A Bayesian Approach to Strong Lens Finding in the Era of Wide-area Surveys

The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from $\mathcal{O}(10^3)$ to $\mathcal{O}(10^5)$. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong-lens experts to remove false positives which is only set to increase with these surveys. In this work we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam (HSC) survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false positive rate of $10^{-3}$ can be achieved with a completeness of $46\%$, compared to $34\%$ for the best individual classifier. Given the large number of galaxy-galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses.Comment: Submitted to MNRAS, 14 pages, 9 figures. Comments welcom

Constraining the multi-scale dark-matter distribution in CASSOWARY 31 with strong gravitational lensing and stellar dynamics

We study the inner structure of the group-scale lens CASSOWARY 31 (CSWA 31) by adopting both strong lensing and dynamical modeling. CSWA 31 is a peculiar lens system. The brightest group galaxy (BGG) is an ultra-massive elliptical galaxy at z = 0.683 with a weighted mean velocity dispersion of $\sigma = 432 \pm 31$ km s$^{-1}$. It is surrounded by group members and several lensed arcs probing up to ~150 kpc in projection. Our results significantly improve previous analyses of CSWA 31 thanks to the new HST imaging and MUSE integral-field spectroscopy. From the secure identification of five sets of multiple images and measurements of the spatially-resolved stellar kinematics of the BGG, we conduct a detailed analysis of the multi-scale mass distribution using various modeling approaches, both in the single and multiple lens-plane scenarios. Our best-fit mass models reproduce the positions of multiple images and provide robust reconstructions for two background galaxies at z = 1.4869 and z = 2.763. The relative contributions from the BGG and group-scale halo are remarkably consistent in our three reference models, demonstrating the self-consistency between strong lensing analyses based on image position and extended image modeling. We find that the ultra-massive BGG dominates the projected total mass profiles within 20 kpc, while the group-scale halo dominates at larger radii. The total projected mass enclosed within $R_{eff}$ = 27.2 kpc is $1.10_{-0.04}^{+0.02} \times 10^{13}$ M$_\odot$. We find that CSWA 31 is a peculiar fossil group, strongly dark-matter dominated towards the central region, and with a projected total mass profile similar to higher-mass cluster-scale halos. The total mass-density slope within the effective radius is shallower than isothermal, consistent with previous analyses of early-type galaxies in overdense environments.Comment: 22 pages, 12 figures, 5 tables, submitted to Astronomy & Astrophysics. We welcome the comments from reader

Planck \u27s Dusty GEMS: VIII. Dense-gas reservoirs in the most active dusty starbursts at z ∼3

We present ALMA, NOEMA, and IRAM-30 m/EMIR observations of the high-density tracer molecules HCN, HCO+, and HNC in three of the brightest lensed dusty star-forming galaxies at z≲ 3-3.5, part of the Planck\u27s Dusty Gravitationally Enhanced subMillimetre Sources (GEMS), with the aim of probing the gas reservoirs closely associated with their exceptional levels of star formation. We obtained robust detections of ten emission lines between Jup = 4 and 6, as well as several additional upper flux limits. In PLCK_G244.8+54.9, the brightest source at z = 3.0, the HNC(5-4) line emission at 0.1″ resolution, together with other spatially-integrated line profiles, suggests comparable distributions of dense and more diffuse gas reservoirs, at least over the most strongly magnified regions. This rules out any major effect from differential lensing. This line is blended with CN(4-3) and in this source, we measure a HNC(5-4)/CN(4-3) flux ratio of 1.76 \ub10. 86. Dense-gas line profiles generally match those of mid-J CO lines, except in PLCK_G145.2+50.8, which also has dense-gas line fluxes that are relatively lower, perhaps due to fewer dense cores and more segregated dense and diffuse gas phases in this source. The HCO+/HCN 1 and HNC/HCN ∼ 1 line ratios in our sample are similar to those of nearby ultraluminous infrared galaxies (ULIRGs) and consistent with photon-dominated regions without any indication of important mechanical heating or active galactic nuclei feedback. We characterize the dense-gas excitation in PLCK_G244.8+54.9 using radiative transfer models assuming pure collisional excitation and find that mid-J HCN, HCO+, and HNC lines arise from a high-density phase with an H2 density of n ∼ 105-106 cm-3, although important degeneracies hinder a determination of the exact conditions. The three GEMS are consistent with extrapolations of dense-gas star-formation laws derived in the nearby Universe, adding further evidence that the extreme star-formation rates observed in the most active galaxies at z ∼ 3 are a consequence of their important dense-gas contents. The dense-gas-mass fractions traced by HCN/[CI] and HCO+/[CI] line ratios are elevated, but not exceptional as compared to other lensed dusty star-forming galaxies at z > 2, and they fall near the upper envelope of local ULIRGs. Despite the higher overall gas fractions and local gas-mass surface densities observed at high redshift, the dense-gas budget of rapidly star-forming galaxies seems to have evolved little between z ∼ 3 and z ∼ 0. Our results favor constant dense-gas depletion times in these populations, which is in agreement with theoretical models of star formation

The impact of human expert visual inspection on the discovery of strong gravitational lenses

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% 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 unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst 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

Photometric redshift estimation with a convolutional neural network: NetZ

Galaxy redshifts are a key characteristic for nearly all extragalactic studies. Since spectroscopic redshifts require additional telescope and human resources, millions of galaxies are known without spectroscopic redshifts. Therefore, it is crucial to have methods for estimating the redshift of a galaxy based on its photometric properties, the so-called photo-z. We have developed NetZ, a new method using a convolutional neural network (CNN) to predict the photo-z based on galaxy images, in contrast to previous methods that often used only the integrated photometry of galaxies without their images. We use data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) in five different filters as the training data. The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-z range, where it fares better than other methods on the same data. We obtained a precision |zpred − zref| of σ = 0.12 (68% confidence interval) with a CNN working for all galaxy types averaged over all galaxies in the redshift range of 0 to ∼4. We carried out a comparison with a network trained on point-like sources, highlighting the importance of morphological information for our redshift estimation. By limiting the scope to smaller redshift ranges or to luminous red galaxies, we find a further notable improvement. We have published more than 34 million new photo-z values predicted with NetZ. This shows that the new method is very simple and swift in application, and, importantly, it covers a wide redshift range that is limited only by the available training data. It is broadly applicable, particularly with regard to upcoming surveys such as the Rubin Observatory Legacy Survey of Space and Time, which will provide images of billions of galaxies with similar image quality as HSC. Our HSC photo-z estimates are also beneficial to the Euclid survey, given the overlap in the footprints of the HSC and Euclid

Searching for Strong Gravitational Lenses

Acknowledgements: We thank the International Space Science Institute in Bern (ISSI) for their hospitality and the conveners for organizing the stimulating workshop on “Strong Gravitational Lensing”.Funder: EPFL LausanneAbstractStrong gravitational lenses provide unique laboratories for cosmological and astrophysical investigations, but they must first be discovered – a task that can be met with significant contamination by other astrophysical objects and asterisms. Here we review strong lens searches, covering various sources (quasars, galaxies, supernovae, FRBs, GRBs, and GWs), lenses (early- and late-type galaxies, groups, and clusters), datasets (imaging, spectra, and lightcurves), and wavelengths. We first present the physical characteristics of the lens and source populations, highlighting relevant details for constructing targeted searches. Search techniques are described based on the main lensing feature that is required for the technique to work, namely one of: (i) an associated magnification, (ii) multiple spatially-resolved images, (iii) multiple redshifts, or (iv) a non-zero time delay between images. To use the current lens samples for science, and for the design of future searches, we list several selection biases that exist due to these discovery techniques. We conclude by discussing the future of lens searches in upcoming surveys and the new population of lenses that will be discovered.</jats:p

Milk fatty acid profile from grazing buffaloes fed a blend of soybean and linseed oils

The aim of the study was to examine the changes in milk fatty acid (FA) profile of grazing buffaloes fed either low (L, 276g/d) or high (H, 572g/d) doses of a blend (70:30, wt/wt) of soybean and linseed oils. Fourteen multiparous Mediterranean buffaloes grazing on a native pasture were fed 4 kg/day of a commercial concentrate containing no supplemental oil over a pre-experimental period of ten days. The baseline milk production and composition and milk FA profile were measured over the last three days. After this pre-experimental period the animals received the same concentrate added with either the L or H oil doses for 26 additional days. Milk yield (g/animal/day) did not differ at the start (1776 ± 522 and 1662 ± 291 for L and H, respectively, P<0.622) or at the end of the trial (4590 ± 991 and 4847 ± 447 in L and H, respectively, P<0.543). Baseline milk fat content (g/kg) averaged 77.1 (±20.5) in L and 74.3 (±9.9) in H (P<0.10) and was reduced (P<0.031) to 60.7 (±23.6) and 49.4 (±11.2) (P<0.0031) respectively after L and H with no differences between treatments (P<0.277). Baseline milk protein content (L=43.2 ± 3.4 and H= 44.3 ± 6.9g/kg) increased after oil supplementation (P<0.0001) in both L (73.2 ± 6.0g/kg) and H (68.4 ± 4.9g/kg) without differences between oil doses (P<0.123). Milk fat content of 14:0 decreased after oil supplementation only in the H treatment (5.29 to 4.03, P<0.007) whereas that of 16:0 was reduced (P<0.001) at both L (24.49 to 19.75g/100g FA) and H (25.92 to 19.17g/100g FA) doses. The reduction of total content of 12:0 to 16:0 was higher (P<0.052) in H (32.02 to 23.93g/100g FA) than L (30.17 to 25.45g/100g FA). Vaccenic acid content increased (P<0.001) from 5.70 to 13.24g/100g FA in L and from 5.25 to 16.77 in H, with higher results in the in H treatment (P<0.001). Baseline rumenic acid was sharply increased (P<0.001) in L (1.80 to 4.09g/100g FA, +127%) and H (1.60 to 4.61g/100g FA, +187%) with no differences between L and H (P<0.19). Overall, these results indicate a pronounced improvement in the nutritional value of milk fat from grazing buffaloes fed little amounts (0.276g/day) of a blend of soybean and linseed oils

HOLISMOKES

We have carried out a systematic search for galaxy-scale strong lenses in multiband imaging from the Hyper Suprime-Cam (HSC) survey. Our automated pipeline, based on realistic strong-lens simulations, deep neural network classification, and visual inspection, is aimed at efficiently selecting systems with wide image separations (Einstein radii θE ∼ 1.0–3.0″), intermediate redshift lenses (z ∼ 0.4–0.7), and bright arcs for galaxy evolution and cosmology. We classified gri images of all 62.5 million galaxies in HSC Wide with i-band Kron radius ≥0.8″ to avoid strict preselections and to prepare for the upcoming era of deep, wide-scale imaging surveys with Euclid and Rubin Observatory. We obtained 206 newly-discovered candidates classified as definite or probable lenses with either spatially-resolved multiple images or extended, distorted arcs. In addition, we found 88 high-quality candidates that were assigned lower confidence in previous HSC searches, and we recovered 173 known systems in the literature. These results demonstrate that, aided by limited human input, deep learning pipelines with false positive rates as low as ≃0.01% can be very powerful tools for identifying the rare strong lenses from large catalogs, and can also largely extend the samples found by traditional algorithms. We provide a ranked list of candidates for future spectroscopic confirmation