ABSTRACT
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 versus 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. We use active learning to focus our volunteer effort on the galaxies which, if labelled, would be most informative for training our ensemble. When measured against confident volunteer classifications, the trained networks are approximately 99 per cent 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.
We report the discovery of 29 promising (and 59 total) new lens candidates from the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) based on about 11 million classifications performed by ...citizen scientists as part of the first Space Warps lens search. The goal of the blind lens search was to identify lens candidates missed by robots (the ringfinder on galaxy scales and arcfinder on group/cluster scales) which had been previously used to mine the CFHTLS for lenses. We compare some properties of the samples detected by these algorithms to the Space Warps sample and find them to be broadly similar. The image separation distribution calculated from the Space Warps sample shows that previous constraints on the average density profile of lens galaxies are robust. Space Warps recovers about 65 per cent of known lenses, while the new candidates show a richer variety compared to those found by the two robots. This detection rate could be increased to 80 per cent by only using classifications performed by expert volunteers (albeit at the cost of a lower purity), indicating that the training and performance calibration of the citizen scientists is very important for the success of Space Warps. In this work we present the SIMCT pipeline, used for generating in situ a sample of realistic simulated lensed images. This training sample, along with the false positives identified during the search, has a legacy value for testing future lens-finding algorithms. We make the pipeline and the training set publicly available.
We describe Space Warps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowdsourced visual inspection. Carefully produced colour composite ...images are displayed to volunteers via a web-based classification interface, which records their estimates of the positions of candidate lensed features. Images of simulated lenses, as well as real images which lack lenses, are inserted into the image stream at random intervals; this training set is used to give the volunteers instantaneous feedback on their performance, as well as to calibrate a model of the system that provides dynamical updates to the probability that a classified image contains a lens. Low-probability systems are retired from the site periodically, concentrating the sample towards a set of lens candidates. Having divided 160 deg2 of Canada–France–Hawaii Telescope Legacy Survey imaging into some 430 000 overlapping 82 by 82 arcsec tiles and displaying them on the site, we were joined by around 37 000 volunteers who contributed 11 million image classifications over the course of eight months. This stage 1 search reduced the sample to 3381 images containing candidates; these were then refined in stage 2 to yield a sample that we expect to be over 90 per cent complete and 30 per cent pure, based on our analysis of the volunteers performance on training images. We comment on the scalability of the Space Warps system to the wide field survey era, based on our projection that searches of 105 images could be performed by a crowd of 105 volunteers in 6 d.
Context. Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, however, they are rare and difficult to find. The number of ...currently known lenses is on the order of 1000. Aims. The aim of this study is 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. Methods. Based on the S16A internal data release of the HSC survey, we chose a sample of ∼300 000 galaxies with photometric redshifts in the range of 0.2 < zphot < 1.2 and photometrically inferred stellar masses of 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 6000 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. Results. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising ∼1500 candidates, which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. By 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 (grade A), 129 probable lenses (grade B), and 581 possible lenses. YATTALENS found half the number of lenses that were discovered via crowdsourcing. Conclusions. Crowdsourcing is able to produce samples of lens candidates with high completeness, when multiple images are clearly detected, and with higher purity compared to the currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s, with forthcoming wide-area surveys such as LSST, Euclid, and WFIRST.
Nasopharyngeal cancer (NPC) is a malignant epithelial carcinoma which is intimately associated with EBV. The latent presence of EBV affects the function of p53, Bcl-2, and survivin. We thus ...investigated the relationship between EBV status, p53, Bcl-2, and survivin in biopsy specimens from patients with primary NPC.
Archival formalin-fixed, paraffin-embedded NPC biopsies were evaluated in 80 patients treated with curative radiation from a single institution. The presence of EBV was determined using EBER in situ hybridization, whereas p53, Bcl-2, and survivin were assessed using immunohistochemistry.
The majority of NPC specimens in this patient cohort were EBER-positive (64 of 78, or 82%), which in turn, was significantly associated with ethnicity (P = 0.0007), and WHO subtype 2A/2B (P = 0.04). EBER-positive tumors were also associated with p53 (P = 0.002), Bcl-2 (P = 0.04), and nuclear survivin (P = 0.03) expression. Patients with EBER-positive NPC fared better, with a 10-year overall survival of 68% versus 48% for EBER-negative patients (P = 0.03). For nuclear survivin, patients with either low or high nuclear survivin fared worse than patients with intermediate survivin expression (P = 0.05), suggesting that there is an optimal proportion of survivin-expressing cells for best function and clinical outcome.
With an extended median follow-up time of 11.4 years, EBV status remains a strong predictor for overall survival in NPC. EBV-positive NPC has strong molecular associations with p53, Bcl-2, and survivin expression. Furthermore, we provide clinical data revealing the potentially dual nature of survivin in predicting clinical outcome.
Context. Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, however, they are rare and difficult to find. The number of ...currently known lenses is on the order of 1000. Aims. The aim of this study is 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. Methods. Based on the S16A internal data release of the HSC survey, we chose a sample of ∼300 000 galaxies with photometric redshifts in the range of 0.2 < z phot < 1.2 and photometrically inferred stellar masses of 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 6000 citizen volunteers participated in the experiment. In parallel, we used Y ATTA L ENS , an automated lens-finding algorithm, to look for lenses in the same sample of galaxies. Results. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising ∼1500 candidates, which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. By including lenses found by Y ATTA L ENS or serendipitously noticed in the discussion section of the Space Warps website, we were able to find 14 definite lenses (grade A), 129 probable lenses (grade B), and 581 possible lenses. Y ATTA L ENS found half the number of lenses that were discovered via crowdsourcing. Conclusions. Crowdsourcing is able to produce samples of lens candidates with high completeness, when multiple images are clearly detected, and with higher purity compared to the currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s, with forthcoming wide-area surveys such as LSST, Euclid , and WFIRST.
ABSTRACT
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 per cent 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 (5000–19 000 deg2) and allows for full overlap with complementary surveys including ALFALFA and MaNGA.
ABSTRACT
We investigate the ability of human ‘expert’ classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed ...more than 25 per cent of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabelled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, while arcs with g-band signal to noise less than ∼25 or Einstein radii less than ∼1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier’s experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.
AbstractWe report modelling follow-up of recently discovered gravitational-lens candidates in the Canada France Hawaii Telescope Legacy Survey. Lens modelling was done by a small group of specially ...interested volunteers from the Space Warps citizen-science community who originally found the candidate lenses. Models are categorized according to seven diagnostics indicating (a) the image morphology and how clear or indistinct it is, (b) whether the mass map and synthetic lensed image appear to be plausible, and (c) how the lens-model mass compares with the stellar mass and the abundance-matched halo mass. The lensing masses range from ∼1011 to >1013 Modot . Preliminary estimates of the stellar masses show a smaller spread in stellar mass (except for two lenses): a factor of a few below or above ∼1011 Modot . Therefore, we expect the stellar-to-total mass fraction to decline sharply as lensing mass increases. The most massive system with a convincing model is J1434+522 (SW 05). The two low-mass outliers are J0206-095 (SW 19) and J2217+015 (SW 42); if these two are indeed lenses, they probe an interesting regime of very low star formation efficiency. Some improvements to the modelling software (SpaghettiLens), and discussion of strategies regarding scaling to future surveys with more and frequent discoveries, are included.
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