Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC

Key Points

• Systematic uncertainty during analyses can lead to significant variations in outcomes, affecting precision.
• The new neural network training approach improves binned-likelihood analyses, achieving better accuracy.
• Analysis focuses on neural networks specifically designed for systematic uncertainties encountered at the LHC.
• Developing these models highlights the need for effective algorithms to address data interpretation challenges.