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 483 (2019) 4847-4865

WC Keel, VN Bennert, A Pancoast, CE Harris, A Nierenberg, SD Chojnowski, AV Moiseev, DV Oparin, CJ Lintott, K Schawinski, G Mitchell, C Cornen

© 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. 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


DS Aguado, R Ahumada, A Almeida, SF Anderson, BH Andrews, B Anguiano, E Aquino Ortiz, A Aragon-Salamanca, M Argudo-Fernandez, M Aubert, V Avila-Reese, C Badenes, SB Rembold, K Barger, J Barrera-Ballesteros, D Bates, J Bautista, RL Beaton, TC Beers, F Belfiore, M Bernardi, M Bershady, F Beutler, J Bird, D Bizyaev, GA Blanc, MR Blanton, M Blomqvist, AS Bolton, M Boquien, J Borissova, J Bovy, WN Brandt, J Brinkmann, JR Brownstein, K Bundy, A Burgasser, N Byler, MC Diaz, M Cappellari, R Carrera, B Cervantes Sodi, Y Chen, B Cherinka, PD Choi, H Chung, D Coffey, JM Comerford, J Comparat, K Covey, GDS Ilha, L da Costa, YS Dai, G Damke, J Darling, R Davies, K Dawson, V de Sainte Agathe, AD Machado, A Del Moro, N De Lee, AM Diamond-Stanic, HD Sanchez, J Donor, N Drory, HDM des Bourboux, C Duckworth, T Dwelly, G Ebelke, E Emsellem, S Escoffier, JG Fernandez-Trincado, D Feuillet, J-L Fischer, SW Fleming, A Fraser-McKelvie, G Freischlad, PM Frinchaboy, H Fu, L Galbany, R Garcia-Dias, DA Garcia-Hernandez, LA Garma Oehmichen, MA Geimba Maia, H Gil-Marin, K Grabowski, M Gu, H Guo, J Ha, E Harrington, S Hasselquist, CR Hayes, F Hearty, H Hernandez Toledo, H Hicks, DW Hogg, K Holley-Bockelmann, JA Holtzman, B-C Hsieh, JAS Hunt, HS Hwang, HJ Ibarra-Medel, CE Jimenez Angel, J Johnson, A Jones, H Jonsson, K Kinemuchi, J Kollmeier, C Krawczyk, K Kreckel, S Kruk, I Lacerna, T-W Lan, RR Lane, DR Law, Y-B Lee, C Li, J Lian, L Lin, Y-T Lin, C Lintott, D Long, P Longa-Pena, JT Mackereth, A de la Macorra, SR Majewski, O Malanushenko, A Manchado, C Maraston, V Mariappan, M Marinelli, R Marques-Chaves, T Masseron, KL Masters, RM McDermid, N Medina Pena, S Meneses-Goytia, A Merloni, M Merrifield, S Meszaros, D Minniti, R Minsley, D Muna, AD Myers, P Nair, JC do Nascimento, JA Newman, C Nitschelm, MD Olmstead, A Oravetz, D Oravetz, RA Ortega Minakata, Z Pace, N Padilla, PA Palicio, K Pan, H-A Pan, T Parikh, PJ III, S Peirani, S Penny, WJ Percival, I Perez-Fournon, T Peterken, MH Pinsonneault, A Prakash, MJ Raddick, A Raichoor, RA Riffel, R Riffel, H-W Rix, AC Robin, A Roman-Lopes, B Rose, AJ Ross, G Rossi, K Rowlands, KHR Rubin, SF Sanchez, JR Sanchez-Gallego, C Sayres, A Schaefer, RP Schiavon, JS Schimoia, E Schlafly, D Schlegel, DP Schneider, M Schultheis, H-J Seo, SJ Shamsi, Z Shao, S Shen, S Shetty, G Simonian, RJ Smethurst, J Sobeck, BJ Souter, A Spindler, DV Stark, KG Stassun, M Steinmetz, T Storchi-Bergmann, GS Stringfellow, G Suarez, J Sun, M Taghizadeh-Popp, MS Talbot, J Tayar, AR Thakar, D Thomas, P Tissera, R Tojeiro, NW Troup, E Unda-Sanzana, O Valenzuela, M Vargas-Magana, J Antonio Vazquez-Mata, D Wake, BA Weaver, A-M Weijmans, KB Westfall, V Wild, J Wilson, E Woods, R Yan, M Yang, O Zamora, G Zasowski, K Zhang, Z Zheng, G Zhu, JC Zinn, H Zou

Machine Learning for the Zwicky Transient Facility


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.

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

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


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 5 (2018) 180124-

FM Jones, C Allen, C Arteta, J Arthur, C Black, LM Emmerson, R Freeman, G Hines, CJ Lintott, Z Macháčková, G Miller, R Simpson, C Southwell, HR Torsey, A Zisserman, T Hart

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


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


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

SDSS-IV MaNGA: the different quenching histories of fast and slow rotatorsSDSS-IV MaNGA: the different quenching histories of fast and slow rotators


RJ Smethurst, KL Masters, CJ Lintott, A Weijmans, M Merrifield, SJ Penny, A Aragon-Salamanca, J Brownstein, K Bundy, N Drory, DR Law, RC Nichol

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


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


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

The First Post-Kepler Brightness Dips of KIC 8462852


TS Boyajian, R Alonso, A Ammerman, D Armstrong, AA Ramos, K Barkaoui, TG Beatty, Z Benkhaldoun, P Benni, RO Bentley, A Berdyugin, S Berdyugina, S Bergeron, A Bieryla, MG Blain, AC Blanco, EHL Bodman, A Boucher, M Bradley, SM Brincat, TG Brink, J Briol, DJA Brown, J Budaj, A Burdanov, B Cale, MA Carbo, RC Garcia, WJ Clark, GC Clayton, JL Clem, PH Coker, EM Cook, CM Copperwheat, JL Curtis, RM Cutri, B Cseh, CH Cynamon, AJ Daniels, JRA Davenport, HJ Deeg, R De Lorenzo, T De Jaeger, J-B Desrosiers, J Dolan, DJ Dowhos, F Dubois, R Durkee, S Dvorak, L Easley, N Edwards, TG Ellis, E Erdelyi, S Ertel, RG Farfan, J Farihi, AV Filippenko, E Foxell, D Gandolfi, F Garcia, F Giddens, M Gillon, J-L Gonzalez-Carballo, C Gonzalez-Fernandez, JIG Hernandez, KA Graham, KA Greene, J Gregorio, N Hallakoun, O Hanyecz, GR Harp, GW Henry, E Herrero, CF Hildbold, D Hinzel, G Holgado, B Ignacz, I Ilyin, VD Ivanov, E Jehin, HE Jermak, S Johnston, S Kafka, C Kalup, E Kardasis, S Kaspi, GM Kennedy, F Kiefer, CL Kielty, D Kessler, H Kiiskinen, TL Killestein, RA King, V Kollar, H Korhonen, C Kotnik, R Konyves-Toth, L Kriskovics, N Krumm, V Krushinsky, E Kundra, F-R Lachapelle, D LaCourse, P Lake, K Lam, GP Lamb, D Lane, MW Lau, P Lewin, C Lintott, C Lisse, L Logie, N Longeard, ML Villanueva, EW Ludington, A Mainzer, L Malo, C Maloney, A Mann, A Mantero, M Marengo, J Marchant, MJM Gonzalez, JR Masiero, JC Mauerhan, J McCormac, A McNeely, HYA Meng, M Miller, LA Molnar, JC Morales, BM Morris, MW Muterspaugh, D Nespral, CR Nugent, KM Nugent, A Odasso, D O'Keeffe, A Oksanen, JM O'Meara, A Ordasi, H Osborn, JJ Ott, JR Parks, DR Perez, V Petriew, R Pickard, A Pal, P Plavchan, D Pollacco, FP Nunez, FJ Pozuelos, S Rau, S Redfield, H Relles, I Ribas, J Richards, JLO Saario, EJ Safron, JM Sallai, K Sarneczky, BE Schaefer, CF Schumer, M Schwartzendruber, MH Siegel, APV Siemion, BD Simmons, JD Simon, S Simon-Diaz, ML Sitko, H Socas-Navarro, A Sodor, D Starkey, IA Steele, G Stone, KG Strassmeier, RA Street, T Sullivan, J Suomela, JJ Swift, GM Szabo, R Szabo, R Szakats, T Szalai, AM Tanner, B Toledo-Padron, T Tordai, AHMJ Triaud, JD Turner, JH Ulowetz, M Urbanik, S Vanaverbeke, A Vanderburg, K Vida, BP Vietje, J Vinko, K Von Braun, EO Waagen, D Walsh, CA Watson, RC Weir, K Wenzel, CW Plaza, MW Williamson, JT Wright, MC Wyatt, W Zheng, G Zsidi

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


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

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

Nonprofit and Voluntary Sector Quarterly (2018)

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

© The Author(s) 2018. The concept of social capital has attracted much attention from researchers and policy makers, largely due to links with positive social outcomes and philanthropic acts such as volunteering and donations. However, a rapid growth in Internet technologies and social media networks has fundamentally affected the formation of social capital, as well as the way in which it potentially associates with prosocial behaviors. This study uses unique data from a survey of online volunteers to explore the interrelationships between social capital and a mix of self-reported and observed philanthropic activities in both online and offline settings. Our results show that while social capital levels associate strongly with offline donations, there are key differences in the relationships between social capital and volunteering in online and offline settings. Using two-stage least squares (2SLS) regression analysis to control for endogeneity, we also infer a number of causal relationships between social capital and philanthropy.

Galaxy Zoo: the interplay of quenching mechanisms in the group environment


RJ Smethurst, CJ Lintott, SP Bamford, RE Hart, SJ Kruk, KL Masters, RC Nichol, BD Simmons

Galaxy Zoo: star formation versus spiral arm number

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 468 (2017) 1850-1863

RE Hart, SP Bamford, KRV Casteels, SJ Kruk, CJ Lintott, KL Masters

SDSS IV MaNGA: Discovery of an Ha Blob Associated with a Dry Galaxy Pair-Ejected Gas or a "Dark" Galaxy Candidate?


L Lin, J-H Lin, C-H Hsu, H Fu, S Huang, SF Sanchez, S Gwyn, JD Gelfand, E Cheung, K Masters, S Peirani, W Rujopakarn, DV Stark, F Belfiore, MS Bothwell, K Bundy, A Hagen, L Hao, S Huang, D Law, C Li, C Lintott, R Maiolino, A Roman-Lopes, W-H Wang, T Xiao, F Yuan, D Bizyaev, E Malanushenko, N Drory, JG Fernandez-Trincado, Z Pace, K Pan, D Thomas