1 research outputs found

    ELDAR, a new method to identify AGN in multi-filter surveys: the ALHAMBRA test case

    No full text
    We present ELDAR, a new method that exploits the potential of medium- and narrow-band filter surveys to securely identify active galactic nuclei (AGN) and determine their redshifts. Our methodology improves on traditional approaches by looking for AGN emission lines expected to be identified against the continuum, thanks to the width of the filters. To assess its performance, we apply ELDAR to the data of the ALHAMBRA (Advance Large Homogeneous Area Medium Band Redshift Astronomical) survey, which covered an effective area of 2.38 deg2 with 20 contiguous medium-band optical filters down to F814W ≃ 24.5. Using two different configurations of  ELDAR in which we require the detection of at least two and three emission lines, respectively, we extract two catalogues of type-I AGN. The first is composed of 585 sources (79  per cent of them spectroscopically unknown) down to  F814W = 22.5 at zphot > 1, which corresponds to a surface density of 209 deg−2. In the second, the 494 selected sources (83  per cent of them spectroscopically unknown) reach F814W = 23 at zphot > 1.5, for a corresponding number density of 176 deg−2. Then, using samples of spectroscopically known AGN in the ALHAMBRA fields, for the two catalogues we estimate a completeness of 73  per cent and 67  per cent, and a redshift precision of 1.01  per cent and 0.86  per cent (with outliers fractions of 8.1  per cent and 5.8  per cent). At z > 2, where our selection performs best, we reach 85  per cent and 77  per cent completeness and we find no contamination from galaxies.We acknowledge support from FITE (Fondos de Inversiones de Teruel), Grupos de Aragon E96 and E103, and the Spanish Ministry of Economy and Competitiveness (MINECO) through projects AYA2016-76682C3-1-P, AYA2015-66211-C2-1, AYA2015-66211-C2-2, AYA201342227-P and AYA2012-30789. This work was supported by FCT (ref. UID/FIS/04434/2013) through national funds and by FEDER through COMPETE2020 (ref. POCI-01-0145-FEDER-007672). JC acknowledges support from the Fundacion Bancaria Ibercaja for developing this research. BA has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 656354. MP acknowledges financial supports from the Ethiopian Space Science and Technology Institute (ESSTI) under the Ethiopian Ministry of Science and Technology (MoST). IM acknowledges support from an FCT postdoctoral grant (ref. SFRH/BPD/95578/2013).Peer Reviewe
    corecore