[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 #gravitational wave<\/p>\n #deep learning<\/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 The direct observation of gravitational waves (GW) by the LIGO and Virgo detectors is one of the breakthrough discoveries of the beginning of the 21st century. However, the searches for GW transient sources are mainly limited by non-Gaussian transient noise artefacts coming from a wide variety of provenances. Statistical modelling of these \u201cinstrumental glitches\u201d has not been feasible, so far, because they vary widely in rate, duration, frequency range and morphology. Their contamination can be partially mitigated by requesting temporal coincidence in two or more detectors as their accidental co-occurrence probability is low. When only one detector is operating this strategy cannot be used. The aim of this project is to use deep learning algorithms to separate the glitches from the astrophysical signal, focusing in particular to periods when only one detector is taking data.<\/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#gravitational wave#deep learning\u00a0 Project SummaryThe direct observation of gravitational waves (GW) by the LIGO and Virgo detectors is one of the breakthrough discoveries of the beginning of the 21st century. However, the searches for GW transient sources are mainly limited by non-Gaussian transient noise artefacts coming from a wide variety… Lire la suite Malvasia \u2013 MAchine Learning to VAlue Single Interferometer Analysis<\/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-1589","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\/1589","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=1589"}],"version-history":[{"count":3,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1589\/revisions"}],"predecessor-version":[{"id":2323,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1589\/revisions\/2323"}],"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=1589"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=1589"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=1589"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Project Summary<\/strong><\/h3>\n
\u00a0Agata Trovato <\/strong><\/h5>\n
Projects in the same discipline
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