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Record W3123698023

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

2017· article· en· W3123698023 on OpenAlexafffund
Terence Tao, Maksym Radziwiłł, Kaisa Matomäki

Bibliographic record

VenueCaltechAUTHORS (California Institute of Technology) · 2017
Typearticle
Languageen
FieldMathematics
TopicAnalytic Number Theory Research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaAcademy of FinlandNational Science Foundation
KeywordsMathematicsCombinatoricsDivisor (algebraic geometry)LogarithmInterval (graph theory)Type (biology)Function (biology)Dirichlet distributionAsymptotic formulaValue (mathematics)Exponential functionUpper and lower boundsMathematical analysisStatistics
DOInot available

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 k th 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. <br />

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.004
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.050
GPT teacher head0.322
Teacher spread0.271 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2017
Admission routes2
Has abstractyes

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