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  1. OA@INAF
  2. PRODOTTI RICERCA INAF
  3. 1 CONTRIBUTI IN RIVISTE (Journal articles)
  4. 1.01 Articoli in rivista
Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12386/31346
Title: Identification of Single Spectral Lines through Supervised Machine Learning in a Large HST Survey (WISP): A Pilot Study for Euclid and WFIRST
Authors: BARONCHELLI, IVANO 
Scarlata, C. M.
Rodighiero, G.
Rodríguez-Muñoz, L.
BONATO, MATTEO 
Bagley, M.
Henry, A.
Rafelski, M.
Malkan, M.
Colbert, J.
Dai, Y. S.
Dickinson, H.
MANCINI, CHIARA 
Mehta, V.
Morselli, L.
Teplitz, H. I.
Issue Date: 2020
Journal: THE ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 
Number: 249
Issue: 1
Abstract: Future surveys focusing on understanding the nature of dark energy (e.g., Euclid and WFIRST) will cover large fractions of the extragalactic sky in near-IR slitless spectroscopy. These surveys will detect a large number of galaxies that will have only one emission line in the covered spectral range. In order to maximize the scientific return of these missions, it is imperative that single emission lines are correctly identified. Using a supervised machine-learning approach, we classified a sample of single emission lines extracted from the WFC3 IR Spectroscopic Parallel survey, one of the closest existing analogs to future slitless surveys. Our automatic software integrates a spectral energy distribution (SED)-fitting strategy with additional independent sources of information. We calibrated it and tested it on a "gold" sample of securely identified objects with multiple lines detected. The algorithm correctly classifies real emission lines with an accuracy of 82.6%, whereas the accuracy of the SED-fitting technique alone is low (∼50%) due to the limited amount of photometric data available (≤6 bands). While not specifically designed for the Euclid and WFIRST surveys, the algorithm represents an important precursor of similar algorithms to be used in these future missions.
URI: http://hdl.handle.net/20.500.12386/31346
URL: https://iopscience.iop.org/article/10.3847/1538-4365/ab9a3a
https://api.elsevier.com/content/abstract/scopus_id/85088692784
ISSN: 0067-0049
DOI: 10.3847/1538-4365/ab9a3a
Bibcode ADS: 2020ApJS..249...12B
Fulltext: open
Appears in Collections:1.01 Articoli in rivista

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2006.12613_pt1.pdfpreprint8.37 MBAdobe PDFView/Open
2006.12613_pt.2.pdfpreprint5.81 MBAdobe PDFView/Open
Baronchelli_2020_ApJS_249_12.pdf[Administrators only]5.59 MBAdobe PDF
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