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A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest.

S. Mucesh, W. Hartley, A. Palmese, O. Lahav, L. Whiteway, A. Amon, K. Bechtol, G. Bernstein, A. Rosell, M. Kind, A. Choi, K. Eckert, S. Everett, D. Gruen, R. Gruendl, I. Harrison, E. Huff, N. Kuropatkin, I. Sevilla-Noarbe, E. Sheldon, B. Yanny, M. Aguena, S. Allam, D. Bacon, E. Bertin, S. Bhargava, D. Brooks, J. Carretero, F. Castander, C. Conselice, M. Costanzi, M. Crocce, L. da Costa, M. Pereira, J. DeVicente, S. Desai, H. Diehl, A. Drlica-Wagner, A. Evrard, I. Ferrero, B. Flaugher, P. Fosalba, J. Frieman, J. García-Bellido, E. Gaztañaga, D. Gerdes, J. Gschwend, G. Gutierrez, S. Hinton, D. Hollowood, K. Honscheid, D. James, K. Kuehn, M. Lima, H. Lin, M. Maia, P. Melchior, F. Menanteau, R. Miquel, R. Morgan, F. Paz-Chinchón, A. Plazas, E. Sánchez, V. Scarpine, M. Schubnell, S. Serrano, M. Smith, E. Suchyta, G. Tarlé, D. Thomas, C. To, T. Varga, and R. Wilkinson. CoRR, (2020)

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Fausto Carrasco

Matias Mieth

Marcio Matias

Matias Martinez

Mauricio Carrasco

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More than Meets the Eye: Towards an Artificial Intelligence Observatory.M. Kind. DeLTA, page 5. SCITEPRESS, (2021)AGNet: Weighing Black Holes with Machine Learning.J. Lin, S. Pandya, D. Pratap, X. Liu, and M. Kind. CoRR, (2020)Discovery of a Candidate Binary Supermassive Black Hole in a Periodic Quasar from Circumbinary Accretion VariabilityW. Liao, Y. Chen, X. Liu, A. Holgado, H. Guo, R. Gruendl, E. Morganson, Y. Shen, T. Davis, R. Kessler and 61 other author(s). (2020)cite arxiv:2008.12317Comment: resubmitted to MNRAS after addressing referee reports; comments are welcome.Batch and online anomaly detection for scientific applications in a Kubernetes environment.S. Hariri, and M. Kind. ScienceCloud@HPDC, page 3:1-3:7. ACM, (2018)Constraints on Decaying Dark Matter with DES-Y1 and external dataA. Chen, D. Huterer, S. Lee, A. Ferté, N. Weaverdyck, O. Alves, C. Leonard, N. MacCrann, M. Raveri, A. Porredon and 75 other author(s). (2020)cite arxiv:2011.04606Comment: 23 pages, 11 figures. DES extensions group.Dark Energy Survey Year 1 Results: Constraining Baryonic Physics in the UniverseH. Huang, T. Eifler, R. Mandelbaum, G. Bernstein, A. Chen, A. Choi, J. García-Bellido, D. Huterer, E. Krause, E. Rozo and 67 other author(s). (2020)cite arxiv:2007.15026Comment: 24 pages, 18 figures, 2 tables. submitted to MNRAS. A brief video summary of this paper is available at https://www.youtube.com/watch?v=QbeNwk5papU.flimview : A software framework to handle, visualize and analyze FLIM data.M. Kind, M. Zurauskas, A. Alex, M. Marjanovic, P. Mukherjee, M. Doan, D. Jr., S. Hood, and S. Boppart. F1000Research, (2020)VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learningF. Ostrovski, R. McMahon, A. Connolly, C. Lemon, M. Auger, M. Banerji, J. Hung, S. Koposov, C. Lidman, S. Reed and 49 other author(s). (2016)cite arxiv:1607.01391Comment: 11 pages, 7 figures, 7 tables, MNRAS submitted.Survey2Survey: A deep learning generative model approach for cross-survey image mapping.B. Buncher, A. Sharma, and M. Kind. CoRR, (2020)Extended Isolation Forest.S. Hariri, M. Kind, and R. Brunner. CoRR, (2018)

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Disambiguation of "Kind, Matias Carrasco"

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A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest.

Please choose a person to relate this publication to

Fausto Carrasco

Matias Mieth

Marcio Matias

Matias Martinez

Mauricio Carrasco

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Kind, Matias Carrasco"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest.

Please choose a person to relate this publication to

Fausto Carrasco

Matias Mieth

Marcio Matias

Matias Martinez

Mauricio Carrasco

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest.