arXiv subjects

Lucy Fortson

Publications and source records attributed to Lucy Fortson.

At least 19 recordsLinked to original sources

arXiv2026

A Multiwavelength Study of a Long-Duration VHE Flare from BL Lacertae with VERITAS

We report the first observations of a long-duration very-high-energy (VHE; $E > 100$ GeV) flare from BL Lacertae (VER J2202+422), taken with the Very Energetic Radiation Imaging Telescope Array System (VERITAS). On October 15, 2022, the Fermi-Large Area Telescope (LAT) detected elevated GeV activity originating from this blazar. This triggered a multiwavelength campaign, which includes observations from VERITAS, Swift, NuSTAR, and select optical and radio observatories. VERITAS observed the source for a total of $\sim 9.8$ hours between September 1, 2022 and December 1, 2022. An analysis of these data yields a $\sim 28 \sigma$ detection of the source. While previously observed VHE flares from BL Lacertae have lasted on time-scales of minutes to days, VERITAS continued to detect flaring activity from the source for over a month ($\sim 40$ days) after the original flaring activity was detected with Fermi-LAT. Broadband spectral modeling shows that a synchrotron self-Compton (SSC) model with an external inverse-Compton (EC) component is preferred over a one-zone SSC model.

astro-ph.HE↗

arXiv2026

Grand Challenges for the Convergence of Computational and Citizen Science Research Workshop Report

This report is an outcome of a Computing Community Consortium (CCC) visioning workshop on Grand Challenges for the Convergence of Computational and Citizen Science Research conducted on April 8-9, 2025, in Washington, D.C. as well as through several precursor virtual input-gathering sessions. These events brought together experts across relevant disciplines to develop a research agenda that brings to fruition the above vision on how humans and machines may team up to solve some of the world's most pressing scientific problems. Citizen science delivers measurable economic and national value. Public participation in scientific research generates millions of dollars in volunteer labor value, extends government agency capacity, and directly supports federal priorities in areas such as disaster management, public health, water, energy, workforce development, and many more. At the same time, 21st-century scientific infrastructure requirements for citizen science (from hardware and cyberinfrastructure to data and computational frameworks) mirror those for computational science more generally. The distributed, collaborative, long-term, and contextual nature of citizen science makes it a demanding real-world use case for a novel robust research infrastructure that accounts for security, privacy, resource adaptability, and transparency. In this report, we outline the key findings, future research directions, and recommendations that emerged from the April 2025 CCC Grand Challenges for the Convergence of Computational and Citizen Science Research Workshop.

cs.CY↗

arXiv2026

Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks

Integral Field Spectroscopy (IFS) surveys offer a unique new landscape in which to learn in both spatial and spectroscopic dimensions and could help uncover previously unknown insights into galaxy evolution. In this work, we demonstrate a new unsupervised deep learning framework using Convolutional Long-Short Term Memory Network Autoencoders to encode generalized feature representations across both spatial and spectroscopic dimensions spanning $19$ optical emission lines (3800A $< \lambda <$ 8000A) among a sample of $\sim 9000$ galaxies from the MaNGA IFS survey. As a demonstrative exercise, we assess our model on a sample of $290$ Active Galactic Nuclei (AGN) and highlight scientifically interesting characteristics of some highly anomalous AGN.

astro-ph.GA↗

arXiv2025

Galaxy Zoo Evo: 1 million human-annotated images of galaxies

We introduce Galaxy Zoo Evo, a labeled dataset for building and evaluating foundation models on images of galaxies. GZ Evo includes 104M crowdsourced labels for 823k images from four telescopes. Each image is labeled with a series of fine-grained questions and answers (e.g. "featured galaxy, two spiral arms, tightly wound, merging with another galaxy"). These detailed labels are useful for pretraining or finetuning. We also include four smaller sets of labels (167k galaxies in total) for downstream tasks of specific interest to astronomers, including finding strong lenses and describing galaxies from the new space telescope Euclid. We hope GZ Evo will serve as a real-world benchmark for computer vision topics such as domain adaption (from terrestrial to astronomical, or between telescopes) or learning under uncertainty from crowdsourced labels. We also hope it will support a new generation of foundation models for astronomy; such models will be critical to future astronomers seeking to better understand our universe.

astro-ph.IM↗

arXiv2025

Design and Performance of the Upgraded Prototype Schwarzschild-Couder Telescope Camera Module

The Cherenkov Telescope Array Observatory (CTAO) is a ground-based observatory that will improve upon the sensitivities of the current generation of very-high-energy gamma-ray instruments. The Schwarzschild-Couder Telescope (SCT) is a dual-mirror candidate design for a CTAO Medium-Sized Telescope (MST). The prototype Schwarzschild-Couder Telescope (pSCT) was inaugurated in 2019 at Fred Lawrence Whipple Observatory (FLWO) in Arizona and observed significant gamma-ray emission from the Crab Nebula with a partially populated camera. The pSCT camera is currently being upgraded to fully instrument the focal plane with 11,328 silicon photomultiplier (SiPM) pixels split between 177 camera modules. Additionally, the modules will feature upgraded electronics designed to reduce electronics crosstalk and noise. A module calibration procedure has been developed using a preproduction test module. Following this calibration procedure, performance testing shows that the upgrade module has low noise, minimal electronics crosstalk, and excellent charge resolution. After calibration and optimization, the 177 production modules will be installed in the pSCT camera for commissioning. This will be followed by observations of known VHE gamma-ray sources for camera performance validation.

astro-ph.IM↗

arXiv2025

NGTS-EB-8: A double-lined eclipsing M+M binary discovered by citizen scientists

We report the identification and characterization of a new binary system composed of two near-equal mass M-dwarfs. The binary NGTS-EB-8 was identified as a planet candidate in data from the Next Generation Transit Survey (NGTS) by citizen scientists participating in the Planet Hunters NGTS project. High-resolution spectroscopic observations reveal the system to be a double-lined binary. By modeling the photometric and radial velocity observations, we determine an orbital period of 4.2 days and the masses and radii of both stars to be $M_A=0.250^{+0.005}_{-0.004}$ M$_{\odot}$, $M_B=0.208^{+0.005}_{-0.004}$ M$_{\odot}$, $R_A=0.255^{+0.004}_{-0.005}$ R$_{\odot}$, $R_B=0.233^{+0.006}_{-0.005}$ R$_{\odot}$. We detect Balmer line emission from at least one of the stars but no significant flare activity. We note that both components lie in the fully convective regime of low-mass stars ($<0.35$ M$_{\odot}$), therefore can be a valuable test for stellar evolutionary models. We demonstrate that the photometric observations, speckle imaging and initial radial velocity measurements were unable to identify the true nature of this system and highlight that high-resolution spectroscopic observations are crucial in determining whether systems such as this are in fact binaries.

astro-ph.SR↗

arXiv2025

Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey

We present morphological classifications of over 41,000 galaxies out to $z_{\rm phot}\sim2.5$ across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey. Galaxy Zoo citizen scientists play a crucial role in the examination of large astronomical data sets through crowdsourced data mining of extragalactic imaging. This iteration, Galaxy Zoo: Cosmic Dawn (GZCD), saw tens of thousands of volunteers and the deep learning foundation model Zoobot collectively classify objects in ultra-deep multiband Hyper Suprime-Cam (HSC) imaging down to a depth of $m_{HSC-i} = 21.5$. Here, we present the details and general analysis of this iteration, including the use of Zoobot in an active learning cycle to improve both model performance and volunteer experience, as well as the discovery of 51 new gravitational lenses in the EDFN. We also announce the public data release of the classifications for over 45,000 subjects, including more than 41,000 galaxies (median $z_{\rm phot}$ of $0.42\pm0.23$), along with their associated image cutouts. This data set provides a valuable opportunity for follow-up imaging of objects in the EDFN as well as acting as a truth set for training deep learning models for application to ground-based surveys like that of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) collaboration and the newly operational Vera C. Rubin Observatory.

astro-ph.GA↗

arXiv2025

VERITAS and multiwavelength observations of the Blazar B3 2247+381 in response to an IceCube neutrino alert

While the sources of the diffuse astrophysical neutrino flux detected by the IceCube Neutrino Observatory are still largely unknown, one of the promising methods used towards understanding this is investigating the potential temporal and spatial correlations between neutrino alerts and the electromagnetic radiation from blazars. We report on the multiwavelength target-of-opportunity observations of the blazar B3 2247+381, taken in response to an IceCube multiplet alert for a cluster of muon neutrino events compatible with the source location between May 20, 2022 and November 10, 2022. B3 2247+381 was not detected with VERITAS during this time period. The source was found to be in a low-flux state in the optical, ultraviolet and gamma-ray bands for the time interval corresponding to the neutrino event, but was detected in the hard X-ray band with NuSTAR during this period. We find the multiwavelength spectral energy distribution is well described using a simple one-zone leptonic synchrotron self-Compton radiation model. Moreover, assuming the neutrinos originate from hadronic processes within the jet, the neutrino flux would be accompanied by a photon flux from the cascade emission, and the integrated photon flux required in such a case would significantly exceed the total multiwavelength fluxes and the VERITAS upper limits presented here. The lack of flaring activity observed with VERITAS, combined with the low multiwavelength flux levels, and given the significance of the neutrino excess is at 3$\sigma$ level (uncorrected for trials), makes B3 2247+381 an unlikely source of the IceCube multiplet. We conclude that the neutrino excess is likely a background fluctuation.

astro-ph.HE↗

arXiv2025

An in-depth study of Gamma rays from the Starburst Galaxy M 82 with VERITAS

Assuming Galactic cosmic rays originate in supernovae and the winds of massive stars, starburst galaxies should produce very-high-energy (VHE; E$>$100 GeV) gamma-ray emission via the interaction of their copious quantities of cosmic rays with the large reservoirs of dense gas within the galaxies. Such VHE emission was detected by VERITAS from the starburst galaxy M 82 in 2008-09. An extensive, multi-year campaign followed these initial observations, yielding a total of 254 h of good quality VERITAS data on M 82. Leveraging modern analysis techniques and the larger exposure, these VERITAS data show a more statistically significant VHE signal ($\sim$6.5 standard deviations ($\sigma$)). The corresponding photon spectrum is well fit by a power law ($\Gamma = 2.3 \pm 0.3_{stat} \pm0.2_{sys}$) and the observed integral flux is F($>$450 GeV) = $(3.2 \pm0.6_{stat} \pm 0.6_{sys}) \times 10^{-13}~\mathrm{cm^{-2}~s}^{-1}$, or $\sim$0.4\% of the Crab Nebula flux above the same energy threshold. The improved VERITAS measurements, when combined with various multi-wavelength data, enable modeling of the underlying emission and transport processes. A purely leptonic scenario is found to be a poor representation of the gamma-ray spectral energy distribution (SED). A lepto-hadronic scenario with cosmic rays following a power-law spectrum in momentum (index $s\simeq 2.25$), and with significant bremsstrahlung below $1$~GeV, provides a good match to the observed SED. The synchrotron emission from the secondary electrons indicates that efficient non-radiative losses of cosmic-ray electrons may be related to advective escape from the starburst core.

astro-ph.HE↗

arXiv2024

Galaxy Zoo: Morphologies based on UKIDSS NIR Imaging for 71,052 Galaxies

We present morphological classifications based on Galaxy Zoo analysis of 71,052 galaxies with imaging from the United Kingdom Infrared Telescope Infrared Deep Sky Survey (UKIDSS). Galaxies were selected out of the Galaxy Zoo 2 (GZ2) sample, so also have gri imaging from the Sloan Digital Sky Survey. An identical classification tree, and vote weighting/aggregation was applied to both UKIDSS and GZ2 classifications enabling direct comparisons. With this Research Note we provide a public release of the GZ:UKIDSS morphologies and discuss some initial comparisons with GZ2.

astro-ph.GA↗

arXiv2024

Jovian Vortex Hunter: a citizen science project to study Jupiter's vortices

The Jovian atmosphere contains a wide diversity of vortices, which have a large range of sizes, colors and forms in different dynamical regimes. The formation processes for these vortices is poorly understood, and aside from a few known, long-lived ovals, such as the Great Red Spot, and Oval BA, vortex stability and their temporal evolution are currently largely unknown. In this study, we use JunoCam data and a citizen-science project on Zooniverse to derive a catalog of vortices, some with repeated observations, through May 2018 to Sep 2021, and analyze their associated properties, such as size, location and color. We find that different colored vortices (binned as white, red, brown and dark), follow vastly different distributions in terms of their sizes and where they are found on the planet. We employ a simplified stability criterion using these vortices as a proxy, to derive a minimum Rossby deformation length for the planet of $\sim1800$ km. We find that this value of $L_d$ is largely constant throughout the atmosphere, and does not have an appreciable meridional gradient.

astro-ph.EP↗

arXiv2024

Scaling Laws for Galaxy Images

We present the first systematic investigation of supervised scaling laws outside of an ImageNet-like context - on images of galaxies. We use 840k galaxy images and over 100M annotations by Galaxy Zoo volunteers, comparable in scale to Imagenet-1K. We find that adding annotated galaxy images provides a power law improvement in performance across all architectures and all tasks, while adding trainable parameters is effective only for some (typically more subjectively challenging) tasks. We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images. We achieve an average relative error rate reduction of 31% across 5 downstream tasks of scientific interest. Our finetuned models are more label-efficient and, unlike their ImageNet-12k-pretrained equivalents, often achieve linear transfer performance equal to that of end-to-end finetuning. We find relatively modest additional downstream benefits from scaling model size, implying that scaling alone is not sufficient to address our domain gap, and suggest that practitioners with qualitatively different images might benefit more from in-domain adaption followed by targeted downstream labelling.

cs.CV↗

arXiv2023

Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN

Giant Star-forming Clumps (GSFCs) are areas of intensive star-formation that are commonly observed in high-redshift (z>1) galaxies but their formation and role in galaxy evolution remain unclear. High-resolution observations of low-redshift clumpy galaxy analogues are rare and restricted to a limited set of galaxies but the increasing availability of wide-field galaxy survey data makes the detection of large clumpy galaxy samples increasingly feasible. Deep Learning, and in particular CNNs, have been successfully applied to image classification tasks in astrophysical data analysis. However, one application of DL that remains relatively unexplored is that of automatically identifying and localising specific objects or features in astrophysical imaging data. In this paper we demonstrate the feasibility of using Deep learning-based object detection models to localise GSFCs in astrophysical imaging data. We apply the Faster R-CNN object detection framework (FRCNN) to identify GSFCs in low redshift (z<0.3) galaxies. Unlike other studies, we train different FRCNN models not on simulated images with known labels but on real observational data that was collected by the Sloan Digital Sky Survey Legacy Survey and labelled by volunteers from the citizen science project `Galaxy Zoo: Clump Scout'. The FRCNN model relies on a CNN component as a `backbone' feature extractor. We show that CNNs, that have been pre-trained for image classification using astrophysical images, outperform those that have been pre-trained on terrestrial images. In particular, we compare a domain-specific CNN -`Zoobot' - with a generic classification backbone and find that Zoobot achieves higher detection performance and also requires smaller training data sets to do so. Our final model is capable of producing GSFC detections with a completeness and purity of >=0.8 while only being trained on ~5,000 galaxy images.

astro-ph.GA↗

arXiv2023

Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)

Development of robust general purpose 3D segmentation frameworks using the latest deep learning techniques is one of the active topics in various bio-medical domains. In this work, we introduce Temporal Cubic PatchGAN (TCuP-GAN), a volume-to-volume translational model that marries the concepts of a generative feature learning framework with Convolutional Long Short-Term Memory Networks (LSTMs), for the task of 3D segmentation. We demonstrate the capabilities of our TCuP-GAN on the data from four segmentation challenges (Adult Glioma, Meningioma, Pediatric Tumors, and Sub-Saharan Africa subset) featured within the 2023 Brain Tumor Segmentation (BraTS) Challenge and quantify its performance using LesionWise Dice similarity and $95\%$ Hausdorff Distance metrics. We demonstrate the successful learning of our framework to predict robust multi-class segmentation masks across all the challenges. This benchmarking work serves as a stepping stone for future efforts towards applying TCuP-GAN on other multi-class tasks such as multi-organelle segmentation in electron microscopy imaging.

eess.IV↗

arXiv2023

TCuPGAN: A novel framework developed for optimizing human-machine interactions in citizen science

In the era of big data in scientific research, there is a necessity to leverage techniques which reduce human effort in labeling and categorizing large datasets by involving sophisticated machine tools. To combat this problem, we present a novel, general purpose model for 3D segmentation that leverages patch-wise adversariality and Long Short-Term Memory to encode sequential information. Using this model alongside citizen science projects which use 3D datasets (image cubes) on the Zooniverse platforms, we propose an iterative human-machine optimization framework where only a fraction of the 2D slices from these cubes are seen by the volunteers. We leverage the patch-wise discriminator in our model to provide an estimate of which slices within these image cubes have poorly generalized feature representations, and correspondingly poor machine performance. These images with corresponding machine proposals would be presented to volunteers on Zooniverse for correction, leading to a drastic reduction in the volunteer effort on citizen science projects. We trained our model on ~2300 liver tissue 3D electron micrographs. Lipid droplets were segmented within these images through human annotation via the `Etch A Cell - Fat Checker' citizen science project, hosted on the Zooniverse platform. In this work, we demonstrate this framework and the selection methodology which resulted in a measured reduction in volunteer effort by more than 60%. We envision this type of joint human-machine partnership will be of great use on future Zooniverse projects.

cs.HC↗

arXiv2023

Galaxy Zoo DESI: Detailed Morphology Measurements for 8.7M Galaxies in the DESI Legacy Imaging Surveys

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our models typically predict the fraction of volunteers selecting each answer to within 5-10\% for every answer to every GZ question. The models are trained on newly-collected votes for DESI-LS DR8 images as well as historical votes from GZ DECaLS. We also release the newly-collected votes. Extending our morphology measurements outside of the previously-released DECaLS/SDSS intersection increases our sky coverage by a factor of 4 (5,000 to 19,000 deg$^2$) and allows for full overlap with complementary surveys including ALFALFA and MaNGA.

astro-ph.GA↗

arXiv2023

A VERITAS/Breakthrough Listen Search for Optical Technosignatures

The Breakthrough Listen Initiative is conducting a program using multiple telescopes around the world to search for "technosignatures": artificial transmitters of extraterrestrial origin from beyond our solar system. The VERITAS Collaboration joined this program in 2018, and provides the capability to search for one particular technosignature: optical pulses of a few nanoseconds duration detectable over interstellar distances. We report here on the analysis and results of dedicated VERITAS observations of Breakthrough Listen targets conducted in 2019 and 2020 and of archival VERITAS data collected since 2012. Thirty hours of dedicated observations of 136 targets and 249 archival observations of 140 targets were analyzed and did not reveal any signals consistent with a technosignature. The results are used to place limits on the fraction of stars hosting transmitting civilizations. We also discuss the minimum-pulse sensitivity of our observations and present VERITAS observations of CALIOP: a space-based pulsed laser onboard the CALIPSO satellite. The detection of these pulses with VERITAS, using the analysis techniques developed for our technosignature search, allows a test of our analysis efficiency and serves as an important proof-of-principle.

astro-ph.IM↗

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$\sigma$ detection with a differential photon spectral index, $\Gamma$ = 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 $\tau_{obs}$ = 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↗