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Record W2732576225 · doi:10.1112/plms.12181

Correlations of the von Mangoldt and higher divisor functions I. Long shift ranges

2018· article· en· W2732576225 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the London Mathematical Society · 2018
Typearticle
Languageen
FieldMathematics
TopicAnalytic Number Theory Research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaAcademy of FinlandNational Science Foundation
KeywordsDivisor (algebraic geometry)LogarithmInterval (graph theory)Asymptotic formulaDivisor functionTerm (time)

Abstract

fetched live from OpenAlex

We study asymptotics of sums of the form ∑ X < n ⩽ 2 X Λ ( n ) Λ ( n + h ) , ∑ X < n ⩽ 2 X d k ( n ) d l ( n + h ) , ∑ X < n ⩽ 2 X Λ ( n ) d k ( n + h ) , and ∑ n Λ ( n ) Λ ( N − n ) , where Λ is the von Mangoldt function, d k is the kth divisor function, and N , X are large. Our main result is that the expected asymptotic for the first three sums holds for almost all h ∈ [ − H , H ] , provided that X σ + ε ⩽ H ⩽ X 1 − ε for some ε > 0 , where σ : = 8 33 = 0.2424 ⋯ , with an error term saving on average an arbitrary power of the logarithm over the trivial bound. This improves upon results of Mikawa and Baier–Browning–Marasingha–Zhao, who obtained statements of this form with σ replaced by 1 3 . We obtain an analogous result for the fourth sum for most N in an interval of the form [ X , X + H ] with X σ + ε ⩽ H ⩽ X 1 − ε . Our method starts with a variant of an argument from a paper of Zhan, using the circle method and some oscillatory integral estimates to reduce matters to establishing some mean-value estimates for certain Dirichlet polynomials associated to ‘Type d 3 ’ and ‘Type d 4 ’ sums (as well as some other sums that are easier to treat). After applying Hölder's inequality to the Type d 3 sum, one is left with two expressions, one of which we can control using a short interval mean value theorem of Jutila, and the other we can control using exponential sum estimates of Robert and Sargos. The Type d 4 sum is treated similarly using the classical L 2 mean value theorem and the classical van der Corput exponential sum estimates. In a sequel to this paper we will obtain related results for the correlations involving d k ( n ) for much smaller values of H but with weaker bounds.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.300
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it