Browsing by Author "Croom, Scott M."

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    Galaxy and Mass Assembly (GAMA): active galactic nuclei in pairs of galaxies
    (OXFORD UNIV PRESS, 2017) Gordon, Yjan A.; Owers, Matt S.; Pimbblet, Kevin A.; Croom, Scott M.; Alpaslan, Mehmet; Baldry, Ivan K.; Brough, Sarah; Brown, Michael J. I.; Cluver, Michelle E.; Conselice, Christopher J.; Davies, Luke J. M.; Holwerda, Benne W.; Hopkins, Andrew M.; Gunawardhana, Madusha L. P.; Loveday, Jonathan; Taylor, Edward N.; Wang, Lingyu
    There exist conflicting observations on whether or not the environment of broad-and narrowline active galatic nuclei (AGN) differ and this consequently questions the validity of the AGN unification model. The high spectroscopic completeness of the Galaxy and Mass Assembly (GAMA) survey makes it ideal for a comprehensive analysis of the close environment of galaxies. To exploit this, and conduct a comparative analysis of the environment of broad-and narrow-line AGN within GAMA, we use a double-Gaussian emission line fitting method to model the more complex line profiles associated with broad-line AGN. We select 209 type 1 (i.e. unobscured), 464 type 1.5-1.9 (partially obscured), and 281 type 2 (obscured) AGN within the GAMA II data base. Comparing the fractions of these with neighbouring galaxies out to a pair separation of 350 kpc h(-1) and triangle z < 0.012 shows no difference between AGN of different type, except at separations less than 20 kpc h(-1) where our observations suggest an excess of type 2 AGN in close pairs. We analyse the properties of the galaxies neighbouring our AGN and find no significant differences in colour or the star formation activity of these galaxies. Further to this, we find that Sigma(5) is also consistent between broad-and narrow-line AGN. We conclude that the observations presented here are consistent with AGN unification.
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    Galaxy Spectra neural Network (GaSNet). II. Using deep learning for spectral classification and redshift predictions
    (2024) Zhong, Fucheng; Napolitano, Nicola R.; Heneka, Caroline; Li, Rui; Bauer, Franz Erik; Bouche, Nicolas; Comparat, Johan; Kim, Young-Lo; Krogager, Jens-Kristian; Longhetti, Marcella; Loveday, Jonathan; Roukema, Boudewijn F.; Rouse, Benedict L.; Salvato, Mara; Tortora, Crescenzo; Assef, Roberto J.; Cassara, Letizia P.; Costantin, Luca; Croom, Scott M.; Davies, Luke J. M.; Fritz, Alexander; Guiglion, Guillaume; Humphrey, Andrew; Pompei, Emanuela; Ricci, Claudio; Sifon, Cristobal; Tempel, Elmo; Zafar, Tayyaba
    The size and complexity reached by the large sky spectroscopic surveys require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multinetwork deep learning tool for spectra classification and redshift prediction. GaSNet-II can be trained to identify a customized number of classes and optimize the redshift predictions. Redshift errors are determined via an ensemble/pseudo-Monte Carlo test obtained by randomizing the weights of the network-of-networks structure. As a demonstration of the capability of GaSNet-II, we use 260k Sloan Digital Sky Survey spectra from Data Release 16, separated into 13 classes including 140k galactic, and 120k extragalactic objects. GaSNet-II achieves 92.4 per cent average classification accuracy over the 13 classes and mean redshift errors of approximately 0.23 per cent for galaxies and 2.1 per cent for quasars. We further train/test the pipeline on a sample of 200k 4MOST (4-metre Multi-Object Spectroscopic Telescope) mock spectra and 21k publicly released DESI (Dark Energy Spectroscopic Instrument) spectra. On 4MOST mock data, we reach 93.4 per cent accuracy in 10-class classification and mean redshift error of 0.55 per cent for galaxies and 0.3 per cent for active galactic nuclei. On DESI data, we reach 96 per cent accuracy in (star/galaxy/quasar only) classification and mean redshift error of 2.8 per cent for galaxies and 4.8 per cent for quasars, despite the small sample size available. GaSNet-II can process similar to 40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses and feedback loops for optimization of Stage-IV survey observations.