Negative-weights suppression in Monte Carlo samples

Session

Presentations

5 May 2025, 12:30

4/3-006 - TH Conference Room (CERN)

2. KrKNLO Matching

James Christopher Whitehead

05/05/2025, 12:30

Among NLO matching methods, the KrkNLO method is unique in exploiting a modification of the PDF factorisation scheme to allow NLO accuracy to be achieved by a multiplicative reweight. This gives positive weights by construction, since it does not use subtraction, and unlike other matching methods has no dependence on an unphysical choice of shower-scale or suppression-factor.
We summarise the...

3. Positive-weight event generation with ESME

Alexander Karlberg (CERN)

05/05/2025, 13:15

In this talk we discuss a recent proposal for a family of NLO matching methods that are positive-definite by construction (Exponentiated Subtraction for Matching Events or ESME), and its implementation within PanScales. The method is general enough that it can be implemented in other frameworks in principle. The trade-off for guaranteed positive weights is the inclusion of higher-order...

4. ARCANE Reweighting to reduce negative weights

Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

05/05/2025, 16:00

Negative weights in next-to-leading-order (NLO) event generation pose a significant computational challenge in collider physics. In this talk, I will describe a new Monte Carlo technique called ARCANE reweighting for tackling the negative weights problem. By applying ARCANE reweighting, one can reduce or even completely eliminate the negative weights in Monte Carlo datasets a) without...

6. Challenge of negative weights in NNLO QCD calculations: insights from the MATRIX framework

Oleksandr Zenaiev

06/05/2025, 13:30

Negative weights are a common challenge in higher-order perturbative calculations, impacting the statistical power of Monte Carlo samples and increasing computational costs. In this talk, we discuss our experience with the MATRIX framework, a state-of-the-art tool for next-to-next-to-leading order (NNLO) quantum chromodynamics (QCD) predictions widely used in LHC
precision physics. Due to its...

7. Cell Resampling to reduce negative weights

Andreas Maier (IFAE)

06/05/2025, 14:15

Cell resampling is a method for suppressing negative weights and generally improving statistical convergence in Monte Carlo event generation. I review the lessons learned from applications to showered and large fixed-order event samples and present a new phase-space metric designed to better match the sensitivity of experimental analyses. I conclude with an overview over some of open...

5. Cell resampling in McMule at NNLO

Yannick Ulrich (University of Liverpool (GB))

06/05/2025, 15:00

McMule, a Monte Carlo for MUons and other LEptons, implements many major QED processes at NNLO (eg. $ee\to ee$, $e\mu\to e\mu$, $ee\to\mu\mu$, $\ell p\to \ell p$, $\mu \to \nu\bar\nu e$) including effects from the lepton masses.
This makes McMule suitable for predictions for low-energy experiments such as MUonE, CMD-III, ULQ2, or KLOE.
In this talk I will...

8. Treatment of non-positive definite integrands with Normalising Flows

Rene Poncelet (IFJ PAN Krakow)

07/05/2025, 16:00

We showcase the application of neural importance sampling for the evaluation of NNLO QCD scattering cross sections. We consider trainable Normalizing Flows in the form of discrete coupling layers and time continuous flows for the integration of the various cross-section contributions when using the sector-improved residue subtraction scheme, the so-called STRIPPER method. We thereby consider...

11. Stay Positive: Neural Refinement of Simulated Event Weights

Dennis Daniel Nick Noll (Lawrence Berkeley National Lab (US))

07/05/2025, 16:45

Monte Carlo simulations are an essential tool for data analysis in particle physics. Simulated events are typically produced alongside weights, that redistribute the cross section across the phase space. The presence of latent degrees of freedom can lead to a distribution of weights with negative values, often complicating analysis. Traditional post-hoc reweighting methods aim to approximate...

12. Improving QCD Simulations via Information-Theoretic Reweighting

Ben Assi

07/05/2025, 17:30

We begin by reviewing how NLL accuracy is achieved in modern parton showers—highlighting the recent Sherpa implementation—and then introduce our new information-theoretic matching framework to achieve beyond NLL accuracy. By minimizing a Kullback–Leibler functional under constraints set by precision QCD inputs observables (including theory uncertainties), we embed high-order predictions into...

9. Resampling for event generators

Simon Platzer (University of Graz (AT))

08/05/2025, 11:00

10. Open discussion

08/05/2025, 14:00