < 1/2, 32 * varpi + 9 * delta + sigma < 1/2 ];
typeI_3 := [ 54 * varpi + 15 * delta + 5 * sigma < 1 ];
typeI_4 := [ 236 * varpi/3 + 64 * delta/3 + 4*sigma < 1];
typeI_6 := [ 56 * varpi + 16*delta + 4*sigma < 1];
typeII_1 := [ 58 * varpi + 10 * delta < 1/2 ];
typeII_1a := [48 * varpi + 7 * delta < 1/2 ];
typeII_1b := [38 * varpi + 7 * delta < 1/2 ];
typeII_1c := [34 * varpi + 7 * delta < 1/2 ];
typeIII_1 := [ (13/2) * (1/2 + sigma) > 8 * (1/2 + 2*varpi) + delta ];
typeIII_2 := [ 1 + 5 * (1/2 + sigma) > 8 * (1/2 + 2*varpi) + delta ];
typeIII_3 := [ 3/2 * (1/2 + sigma) > (7/4) * (1/2 + 2*varpi) + (3/8) * delta ];
typeIII_4 := [ 1/4 + (3/4) * (3/2) * (1/2 + sigma) > (7/4) * (1/2 + 2*varpi) + (1/4) * delta ];
constraints := [ op(base), op(typeI_6), op(typeII_1c), op(typeIII_4) ];
LinearMultivariateSystem(constraints, [varpi,delta,sigma]);
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