Publications by Chris Lintott


AGN photoionization of gas in companion galaxies as a probe of AGN radiation in time and direction

Monthly Notices of the Royal Astronomical Society Oxford University Press 483 (2018) 4847–4865-

VN Bennert, WC Keel, A Pancoast, CE Harris, SD Chojnowaki, A Nierenberg, C Lintott, K Schawinski, G Mitchell, C Cornen

We consider active galactic nucleus (AGN) photoionization of gas in companion galaxies (cross-ionization) as a way to sample the intensity of AGN radiation in both direction and time, independent of the gas properties of the AGN host galaxies. From an initial set of 212 AGN+companion systems, identified with the help of Galaxy Zoo participants, we obtained long-slit optical spectra of 32 pairs that were a priori likely to show cross-ionization based on projected separation or angular extent of the companion. From emission-line ratios, 10 of these systems are candidates for cross-ionization, roughly the fraction expected if most AGNs have ionization cones with 70° opening angles. Among these, Was 49 remains the strongest nearby candidate. NGC 5278/9 and UGC 6081 are dual-AGN systems with tidal debris, complicating identification of cross-ionization. The two weak AGNs in the NGC 5278/9 system ionize gas filaments to a projected radius 14 kpc from each galaxy. In UGC 6081, an irregular high-ionization emission region encompasses both AGNs, extending more than 15 kpc from each. The observed AGN companion galaxies with and without signs of external AGN photoionization have similar distributions in estimated incident AGN flux, suggesting that geometry of escaping radiation or long-term variability controls this facet of the AGN environment. This parallels conclusions for luminous QSOs based on the proximity effect among Lyman α absorbers. In some galaxies, mismatch between spectroscopic classifications in the common BPT diagram and the intensity of weaker He II and [Ne V] emission lines highlights the limits of common classifications in low-metallicity environments.


The fifteenth data release of the Sloan Digital Sky Surveys: First release of MaNGA-derived quantities, data visualization tools, and Stellar Library

Astrophysical Journal Supplement Institute of Physics 240 (2019)

DS Aguado, R Ahumada, A Almeida, M Cappellari, R Davies, C Lintott

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data.


The Frequency of Dust Lanes in Edge-on Spiral Galaxies Identified by Galaxy Zoo in KiDS Imaging of GAMA Targets

ASTRONOMICAL JOURNAL 158 (2019) ARTN 103

BW Holwerda, L Kelvin, I Baldry, C Lintott, M Alpaslan, KA Pimbblet, J Liske, T Kitching, S Bamford, J de Jong, M Bilicki, A Hopkins, J Bridge, R Steele, A Jacques, S Goswami, S Kusmic, W Roemer, S Kruk, CC Popescu, K Kuijken, L Wang, A Wright, T Kitching


The effect of minor and major mergers on the evolution of low-excitation radio galaxies

Astrophysical Journal American Astronomical Society 878 (2019) 88

YA Gordon, KA Pimbblet, S Kaviraj, Owers, CP O'Dea, M Walmsley, JP Crossett, Baum, A Fraser-Mckelvie, C Lintott, JCS Pierce

We use deep, μ r ≲ 28 mag arcsec−2, r-band imaging from the Dark Energy Camera Legacy Survey to search for past, or ongoing, merger activity in a sample of 282 low-excitation radio galaxies (LERGs) at z < 0.07. Our principal aim is to assess the the role of mergers in the evolution of LERGs. Exploiting the imaging depth, we classify tidal remnants around galaxies as both minor and major morphological disturbances for our LERG sample and 1622 control galaxies matched in redshift, stellar mass, and environment. In groups and in the field, the LERG minor merger fraction is consistent with the control population. In galaxy clusters, 8.8 ± 2.9% of LERGs show evidence of recent minor mergers in contrast to 23.0 ± 2.0% of controls. This ~4σ deficit of minor mergers in cluster LERGs suggests these events may inhibit this type of nuclear activity for galaxies within the cluster environment. We observe a >4σ excess of major mergers in the LERGs with M * ≲ 1011 M⊙, with 10 ± 1.5% of these active galactic nuclei involved in such large-scale interactions compared to 3.2 ± 0.4% of control galaxies. This excess of major mergers in LERGs decreases with increasing stellar mass, vanishing by M * > 1011.3 M⊙. These observations show that minor mergers do not fuel LERGs, and are consistent with typical LERGs being powered by accretion of matter from their halo. Where LERGs are associated with major mergers, these objects may evolve into more efficiently accreting active galactic nuclei as the merger progresses and more gas falls on to the central engine.


Galaxy Zoo: unwinding the winding problem – observations of spiral bulge prominence and arm pitch angles suggest local spiral galaxies are winding

Monthly Notices of the Royal Astronomical Society Oxford University Press 487 (2019) 1808–1820-

KL Masters, C Lintott, RE Hart, SJ Kruk, RJ Smethurst, K Casteels, WC Keel, BD Simmons, Stanescu, J Tate, S Tomi

We use classifications provided by citizen scientists though Galaxy Zoo to investigate the correlation between bulge size and arm winding in spiral galaxies. Whilst the traditional spiral sequence is based on a combination of both measures, and is supposed to favour arm winding where disagreement exists, we demonstrate that, in modern usage, the spiral classifications Sa–Sd are predominantly based on bulge size, with no reference to spiral arms. Furthermore, in a volume limited sample of galaxies with both automated and visual measures of bulge prominence and spiral arm tightness, there is at best a weak correlation between the two. Galaxies with small bulges have a wide range of arm winding, while those with larger bulges favour tighter arms. This observation, interpreted as revealing a variable winding speed as a function of bulge size, may be providing evidence that the majority of spiral arms are not static density waves, but rather wind-up over time. This suggests the ‘winding problem’ could be solved by the constant reforming of spiral arms, rather than needing a static density wave. We further observe that galaxies exhibiting strong bars tend to have more loosely wound arms at a given bulge size than unbarred spirals. This observations suggests that the presence of a bar may slow the winding speed of spirals, and may also drive other processes (such as density waves) that generate spiral arms. It is remarkable that after over 170 years of observations of spiral arms in galaxies our understanding of them remains incomplete.


Machine Learning for the Zwicky Transient Facility

PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC 131 (2019) ARTN 038002

A Mahabal, U Rebbapragada, R Walters, FJ Masci, N Blagorodnova, J van Roestel, Q-Z Ye, R Biswas, K Burdge, C-K Chang, DA Duev, VZ Golkhou, AA Miller, J Nordin, C Ward, S Adams, EC Bellm, D Branton, B Bue, C Cannella, A Connolly, R Dekany, U Feindt, T Hung, L Fortson, S Frederick, C Fremling, S Gezari, M Graham, S Groom, MM Kasliwal, S Kulkarni, T Kupfer, HW Lin, C Lintott, R Lunnan, J Parejko, TA Prince, R Riddle, B Rusholme, N Saunders, N Sedaghat, DL Shupe, LP Singer, MT Soumagnac, P Szkody, Y Tachibana, K Tirumala, S van Velzen, D Wright


Everyone counts? Design considerations in online citizen science

Journal of Science Communication 18 (2019)

H Spiers, A Swanson, L Fortson, BD Simmons, L Trouille, S Blickhan, C Lintott

© 2019, Scuola Internazionale Superiore di Studi Avanzati. Effective classification of large datasets is a ubiquitous challenge across multiple knowledge domains. One solution gaining in popularity is to perform distributed data analysis via online citizen science platforms, such as the Zooniverse. The resulting growth in project numbers is increasing the need to improve understanding of the volunteer experience; as the sustainability of citizen science is dependent on our ability to design for engagement and usability. Here, we examine volunteer interaction with 63 projects, representing the most comprehensive collection of online citizen science project data gathered to date. Together, this analysis demonstrates how subtle project design changes can influence many facets of volunteer interaction, including when and how much volunteers interact, and, importantly, who participates. Our findings highlight the tension between designing for social good and broad community engagement, versus optimizing for scientific and analytical efficiency.


Citizen science frontiers: Efficiency, engagement, and serendipitous discovery with human-machine systems.

Proceedings of the National Academy of Sciences of the United States of America 116 (2019) 1902-1909

L Trouille, CJ Lintott, LF Fortson

Citizen science has proved to be a unique and effective tool in helping science and society cope with the ever-growing data rates and volumes that characterize the modern research landscape. It also serves a critical role in engaging the public with research in a direct, authentic fashion and by doing so promotes a better understanding of the processes of science. To take full advantage of the onslaught of data being experienced across the disciplines, it is essential that citizen science platforms leverage the complementary strengths of humans and machines. This Perspectives piece explores the issues encountered in designing human-machine systems optimized for both efficiency and volunteer engagement, while striving to safeguard and encourage opportunities for serendipitous discovery. We discuss case studies from Zooniverse, a large online citizen science platform, and show that combining human and machine classifications can efficiently produce results superior to those of either one alone and how smart task allocation can lead to further efficiencies in the system. While these examples make clear the promise of human-machine integration within an online citizen science system, we then explore in detail how system design choices can inadvertently lower volunteer engagement, create exclusionary practices, and reduce opportunity for serendipitous discovery. Throughout we investigate the tensions that arise when designing a human-machine system serving the dual goals of carrying out research in the most efficient manner possible while empowering a broad community to authentically engage in this research.


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

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

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

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.


Identification of low surface brightness tidal features in galaxies using convolutional neural networks

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 483 (2019) 2968-2982

M Walmsley, AMN Ferguson, RG Mann, CJ Lintott


LSST: From science drivers to reference design and anticipated data products

Astrophysical Journal American Astronomical Society 873 (2019) 111

SP Newbry, J-Y Nief, A Nomerotski, M Nordby, P O'Connor, J Oliver, SS Olivier, K Olsen, W O'Mullane, S Ortiz, S Osier, RE Owen, R Pain, PE Palecek, JK Parejko, JB Parsons, NM Pease, JM Peterson, DL Petravick, MEL Petrick, F Pierfederici, CE Petry, S Pietrowicz, PA Pinto

We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, exploring the transient optical sky, and mapping the Milky Way. LSST will be a large, wide-field ground-based system designed to obtain repeated images covering the sky visible from Cerro Pachón in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg2 field of view, a 3.2-gigapixel camera, and six filters (ugrizy) covering the wavelength range 320–1050 nm. The project is in the construction phase and will begin regular survey operations by 2022. About 90% of the observing time will be devoted to a deep-wide-fast survey mode that will uniformly observe a 18,000 deg2 region about 800 times (summed over all six bands) during the anticipated 10 yr of operations and will yield a co-added map to r ~ 27.5. These data will result in databases including about 32 trillion observations of 20 billion galaxies and a similar number of stars, and they will serve the majority of the primary science programs. The remaining 10% of the observing time will be allocated to special projects such as Very Deep and Very Fast time domain surveys, whose details are currently under discussion. We illustrate how the LSST science drivers led to these choices of system parameters, and we describe the expected data products and their characteristics.


Getting Connected: An Empirical Investigation of the Relationship Between Social Capital and Philanthropy Among Online Volunteers

NONPROFIT AND VOLUNTARY SECTOR QUARTERLY 48 (2019) 151S-173S

J Cox, EY Oh, B Simmons, G Graham, A Greenhill, C Lintott, K Masters, R Meriton


K2-288Bb: A Small Temperate Planet in a Low-mass Binary System Discovered by Citizen Scientists

ASTRONOMICAL JOURNAL 157 (2019) ARTN 40

AD Feinstein, JE Schlieder, JH Livingston, DR Ciardi, AW Howard, L Arnold, G Barentsen, M Bristow, JL Christiansen, IJM Crossfield, CD Dressing, EJ Gonzales, M Kosiarek, CJ Lintott, G Miller, FY Morales, EA Petigura, B Thackeray, J Ault, E Baeten, AF Jonkeren, J Langley, H Moshinaly, K Pearson, C Tanner, J Treasure


Integrating human and machine intelligence in galaxy morphology classification tasks

Monthly Notices of the Royal Astronomical Society Blackwell Publishing Inc. (2018)

MR Beck, C Scarlata, LF Fortson, CJ Lintott, BD Simmons, MA Galloway, KW Willett, H Dickinson, KL Masters, PJ Marshall, D Wright

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold, classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of nonparametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine, and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computation cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large scale surveys.


Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project

Scientific Data Springer Nature 5 (2018) 180124

G Miller, R Freeman, G Hines, CJ Lintott, R Simpson, C Southwell, A Zisserman, T Hart, C Allen, C Arteta, C Black, LM Emmerson, FM Jones

Automated time-lapse cameras can facilitate reliable and consistent monitoring of wild animal populations. In this report, data from 73,802 images taken by 15 different Penguin Watch cameras are presented, capturing the dynamics of penguin (Spheniscidae; Pygoscelis spp.) breeding colonies across the Antarctic Peninsula, South Shetland Islands and South Georgia (03/2012 to 01/2014). Citizen science provides a means by which large and otherwise intractable photographic data sets can be processed, and here we describe the methodology associated with the Zooniverse project Penguin Watch, and provide validation of the method. We present anonymised volunteer classifications for the 73,802 images, alongside the associated metadata (including date/time and temperature information). In addition to the benefits for ecological monitoring, such as easy detection of animal attendance patterns, this type of annotated time-lapse imagery can be employed as a training tool for machine learning algorithms to automate data extraction, and we encourage the use of this data set for computer vision development.


Doing Good Online: The Changing Relationships Between Motivations, Activity, and Retention Among Online Volunteers

NONPROFIT AND VOLUNTARY SECTOR QUARTERLY 47 (2018) 1031-1056

J Cox, EY Oh, B Simmons, G Graham, A Greenhill, C Lintott, K Masters, J Woodcock


Normal black holes in bulge-less galaxies: the largely quiescent, merger-free growth of black holes over cosmic time

Monthly Notices of the Royal Astronomical Society Oxford University Press 476 (2018) 2801–2812-

G Martin, S Kaviraj, M Volonteri, JEG Devriendt, BD Simmons, C Lintott, RJ Smethurst, Y Dubois, C Pichon

Understanding the processes that drive the formation of black holes (BHs) is a key topic in observational cosmology. While the observed $M_{\mathrm{BH}}$--$M_{\mathrm{Bulge}}$ correlation in bulge-dominated galaxies is thought to be produced by major mergers, the existence of a $M_{\mathrm{BH}}$--$M_{\star}$ relation, across all galaxy morphological types, suggests that BHs may be largely built by secular processes. Recent evidence that bulge-less galaxies, which are unlikely to have had significant mergers, are offset from the $M_{\mathrm{BH}}$--$M_{\mathrm{Bulge}}$ relation, but lie on the $M_{\mathrm{BH}}$--$M_{\star}$ relation, has strengthened this hypothesis. Nevertheless, the small size and heterogeneity of current datasets, coupled with the difficulty in measuring precise BH masses, makes it challenging to address this issue using empirical studies alone. Here, we use Horizon-AGN, a cosmological hydrodynamical simulation to probe the role of mergers in BH growth over cosmic time. We show that (1) as suggested by observations, simulated bulge-less galaxies lie offset from the main $M_{\mathrm{BH}}$--$M_{\mathrm{Bulge}}$ relation, but on the $M_{\mathrm{BH}}$--$M_{\star}$ relation, (2) the positions of galaxies on the $M_{\mathrm{BH}}$--$M_{\star}$ relation are not affected by their merger histories and (3) only $\sim$35 per cent of the BH mass in today's massive galaxies is directly attributable to merging -- the majority ($\sim$65 per cent) of BH growth, therefore, takes place gradually, via secular processes, over cosmic time.


Galaxy Zoo: secular evolution of barred galaxies from structural decomposition of multiband images

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 473 (2018) 4731-4753

SJ Kruk, CJ Lintott, SP Bamford, KL Masters, BD Simmons, B Haussler, CN Cardamone, RE Hart, L Kelvin, K Schawinski, RJ Smethurst, M Vika


The K2-138 System: A Near-resonant Chain of Five Sub-Neptune Planets Discovered by Citizen Scientists

ASTRONOMICAL JOURNAL 155 (2018) ARTN 57

JL Christiansen, IJM Crossfield, G Barentsen, CJ Lintott, T Barclay, BD Simmons, E Petigura, JE Schlieder, CD Dressing, A Vanderburg, C Allen, A McMaster, G Miller, M Veldthuis, S Allen, Z Wolfenbarger, B Cox, J Zemiro, AW Howard, J Livingston, E Sinukoff, T Catron, A Grey, JJE Kusch, I Terentev, M Vales, MH Kristiansen


Galaxy Zoo: Morphological Classification of Galaxy Images from the Illustris Simulation

ASTROPHYSICAL JOURNAL 853 (2018) ARTN 194

H Dickinson, L Fortson, C Lintott, C Scarlata, K Willett, S Bamford, M Beck, C Cardamone, M Galloway, B Simmons, W Keel, S Kruk, K Masters, M Vogelsberger, P Torrey, GF Snyder

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