ABSTRACT
We present a new sample of galaxy-scale strong gravitational lens candidates, selected from 904 deg2 of Data Release 4 of the Kilo-Degree Survey, i.e. the ‘Lenses in the Kilo-Degree Survey’ ...(LinKS) sample. We apply two convolutional neural networks (ConvNets) to ${\sim }88\,000$ colour–magnitude-selected luminous red galaxies yielding a list of 3500 strong lens candidates. This list is further downselected via human inspection. The resulting LinKS sample is composed of 1983 rank-ordered targets classified as ‘potential lens candidates’ by at least one inspector. Of these, a high-grade subsample of 89 targets is identified with potential strong lenses by all inspectors. Additionally, we present a collection of another 200 strong lens candidates discovered serendipitously from various previous ConvNet runs. A straightforward application of our procedure to future Euclid or Large Synoptic Survey Telescope data can select a sample of ∼3000 lens candidates with less than 10 per cent expected false positives and requiring minimal human intervention.
A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z). A wide plethora of methods have been developed, based either on ...template models fitting or on empirical explorations of the photometric parameter space. Machine-learning-based techniques are not explicitly dependent on the physical priors and able to produce accurate photo-z estimations within the photometric ranges derived from the spectroscopic training set. These estimates, however, are not easy to characterize in terms of a photo-z probability density function (PDF), due to the fact that the analytical relation mapping the photometric parameters on to the redshift space is virtually unknown. We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method designed to provide a reliable PDF of the error distribution for empirical techniques. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine-learning model chosen to predict photo-z. We present a summary of results on SDSS-DR9 galaxy data, used also to perform a direct comparison with PDFs obtained by the LE PHARE spectral energy distribution template fitting. We show that METAPHOR is capable to estimate the precision and reliability of photometric redshifts obtained with three different self-adaptive techniques, i.e. MLPQNA, Random Forest and the standard K-Nearest Neighbors models.
Given a flurry of recent claims for systematic variations in the stellar initial mass function (IMF), we carry out the first inventory of the observational evidence using different approaches. This ...includes literature results, as well as our own new findings from combined stellar population synthesis (SPS) and Jeans dynamical analyses of data on ~4500 early-type galaxies (ETGs) from the SPIDER project. We focus on the mass-to-light ratio mismatch relative to the Milky Way IMF, delta sub(IMF), correlated against the central stellar velocity dispersion, sigmalow *. We find a strong correlation between delta sub(IMF) and sigmalow *, for a wide set of dark matter (DM) model profiles. These results are robust if a uniform halo response to baryons is adopted across the sample. The overall normalization of delta sub(IMF) and the detailed DM profile are less certain, but the data are consistent with standard cold DM halos and a central DM fraction that is roughly constant with sigmalow *. For a variety of related studies in the literature, using SPS, dynamics, and gravitational lensing, similar results are found. Studies based solely on spectroscopic line diagnostics agree on a Salpeter-like IMF at high sigmalow * but differ at low sigmalow *. Overall, we find that multiple independent lines of evidence appear to be converging on a systematic variation in the IMF, such that high-sigmalow * ETGs have an excess of low-mass stars relative to spirals and low-sigmalow * ETGs. Robust verification of super-Salpeter IMFs in the highest-sigmalow * galaxies will require additional scrutiny of scatter and systematic uncertainties. The implications for the distribution of DM are still inconclusive.
Spermiogenesis is a radical process of differentiation whereby sperm cells acquire a compact and specialized morphology to cope with the constraints of sexual reproduction while preserving their main ...cargo, an intact copy of the paternal genome. In animals, this often involves the replacement of most histones by sperm-specific nuclear basic proteins (SNBPs). Yet, how the SNBP-structured genome achieves compaction and accommodates shaping remain largely unknown. Here, we exploit confocal, electron and super-resolution microscopy, coupled with polymer modeling to identify the higher-order architecture of sperm chromatin in the needle-shaped nucleus of the emerging model cricket Gryllus bimaculatus. Accompanying spermatid differentiation, the SNBP-based genome is strikingly reorganized as ~25nm-thick fibers orderly coiled along the elongated nucleus axis. This chromatin spool is further found to achieve large-scale helical twisting in the final stages of spermiogenesis, favoring its ultracompaction. We reveal that these dramatic transitions may be recapitulated by a surprisingly simple biophysical principle based on a nucleated rigidification of chromatin linked to the histone-to-SNBP transition within a confined nuclear space. Our work highlights a unique, liquid crystal-like mode of higher-order genome organization in ultracompact cricket sperm, and establishes a multidisciplinary methodological framework to explore the diversity of non-canonical modes of DNA organization.
Abstract
The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyse sources. Indeed, this is the case for the search for strong ...gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in 255 deg2 of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii ≳1.4 arcsec, about twice the r-band seeing in KiDS. In a sample of 21 789 colour–magnitude selected luminous red galaxies (LRGs), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find ∼100 massive LRG-galaxy lenses at z ≲ 0.4 in KiDS when completed. In the most optimistic scenario, this number can grow considerably (to maximally ∼2400 lenses), when widening the colour–magnitude selection and training the CNN to recognize smaller image-separation lens systems.
Abstract
We analyse the mass density distribution in the centres of galaxies across five orders of magnitude in mass range. Using high-quality spiral galaxy rotation curves and infrared photometry ...from SPARC, we conduct a systematic study of their central dark matter (DM) fraction (fDM) and their mass density slope (α), within their effective radius. We show that lower mass spiral galaxies are more DM dominated and have more shallow mass density slopes when compared with more massive galaxies, which have density profiles closer to isothermal. Low-mass (${M_{*}}\lesssim 10^{10}\, {\mathrm{M}_\odot}$) gas-rich spirals span a wide range of fDM values, but systematically lower than in gas-poor systems of similar mass. With increasing galaxy mass, the values of fDM decrease and the density profiles steepen. In the most massive late-type gas-poor galaxies, a possible flattening of these trends is observed. When comparing these results to massive (${M_{*}}\gtrsim 10^{10}\, {\mathrm{M}_\odot}$) elliptical galaxies from SPIDER and to dwarf ellipticals (dEs) from SMACKED, these trends result to be inverted. Hence, the values of both fDM and α, as a function of M*, exhibit a U-shape trend. At a fixed stellar mass, the mass density profiles in dEs are steeper than in spirals. These trends can be understood by stellar feedback from a more prolonged star formation period in spirals, causing a transformation of the initial steep density cusp to a more shallow profile via differential feedback efficiency by supernovae, and by galaxy mergers or AGN feedback in higher mass galaxies.
Abstract
We present 97 new high-quality strong lensing candidates found in the final ∼350 deg
2
that complete the full ∼1350 deg
2
area of the Kilo-Degree Survey (KiDS). Together with our previous ...findings, the final list of high-quality candidates from KiDS sums up to 268 systems. The new sample is assembled using a new convolutional neural network (CNN) classifier applied to
r
-band (best-seeing) and
g
,
r
, and
i
color-composited images separately. This optimizes the complementarity of the morphology and color information on the identification of strong lensing candidates. We apply the new classifiers to a sample of luminous red galaxies (LRGs) and a sample of bright galaxies (BGs) and select candidates that received a high probability to be a lens from the CNN (
P
CNN
). In particular, setting
P
CNN
> 0.8 for the LRGs, the one-band CNN predicts 1213 candidates, while the three-band classifier yields 1299 candidates, with only ∼30% overlap. For the BGs, in order to minimize the false positives, we adopt a more conservative threshold,
P
CNN
> 0.9, for both CNN classifiers. This results in 3740 newly selected objects. The candidates from the two samples are visually inspected by seven coauthors to finally select 97 “high-quality” lens candidates which received mean scores larger than 6 (on a scale from 0 to 10). We finally discuss the effect of the seeing on the accuracy of CNN classification and possible avenues to increase the efficiency of multiband classifiers, in preparation of next-generation surveys from ground and space.
Context. The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope, specifically designed for measuring weak gravitational ...lensing by galaxies and large-scale structure. When completed it will consist of 1350 square degrees imaged in four filters (ugri). Aims. Here we present the fourth public data release which more than doubles the area of sky covered by data release 3. We also include aperture-matched ZYJHKs photometry from our partner VIKING survey on the VISTA telescope in the photometry catalogue. We illustrate the data quality and describe the catalogue content. Methods. Two dedicated pipelines are used for the production of the optical data. The ASTRO-WISE information system is used for the production of co-added images in the four survey bands, while a separate reduction of the r-band images using the THELI pipeline is used to provide a source catalogue suitable for the core weak lensing science case. All data have been re-reduced for this data release using the latest versions of the pipelines. The VIKING photometry is obtained as forced photometry on the THELI sources, using a re-reduction of the VIKING data that starts from the VISTA pawprints. Modifications to the pipelines with respect to earlier releases are described in detail. The photometry is calibrated to the Gaia DR2 G band using stellar locus regression. Results. In this data release a total of 1006 square-degree survey tiles with stacked ugri images are made available, accompanied by weight maps, masks, and single-band source lists. We also provide a multi-band catalogue based on r-band detections, including homogenized photometry and photometric redshifts, for the whole dataset. Mean limiting magnitudes (5σ in a 2″ aperture) and the tile-to-tile rms scatter are 24.23 ± 0.12, 25.12 ± 0.14, 25.02 ± 0.13, 23.68 ± 0.27 in ugri, respectively, and the mean r-band seeing is 0.″70.
We report new high-quality galaxy-scale strong lens candidates found in the Kilo-Degree Survey data release 4 using machine learning. We have developed a new convolutional neural network (CNN) ...classifier to search for gravitational arcs, following the prescription by Petrillo et al. and using only r-band images. We have applied the CNN to two "predictive samples": a luminous red galaxy (LRG) and a "bright galaxy" (BG) sample (r < 21). We have found 286 new high-probability candidates, 133 from the LRG sample and 153 from the BG sample. We have ranked these candidates based on a value that combines the CNN likelihood of being a lens and the human score resulting from visual inspection (P-value), and here we present the highest 82 ranked candidates with P-values ≥0.5. All of these high-quality candidates have obvious arc or pointlike features around the central red defector. Moreover, we define the best 26 objects, all with P-values ≥0.7, as a "golden sample" of candidates. This sample is expected to contain very few false positives; thus, it is suitable for follow-up observations. The new lens candidates come partially from the more extended footprint adopted here with respect to the previous analyses and partially from a larger predictive sample (also including the BG sample). These results show that machine-learning tools are very promising for finding strong lenses in large surveys and more candidates can be found by enlarging the predictive samples beyond the standard assumption of LRGs. In the future, we plan to apply our CNN to the data from next-generation surveys such as the Large Synoptic Survey Telescope, Euclid, and the Chinese Space Station Optical Survey.
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