{"id":9111,"date":"2022-10-11T12:28:34","date_gmt":"2022-10-11T12:28:34","guid":{"rendered":"https:\/\/www.istp.cnr.it\/?post_type=product&#038;p=9111"},"modified":"2022-11-11T11:03:52","modified_gmt":"2022-11-11T11:03:52","slug":"bayesian-inference-applied-to-electron-temperature-data-computational-performances-and-diagnostics-integration","status":"publish","type":"product","link":"https:\/\/www.istp.cnr.it\/it\/research-product\/bayesian-inference-applied-to-electron-temperature-data-computational-performances-and-diagnostics-integration\/","title":{"rendered":"Bayesian inference applied to electron temperature data: computational performances and diagnostics integration"},"content":{"rendered":"<p>Bayesian inference proves to be a robust tool for the fitting of parametric models on experimental datasets. In the case of electron kinetics, it can help the identification of non-thermal components in electron population and their relation with plasma parameters and dynamics. We present here a tool for electron distribution reconstruction based on MCMC (Monte Carlo Markov Chain) based Bayesian inference on Thomson Scattering data, discussing the computational performances of different algorithms and information metrics. Along, a possible integration between Soft X-ray spectroscopy and Thomson Scattering is presented, focusing on the parametric optimization of diagnostics spectral channels in different plasma regimes.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Fassina A.; Abate D.; Franz P.<\/p>\n","protected":false},"featured_media":1294,"comment_status":"closed","ping_status":"open","template":"","meta":[],"product_cat":[574],"product_tag":[2149,2828,4062,4063,4064],"class_list":["post-9111","product","type-product","status-publish","has-post-thumbnail","hentry","product_cat-journal-articles","product_tag-data-processing-methods","product_tag-rf","product_tag-plasma-generation","product_tag-laser-produced","product_tag-x-ray-produced","prodpage-style2"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product\/9111","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/comments?post=9111"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/media\/1294"}],"wp:attachment":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/media?parent=9111"}],"wp:term":[{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product_cat?post=9111"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product_tag?post=9111"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}