Zooniverse

Abstract:

Modern radio telescopes are data-intensive machines, producing many TB of data every night. Amongst this deluge of data are transient and variable phenomena, whose study can shed new light on processes as varied as stellar dynamos and the accretion discs in supermassive black holes. In this work I demonstrate the applicability of different methods to the discovery of these astrophysical transients and variables coming from telescopes such as MeerKAT.

I first consider a standard approach to discovering transients by characterising their variability. By making use of even modest sampling with the high sensitivity and wide field of view of MeerKAT, I demonstrate how we are now able to uncover new transients almost by accident - if we exclude the vast amount of time spent planning, building and operating excellent telescopes, efficient pipelines and well- crafted observing proposals. In this work I found a stellar flare from a nearby M dwarf, which was then followed up and complemented by optical and X-ray photometry and spectroscopy, providing new insights on the system.

Next I built a citizen science platform in order to perform such transient searches at scale, making use of a wide range of data available in the MeerKAT archive. I detail the process of review and beta-testing that resulted in the final design of the Bursts from Space: MeerKAT project. Over 1000 volunteers took part, demonstrating a healthy appetite for further Zooniverse data releases. Volunteers discovered or recovered a wide range of phenomena, from flare stars and pulsars to scintillating AGN and transient OH maser emission. I was also able to use the known transients in our fields to understand some reasons why interesting sources may be missed and will fold this learning through to future iterations of the project. This is the first demonstration of volunteers finding radio transients in images.

Finally, I show how anomaly detection, an unsupervised machine learning approach, is a suitable tool for finding these variable phenomena at scale, as is required for modern astronomical surveys. I use three feature sets as applied to two anomaly detection techniques in the Astronomaly package and analyse anomaly detection performance by comparison with citizen science labels. By using transients found by citizen scientists as a ground truth I demonstrate that anomaly detection techniques can recall over half of the radio transients within 10% of the sample dataset. I find that the choice of feature set is crucial, especially when considering available resources for human inspection and follow-up. I find that active learning on ∼2% of the data improves recall by up to 10%, depending on the feature-model pair. The best performing feature-model pairs result in a factor of 5 times fewer sources requiring vetting by humans. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other datasets, a real-time transient detection system and upcoming large surveys.

Abstract:

We present HST WFPC2 I band imaging for a sample of 9 Hyperluminous Infrared Galaxies spanning a redshift range 0.45 < z < 1.34. Three of the sample have morphologies showing evidence for interactions, six are QSOs. Host galaxies in the QSOs are reliably detected out to z ~ 0.8. The detected QSO host galaxies have an elliptical morphology with scalelengths spanning 6.5 < r_{e}(Kpc) < 88 and absolute k corrected magnitudes spanning -24.5 < M_{I} < -25.2. There is no clear correlation between the IR power source and the optical morphology. None of the sources in the sample, including F15307+3252, show any evidence for gravitational lensing. We infer that the IR luminosities are thus real. Based on these results, and previous studies of HLIRGs, we conclude that this class of object is broadly consistent with being a simple extrapolation of the ULIRG population to higher luminosities; ULIRGs being mainly violently interacting systems powered by starbursts and/or AGN. Only a small number of sources whose infrared luminosities exceed 10^{13}Lsun are intrinsically less luminous objects which have been boosted by gravitational lensing.

Abstract:

We report on the discovery and validation of TOI 813b (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 ($R_\star=1.94\,R_\odot$, $M_\star=1.32\,M_\odot$). 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 $2 M_{Jup}$ (99 % confidence) on the mass of the companion, and to statistically validate its planetary nature. Detailed modelling of the transits yields a period of $83.8911_{ - 0.0031 } ^ { + 0.0027 }$ days, a planet radius of $6.71 \pm 0.38$ $R_{\oplus}$, and a semi major axis of $0.423_{ - 0.037 } ^ { + 0.031 }$ AU. The planet's orbital period combined with the evolved nature of the host star places this object in a relatively under-explored region of parameter space. We estimate that TOI-813b induces a reflex motion in its host star with a semi-amplitude of $\sim6$ ms$^{-1}$, making this system a promising target to measure the mass of a relatively long-period transiting planet.

Abstract:

We report on the discovery and validation of a two-planet system around a bright (V = 8.85 mag) early G dwarf (1.43 $R_{\odot}$, 1.15 $M_{\odot}$, TOI 2319) using data from NASA's Transiting Exoplanet Survey Satellite (TESS). Three transit events from two planets were detected by citizen scientists in the month-long TESS light curve (sector 25), as part of the Planet Hunters TESS project. Modelling of the transits yields an orbital period of \Pb\ and radius of $3.41 _{ - 0.12 } ^ { + 0.14 }$ $R_{\oplus}$ for the inner planet, and a period in the range 19.26-35 days and a radius of $5.83 _{ - 0.14 } ^ { + 0.14 }$ $R_{\oplus}$ for the outer planet, which was only seen to transit once. Each signal was independently statistically validated, taking into consideration the TESS light curve as well as the ground-based spectroscopic follow-up observations. Radial velocities from HARPS-N and EXPRES yield a tentative detection of planet b, whose mass we estimate to be $11.56 _{ - 6.14 } ^ { + 6.58 }$ $M_{\oplus}$, and allow us to place an upper limit of $27.5$ $M_{\oplus}$ (99 per cent confidence) on the mass of planet c. Due to the brightness of the host star and the strong likelihood of an extended H/He atmosphere on both planets, this system offers excellent prospects for atmospheric characterisation and comparative planetology.

Abstract:

Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, but are rare and difficult to find. The number of currently known lenses is on the order of 1,000. We wish 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. We selected a sample of $\sim300,000$ galaxies with photometric redshifts in the range $0.2 < z_{phot} < 1.2$ and photometrically inferred stellar masses $\log{M_*} > 11.2$. 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 6,000 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. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising $\sim1,500$ candidates which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. 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, 129 probable lenses and 581 possible lenses. YattaLens found half the number of lenses discovered via crowdsourcing. Crowdsourcing is able to produce samples of lens candidates with high completeness and purity, compared to currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms and/or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s.