{"id":21455,"date":"2020-10-11T14:45:49","date_gmt":"2020-10-11T14:45:49","guid":{"rendered":"https:\/\/www.xatakaciencia.com\/astronomia\/ocupa-300-gb-catalogo-galaxias-3d-que-abarca-tres-cuartas-partes-cielo"},"modified":"2020-10-11T14:45:49","modified_gmt":"2020-10-11T14:45:49","slug":"ocupa-300-gb-y-es-un-catalogo-de-galaxias-3d-que-abarca-tres-cuartas-partes-del-cielo","status":"publish","type":"post","link":"http:\/\/forocilac.org\/en\/ocupa-300-gb-y-es-un-catalogo-de-galaxias-3d-que-abarca-tres-cuartas-partes-del-cielo\/","title":{"rendered":"It occupies 300 GB and is a 3D galaxy catalog that covers three quarters of the sky"},"content":{"rendered":"<p>\n      <img decoding=\"async\" src=\"https:\/\/i.blogs.es\/fd5b3f\/manoa-ifa-pan-starr1-survey-1\/1024_2000.jpg\" alt=\"Ocupa 300 GB y es un cat\u00e1logo de galaxias 3D que abarca tres cuartas partes del cielo\">\n    <\/p>\n<p>He <strong>largest catalog of 3D astronomical images of stars, galaxies and quasars<\/strong> has been created by a group of astronomers from the University of Hawaii at the Manoa Institute for Astronomy (IfA).<\/p>\n<p><!-- BREAK 1 --><\/p>\n<p>The team used data from the Panoramic Telescope and the UH Rapid Response System or <a href=\"https:\/\/archive.stsci.edu\/hlsp\/ps1-strm\">Pan-STARRS1<\/a> (PS1) to decipher which of the 3 billion objects are stars, galaxies or quasars using new computational tools. <\/p>\n<p><!-- BREAK 2 --><!--more--><\/p>\n<h2>New computational tools<\/h2>\n<p>Previously, the largest map of the universe was created by <a href=\"https:\/\/www.hawaii.edu\/news\/2020\/10\/08\/largest-3d-catalog-of-galaxies\/\">Sloan Digital Sky Survey<\/a> (SDSS), which covers only a third of the sky. To achieve this, astronomers took publicly available spectroscopic measurements that provide definitive object classifications and distances, and <strong>They sent them to an artificial intelligence algorithm<\/strong>.<\/p>\n<p><!-- BREAK 3 --><\/p>\n<div class=\"article-asset-image article-asset-normal\">\n<div class=\"asset-content\">\n<p>  <img decoding=\"async\" alt=\"Overdensity Web\" class=\"centro_sinmarco\" src=\"https:\/\/i.blogs.es\/e816da\/overdensity_web\/450_1000.png\"><\/p><\/div>\n<\/div>\n<p>This artificial intelligence or machine learning approach with a \u201cfeedback neural network\u201d achieved an overall classification accuracy of 98.1% for galaxies, 97.8% for stars, and 96.6% for quasars. The galaxy&#039;s distance estimates are accurate to nearly 3%.<\/p>\n<p><!-- BREAK 4 --><\/p>\n<p>It is approximately 300 GB in size and scientific users can query the catalog via the MAST CasJobs SQL interface or download the entire collection as a readable table. According to the main author of the study, <strong>Robert Beck<\/strong>, former cosmology postdoctoral fellow at IfA:<\/p>\n<p><!-- BREAK 5 --><\/p>\n<blockquote>\n<p>Using a state-of-the-art optimization algorithm, we leverage the spectroscopic training set of nearly 4 million light sources to teach the neural network to predict source types and galaxy distances, while at the same time correcting for extinction of light by dust in the Milky Way.<\/p>\n<\/blockquote>\n<p><script>\n (function() {\n  window._JS_MODULES = window._JS_MODULES || {};\n  var headElement = document.getElementsByTagName('head')[0];\n  if (_JS_MODULES.instagram) {\n   var instagramScript = document.createElement('script');\n   instagramScript.src = 'https:\/\/platform.instagram.com\/en_US\/embeds.js';\n   instagramScript.async = true;\n   instagramScript.defer = true;\n   headElement.appendChild(instagramScript);\n  }\n })();\n<\/script><\/p>\n<p> &#8211; <br \/> The news<br \/>\n      <a href=\"https:\/\/www.xatakaciencia.com\/astronomia\/ocupa-300-gb-catalogo-galaxias-3d-que-abarca-tres-cuartas-partes-cielo?utm_source=feedburner&#038;utm_medium=feed&#038;utm_campaign=11_Oct_2020\"><br \/>\n       <em> It occupies 300 GB and is a 3D galaxy catalog that covers three quarters of the sky <\/em><br \/>\n      <\/a><br \/>\n      was originally published in<br \/>\n      <a href=\"https:\/\/www.xatakaciencia.com\/?utm_source=feedburner&#038;utm_medium=feed&#038;utm_campaign=11_Oct_2020\"><br \/>\n       <strong> Xataka Science <\/strong><br \/>\n      <\/a><br \/>\n            by <a\n       href=\"https:\/\/www.xatakaciencia.com\/autor\/sergio-parra?utm_source=feedburner&#038;utm_medium=feed&#038;utm_campaign=11_Oct_2020\"><br \/>\n       Sergio Parra<br \/>\n      <\/a><br \/>\n      . <\/p>\n<p><img decoding=\"async\" src=\"http:\/\/feeds.feedburner.com\/~r\/xatakaciencia\/~4\/wvnVREjhlWo\" height=\"1\" width=\"1\" alt=\"\"\/><\/p>","protected":false},"excerpt":{"rendered":"<p>\n      <img decoding=\"async\" src=\"https:\/\/i.blogs.es\/fd5b3f\/manoa-ifa-pan-starr1-survey-1\/1024_2000.jpg\" alt=\"Ocupa 300 GB y es un cat\u00e1logo de galaxias 3D que abarca tres cuartas partes del cielo\"><\/p>\n<p>He <strong>largest catalog of 3D astronomical images of stars, galaxies and quasars<\/strong> has been created by a group of astronomers from the University of Hawaii at the Manoa Institute for Astronomy (IfA).<\/p>\n<p><!-- BREAK 1 --><\/p>\n<p>The team used data from the Panoramic Telescope and the UH Rapid Response System or <a href=\"https:\/\/archive.stsci.edu\/hlsp\/ps1-strm\">Pan-STARRS1<\/a> (PS1) to decipher which of the 3 billion objects are stars, galaxies or quasars using new computational tools. <\/p>\n<p><!-- BREAK 2 --><!--more--><\/p>\n<h2>New computational tools<\/h2>\n<p>Previously, the largest map of the universe was created by <a href=\"https:\/\/www.hawaii.edu\/news\/2020\/10\/08\/largest-3d-catalog-of-galaxies\/\">Sloan Digital Sky Survey<\/a> (SDSS), which covers only a third of the sky. To achieve this, astronomers took publicly available spectroscopic measurements that provide definitive object classifications and distances, and <strong>They sent them to an artificial intelligence algorithm<\/strong>.<\/p>\n<p><!-- BREAK 3 --><\/p>\n<div class=\"article-asset-image article-asset-normal\">\n<div class=\"asset-content\">\n<p>  <img decoding=\"async\" alt=\"Overdensity Web\" class=\"centro_sinmarco\" src=\"https:\/\/i.blogs.es\/e816da\/overdensity_web\/450_1000.png\">\n<\/div>\n<\/div>\n<p>This artificial intelligence or machine learning approach with a \u201cfeedback neural network\u201d achieved an overall classification accuracy of 98.1% for galaxies, 97.8% for stars, and 96.6% for quasars. The galaxy&#039;s distance estimates are accurate to nearly 3%.<\/p>\n<p><!-- BREAK 4 --><\/p>\n<p>It is approximately 300 GB in size and scientific users can query the catalog via the MAST CasJobs SQL interface or download the entire collection as a readable table. According to the main author of the study, <strong>Robert Beck<\/strong>, former cosmology postdoctoral fellow at IfA:<\/p>\n<p><!-- BREAK 5 --><\/p>\n<blockquote>\n<p>Using a state-of-the-art optimization algorithm, we leverage the spectroscopic training set of nearly 4 million light sources to teach the neural network to predict source types and galaxy distances, while at the same time correcting for extinction of light by dust in the Milky Way.<\/p>\n<\/blockquote>\n<p> &#8211; <br \/> The news<br \/>\n      <a href=\"https:\/\/www.xatakaciencia.com\/astronomia\/ocupa-300-gb-catalogo-galaxias-3d-que-abarca-tres-cuartas-partes-cielo?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=11_Oct_2020\"><br \/>\n       <em> It occupies 300 GB and is a 3D galaxy catalog that covers three quarters of the sky <\/em><br \/>\n      <\/a><br \/>\n      was originally published in<br \/>\n      <a href=\"https:\/\/www.xatakaciencia.com\/?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=11_Oct_2020\"><br \/>\n       <strong> Xataka Science <\/strong><br \/>\n      <\/a><br \/>\n            by <a href=\"https:\/\/www.xatakaciencia.com\/autor\/sergio-parra?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=11_Oct_2020\"><br \/>\n       Sergio Parra<br \/>\n      <\/a><br \/>\n      . <\/p>\n<p><img decoding=\"async\" src=\"http:\/\/feeds.feedburner.com\/~r\/xatakaciencia\/~4\/wvnVREjhlWo\" height=\"1\" width=\"1\" alt=\"\"><\/p>","protected":false},"author":19,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[125],"tags":[],"class_list":{"0":"post-21455","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-portal-3"},"aioseo_notices":[],"_links":{"self":[{"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/posts\/21455","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/comments?post=21455"}],"version-history":[{"count":4,"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/posts\/21455\/revisions"}],"predecessor-version":[{"id":21680,"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/posts\/21455\/revisions\/21680"}],"wp:attachment":[{"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/media?parent=21455"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/categories?post=21455"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/forocilac.org\/en\/wp-json\/wp\/v2\/tags?post=21455"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}