[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
2022<\/em><\/p>\n Strategic Projects<\/em><\/p>\n @Physics & Astronomy<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”3.22.1″ text_orientation=”right” z_index_tablet=”500″]<\/p>\n +Biology<\/p>\n +Earth Sciences\/Geosciences<\/p>\n \u00a0<\/p>\n #Neutrinos<\/p>\n #Machine Learning<\/p>\n #Bioluminescence<\/p>\n #Graph Neural Networks<\/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 Neutrinos are fundamental particles that are produced in a multitude of nuclear processes and permeate our universe. Despite their abundance, neutrinos are extremely difficult to observe due to their very weak interaction with matter. Neutrinos produced in the atmosphere will typically traverse the whole Earth as if it wasn\u2019t even there. In order to detect such elusive particles, the KM3neT experiment is building gigantic arrays of photosensors submerged in the deepest regions of the Mediterranean, where few other particles can reach and the clear seawater provides a huge natural target for neutrino interactions. \u00a0<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-content\/uploads\/sites\/27\/2024\/10\/Learning-from-deep-sea-light-with-KM3NeT_compressed_page-0001-1.jpg” _builder_version=”3.22.1″ z_index_tablet=”500″][\/et_pb_image][\/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":" 2022Strategic Projects@Physics & Astronomy +Biology+Earth Sciences\/Geosciences\u00a0#Neutrinos#Machine Learning#Bioluminescence#Graph Neural Networks Project SummaryNeutrinos are fundamental particles that are produced in a multitude of nuclear processes and permeate our universe. Despite their abundance, neutrinos are extremely difficult to observe due to their very weak interaction with matter. Neutrinos produced in the atmosphere will typically traverse the whole Earth… Lire la suite Learning from deep sea light with KM3NeT<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":1601,"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":[55,1,37,26],"tags":[],"class_list":["post-1597","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-55","category-diip","category-physics-astronomy","category-strategic-projects"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1597","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=1597"}],"version-history":[{"count":2,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1597\/revisions"}],"predecessor-version":[{"id":2329,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1597\/revisions\/2329"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/1601"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=1597"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=1597"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=1597"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Project Summary<\/strong><\/h3>\n
In the rare occasions when these neutrinos interact inside or near the KM3NeT detectors, multiple charged particles are created which in turn produce light as they travel through the seawater. By observing the pattern that these light signals leave in the detector, KM3NeT is able to reconstruct basic properties of the neutrino interactions such as energy, momentum, and flavour. Currently, these tasks are performed mostly by hand-crafted algorithms based on fundamental physics knowledge. The goal of this project is to enhance the capabilities of KM3NeT by exploring cutting-edge deep learning techniques to replace traditional reconstruction methods, pushing the boundaries of what is possible and enabling new areas of research with the KM3NeT infrastructure.<\/p>\nJoao Coehlo <\/strong><\/h5>\n
Projects in the same discipline
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