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.
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.
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.
We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce ...a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions.
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.
This paper has been prepared by the HEP Software Foundation (HSF) Physics Event Generator Working Group (WG), as an input to the second phase of the LHCC review of High-Luminosity LHC (HL-LHC) ...computing, which is due to take place in November 2021. It complements previous documents prepared by the WG in the context of the first phase of the LHCC review in 2020, including in particular the WG paper on the specific challenges in Monte Carlo event generator software for HL-LHC, which has since been updated and published, and which we are also submitting to the November 2021 review as an integral part of our contribution.
