arXiv subjects

Daniel Maître

Publications and source records attributed to Daniel Maître.

13 recordsLinked to original sources

arXiv2026

Globally Optimal Contour Deformations with Neural Networks

In this article, we explore the use of contour deformation for the numerical evaluation of Feynman integrals after sector decomposition. In existing codes, the contour of integration is determined heuristically for each phase-space point by sampling the integrand. In this work, we introduce a method for choosing the contour deformation for an entire phase-space region using only an initial sampling or training step. We demonstrate that the resulting integrand has a lower variance than that obtained with heuristic methods and show that optimising a contour to reduce the estimated error of a Quasi-Monte Carlo sample is an ill-defined problem. The a priori knowledge of the integration path obtained in this work can be used to improve the speed of conventional integration methods or be leveraged for integration using neural networks, where, crucially, it removes the need to retrain the neural network for each phase-space point. The techniques described in this work can be adapted to other problems where a non-trivial integration path has to be chosen subject to a set of constraints.

hep-ph↗

arXiv2024

Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoods

The Matrix-Element Method (MEM) has long been a cornerstone of data analysis in high-energy physics. It leverages theoretical knowledge of parton-level processes and symmetries to evaluate the likelihood of observed events. In parallel, the advent of geometric deep learning has enabled neural network architectures that incorporate known symmetries directly into their design, leading to more efficient learning. This paper presents a novel approach that combines MEM-inspired symmetry considerations with equivariant neural network design for particle physics analysis. Even though Lorentz invariance and permutation invariance overall reconstructed objects are the largest and most natural symmetry in the input domain, we find that they are sub-optimal in most practical search scenarios. We propose a longitudinal boost-equivariant message-passing neural network architecture that preserves relevant discrete symmetries. We present numerical studies demonstrating MEM-inspired architectures achieve new state-of-the-art performance in distinguishing di-Higgs decays to four bottom quarks from the QCD background, with enhanced sample and parameter efficiencies. This synergy between MEM and equivariant deep learning opens new directions for physics-informed architecture design, promising more powerful tools for probing physics beyond the Standard Model.

hep-ph↗

arXiv2023

Two-Loop Five-Parton Leading-Colour Finite Remainders in the Spinor-Helicity Formalism

We present all two-loop five-parton leading-colour finite remainders in the spinor-helicity formalism by analysing numerical evaluations of their known expressions in terms of Mandelstam invariants. Recasting them in terms of spinor-helicity variables allows us to obtain expressions which are more compact, faster to evaluate, numerically more stable and manifestly free from poles of higher order than necessary. At the same time, due to the better scaling of our reconstruction strategy with the complexity of the input, we required one order of magnitude fewer numerical samples to complete the analytical reconstruction than were needed by the authors of Ref. \cite{Abreu:2019odu}, albeit using higher numerical working precision. This places our reconstruction technique as an alternative to the finite-field single-numerator reconstruction for future applications.

hep-ph↗

arXiv2023

Extracting analytical one-loop amplitudes from numerical evaluations

In this article we present a method to generate analytic expressions for the integral coefficients of loop amplitudes using numerical evaluations only. We use high-precision arithmetic to explore the singularity structure of the coefficients and decompose them into parts of manageable complexity. To illustrate the usability of our method we provide analytical expressions for all helicity configurations of the colour-ordered six-point gluon amplitudes at one loop with a gluon in the loop.

hep-ph↗

arXiv2023

Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples

We demonstrate that cell resampling can eliminate the bulk of negative event weights in large event samples of high multiplicity processes without discernible loss of accuracy in the predicted observables. The application of cell resampling to much larger data sets and higher multiplicity processes such as vector boson production with up to five jets has been made possible by improvements in the method paired with drastic enhancement of the computational efficiency of the implementation.

hep-ph↗

arXiv2023

Unweighting multijet event generation using factorisation-aware neural networks

In this article we combine a recently proposed method for factorisation-aware matrix element surrogates with an unbiased unweighting algorithm. We show that employing a sophisticated neural network emulation of QCD multijet matrix elements based on dipole factorisation can lead to a drastic acceleration of unweighted event generation. We train neural networks for a selection of partonic channels contributing at the tree-level to $Z+4,5$ jets and $t\bar{t}+3,4$ jets production at the LHC which necessitates a generalisation of the dipole emulation model to include initial state partons as well as massive final state quarks. We also present first steps towards the emulation of colour-sampled amplitudes. We incorporate these emulations as fast and accurate surrogates in a two-stage rejection sampling algorithm within the Sherpa Monte Carlo that yields unbiased unweighted events suitable for phenomenological analyses and post-processing in experimental workflows, e.g. as input to a time-consuming detector simulation. For the computational cost of unweighted events we achieve a reduction by factors between $16$ and $350$ for the considered channels.

hep-ph↗

arXiv2023

Multi-variable Integration with a Neural Network

In this article we present a method for automatic integration of parametric integrals over the unit hypercube using a neural network. The method fits a neural network to the primitive of the integrand using a loss function designed to minimize the difference between multiple derivatives of the network and the function to be integrated. We apply this method to two example integrals resulting from the sector decomposition of a one-loop and two-loop scalar integrals. Our method can achieve per-mil and percent accuracy for these integrals over a range of invariant values. Once the neural network is fitted, the evaluation of the integral is between 40 and 125 times faster than the usual numerical integration method for our examples, and we expect the speed gain to increase with the complexity of the integrand.

hep-ph↗

arXiv2021

A factorisation-aware Matrix element emulator

In this article we present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements. In so doing we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network. We apply our model to the case of leading-order jet production in $e^+e^-$ collisions with up to five jets. Our results show that this model can reproduce the matrix elements with errors below the one-percent level on the phase-space covered during fitting and testing, and a robust extrapolation to the parts of the phase-space where the matrix elements are more singular than seen at the fitting stage.

hep-ph↗

arXiv2017

Strong coupling constant extraction from high-multiplicity Z+jets observables

We present a strong coupling constant extraction at Next-to-Leading Order QCD accuracy using ATLAS Z+2,3,4 jets data. This is the first extraction using processes with a dependency to high powers of the coupling constant. We obtain values of the strong coupling constant at the $Z$ mass compatible with the world average and with uncertainties commensurate with other NLO extractions at hadron colliders. Our most conservative result for the strong coupling constant is $α_S=0.1178 ^{+0.0051}_{- 0.0043}$.

hep-ph↗

arXiv2014

High multiplicity processes with BlackHat and Sherpa

In this contribution, we present an intermediate storage format for next-to-leading order (NLO) events and explain the advantages of presenting a NLO calculation in this format. We also present some recent applications, including the calculation of PDF uncertainties and the combination of different multiplicity samples for the prediction of gap fractions in inclusive dijet events.

hep-ph↗

arXiv2014

Les Houches 2013: Physics at TeV Colliders: Standard Model Working Group Report

This Report summarizes the proceedings of the 2013 Les Houches workshop on Physics at TeV Colliders. Session 1 dealt primarily with (1) the techniques for calculating standard model multi-leg NLO and NNLO QCD and NLO EW cross sections and (2) the comparison of those cross sections with LHC data from Run 1, and projections for future measurements in Run 2.

hep-ph↗

arXiv2013

Simulated NNLO for high-pT observables in vector boson + jets production at the LHC

We present a study of higher order QCD corrections beyond NLO to processes with an electroweak vector boson, W or Z, in association with jets. We focus on the regions of high transverse momenta of commonly used differential distributions. We employ the LoopSim method to merge NLO samples of different multiplicity obtained from MCFM and from BLACKHAT+SHERPA in order to compute the dominant part of the NNLO corrections for high-pT observables. We find that these corrections are indeed substantial for a number of experimentally relevant observables. For other observables, they lead to significant reduction of scale uncertainties.

hep-ph↗

arXiv2012

High multiplicity processes at NLO with BlackHat and Sherpa

In this contribution we review recent progress with fixed-order QCD predictions for the production of a vector boson in association with jets at hadron colliders, using the programs BlackHat and SHERPA. We review general features of next-to-leading-order (NLO) predictions for the production of a massive vector boson in association with four jets. We also discuss how precise descriptions of vector-boson production can be applied to the determination of backgrounds to new physics signals. Here we focus on data-driven backgrounds to a missing-energy-plus-jets search performed by CMS. Finally, we review recent progress in developing theoretical tools for high-multiplicity loop-computation within the BlackHat-library. In particular, we discuss methods for handling the color degrees of freedom in multi-jet predictions at NLO.

hep-ph↗