Prof Chris Lintott

Abstract:

The challenge of consistent identification of internal structure in galaxies - in particular disc galaxy components like spiral arms, bars, and bulges - has hindered our ability to study the physical impact of such structure across large samples. In this paper we present Galaxy Zoo: 3D (GZ:3D) a crowdsourcing project built on the Zooniverse platform that we used to create spatial pixel (spaxel) maps that identify galaxy centres, foreground stars, galactic bars, and spiral arms for 29 831 galaxies that were potential targets of the MaNGA survey (Mapping Nearby Galaxies at Apache Point Observatory, part of the fourth phase of the Sloan Digital Sky Surveys or SDSS-IV), including nearly all of the 10 010 galaxies ultimately observed. Our crowdsourced visual identification of asymmetric internal structures provides valuable insight on the evolutionary role of non-axisymmetric processes that is otherwise lost when MaNGA data cubes are azimuthally averaged. We present the publicly available GZ:3D catalogue alongside validation tests and example use cases. These data may in the future provide a useful training set for automated identification of spiral arm features. As an illustration, we use the spiral masks in a sample of 825 galaxies to measure the enhancement of star formation spatially linked to spiral arms, which we measure to be a factor of three over the background disc, and how this enhancement increases with radius.

Abstract:

Many of the species in decline around the world are subject to different environmental stressors across their range, so replicated large-scale monitoring programmes, are necessary to disentangle the relative impacts of these threats. At the same time as funding for long-term monitoring is being cut, studies are increasingly being criticised for lacking statistical power. For those taxa or environments where a single vantage point can observe individuals or ecological processes, time-lapse cameras can provide a cost-effective way of collecting time series data replicated at large spatial scales that would otherwise be impossible. However, networks of time-lapse cameras needed to cover the range of species or processes create a problem in that the scale of data collection easily exceeds our ability to process the raw imagery manually. Citizen science and machine learning provide solutions to scaling up data extraction (such as locating all animals in an image). Crucially, citizen science, machine learning-derived classifiers, and the intersection between them, are key to understanding how to establish monitoring systems that are sensitive to – and sufficiently powerful to detect –changes in the study system. Citizen science works relatively ‘out of the box’, and we regard it as a first step for many systems until machine learning algorithms are sufficiently trained to automate the process. Using Penguin Watch (www.penguinwatch.org) data as a case study, we discuss a complete workflow from images to parameter estimation and interpretation: the use of citizen science and computer vision for image processing, and parameter estimation and individual recognition for investigating biological questions. We discuss which techniques are easily generalizable to a range of questions, and where more work is needed to supplement ‘out of the box’ tools. We conclude with a horizon scan of the advances in camera technology, such as on-board computer vision and decision making.

Abstract:

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realizing the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope (NE) of HeLa cells imaged with serial blockface scanning electron microscopy. We present our approach for aggregating multiple volunteer annotations to generate a high-quality consensus segmentation and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the NE, which we share here, in addition to our archived benchmark data.

Abstract:

We use new Hubble Space Telescope (HST) images of nine Green Pea galaxies (GPGs) to study their resolved structure and color. The choice of filters, F555W and F850LP, together with the redshift of the galaxies (z ~ 0.25), minimizes the contribution of the nebular [O iii] and Hα emission lines to the broadband images. While these galaxies are typically very blue in color, our analysis reveals that it is only the dominant stellar clusters that are blue. Each GPG does clearly show the presence of at least one bright and compact star-forming region, but these are invariably superimposed on a more extended and lower surface brightness emission. Moreover, the colors of the star-forming regions are on average bluer than those of the diffuse emission, reaching up to 0.6 magnitudes bluer. Assuming that the diffuse and compact components have constant and single-burst star formation histories, respectively, the observed colors imply that the diffuse components (possibly the host galaxy of the star formation episode) have, on average, old stellar ages (>1 Gyr), while the star clusters are younger than 500 Myr. While a redder stellar component is perhaps the most plausible explanation for these results, the limitations of our current data set lead us to examine possible alternative mechanisms, particularly recombination emission processes, which are unusually prominent in systems with such strong line emission. With the available data, however, it is not possible to distinguish between these two interpretations. A substantial presence of old stars would indicate that the mechanisms allowing large escape fractions in these local galaxies may be different from those at play during the reionization epoch.

Abstract:

© 2021 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. 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.