arXiv subjects

Chris Lintott

Publications and source records attributed to Chris Lintott.

At least 55 records · Page 3Linked to original sources

arXiv2021

Galaxy Zoo Builder: Morphological Dependence of Spiral Galaxy Pitch Angle

Spiral structure is ubiquitous in the Universe, and the pitch angle of arms in spiral galaxies provide an important observable in efforts to discriminate between different mechanisms of spiral arm formation and evolution. In this paper, we present a hierarchical Bayesian approach to galaxy pitch angle determination, using spiral arm data obtained through the Galaxy Builder citizen science project. We present a new approach to deal with the large variations in pitch angle between different arms in a single galaxy, which obtains full posterior distributions on parameters. We make use of our pitch angles to examine previously reported links between bulge and bar strength and pitch angle, finding no correlation in our data (with a caveat that we use observational proxies for both bulge size and bar strength which differ from other work). We test a recent model for spiral arm winding, which predicts uniformity of the cotangent of pitch angle between some unknown upper and lower limits, finding our observations are consistent with this model of transient and recurrent spiral pitch angle as long as the pitch angle at which most winding spirals dissipate or disappear is larger than 10 degrees.

astro-ph.GA↗

arXiv2020

An old stellar population or diffuse nebular continuum emission discovered in green pea galaxies

We use new 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 ($z\sim 0.25$), minimizes the contribution of the nebular [O\thinspace{\sc iii}] and H$α$ emission lines to the broad-band 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 500Myrs. We also discuss the possibility that the diffuse red emission is due to a varying relative contribution of nebular continuum, rather than a different stellar population. 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 that allow large escape fractions in these local galaxies may be different from those at play during the reionization epoch.

astro-ph.GA↗

arXiv2020

Planet Hunters TESS II: Findings from the first two years of TESS

We present the results from the first two years of the Planet Hunters TESS citizen science project, which identifies planet candidates in the TESS 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 % of the TOIs with radii greater than 4 Earth radii and 51 % of those with radii between 3 and 4 Earth radii. 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.

astro-ph.EP↗

arXiv2020

Galaxy Zoo Builder: Four Component Photometric decomposition of Spiral Galaxies Guided by Citizen Science

Multi-component modelling of galaxies is a valuable tool in the effort to quantitatively understand galaxy evolution, yet the use of the technique is plagued by issues of convergence, model selection and parameter degeneracies. These issues limit its application over large samples to the simplest models, with complex models being applied only to very small samples. We attempt to resolve this dilemma of "quantity or quality" by developing a novel framework, built inside the Zooniverse citizen science platform, to enable the crowdsourcing of model creation for Sloan Digitial Sky Survey galaxies. We have applied the method, including a final algorithmic optimisation step, on a test sample of 198 galaxies, and examine the robustness of this new method. We also compare it to automated fitting pipelines, demonstrating that it is possible to consistently recover accurate models that either show good agreement with, or improve on, prior work. We conclude that citizen science is a promising technique for modelling images of complex galaxies, and release our catalogue of models.

astro-ph.GA↗

arXiv2020

TOI-1338: TESS' First Transiting Circumbinary Planet

We report the detection of the first circumbinary planet found by TESS. The target, a known eclipsing binary, was observed in sectors 1 through 12 at 30-minute cadence and in sectors 4 through 12 at two-minute cadence. It consists of two stars with masses of 1.1 MSun and 0.3 MSun on a slightly eccentric (0.16), 14.6-day orbit, producing prominent primary eclipses and shallow secondary eclipses. The planet has a radius of ~6.9 REarth and was observed to make three transits across the primary star of roughly equal depths (~0.2%) but different durations -- a common signature of transiting circumbinary planets. Its orbit is nearly circular (e ~ 0.09) with an orbital period of 95.2 days. The orbital planes of the binary and the planet are aligned to within ~1 degree. To obtain a complete solution for the system, we combined the TESS photometry with existing ground-based radial-velocity observations in a numerical photometric-dynamical model. The system demonstrates the discovery potential of TESS for circumbinary planets, and provides further understanding of the formation and evolution of planets orbiting close binary stars.

astro-ph.EP↗

arXiv2020

Defining the Really Habitable Zone

Since the discovery of the first confirmed exoplanet, observations have revealed a remarkable diversity of worlds. A wide variety of orbital and physical characteristics are detected in the exoplanet population, and much work has been devoted to deciding which of these planets may be suitable for life. Until now, though, little work has been devoted to deciding which of the potentially habitable planets might actually be \textit{worth} existing on. To this end, we present the Really Habitable Zone (RHZ), defined as the region around a star where acceptable gins and tonic are likely to be abundant. In common with much of the work in the field, we rely throughout on assumptions which are difficult if not impossible to test and present some plots which astronomers can use in their own talks, stripped of all caveats. We suggest that planets in the Really Habitable Zone be early targets for the JWST, because by the time that thing finally launches we're all going to need a drink.

astro-ph.EP↗

arXiv2019

Revealing the cosmic evolution of boxy/peanut-shaped bulges from HST COSMOS and SDSS

Vertically thickened bars, observed in the form of boxy/peanut (B/P) bulges, are found in the majority of massive barred disc galaxies in the local Universe, including our own. B/P bulges indicate that their host bars have suffered violent bending instabilities driven by anisotropic velocity distributions. We investigate for the first time how the frequency of B/P bulges in barred galaxies evolves from $z = 1$ to $z\approx 0$, using a large sample of non-edge-on galaxies with masses $M_{\star} > 10^{10}\:M_{\odot}$, selected from the HST COSMOS survey. We find the observed fraction increases from $0^{+3.6}_{-0.0}\%$ at $z = 1$ to $37.8^{+5.4}_{-5.1}\%$ at $z = 0.2$. We account for problems identifying B/P bulges in galaxies with low inclinations and unfavourable bar orientations, and due to redshift-dependent observational biases with the help of a sample from the Sloan Digital Sky Survey, matched in resolution, rest-frame band, signal-to-noise ratio and stellar mass and analysed in the same fashion. From this, we estimate that the true fraction of barred galaxies with B/P bulges increases from $\sim 10\%$ at $z \approx 1$ to $\sim 70\%$ at $z = 0$. In agreement with previous results for nearby galaxies, we find a strong dependence of the presence of a B/P bulge on galaxy stellar mass. This trend is observed in both local and high-redshift galaxies, indicating that it is an important indicator of vertical instabilities across a large fraction of the age of the Universe. We propose that galaxy formation processes regulate the thickness of galaxy discs, which in turn affect which galaxies experience violent bending instabilities of the bar.

astro-ph.GA↗

arXiv2019

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 11.8% within a vote fraction deviation of 0.2) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35-60% fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy Zoo will be able to classify surveys of any conceivable scale on a timescale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.

astro-ph.GA↗

arXiv2019

Secularly powered outflows from AGN: the dominance of non-merger driven supermassive black hole growth

Recent observations and simulations have revealed the dominance of secular processes over mergers in driving the growth of both supermassive black holes (SMBH) and galaxy evolution. Here we obtain narrowband imaging of AGN powered outflows in a sample of $12$ galaxies with disk-dominated morphologies, whose history is assumed to be merger-free. We detect outflows in $10/12$ sources in narrow band imaging of the [OIII] $5007 \unicode{x212B}$ emission using filters on the Shane-3m telescope. We calculate a mean outflow rate for these AGN of $0.95\pm0.14~\rm{M}_{\odot}~\rm{yr}^{-1}$. This exceeds the mean accretion rate of their SMBHs $0.054\pm0.039~\rm{M}_{\odot}~\rm{yr}^{-1}$) by a factor of $\sim18$. Assuming that the galaxy must provide at least enough material to power both the AGN and the outflow, this gives a lower limit on the average inflow rate of $\sim1.01\pm0.14~\rm{M}_{\odot}~\rm{yr}^{-1}$, a rate which simulations show can be achieved by bars, spiral arms and cold accretion. We compare our disk dominated sample to a sample of nearby AGN with merger dominated histories and show that the black hole accretion rates in our sample are 5 times higher ($4.2σ$) and the outflow rates are 5 times lower ($2.6σ$}. We suggest that this could be a result of the geometry of the smooth, planar inflow in a secular dominated system, which is both spinning up the black hole to increase accretion efficiency and less affected by feedback from the outflow, than in a merger-driven system with chaotic quasi-spherical inflows. This work provides further evidence that secular processes are sufficient to fuel SMBH growth.

astro-ph.GA↗

arXiv2019

Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images

This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a Self-Organising Map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand for machine learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labelled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighbourhood similarity and K-means clustering of radio-astronomical features complexity. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) dataset image features which can be applied to new radio survey data.

astro-ph.IM↗

arXiv2019

Machine Learning for the Zwicky Transient Facility

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.

astro-ph.IM↗

arXiv2018

Optimizing the Human-Machine Partnership with Zooniverse

Over the past decade, Citizen Science has become a proven method of distributed data analysis, enabling research teams from diverse domains to solve problems involving large quantities of data with complexity levels which require human pattern recognition capabilities. With over 120 projects built reaching nearly 1.7 million volunteers, the Zooniverse.org platform has led the way in the application of Citizen Science as a method for closing the Big Data analysis gap. Since the launch in 2007 of the Galaxy Zoo project, the Zooniverse platform has enabled significant contributions across many disciplines; e.g., in ecology, humanities, and astronomy. Citizen science as an approach to Big Data combines the twin advantages of the ability to scale analysis to the size of modern datasets with the ability of humans to make serendipitous discoveries. To cope with the larger datasets looming on the horizon such as astronomy's Large Synoptic Survey Telescope (LSST) or the 100's of TB from ecology projects annually, Zooniverse has been researching a system design that is optimized for efficiency in task assignment and incorporating human and machine classifiers into the classification engine. By making efficient use of smart task assignment and the combination of human and machine classifiers, we can achieve greater accuracy and flexibility than has been possible to date. We note that creating the most efficient system must consider how best to engage and retain volunteers as well as make the most efficient use of their classifications. Our work thus focuses on understanding the factors that optimize efficiency of the combined human-machine system. This paper summarizes some of our research to date on integration of machine learning with Zooniverse, while also describing new infrastructure developed on the Zooniverse platform to carry out this research.

cs.HC↗

arXiv2018

Galaxy Nurseries: Crowdsourced analysis of slitless spectroscopic data

We present the results of Galaxy Nurseries project, which was designed to enable crowdsourced analysis of slitless spectroscopic data by volunteer citizen scientists using the Zooniverse online interface. The dataset was obtained by the WFC3 Infrared Spectroscopic Parallel (WISP) Survey collaboration and comprises NIR grism (G102 and G141) and direct imaging. Volunteers were instructed to evaluate indicated spectral features and decide whether it was a genuine emission line or more likely an artifact. Galaxy Nurseries was completed in only 40 days, gathering 414,360 classifications from 3003 volunteers for 27,333 putative emission lines. The results of Galaxy Nurseries demonstrate the feasibility of identifying genuine emission lines in slitless spectra by citizen scientists. Volunteer responses for each subject were aggregated to compute $f_{\mathrm{Real}}$, the fraction of volunteers who classified the corresponding emission line as "Real". To evaluate the accuracy of volunteer classifications, their aggregated responses were compared with independent assessments provided by members of the WISP Survey Science Team (WSST). Overall, there is a broad agreement between the WSST and volunteers' classifications, although we recognize that robust scientific analyses typically require samples with higher purity and completeness than raw volunteer classifications provide. Nonetheless, choosing optimal threshold values for $f_{\mathrm{Real}}$ allows a large fraction of spurious lines to be vetoed, substantially reducing the timescale for subsequent professional analysis of the remaining potential lines.

astro-ph.GA↗

arXiv2018

Galaxy Zoo: Morphological classification of galaxy images from the Illustris simulation

Modern cosmological simulations model the universe with increasing sophistication and at higher spatial and temporal resolutions. These enhancements permit detailed comparisons between the simulation outputs and real observational data. Recent projects such as Illustris are capable of producing simulated images that are comparable to those obtained from local surveys. This paper tests how well Illustris achieves this goal across a diverse population of galaxies using visual morphologies derived from Galaxy Zoo citizen scientists. Morphological classifications provided by volunteers for simulated galaxies are compared with similar data for a compatible sample of images drawn from the SDSS Legacy Survey. This paper investigates how simple morphological characterization by human volunteers asked to distinguish smooth from featured systems differs between simulated and real galaxy images. Differences are identified, which are likely due to the limited resolution of the simulation, but which could be revealing real differences in the dynamical evolution of populations of galaxies in the real and model universes. Specifically, for stellar masses $M_{\star}\lesssim10^{11}M_{\odot}$, a larger proportion of Illustris galaxies that exhibit disk-like morphology or visible substructure, relative to their SDSS counterparts. Toward higher masses, simulated and observed galaxies converge and exhibit similar morphology distributions. The stellar mass threshold indicated by this divergent behavior confirms recent works using parametric measures of morphology from Illustris simulated images. When $M_{\star}\gtrsim10^{11}M_{\odot}$, the Illustris dataset contains fewer galaxies that classifiers regard as unambiguously featured. These results suggest that comparison between the detailed properties of observed and simulated galaxies, even when limited to reasonably massive systems, may be misleading.

astro-ph.GA↗

arXiv2017

SDSS-IV MaNGA: The Different Quenching Histories of Fast and Slow Rotators

Do the theorised different formation mechanisms of fast and slow rotators produce an observable difference in their star formation histories? To study this we identify quenching slow rotators in the MaNGA sample by selecting those which lie below the star forming sequence and identify a sample of quenching fast rotators which were matched in stellar mass. This results in a total sample of 194 kinematically classified galaxies, which is agnostic to visual morphology. We use u-r and NUV-u colours from SDSS and GALEX and an existing inference package, STARPY, to conduct a first look at the onset time and exponentially declining rate of quenching of these galaxies. An Anderson-Darling test on the distribution of the inferred quenching rates across the two kinematic populations reveals they are statistically distinguishable ($3.2σ$). We find that fast rotators quench at a much wider range of rates than slow rotators, consistent with a wide variety of physical processes such as secular evolution, minor mergers, gas accretion and environmentally driven mechanisms. Quenching is more likely to occur at rapid rates ($τ\lesssim 1~\rm{Gyr}$) for slow rotators, in agreement with theories suggesting slow rotators are formed in dynamically fast processes, such as major mergers. Interestingly, we also find that a subset of the fast rotators quench at these same rapid rates as the bulk of the slow rotator sample. We therefore discuss how the total gas mass of a merger, rather than the merger mass ratio, may decide a galaxy's ultimate kinematic fate.

astro-ph.GA↗

arXiv2017

Large Synoptic Survey Telescope Galaxies Science Roadmap

The Large Synoptic Survey Telescope (LSST) will enable revolutionary studies of galaxies, dark matter, and black holes over cosmic time. The LSST Galaxies Science Collaboration has identified a host of preparatory research tasks required to leverage fully the LSST dataset for extragalactic science beyond the study of dark energy. This Galaxies Science Roadmap provides a brief introduction to critical extragalactic science to be conducted ahead of LSST operations, and a detailed list of preparatory science tasks including the motivation, activities, and deliverables associated with each. The Galaxies Science Roadmap will serve as a guiding document for researchers interested in conducting extragalactic science in anticipation of the forthcoming LSST era.

astro-ph.GA↗

arXiv2017

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

gr-qc↗

arXiv2017

SDSS IV MaNGA: Discovery of an $H_α$ blob associated with a dry galaxy pair -- ejected gas or a `dark' galaxy candidate?

We report the discovery of a mysterious giant $H_α$ blob that is $\sim 8$ kpc away from the main MaNGA target 1-24145, one component of a dry galaxy merger, identified in the first-year SDSS-IV MaNGA data. The size of the $H_α$ blob is $\sim$ 3-4 kpc in radius, and the $H_α$ distribution is centrally concentrated. However, there is no optical continuum counterpart in deep broadband images reaching $\sim$26.9 mag arcsec$^{-2}$ in surface brightness. We estimate that the masses of ionized and cold gases are $3.3 \times 10^{5}$ $\rm M_{\odot}$ and $< 1.3 \times 10^{9}$ $\rm M_{\odot}$, respectively. The emission-line ratios indicate that the $H_α$ blob is photoionized by a combination of massive young stars and AGN. Furthermore, the ionization line ratio decreases from MaNGA 1-24145 to the $H_α$ blob, suggesting that the primary ionizing source may come from MaNGA 1-24145, likely a low-activity AGN. Possible explanations of this $H_α$ blob include AGN outflow, the gas remnant being tidally or ram-pressure stripped from MaNGA 1-24145, or an extremely low surface brightness (LSB) galaxy. However, the stripping scenario is less favoured according to galaxy merger simulations and the morphology of the $H_α$ blob. With the current data, we can not distinguish whether this $H_α$ blob is ejected gas due to a past AGN outburst, or a special category of `ultra-diffuse galaxy' (UDG) interacting with MaNGA 1-24145 that further induces the gas inflow to fuel the AGN in MaNGA 1-24145.

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