In this paper, we present the identification of five previously unknown giant radio galaxies (GRGs) using Data Release 1 of the Radio Galaxy Zoo citizen science project and a selection method appropriate to the training and validation of deep learning algorithms for new radio surveys. We associate one of these new GRGs with the brightest cluster galaxy (BCG) in the galaxy cluster GMBCG J251.67741+36.45295 and use literature data to identify a further 13 previously known GRGs as BCG candidates, increasing the number of known BCG GRGs by >60%. By examining local galaxy number densities for the number of all known BCG GRGs, we suggest that the existence of this growing number implies that GRGs are able to reside in the centers of rich ($\sim 10^{14}$ M$_{\odot}$) galaxy clusters and challenges the hypothesis that GRGs grow to such sizes only in locally under-dense environments.
Hybrid morphology radio sources are a rare type of radio galaxy that display different Fanaroff-Riley classes on opposite sides of their nuclei. To enhance the statistical analysis of hybrid morphology radio sources, we embarked on a large-scale search of these sources within the international citizen science project, Radio Galaxy Zoo (RGZ). Here, we present 25 new candidate hybrid morphology radio galaxies. Our selected candidates are moderate power radio galaxies (L_median = 4.7x10^{24} W/(Hz sr) at redshifts 0.14<z<1.0. Hosts of nine candidates have spectroscopic observations, of which six …
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-t…
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 probabilitie…
We analyze two binary systems containing giant stars, V723 Mon ("the Unicorn") and 2M04123153+6738486 ("the Giraffe"). Both giants orbit more massive but less luminous companions, previously proposed to be mass-gap black holes. Spectral disentangling reveals luminous companions with star-like spectra in both systems. Joint modeling of the spectra, light curves, and spectral energy distributions robustly constrains the masses, temperatures, and radii of both components: the primaries are luminous, cool giants ($T_{\rm eff,\,giant} = 3,800\,\rm K$ and $4,000\,\rm K$, $R_{\rm giant}= 22.5\,R_{\od…
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…
We use deep learning to explore the nature of observed giant clumps in high-redshift disc galaxies, based on their identification and classification in cosmological simulations. Simulated clumps are detected using the 3D gas and stellar densities in the VELA zoom-in cosmological simulation suite, with $\sim \!\! 25\ \!\rm{pc}$ maximum resolution, targeting main sequence galaxies at $1\!<\!z\!<\!3$. The clumps are classified as long-lived clumps (LLCs) or short-lived clumps (SLCs) based on their longevity in the simulations. We then train neural networks to detect and classify the simulated clu…
A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work we first present a new method based on neural networks to detect clumps in galaxy images. We use this method to detect clumps in the rest-frame optical and UV images of a complete sample of $\sim1500$ star forming galaxies at $1<z<3$ in the CANDELS survey as well as in images from the VELA zoom-in cosmological simulations. We show that observational effects have a dramatic impact on the derived clump properties leadi…
Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP
The Open University（开放大学）；University of Minnesota（明尼苏达大学）；University of Toronto（多伦多大学）；Lancaster University（兰卡斯特大学）；California Institute of Technology（加州理工学院）
利用深度学习的目标检测模型，通过Faster R - CNN处理$ugrizy$波段图像，检测低红移星系中潜在恒星形成团块，验证了模型的检测完整性和纯度，还展示了Zoobot在目标检测下游任务中的应用。
