Constraining $$ \mathcal{CP} $$-violation in the Higgs-top-quark interaction using machine-learning-based inference

Bahl, Henning; Braß, Simon

doi:10.1007/jhep03(2022)017

“…In the left panel we consider t t H production to constrain the two SMEFT Wilson coefficients c u and c G . In the right panel we consider the same process to constrain CP-violation in the top-Higgs coupling, as parameterized by the magnitude κ t and CP-phase α of the top Yukawa coupling [116,121].…”

Section: Madminermentioning

confidence: 99%

Machine learning and LHC event generation

Butter

¹

,

Plehn

²

,

Schumann

³

et al. 2023

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First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

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“…In the left panel we consider t t H production to constrain the two SMEFT Wilson coefficients c u and c G . In the right panel we consider the same process to constrain CP-violation in the top-Higgs coupling, as parameterized by the magnitude κ t and CP-phase α of the top Yukawa coupling [115,120].…”

Section: Madminermentioning

confidence: 99%

Machine Learning and LHC Event Generation

Butter¹,

Plehn²,

Schumann³

et al. 2022

Preprint

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First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

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“…First phenomenological studies on synthetic data have shown that these methods have the potential to substantially improve the precision of Higgs measurements at the LHC [165,166,167,185,186,187,188]. It remains a challenge to scale these analyses to real data and in particular to a realistic modeling of systematic uncertainties, which are usually modeled with thousands of nuisance parameters.…”

Section: Examplesmentioning

confidence: 99%

Simulation Intelligence: Towards a New Generation of Scientific Methods

Lavin¹,

Zenil²,

Krakauer³

et al. 2021

Preprint

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The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. 1 We present the Nine Motifs of Simulation Intelligence, a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence "operating system" stack (SI-stack) and the motifs therein:1. Multi-physics and multi-scale modeling 2. Surrogate modeling and emulation 3. Simulation-based inference 4. Causal modeling and inference 5. Agent-based modeling 6. Probabilistic programming 7. Differentiable programming 8. Open-ended optimization Machine programmingWe believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.

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Constraining $$ \mathcal{CP} $$-violation in the Higgs-top-quark interaction using machine-learning-based inference

Cited by 16 publications

References 87 publications

Machine learning and LHC event generation

Machine learning and LHC event generation

Machine Learning and LHC Event Generation

Simulation Intelligence: Towards a New Generation of Scientific Methods

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