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Weight optimization in multichannel Monte Carlo

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R.Kleiss, R.Pittau

High Energy Physics - Phenomenology · Submitted 10 May 1994

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We describe our experience with this method in a realistic problem, where an effective increase in program speed by almost an order of magnitude is observed.

how it is stated · asserted 0.99 read the record →

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arXiv IDhep-ph/9405257 (version 2)
Pith IDMNDPKYTN, Pith's permanent record of this paper and its review
CitationR.Kleiss, R.Pittau. "Weight optimization in multichannel Monte Carlo." arXiv:hep-ph/9405257 (1994). https://pith.science/paper/MNDPKYTN
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,594 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.09 · human participants 0.02 · controlled comparison 0.11 · randomized assignment 0.02 · states quantitative result 0.94 · compares with prior results 0.23 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We discuss the improvement in the accuracy of a Monte Carlo integration that can be obtained by optimization of the `a-priori weights' of the various channels. These channels may be either the strata in a stratified-sampling approach, or the several `approximate' distributions such as are used in event generators for particle phenomenology. The optimization algorithm does not require any initialization, and each Monte Carlo integration point can be used in the evaluation of the integral. We describe our experience with this method in a realistic problem, where an effective increase in program speed by almost an order of magnitude is observed.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We discuss the improvement in the accuracy of a Monte Carlo integration that can be obtained by optimization of the `a-priori weights' of the various channels. contribution 0.93 0.60 0.50 asserted 0.59
s1 These channels may be either the strata in a stratified-sampling approach, or the several `approximate' distributions such as are used in event generators for particle phenomenology. method 0.63 0.12 0.04 hedged possibility 0.97
s2 The optimization algorithm does not require any initialization, and each Monte Carlo integration point can be used in the evaluation of the integral. method 0.98 0.74 0.10 asserted 1.00
s3 We describe our experience with this method in a realistic problem, where an effective increase in program speed by almost an order of magnitude is observed. result 1.00 0.95 0.49 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We discuss the improvement in the accuracy of a Monte Carlo integration that can be obtained by optimization of the `a-priori weights' of the various channels. These channels may be either the strata in a stratified-sampling approach, or the several `approximate' distributions such as are used in event generators for particle phenomenology. The optimization algorithm does not require any initialization, and each Monte Carlo integration point can be used in the evaluation of the integral. We describe our experience with this method in a realistic problem, where an effective increase in program speed by almost an order of magnitude is observed.

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References
How later papers used it

50 later papers cite it

Evidence: substantial. 47 of the citing papers share no author with it, and 23 of those carry a confident label from Pith's citation classifier.

Pith read 25 of the 61 citing sentences, choosing only those whose citation markers point at this paper alone, and wrote the paraphrases from them. The summary, the counts, the evidence level, the links and the quotes come from code; the summary counts the papers listed below. A later paper is listed under a label when Pith's citation classifier gives one of its sentences that label at probability 0.8 or more (0.95 for support), Pith's reading finds that the citing paper itself did it, and a second check confirms the paraphrase against the sentence. This section lists uses only. It does not list criticism or disagreement, so their absence here says nothing about how the paper has been received. Papers that share an author with this one are listed apart, under "By the same authors". A quote is the citing authors' own sentence, shown when their paper's licence is CC BY, CC BY-SA or CC0. JSON

Uses its method or tool 9 of 25 papers with this label · 1 by the same authors

By the same authors

How the literature uses it

40 arXiv papers cite this in 51 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 18 sentences without a confident label

Uses its method or tool 21 papers
Compares with it 1 paper
  • This decomposition is different from the original multi-channel method [ref] , which is used by [ref] , or [ref] , as discussed in Ref. [ref] .

    The MadNIS Reloaded · Improving · 0.86

Cites it as background 8 papers
  • Early foundational work established the general structure of multi-particle phase space and its recursive decomposition [ref] , while subsequent developments introduced adaptive and multi-channel techniques tailored to the singularity structure of perturbative amplitudes [ref] .

    Multi-channel phase space with Feynman-diagram-gauge amplitudes · Introduction · 1.00

  • In the literature [ref] a common approach when using surrogates is to use it within importance sampling, while relying on standard hit and miss to generate event following the surrogate density function, which is conceptually equivalent to other importance sampling algorithm (based on ML or not) [ref] .

    Amplitude Surrogates for Multi-Jet Processes · Achievable speed-up without double-unweighting · 0.91

  • Methods using the latter are referred to as adaptive methods, and the ones used within particle physics are based on adaptive grids dividing phase space into subspaces [ref] , concern the optimization of the parameters in mixture distributions (the so-called adaptive multi-channel method) [ref] , or combine both [ref] .

    Kaleu: a general-purpose parton-level phase space generator · Introduction · 0.96

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