Planet Hunters TESS I: TOI 813, a subgiant hosting a transiting Saturn-sized planet on an 84-day orbit

Monthly Notices of the Royal Astronomical Society, Volume 494, Issue 1, May 2020, Pages 750–763,

Authors:

N L Eisner, O Barragán, S Aigrain, C Lintott, G Miller, N Zicher, T S Boyajian, C Briceño, E M Bryant, J L Christiansen, A D Feinstein, L M Flor-Torres, M Fridlund, D Gandolfi, J Gilbert, N Guerrero, J M Jenkins, K Jones, M H Kristiansen, A Vanderburg, N Law, A R López-Sánchez, A W Mann, E J Safron, M E Schwamb, K G Stassun, H P Osborn, J Wang, A Zic, C Ziegler, F Barnet, S J Bean, D M Bundy, Z Chetnik, J L Dawson, J Garstone, A G Stenner, M Huten, S Larish, L D Melanson, T Mitchell, C Moore, K Peltsch, D J Rogers, C Schuster, D S Smith, D J Simister, C Tanner, I Terentev, A Tsymbal

Abstract:

We report on the discovery and validation of TOI 813 b (TIC 55525572 b), a transiting exoplanet identified by citizen scientists in data from NASA’s Transiting Exoplanet Survey Satellite (TESS) and the first planet discovered by the Planet Hunters TESS project. The host star is a bright (V = 10.3 mag) subgiant (⁠R⋆=1.94R⊙⁠, M⋆=1.32M⊙⁠). It was observed almost continuously by TESS during its first year of operations, during which time four individual transit events were detected. The candidate passed all the standard light curve-based vetting checks, and ground-based follow-up spectroscopy and speckle imaging enabled us to place an upper limit of 2MJup (99 per cent confidence) on the mass of the companion, and to statistically validate its planetary nature. Detailed modelling of the transits yields a period of 83.8911+0.0027−0.0031 d, a planet radius of 6.71 ± 0.38 R⊕ and a semimajor axis of 0.423+0.031−0.037 AU. The planet’s orbital period combined with the evolved nature of the host star places this object in a relatively underexplored region of parameter space. We estimate that TOI 813 b induces a reflex motion in its host star with a semi-amplitude of ∼6 m s−1, making this a promising system to measure the mass of a relatively long-period transiting planet.

LATTE: Lightcurve Analysis Tool for Transiting Exoplanets

The Journal of Open Source Software The Open Journal 5:49 (2020) 2101

Authors:

Nora Eisner, Chris Lintott, Suzanne Aigrain

Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). VI. Crowdsourced lens finding with Space Warps

(2020)

Authors:

Alessandro Sonnenfeld, Aprajita Verma, Anupreeta More, Elisabeth Baeten, Christine Macmillan, Kenneth C Wong, James HH Chan, Anton T Jaelani, Chien-Hsiu Lee, Masamune Oguri, Cristian E Rusu, Marten Veldthuis, Laura Trouille, Philip J Marshall, Roger Hutchings, Campbell Allen, James O' Donnell, Claude Cornen, Christopher Davis, Adam McMaster, Chris Lintott, Grant Miller

Defining the Really Habitable Zone

(2020)

Authors:

Marven F Pedbost, Trillean Pomalgu, Chris Lintott, Nora Eisner, Belinda Nicholson

Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics

Scientific Data Nature Research 7:1 (2020) 102

Authors:

Fiona M Jones, Carlos Arteta, Andrew Zisserman, Victor Lempitsky, Chris J Lintott, Tom Hart

Abstract:

Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in count data (number of penguins per image) and ‘nearest neighbour distance’ measurements. The latter provide useful summaries of colony spatial structure (which can indicate phenological stage) and can be used to detect movement – metrics which could be valuable for a number of different monitoring scenarios, including image capture during aerial surveys. We present two alternative pathways for producing counts: (1) via the Zooniverse citizen science project Penguin Watch and (2) via a computer vision algorithm (Pengbot), and share a comparison of citizen science-, machine learning-, and expert- derived counts. We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development.