arXiv subjects

Lucy Fortson

Publications and source records attributed to Lucy Fortson.

At least 37 records · Page 2Linked to original sources

arXiv2023

VERITAS discovery of very high energy gamma-ray emission from S3 1227+25 and multiwavelength observations

We report the detection of very high energy gamma-ray emission from the blazar S3 1227+25 (VER J1230+253) with the Very Energetic Radiation Imaging Telescope Array System (VERITAS). VERITAS observations of the source were triggered by the detection of a hard-spectrum GeV flare on May 15, 2015 with the Fermi-Large Area Telescope (LAT). A combined five-hour VERITAS exposure on May 16th and May 18th resulted in a strong 13$σ$ detection with a differential photon spectral index, $Γ$ = 3.8 $\pm$ 0.4, and a flux level at 9% of the Crab Nebula above 120 GeV. This also triggered target of opportunity observations with Swift, optical photometry, polarimetry and radio measurements, also presented in this work, in addition to the VERITAS and Fermi-LAT data. A temporal analysis of the gamma-ray flux during this period finds evidence of a shortest variability timescale of $τ_{obs}$ = 6.2 $\pm$ 0.9 hours, indicating emission from compact regions within the jet, and the combined gamma-ray spectrum shows no strong evidence of a spectral cut-off. An investigation into correlations between the multiwavelength observations found evidence of optical and gamma-ray correlations, suggesting a single-zone model of emission. Finally, the multiwavelength spectral energy distribution is well described by a simple one-zone leptonic synchrotron self-Compton radiation model.

astro-ph.HE↗

arXiv2022

From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.

cs.LG↗

arXiv2022

Galaxy Zoo: Clump Scout -- Design and first application of a two-dimensional aggregation tool for citizen science

Galaxy Zoo: Clump Scout is a web-based citizen science project designed to identify and spatially locate giant star forming clumps in galaxies that were imaged by the Sloan Digital Sky Survey Legacy Survey. We present a statistically driven software framework that is designed to aggregate two-dimensional annotations of clump locations provided by multiple independent Galaxy Zoo: Clump Scout volunteers and generate a consensus label that identifies the locations of probable clumps within each galaxy. The statistical model our framework is based on allows us to assign false-positive probabilities to each of the clumps we identify, to estimate the skill levels of each of the volunteers who contribute to Galaxy Zoo: Clump Scout and also to quantitatively assess the reliability of the consensus labels that are derived for each subject. We apply our framework to a dataset containing 3,561,454 two-dimensional points, which constitute 1,739,259 annotations of 85,286 distinct subjects provided by 20,999 volunteers. Using this dataset, we identify 128,100 potential clumps distributed among 44,126 galaxies. This dataset can be used to study the prevalence and demographics of giant star forming clumps in low-redshift galaxies. The code for our aggregation software framework is publicly available at: https://github.com/ou-astrophysics/BoxAggregator

astro-ph.GA↗

arXiv2022

Practical Galaxy Morphology Tools from Deep Supervised Representation Learning

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. "#diffuse"), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100% accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly-labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled datasets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code Zoobot. Zoobot is accessible to researchers with no prior experience in deep learning.

astro-ph.GA↗

arXiv2022

Gems of the Galaxy Zoos -- a Wide-Ranging Hubble Space Telescope Gap-Filler Program

We describe the Gems of the Galaxy Zoos (Zoo Gems) project, a gap-filler project using short windows in the Hubble Space Telescope's schedule. As with previous snapshot programs, targets are taken from a pool based on position; we combine objects selected by volunteers in both the Galaxy Zoo and Radio Galaxy Zoo citizen-science projects. Zoo Gems uses exposures with the Advanced Camera for Surveys (ACS) to address a broad range of topics in galaxy morphology, interstellar-medium content, host galaxies of active galactic nuclei, and galaxy evolution. Science cases include studying galaxy interactions, backlit dust in galaxies, post-starburst systems, rings and peculiar spiral patterns, outliers from the usual color-morphology relation, Green Pea compact starburst systems, double radio sources with spiral host galaxies, and extended emission-line regions around active galactic nuclei. For many of these science categories, final selection of targets from a larger list used public input via a voting process. Highlights to date include the prevalence of tightly-wound spiral structure in blue, apparently early-type galaxies, a nearly complete Einstein ring from a group lens, redder components at lower surface brightness surrounding compact Green Pea starbursts, and high-probability examples of spiral galaxies hosting large double radio sources.

astro-ph.GA↗

arXiv2022

Galaxy Zoo: Clump Scout: Surveying the Local Universe for Giant Star-forming Clumps

Massive, star-forming clumps are a common feature of high-redshift star-forming galaxies. How they formed, and why they are so rare at low redshift, remains unclear. In this paper we identify the largest yet sample of clumpy galaxies (7,052) at low redshift using data from the citizen science project \textit{Galaxy Zoo: Clump Scout}, in which volunteers classified over 58,000 Sloan Digital Sky Survey (SDSS) galaxies spanning redshift $0.02 < z < 0.15$. We apply a robust completeness correction by comparing with simulated clumps identified by the same method. Requiring that the ratio of clump-to-galaxy flux in the SDSS $u$ band be greater than 8\% (similar to clump definitions used by other works), we estimate the fraction of local galaxies hosting at least one clump ($f_{clumpy}$) to be $2.68_{-0.30}^{+0.33}\%$. We also compute the same fraction with a less stringent cut of 3\% ($11.33_{-1.16}^{+0.89}\%$), as the higher number count and lower statistical noise of this fraction permits sharper comparison with future low-redshift clumpy galaxy studies. Our results reveal a sharp decline in $f_{clumpy}$ over $0 < z < 0.5$. The minor merger rate remains roughly constant over the same span, so we suggest that minor mergers are unlikely to be the primary driver of clump formation. Instead, the rate of galaxy turbulence is a better tracer for $f_{clumpy}$ over $0 < z < 1.5$ for galaxies of all masses, which supports the idea that clump formation is primarily driven by violent disk instability for all galaxy populations during this period.

astro-ph.GA↗

arXiv2022

Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies

We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r=23.6 vs. r=22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314,000 galaxies. 140,000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314,000 galaxies. When measured against confident volunteer classifications, the networks are approximately 99% accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.

astro-ph.GA↗

arXiv2021

A Search for TeV Gamma-ray Emission from Pulsar Tails by VERITAS

We report on the search for very-high-energy gamma-ray emission from the regions around three nearby supersonic pulsars (PSR B0355+54, PSR J0357+3205 and PSR J1740+1000) that exhibit long X-ray tails. To date there is no clear detection of TeV emission from any pulsar tail that is prominent in X-ray or radio. We provide upper limits on the TeV flux, and luminosity, and also compare these limits with other pulsar wind nebulae detected in X-rays and the tail emission model predictions. We find that at least one of the three tails is likely to be detected in observations that are a factor of 2-3 more sensitive. The analysis presented here also has implications for deriving the properties of pulsar tails, for those pulsars whose tails could be detected in TeV.

astro-ph.HE↗

arXiv2021

Investigating Clumpy Galaxies in the Sloan Digital Sky Survey Stripe 82 using the Galaxy Zoo

Giant, star-forming clumps are a common feature prevalent amongst high-redshift star-forming galaxies and play a critical role in shaping their chaotic morphologies and yet, their nature and role in galaxy evolution remains to be fully understood. A majority of the effort to study clumps has been focused at high redshifts, and local clump studies have often suffered from small sample sizes. In this work, we present an analysis of clump properties in the local universe, and for the first time, performed with a statistically significant sample. With the help of the citizen science-powered Galaxy Zoo: Hubble project, we select a sample of 92 $z<0.06$ clumpy galaxies in Sloan Digital Sky Survey Stripe 82 galaxies. Within this sample, we identify 543 clumps using a contrast-based image analysis algorithm and perform photometry as well as estimate their stellar population properties. The overall properties of our $z<0.06$ clump sample are comparable to the high-redshift clumps. However, contrary to the high-redshift studies, we find no evidence of a gradient in clump ages or masses as a function of their galactocentric distances. Our results challenge the inward migration scenario for clump evolution for the local universe, potentially suggesting a larger contribution of ex-situ clumps and/or longer clump migration timescales.

astro-ph.GA↗

arXiv2021

Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research

Machine learning, artificial intelligence, and deep learning have advanced significantly over the past decade. Nonetheless, humans possess unique abilities such as creativity, intuition, context and abstraction, analytic problem solving, and detecting unusual events. To successfully tackle pressing scientific and societal challenges, we need the complementary capabilities of both humans and machines. The Federal Government could accelerate its priorities on multiple fronts through judicious integration of citizen science and crowdsourcing with artificial intelligence (AI), Internet of Things (IoT), and cloud strategies.

cs.CY↗

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↗

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

Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse

LSST and Euclid must address the daunting challenge of analyzing the unprecedented volumes of imaging and spectroscopic data that these next-generation instruments will generate. A promising approach to overcoming this challenge involves rapid, automatic image processing using appropriately trained Deep Learning (DL) algorithms. However, reliable application of DL requires large, accurately labeled samples of training data. Galaxy Zoo Express (GZX) is a recent experiment that simulated using Bayesian inference to dynamically aggregate binary responses provided by citizen scientists via the Zooniverse crowd-sourcing platform in real time. The GZX approach enables collaboration between human and machine classifiers and provides rapidly generated, reliably labeled datasets, thereby enabling online training of accurate machine classifiers. We present selected results from GZX and show how the Bayesian aggregation engine it uses can be extended to efficiently provide object-localization and bounding-box annotations of two-dimensional data with quantified reliability. DL algorithms that are trained using these annotations will facilitate numerous panchromatic data modeling tasks including morphological classification and substructure detection in direct imaging, as well as decontamination and emission line identification for slitless spectroscopy. Effectively combining the speed of modern computational analyses with the human capacity to extrapolate from few examples will be critical if the potential of forthcoming large-scale surveys is to be realized.

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

A transient search using combined human and machine classifications

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.

astro-ph.IM↗