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Found 5 papers, 1 papers with code
Shared Data and Algorithms for Deep Learning in Fundamental Physics
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies.
Carbon Leakage in a European Power System with Inhomogeneous Carbon Prices
Global warming is one of the main threats to the future of humanity and extensive emissions of greenhouse gases are found to be the main cause of global temperature rise as well as climate change.
Renewable Generation Data for European Energy System Analysis
We find significant differences between these datasets and cost-difference of about 10% result in the different energy mix.
Physics and Society
An equation-of-state-meter of quantum chromodynamics transition from deep learning
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions.
An equation-of-state-meter of QCD transition from deep learning
Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra $\rho(p_T,\Phi)$.
