{"id":4958,"date":"2025-11-05T12:03:32","date_gmt":"2025-11-05T15:03:32","guid":{"rendered":"https:\/\/iate.oac.uncor.edu\/?p=4958"},"modified":"2025-11-05T12:03:32","modified_gmt":"2025-11-05T15:03:32","slug":"viernes-07-11-analisis-bayesiano-de-esperimentos-de-deteccion-directa-estimacion-de-perfiles-de-materia-oscura-con-ml","status":"publish","type":"post","link":"https:\/\/iate.oac.uncor.edu\/en\/2025\/11\/05\/viernes-07-11-analisis-bayesiano-de-esperimentos-de-deteccion-directa-estimacion-de-perfiles-de-materia-oscura-con-ml\/","title":{"rendered":"Viernes 07\/11: An\u00e1lisis Bayesiano de Esperimentos de Detecci\u00f3n Directa + Estimaci\u00f3n de perfiles de materia oscura con ML"},"content":{"rendered":"<p><strong>Expositor:<\/strong> Mart\u00edn Emilio de los R\u00edos (IATE &#8211; UNC\/CONICET)<\/p>\n\n\n\n<p><strong>Date: <\/strong>viernes 07 de noviembre, 11:45 hs.<\/p>\n\n\n\n<p><strong>Abstract:<\/strong> En este seminario presentar\u00e9 un m\u00e9todo de machine learning (Truncated Marginal Neural Ratio Estimation) para realizar an\u00e1lisis bayesianos. Esta estrategia evita el c\u00e1lculo expl\u00edcito del likelihood, estim\u00e1ndolo mediante redes neuronales entrenadas con datos simulados, lo que permite acelerar la obtenci\u00f3n de posteriors en comparaci\u00f3n con los algoritmos tradicionales de Monte Carlo (MCMC).<\/p>\n\n\n\n<p>Como ejemplo contare brevemente <a href=\"https:\/\/arxiv.org\/pdf\/2407.21008\">el trabajo<\/a>  donde aplicamos este m\u00e9todo para estimar los par\u00e1metros de un modelo de materia oscura, utilizando datos simulados del experimento XENONnT.<\/p>\n\n\n\n<p>En la segunda parte del seminario presentar\u00e9 <a href=\"https:\/\/arxiv.org\/abs\/2111.08725\">el trabajo<\/a>, donde utilizamos redes neuronales convolucionales (CNN) para inferir los perfiles de materia oscura en galaxias a partir de su fotometr\u00eda e interferometr\u00eda, obteniendo mejores resultados que con el an\u00e1lisis tradicional de las curvas de rotaci\u00f3n.<\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Expositor: Mart\u00edn Emilio de los R\u00edos (IATE &#8211; UNC\/CONICET) Fecha: viernes 07 de noviembre, 11:45 hs. Resumen: En este seminario presentar\u00e9 un m\u00e9todo de machine learning (Truncated Marginal Neural Ratio Estimation) para realizar an\u00e1lisis bayesianos. Esta estrategia evita el c\u00e1lculo expl\u00edcito del likelihood, estim\u00e1ndolo mediante redes neuronales entrenadas con datos&hellip;<\/p>","protected":false},"author":1,"featured_media":4775,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,9],"tags":[],"class_list":["post-4958","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-novedades","category-seminarios"],"_links":{"self":[{"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/posts\/4958","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/comments?post=4958"}],"version-history":[{"count":1,"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/posts\/4958\/revisions"}],"predecessor-version":[{"id":4959,"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/posts\/4958\/revisions\/4959"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/media\/4775"}],"wp:attachment":[{"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/media?parent=4958"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/categories?post=4958"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/iate.oac.uncor.edu\/en\/wp-json\/wp\/v2\/tags?post=4958"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}