{"id":16840,"date":"2016-12-20T23:29:51","date_gmt":"2016-12-20T21:29:51","guid":{"rendered":"http:\/\/hgpu.org\/?p=16840"},"modified":"2016-12-20T23:29:51","modified_gmt":"2016-12-20T21:29:51","slug":"an-eos-meter-of-qcd-transition-from-deep-learning","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=16840","title":{"rendered":"An EoS-meter of QCD transition from deep learning"},"content":{"rendered":"<p>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. The final-state particle spectra $rho(p_T,Phi)$ provide directly accessible information from experiments. High-level correlations of $rho(p_T,Phi)$ learned by the neural network act as an &quot;EoS-meter&quot;, effective in detecting the nature of the QCD transition. The EoS-meter is model independent and insensitive to other simulation input, especially the initial conditions. Thus it provides a formidable direct-connection of heavy-ion collision observable with the bulk properties of QCD.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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. The final-state particle spectra $rho(p_T,Phi)$ provide directly accessible information from experiments. High-level correlations of $rho(p_T,Phi)$ learned by the neural network act as an &quot;EoS-meter&quot;, effective in detecting the nature [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,3,12],"tags":[14,1673,100,1930,1025,34,1171,20,1783,335,1909,1390],"class_list":["post-16840","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-cuda","tag-deep-learning","tag-high-energy-physics-phenomenology","tag-keras","tag-machine-learning","tag-neural-networks","tag-nuclear-theory","tag-nvidia","tag-physics","tag-qcd","tag-tensorflow","tag-tesla-k20"],"views":5098,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16840","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=16840"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16840\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16840"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16840"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16840"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}