{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T13:11:07Z","timestamp":1649164267278},"reference-count":8,"publisher":"Cambridge University Press (CUP)","issue":"S325","license":[{"start":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T00:00:00Z","timestamp":1496102400000},"content-version":"unspecified","delay-in-days":241,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. IAU"],"published-print":{"date-parts":[[2016,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The need for accurate photometric redshifts estimation is a topic that has fundamental importance in Astronomy, due to the necessity of efficiently obtaining redshift information without the need of spectroscopic analysis. We propose a method for determining accurate multi-modal photo-z probability density functions (PDFs) using Mixture Density Networks (MDN) and Deep Convolutional Networks (DCN). A comparison with a Random Forest (RF) is performed.<\/jats:p>","DOI":"10.1017\/s1743921316013090","type":"journal-article","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T11:04:51Z","timestamp":1496142291000},"page":"209-212","source":"Crossref","is-referenced-by-count":0,"title":["Uncertain Photometric Redshifts with Deep Learning Methods"],"prefix":"10.1017","volume":"12","author":[{"given":"A.","family":"D\u2019Isanto","sequence":"first","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2017,5,30]]},"reference":[{"key":"S1743921316013090_ref003","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"S1743921316013090_ref006","unstructured":"Richards G. T. , Hall P. B. , & Schneider D. P. , et al., VizieR Online Data Catalog: The SDSS-DR7 quasar catalog (Schneider+, 2010). VizieR Online Data Catalog, 7260, May 2010"},{"key":"S1743921316013090_ref007","first-page":"A114","article-title":"Hierarchical progressive surveys. Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes","volume":"578","author":"Fernique","year":"2015","journal-title":"AandA"},{"key":"S1743921316013090_ref002","doi-asserted-by":"crossref","unstructured":"LeCun Y. , Bottou L. , bengio Y. , & Haffner P. , Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278\u20132324, November 1998","DOI":"10.1109\/5.726791"},{"key":"S1743921316013090_ref008","doi-asserted-by":"publisher","DOI":"10.1086\/301004"},{"key":"S1743921316013090_ref001","unstructured":"Christopher M. Bishop. Mixture density networks. Technical report, 1994."},{"key":"S1743921316013090_ref005","unstructured":"Polsterer K. L. , D\u2019Isanto A. , & Gieseke F. Uncertain photometric redshifts. 2016"},{"key":"S1743921316013090_ref004","doi-asserted-by":"publisher","DOI":"10.1175\/MWR2904.1"}],"container-title":["Proceedings of the International Astronomical Union"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S1743921316013090","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T23:37:10Z","timestamp":1555630630000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S1743921316013090\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,10]]},"references-count":8,"journal-issue":{"issue":"S325","published-print":{"date-parts":[[2016,10]]}},"alternative-id":["S1743921316013090"],"URL":"https:\/\/doi.org\/10.1017\/s1743921316013090","relation":{},"ISSN":["1743-9213","1743-9221"],"issn-type":[{"value":"1743-9213","type":"print"},{"value":"1743-9221","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,10]]}}}