Phil Ilten

We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (\homer), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of \homer on a simplified hadronization problem, a $q\bar{q}$ string fragmenting into pions, and extract a modified Lund string fragmentation function $f(z)$. We then demonstrate the use of \homer on three types of experimental data: (i) binned distributions of high level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that $f(z)$ can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted $f(z)$ suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad.

Hadronization

10.48550/arxiv.2410.06342

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LHCb future dark-sector sensitivity projections for Snowmass 2021

arXiv (Cornell University) (2022)

D. C. Craik Phil Ilten Daniel Johnson M. Williams

We provide future LHCb dark-sector sensitivity projections for use in the Snowmass reports. These include updated projections for dark photons and the Higgs portal, along with new projections for axion-like particles that couple predominantly to gluons.

Higgs sector

10.48550/arxiv.2203.07048

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Report on 2308.13459v3

arXiv (Cornell University) (2024)

Christian Bierlich Phil Ilten Tony Menzo S. Mrenna Manuel Szewc

This work reports on a method for uncertainty estimation in simulated collider-event predictions.The method is based on a Monte Carlo-veto algorithm, and extends previous work on uncertainty estimates in parton showers by including uncertainty estimates for the Lund string-fragmentation model.This method is advantageous from the perspective of simulation costs: a single ensemble of generated events can be reinterpreted as though it was obtained using a different set of input parameters, where each event now is accompanied with a corresponding weight.This allows for a robust exploration of the uncertainties arising from the choice of input model parameters, without the need to rerun full simulation pipelines for each input parameter choice.Such explorations are important when determining the sensitivities of precision physics measurements.

10.21468/scipost.report.8768

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Report on scipost_202204_00035v1

Phil Ilten Tony Menzo Ahmed Youssef Jure Zupan

We present the first steps in the development of a new class of hadronization models utilizing machine learning techniques.We successfully implement, validate, and train a conditional sliced-Wasserstein autoencoder to replicate the Pythia generated kinematic distributions of first-hadron emissions, when the Lund string model of hadronization implemented in Pythia is restricted to the emissions of pions only.The trained models are then used to generate the full hadronization chains, with an IR cutoff energy imposed externally.The hadron multiplicities and cumulative kinematic distributions are shown to match the Pythia generated ones.We also discuss possible future generalizations of our results.

10.21468/scipost.report.5322

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MLHAD-NF datasets

Zenodo (CERN European Organization for Nuclear Research) (2023)

Tony Menzo Christian Bierlich Phil Ilten S. Mrenna Manuel Szewc

Datasets associated with the MLHAD-BNF repository https://gitlab.com/uchep/mlhad/BNF based on work the presented in https://arxiv.org/pdf/2311.09296.pdf. This data should be downloaded to the path '<>/mlhad/BNF/data/' for proper integration with the repository.

10.5281/zenodo.10379696

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Modeling hadronization using machine learning

arXiv (Cornell University) (2022)

Phil Ilten Tony Menzo Ahmed M. Youssef Jure Zupan

We present the first steps in the development of a new class of hadronization models utilizing machine learning techniques. We successfully implement, validate, and train a conditional sliced-Wasserstein autoencoder to replicate the Pythia generated kinematic distributions of first-hadron emissions, when the Lund string model of hadronization implemented in Pythia is restricted to the emissions of pions only. The trained models are then used to generate the full hadronization chains, with an IR cutoff energy imposed externally. The hadron multiplicities and cumulative kinematic distributions are shown to match the Pythia generated ones. We also discuss possible future generalizations of our results.

Hadronization

10.48550/arxiv.2203.04983

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Report on 2308.13459v2

arXiv (Cornell University) (2023)

Christian Bierlich Phil Ilten Tony Menzo S. Mrenna Manuel Szewc

This work reports on a method for uncertainty estimation in simulated collider-event predictions.The method is based on a Monte Carlo-veto algorithm, and extends previous work on uncertainty estimates in parton showers by including uncertainty estimates for the Lund string-fragmentation model.This method is advantageous from the perspective of simulation costs: a single ensemble of generated events can be reinterpreted as though it was obtained using a different set of input parameters, where each event now is accompanied with a corresponding weight.This allows for a robust exploration of the uncertainties arising from the choice of input model parameters, without the need to rerun full simulation pipelines for each input parameter choice.Such explorations are important when determining the sensitivities of precision physics measurements.Accompanying code is available at gitlab.com/uchep/mlhad-weights-validation.

10.21468/scipost.report.8212

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Dark Sector Physics at High-Intensity Experiments

arXiv (Cornell University) (2022)

Stefania Gori M. Williams Phil Ilten Nhan Viet Tran Gordan Krnjaic

Is Dark Matter part of a Dark Sector? The possibility of a dark sector neutral under Standard Model (SM) forces furnishes an attractive explanation for the existence of Dark Matter (DM), and is a compelling new-physics direction to explore in its own right, with potential relevance to fundamental questions as varied as neutrino masses, the hierarchy problem, and the Universe's matter-antimatter asymmetry. Because dark sectors are generically weakly coupled to ordinary matter, and because they can naturally have MeV-to-GeV masses and respect the symmetries of the SM, they are only mildly constrained by high-energy collider data and precision atomic measurements. Yet upcoming and proposed intensity-frontier experiments will offer an unprecedented window into the physics of dark sectors, highlighted as a Priority Research Direction in the 2018 Dark Matter New Initiatives (DMNI) BRN report. Support for this program -- in the form of dark-sector analyses at multi-purpose experiments, realization of the intensity-frontier experiments receiving DMNI funds, an expansion of DMNI support to explore the full breadth of DM and visible final-state signatures (especially long-lived particles) called for in the BRN report, and support for a robust dark-sector theory effort -- will enable comprehensive exploration of low-mass thermal DM milestones, and greatly enhance the potential of intensity-frontier experiments to discover dark-sector particles decaying back to SM particles.

Hidden sector

Light dark matter

Intensity mapping

10.48550/arxiv.2209.04671

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Rejection Sampling with Autodifferentiation -- Case study: Fitting a Hadronization Model

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2024)

Noah Heller Phil Ilten Tony Menzo Stephen Mrenna Benjamin Nachman

We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be used for efficient parameter estimation and model exploration. Additionally, this approach facilitates the use of unbinned machine-learning-based observables, allowing for more precise, data-driven fits. To showcase these capabilities, we apply an RSA-based parameter fit to a simplified hadronization model.

Hadronization

Source

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Report on 2308.13459v2

arXiv (Cornell University) (2024)

Christian Bierlich Phil Ilten Tony Menzo S. Mrenna Manuel Szewc

This work reports on a method for uncertainty estimation in simulated collider-event predictions.The method is based on a Monte Carlo-veto algorithm, and extends previous work on uncertainty estimates in parton showers by including uncertainty estimates for the Lund string-fragmentation model.This method is advantageous from the perspective of simulation costs: a single ensemble of generated events can be reinterpreted as though it was obtained using a different set of input parameters, where each event now is accompanied with a corresponding weight.This allows for a robust exploration of the uncertainties arising from the choice of input model parameters, without the need to rerun full simulation pipelines for each input parameter choice.Such explorations are important when determining the sensitivities of precision physics measurements.Accompanying code is available at gitlab.com/uchep/mlhad-weights-validation.

10.21468/scipost.report.8362

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