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Found 5 papers, 3 papers with code
Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test
We here propose a machine learning approach for monitoring particle detectors in real-time.
Learning new physics efficiently with nonparametric methods
We present a machine learning approach for model-independent new physics searches.
Learning Multivariate New Physics
It is designed to be sensitive to small discrepancies that arise in datasets dominated by the reference model.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics
Training of the neural network models, defined using the Keras API, is performed in a distributed fashion on Spark clusters by using BigDL with Analytics Zoo and also by using TensorFlow, notably for distributed training on CPU and GPU resourcess.
Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum
This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches.
High Energy Physics - Experiment High Energy Physics - Phenomenology
