K2-138 is a moderately bright (V=12.2, K=10.3) main-sequence K star observed in Campaign 12 of the NASA K2 mission. It hosts five small (1.6-3.3 R Å ) transiting planets in a compact architecture. The periods of the five planets are 2. 35, 3.56, 5.40, 8.26, and 12.76 days, forming an unbroken chain of near 3:2 resonances. Although we do not detect the predicted 2-5 minute transit timing variations (TTVs) with the K2 timing precision, they may be observable by higher-cadence observations with, for example, Spitzer or CHEOPS. The planets are amenable to mass measurement by precision radial velocity measurements, and therefore K2-138 could represent a new benchmark system for comparing radial velocity and TTV masses. K2-138 is the first exoplanet discovery by citizen scientists participating in the Exoplanet Explorers project on the Zooniverse platform.

Context. Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, however, they are rare and difficult to find. The number of currently known lenses is on the order of 1000. Aims. The aim of this study is to use crowdsourcing to carry out a lens search targeting massive galaxies selected from over 442 square degrees of photometric data from the Hyper Suprime-Cam (HSC) survey. Methods. Based on the S16A internal data release of the HSC survey, we chose a sample of ∼300 000 galaxies with photometric redshifts in the range of 0.2 < zphot < 1.2 and photometrically inferred stellar masses of log M* > 11.2. We crowdsourced lens finding on this sample of galaxies on the Zooniverse platform as part of the Space Warps project. The sample was complemented by a large set of simulated lenses and visually selected non-lenses for training purposes. Nearly 6000 citizen volunteers participated in the experiment. In parallel, we used YATTALENS, an automated lens-finding algorithm, to look for lenses in the same sample of galaxies. Results. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising ∼1500 candidates, which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. By including lenses found by YATTALENS or serendipitously noticed in the discussion section of the Space Warps website, we were able to find 14 definite lenses (grade A), 129 probable lenses (grade B), and 581 possible lenses. YATTALENS found half the number of lenses that were discovered via crowdsourcing. Conclusions. Crowdsourcing is able to produce samples of lens candidates with high completeness, when multiple images are clearly detected, and with higher purity compared to the currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s, with forthcoming wide-area surveys such as LSST, Euclid, and WFIRST.

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.

We present the results from the first two years of the Planet Hunters TESS (PHT) citizen science project, which identifies planet candidates in the TESS (Transiting Exoplanet Survey Satellite) data by engaging members of the general public. Over 22 000 citizen scientists from around the world visually inspected the first 26 sectors of TESS data in order to help identify transit-like signals. We use a clustering algorithm to combine these classifications into a ranked list of events for each sector, the top 500 of which are then visually vetted by the science team. We assess the detection efficiency of this methodology by comparing our results to the list of TESS Objects of Interest (TOIs) and show that we recover 85 per cent of the TOIs with radii greater than 4 R⊕ and 51 per cent of those with radii between 3 and 4 R⊕. Additionally, we present our 90 most promising planet candidates that had not previously been identified by other teams, 73 of which exhibit only a single-transit event in the TESS light curve, and outline our efforts to follow these candidates up using ground-based observatories. Finally, we present noteworthy stellar systems that were identified through the Planet Hunters TESS project.

Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of LSST and other large-throughput surveys.