[et_pb_section fb_built=”1″ _builder_version=”3.22″][et_pb_row _builder_version=”3.22″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ background_color=”#072c72″ border_color_all=”#3255c9″ text_orientation=”right” background_layout=”dark” custom_padding=”20px|15px|15px|” z_index_tablet=”500″]<\/p>\n
2021<\/em><\/p>\n Masters Projects<\/em><\/p>\n @Physics and Astronomy<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”3.22.1″ text_orientation=”right” z_index_tablet=”500″]<\/p>\n +Computer Science<\/p>\n +Mathematics\/Statistics<\/p>\n +Physics\/Astronomy<\/p>\n \u00a0<\/p>\n #cosmology<\/p>\n #astrophysics<\/p>\n #probabilistic deep learning<\/p>\n #image processing<\/p>\n \u00a0<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ z_index_tablet=”500″]<\/p>\n Astronomy, as many other disciplines, is entering a big data era. The next generation of space (e.g. Euclid) and ground based (e.g. LSST) imaging survey will produce images of billions of galaxies at unprecedented depth. This new regime requires revisiting the existing methods to process the data both in terms of speed and accuracy in order for these surveys to achieve the main scientific goals. A particularly important source of bias is the one caused by overlapping sources (or blending) in the 2D projected plane of the sky. Galaxies which are at very different distances end up blended together. Built on previous synergic experiences of two research groups, this project explores the use of state-of-the art Artificial Intelligence techniques for deblending of galaxy images. We will in particular explore synergies between LSST and Euclid.<\/p>\n \u00a0<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_margin=”120px||” _builder_version=”3.22.1″ admin_label=”Row” locked=”off”][et_pb_column type=”4_4″ _builder_version=”3.22.1″][et_pb_divider _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”\u00c0 lire aussi” _builder_version=”3.22.1″ z_index_tablet=”500″ locked=”off”]<\/p>\n [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”37″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.22.1″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|” z_index_tablet=”500″ locked=”off”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" 2021Masters Projects@Physics and Astronomy +Computer Science+Mathematics\/Statistics+Physics\/Astronomy\u00a0#cosmology#astrophysics#probabilistic deep learning#image processing\u00a0 Project SummaryAstronomy, as many other disciplines, is entering a big data era. The next generation of space (e.g. Euclid) and ground based (e.g. LSST) imaging survey will produce images of billions of galaxies at unprecedented depth. This new regime requires revisiting the existing methods to process… Lire la suite Artificial Intelligence for source deblending in the next generation of astrophysical big data imaging surveys \u2013 Combining Euclid and LSST<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":2263,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[57,1,29,37],"tags":[],"class_list":["post-1587","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-57","category-diip","category-masters-internship","category-physics-astronomy"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1587","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=1587"}],"version-history":[{"count":3,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1587\/revisions"}],"predecessor-version":[{"id":2321,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1587\/revisions\/2321"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/2263"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=1587"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=1587"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=1587"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Project Summary<\/strong><\/h3>\n
Marc Huertas-Company<\/strong><\/h5>\n
Projects in the same discipline
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