Publications by Becky Smethurst

The 16th data release of the Sloan Digital Sky Surveys: first release from the APOGEE-2 Southern Survey and full release of eBOSS spectra

Astrophysical Journal Supplement American Astronomical Society 249 (2020) 3

R Ahumada, C Allende Prieto, A Almeida, M Bureau, M Cappellari, R Davies, E-M Mueller, R Smethurst, SDSS-IVC SDSS-IV Collaboration

This paper documents the 16th data release (DR16) from the Sloan Digital Sky Surveys (SDSS), the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the Southern Hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey and new data from the SPectroscopic IDentification of ERosita Survey programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library "MaStar"). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17).

Galaxy zoo: Probabilistic morphology through Bayesian CNNs and active learning

Monthly Notices of the Royal Astronomical Society Oxford University Press 491 (2019) 1554-1574

M Walmsley, L Smith, C Lintott, Y Gal, S Bamford, H Dickinson, L Fortson, S Kruk, K Masters, C Scarlata, B Simmons, R Smethurst, D Wright

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 11.8 per cent within a vote fraction deviation of 0.2) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35–60 per cent fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy zoo will be able to classify surveys of any conceivable scale on a time-scale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.

Secularly powered outflows from AGN: the dominance of non-merger driven supermassive black hole growth

Monthly notices of the Royal Astronomical Society Oxford University Press 489 (2019) 4014-4031

RJ Smethurst, BD Simmons, C Lintott, J Shanahan

Recent observations and simulations have revealed the dominance of secular processes over mergers in driving the growth of both supermassive black holes (SMBH) and galaxy evolution. Here we obtain narrowband imaging of AGN powered outflows in a sample of 12 galaxies with disk-dominated morphologies, whose history is assumed to be merger-free. We detect outflows in 10/12 sources in narrow band imaging of the [OIII] 5007 A˚ emission using filters on the Shane-3m telescope. We calculate a mean outflow rate for these AGN of 0.95±0.14 M⊙ yr−1⁠. This exceeds the mean accretion rate of their SMBHs (⁠0.054±0.039 M⊙ yr−1⁠) by a factor of ∼18. Assuming that the galaxy must provide at least enough material to power both the AGN and the outflow, this gives a lower limit on the average inflow rate of ∼1.01±0.14 M⊙ yr−1⁠, a rate which simulations show can be achieved by bars, spiral arms and cold accretion. We compare our disk dominated sample to a sample of nearby AGN with merger dominated histories and show that the black hole accretion rates in our sample are 5 times higher (4.2σ) and the outflow rates are 5 times lower ( 2.6σ). We suggest that this could be a result of the geometry of the smooth, planar inflow in a secular dominated system, which is both spinning up the black hole to increase accretion efficiency and less affected by feedback from the outflow, than in a merger-driven system with chaotic quasi-spherical inflows. This work provides further evidence that secular processes are sufficient to fuel SMBH growth.

SNITCH: seeking a simple, informative star formation history inference tool

Monthly Notices of the Royal Astronomical Society Oxford University Press 484 (2019) 3590-3603

RJ Smethurst, M Merrifield, C Lintott, KL Masters, BD Simmons, A Fraser-Mckelvie, T Peterken, M Boquien, RA Riffel, N Drory

Deriving a simple, analytic galaxy star formation history (SFH) using observational data is a complex task without the proper tool to hand. We therefore present SNITCH, an open source code written in PYTHON, developed to quickly (2 min) infer the parameters describing an analytic SFH model from the emission and absorption features of a galaxy spectrum dominated by star formation gas ionization. SNITCH uses the Flexible Stellar Population Synthesis models of Conroy, Gunn & White (2009), the MaNGA Data Analysis Pipeline and a Markov Chain Monte Carlo method in order to infer three parameters (time of quenching, rate of quenching, and model metallicity) which best describe an exponentially declining quenching history. This code was written for use on the MaNGA spectral data cubes but is customizable by a user so that it can be used for any scenario where a galaxy spectrum has been obtained, and adapted to infer a user defined analytic SFH model for specific science cases. Herein, we outline the rigorous testing applied to SNITCH and show that it is both accurate and precise at deriving the SFH of a galaxy spectra. The tests suggest that SNITCHis sensitive to the most recent epoch of star formation but can also trace the quenching of star formation even if the true decline does not occur at an exponential rate. With the use of both an analytical SFH and only five spectral features, we advocate that this code be used as a comparative tool across a large population of spectra, either for integral field unit data cubes or across a population of galaxy spectra.

Supermassive black holes in disk-dominated galaxies outgrow their bulges and co-evolve with their host galaxies

Monthly Notices of the Royal Astronomical Society Oxford University Press 470 (2017) 1559-1569

BD Simmons, RJ Smethurst, C Lintott

The deep connection between galaxies and their supermassive black holes is central to modern astrophysics and cosmology. The observed correlation between galaxy and black hole mass is usually attributed to the contribution of major mergers to both. We make use of a sample of galaxies whose disk-dominated morphologies indicate a major-merger-free history and show that such systems are capable of growing supermassive black holes at rates similar to quasars. Comparing black hole masses to conservative upper limits on bulge masses, we show that the black holes in the sample are typically larger than expected if processes creating bulges are also the primary driver of black hole growth. The same relation between black hole and total stellar mass of the galaxy is found for the merger-free sample as for a sample which has experienced substantial mergers, indicating that major mergers do not play a significant role in controlling the coevolution of galaxies and black holes. We suggest that more fundamental processes which contribute to galaxy assembly are also responsible for black hole growth.