Using GMOS Spectroscopy to Study Star Formation and AGN in the CANDELS COSMOS and UDS Fields

Abstract

Spectroscopic measurements of galaxies can help us to better understand their properties. Spectroscopy allows for the measurement of spectroscopic redshifts, which have better precision than redshifts derived from photometry. Precise redshifts are needed to eliminate distance un- certainties when deriving distance-dependent properties of galaxies. Additionally, spectroscopy provides insight into the physical state and processes within a galaxy, such as its rate of on- going star formation, its chemical abundance, and the properties of the interstellar medium within the galaxy. Spectroscopy is particularly powerful when coupled with broad, multiwavelength photometric data. The five extragalactic fields from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) have excellent multiwavelength photometric coverage. Two of the fields however, were lacking in spectroscopic coverage. To rectify this, spectroscopic observations were taken for sources in those particular fields in 2014, 2016, and 2019 with the Gemini Multi-Object Spectrometer on the twin Gemini Telescopes. We reduced the spectroscopic observations, which were taken in a configuration not compatible with existing reduction pipelines. We were able to extract spectra for 196 sources and measured their spectroscopic redshifts. We also measured emission fluxes from the spectra. We used the Halpha and [OII]3727 emission lines as star formation rate indicators to measure the star formation rates of our galaxies, and compared these results to star formation rates obtained from other techniques. We also used Hbeta, [OIII]5007, Halpha, and [NII]6583 to construct diagnostics to detect AGN in our sample, and compared those classifications to sources in our sample detected as an AGN with other methods (e.g., mid-infrared colors, X-ray flux). Using the spectroscopic data, we identified seven possible AGN sources not identified with X-ray or infrared data that could be followed up

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