arXiv subjects

M. Brescia

Publications and source records attributed to M. Brescia.

At least 307 records · Page 17Linked to original sources

arXiv2020

Euclid: impact of nonlinear prescriptions on cosmological parameter estimation from weak lensing cosmic shear

Upcoming surveys will map the growth of large-scale structure with unprecented precision, improving our understanding of the dark sector of the Universe. Unfortunately, much of the cosmological information is encoded by the small scales, where the clustering of dark matter and the effects of astrophysical feedback processes are not fully understood. This can bias the estimates of cosmological parameters, which we study here for a joint analysis of mock Euclid cosmic shear and Planck cosmic microwave background data. We use different implementations for the modelling of the signal on small scales and find that they result in significantly different predictions. Moreover, the different nonlinear corrections lead to biased parameter estimates, especially when the analysis is extended into the highly nonlinear regime, with both the Hubble constant, $H_0$, and the clustering amplitude, $σ_8$, affected the most. Improvements in the modelling of nonlinear scales will therefore be needed if we are to resolve the current tension with more and better data. For a given prescription for the nonlinear power spectrum, using different corrections for baryon physics does not significantly impact the precision of Euclid, but neglecting these correction does lead to large biases in the cosmological parameters. In order to extract precise and unbiased constraints on cosmological parameters from Euclid cosmic shear data, it is therefore essential to improve the accuracy of the recipes that account for nonlinear structure formation, as well as the modelling of the impact of astrophysical processes that redistribute the baryons.

astro-ph.CO↗

arXiv2020

Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

We present a new, updated version of the EuclidEmulator (called EuclidEmulator2), a fast and accurate predictor for the nonlinear correction of the matter power spectrum. Percent-level accurate emulation is now supported in the eight-dimensional parameter space of $w_0w_a$CDM$+\sum m_ν$models between redshift $z=0$ and $z=3$ for spatial scales within the range 0.01 $h$/Mpc $\leq k \leq$ 10 $h$/Mpc. In order to achieve this level of accuracy, we have had to improve the quality of the underlying N-body simulations used as training data: (1) we use self-consistent linear evolution of non-dark matter species such as massive neutrinos, photons, dark energy and the metric field, (2) we perform the simulations in the so-called N-body gauge, which allows one to interpret the results in the framework of general relativity, (3) we run over 250 high-resolution simulations with $3000^3$ particles in boxes of 1 (Gpc/$h$)${}^3$ volumes based on paired-and-fixed initial conditions and (4) we provide a resolution correction that can be applied to emulated results as a post-processing step in order to drastically reduce systematic biases on small scales due to residual resolution effects in the simulations. We find that the inclusion of the dynamical dark energy parameter $w_a$ significantly increases the complexity and expense of creating the emulator. The high fidelity of EuclidEmulator2 is tested in various comparisons against N-body simulations as well as alternative fast predictors like Halofit, HMCode and CosmicEmu. A blind test is successfully performed against the Euclid Flagship v2.0 simulation. Nonlinear correction factors emulated with EuclidEmulator2 are accurate at the level of 1% or better for 0.01 $h$/Mpc $\leq k \leq$ 10 $h$/Mpc and $z\leq3$ compared to high-resolution dark matter only simulations. EuclidEmulator2 is publicly available at https://github.com/miknab/EuclidEmulator2 .

astro-ph.CO↗

arXiv2020

The search for galaxy cluster members with deep learning of panchromatic HST imaging and extensive spectroscopy

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the classification capabilities of convolution neural networks (CNNs) to identify galaxy cluster members (CLMs) by using Hubble Space Telescope (HST) images of 15 galaxy clusters at redshift 0.19<z<0.60, observed as part of the CLASH and Hubble Frontier Field programmes. We used extensive spectroscopic information, based on the CLASH-VLT VIMOS programme combined with MUSE observations, to define the knowledge base. We performed various tests to quantify how well CNNs can identify cluster members on the basis of imaging information only. We investigated the CNN capability to predict source memberships outside the training coverage, by identifying CLMs at the faint end of the magnitude distributions. We find that the CNNs achieve a purity-completeness rate ~90%, demonstrating stable behaviour, along with a remarkable generalisation capability with respect to cluster redshifts. We concluded that if extensive spectroscopic information is available as a training base, the proposed approach is a valid alternative to catalogue-based methods because it has the advantage of avoiding photometric measurements, which are particularly challenging and time-consuming in crowded cluster cores. As a byproduct, we identified 372 photometric CLMs, with mag(F814)<25, to complete the sample of 812 spectroscopic CLMs in four galaxy clusters RX~J2248-4431, MACS~J0416-2403, MACS~J1206-0847 and MACS~J1149+2223. When this technique is applied to the data that are expected to become available from forthcoming surveys, it will be an efficient tool for a variety of studies requiring CLM selection, such as galaxy number densities, luminosity functions, and lensing mass reconstruction.

astro-ph.GA↗

arXiv2020

Statistical characterization and classification of astronomical transients with Machine Learning in the era of the Vera C. Rubin Observatory

Astronomy has entered the multi-messenger data era and Machine Learning has found widespread use in a large variety of applications. The exploitation of synoptic (multi-band and multi-epoch) surveys, like LSST (Legacy Survey of Space and Time), requires an extensive use of automatic methods for data processing and interpretation. With data volumes in the petabyte domain, the discrimination of time-critical information has already exceeded the capabilities of human operators and crowds of scientists have extreme difficulty to manage such amounts of data in multi-dimensional domains. This work is focused on an analysis of critical aspects related to the approach, based on Machine Learning, to variable sky sources classification, with special care to the various types of Supernovae, one of the most important subjects of Time Domain Astronomy, due to their crucial role in Cosmology. The work is based on a test campaign performed on simulated data. The classification was carried out by comparing the performances among several Machine Learning algorithms on statistical parameters extracted from the light curves. The results make in evidence some critical aspects related to the data quality and their parameter space characterization, propaedeutic to the preparation of processing machinery for the real data exploitation in the incoming decade.

astro-ph.IM↗

arXiv2020

Euclid preparation: VIII. The Complete Calibration of the Colour-Redshift Relation survey: VLT/KMOS observations and data release

The Complete Calibration of the Colour-Redshift Relation survey (C3R2) is a spectroscopic effort involving ESO and Keck facilities designed to empirically calibrate the galaxy colour-redshift relation - P(z|C) to the Euclid depth (i_AB=24.5) and is intimately linked to upcoming Stage IV dark energy missions based on weak lensing cosmology. The aim is to build a spectroscopic calibration sample that is as representative as possible of the galaxies of the Euclid weak lensing sample. In order to minimise the number of spectroscopic observations to fill the gaps in current knowledge of the P(z|C), self-organising map (SOM) representations of the galaxy colour space have been constructed. Here we present the first results of an ESO@ VLT Large Programme approved in the context of C3R2, which makes use of the two VLT optical and near-infrared multi-object spectrographs, FORS2 and KMOS. This paper focuses on high-quality spectroscopic redshifts of high-z galaxies observed with the KMOS spectrograph in the H- and K-bands. A total of 424 highly-reliable z are measured in the 1.3<=z<=2.5 range, with total success rates of 60.7% in the H-band and 32.8% in the K-band. The newly determined z fill 55% of high and 35% of lower priority empty SOM grid cells. We measured Halpha fluxes in a 1."2 radius aperture from the spectra of the spectroscopically confirmed galaxies and converted them into star formation rates. In addition, we performed an SED fitting analysis on the same sample in order to derive stellar masses, E(B-V), total magnitudes, and SFRs. We combine the results obtained from the spectra with those derived via SED fitting, and we show that the spectroscopic failures come from either weakly star-forming galaxies (at z<1.7, i.e. in the H-band) or low S/N spectra (in the K-band) of z>2 galaxies.

astro-ph.GA↗

arXiv2020

Nature versus nurture: relic nature and environment of the most massive passive galaxies at $z < 0.5$

Relic galaxies are thought to be the progenitors of high-redshift red nuggets that for some reason missed the channels of size growth and evolved passively and undisturbed since the first star formation burst (at $z>2$). These local ultracompact old galaxies are unique laboratories for studying the star formation processes at high redshift and thus the early stage of galaxy formation scenarios. Counterintuitively, theoretical and observational studies indicate that relics are more common in denser environments, where merging events predominate. To verify this scenario, we compared the number counts of a sample of ultracompact massive galaxies (UCMGs) selected within the third data release of the Kilo Degree Survey, that is, systems with sizes $R_{\rm e} < 1.5 \, \rm kpc$ and stellar masses $M_{\rm \star} > 8 \times 10^{10}\, \rm M_{\odot}$, with the number counts of galaxies with the same masses but normal sizes in field and cluster environments. Based on their optical and near-infrared colors, these UCMGs are likely to be mainly old, and hence representative of the relic population. We find that both UCMGs and normal-size galaxies are more abundant in clusters and their relative fraction depends only mildly on the global environment, with denser environments penalizing the survival of relics. Hence, UCMGs (and likely relics overall) are not special because of the environment effect on their nurture, but rather they are just a product of the stochasticity of the merging processes regardless of the global environment in which they live.

astro-ph.GA↗

arXiv2020

Euclid: The importance of galaxy clustering and weak lensing cross-correlations within the photometric Euclid survey

The data from the Euclid mission will enable the measurement of the photometric redshifts, angular positions, and weak lensing shapes for over a billion galaxies. This large dataset will allow for cosmological analyses using the angular clustering of galaxies and cosmic shear. The cross-correlation (XC) between these probes can tighten constraints and it is therefore important to quantify their impact for Euclid. In this study we carefully quantify the impact of XC not only on the final parameter constraints for different cosmological models, but also on the nuisance parameters. In particular, we aim at understanding the amount of additional information that XC can provide for parameters encoding systematic effects, such as galaxy bias or intrinsic alignments (IA). We follow the formalism presented in Euclid Collaboration: Blanchard et al. (2019) and make use of the codes validated therein. We show that XC improves the dark energy Figure of Merit (FoM) by a factor $\sim 5$, whilst it also reduces the uncertainties on galaxy bias by $\sim 17\%$ and the uncertainties on IA by a factor $\sim 4$. We observe that the role of XC on the final parameter constraints is qualitatively the same irrespective of the galaxy bias model used. We also show that XC can help in distinguishing between different IA models, and that if IA terms are neglected then this can lead to significant biases on the cosmological parameters. We find that the XC terms are necessary to extract the full information content from the data in future analyses. They help in better constraining the cosmological model, and lead to a better understanding of the systematic effects that contaminate these probes. Furthermore, we find that XC helps in constraining the mean of the photometric-redshift distributions, but it requires a more precise knowledge of this mean in order not to degrade the final FoM. [Abridged]

astro-ph.CO↗

arXiv2020

Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

Stage IV weak lensing experiments will offer more than an order of magnitude leap in precision. We must therefore ensure that our analyses remain accurate in this new era. Accordingly, previously ignored systematic effects must be addressed. In this work, we evaluate the impact of the reduced shear approximation and magnification bias, on the information obtained from the angular power spectrum. To first-order, the statistics of reduced shear, a combination of shear and convergence, are taken to be equal to those of shear. However, this approximation can induce a bias in the cosmological parameters that can no longer be neglected. A separate bias arises from the statistics of shear being altered by the preferential selection of galaxies and the dilution of their surface densities, in high-magnification regions. The corrections for these systematic effects take similar forms, allowing them to be treated together. We calculated the impact of neglecting these effects on the cosmological parameters that would be determined from Euclid, using cosmic shear tomography. To do so, we employed the Fisher matrix formalism, and included the impact of the super-sample covariance. We also demonstrate how the reduced shear correction can be calculated using a lognormal field forward modelling approach. These effects cause significant biases in Omega_m, sigma_8, n_s, Omega_DE, w_0, and w_a of -0.53 sigma, 0.43 sigma, -0.34 sigma, 1.36 sigma, -0.68 sigma, and 1.21 sigma, respectively. We then show that these lensing biases interact with another systematic: the intrinsic alignment of galaxies. Accordingly, we develop the formalism for an intrinsic alignment-enhanced lensing bias correction. Applying this to Euclid, we find that the additional terms introduced by this correction are sub-dominant.

astro-ph.CO↗

arXiv2019

The Hi-GAL catalogue of dusty filamentary structures in the Galactic Plane

The recent data collected by {\it Herschel} have confirmed that interstellar structures with filamentary shape are ubiquitously present in the Milky Way. Filaments are thought to be formed by several physical mechanisms acting from the large Galactic scales down to the sub-pc fractions of molecular clouds, and they might represent a possible link between star formation and the large-scale structure of the Galaxy. In order to study this potential link, a statistically significant sample of filaments spread throughout the Galaxy is required. In this work we present the first catalogue of $32,059$ candidate filaments automatically identified in the Hi-GAL survey of the entire Galactic Plane. For these objects we determined morphological (length, $l^{a}$, and geometrical shape) and physical (average column density, $N_{\rm H_{2}}$, and average temperature, $T$) properties. We identified filaments with a wide range of properties: 2$'$\,$\leq l^{a}\leq$\, 100$'$, $10^{20} \leq N_{\rm H_{2}} \leq 10^{23}$\,cm$^{-2}$ and $10 \leq T\leq$ 35\,K. We discuss their association with the Hi-GAL compact sources, finding that the most tenuous (and stable) structures do not host any major condensation and we also assign a distance to $\sim 18,400$ filaments for which we determine mass, physical size, stability conditions and Galactic distribution. When compared to the spiral arms structure, we find no significant difference between the physical properties of on-arm and inter-arm filaments. We compared our sample with previous studies, finding that our Hi-GAL filament catalogue represents a significant extension in terms of Galactic coverage and sensitivity. This catalogue represents an unique and important tool for future studies devoted to understanding the filament life-cycle.

astro-ph.GA↗

arXiv2019

Euclid preparation: V. Predicted yield of redshift 7<z<9 quasars from the wide survey

We provide predictions of the yield of $7 8$ may be selected from Euclid $OYJH$ photometry alone, but selection over the redshift interval $7 7.5$ should be found in the DR1 data release, expected in 2024. It will be possible to determine the bright-end slope of the QLF, $7<z<8$, $M_{1450}<-25$, using 8m class telescopes to confirm candidates, but follow-up with JWST or E-ELT will be required to measure the faint-end slope. Contamination of the candidate lists is predicted to be modest even at $J_{AB}\sim23$. The precision with which $k$ can be determined over $7<z<8$ depends on the value of $k$, but assuming $k=-0.72$ it can be measured to a 1 sigma uncertainty of 0.07.

astro-ph.GA↗

arXiv2019

Euclid preparation: VI. Verifying the Performance of Cosmic Shear Experiments

Our aim is to quantify the impact of systematic effects on the inference of cosmological parameters from cosmic shear. We present an end-to-end approach that introduces sources of bias in a modelled weak lensing survey on a galaxy-by-galaxy level. Residual biases are propagated through a pipeline from galaxy properties (one end) through to cosmic shear power spectra and cosmological parameter estimates (the other end), to quantify how imperfect knowledge of the pipeline changes the maximum likelihood values of dark energy parameters. We quantify the impact of an imperfect correction for charge transfer inefficiency (CTI) and modelling uncertainties of the point spread function (PSF) for Euclid, and find that the biases introduced can be corrected to acceptable levels.

astro-ph.CO↗

arXiv2019

Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era

With the launch of eROSITA (extended Roentgen Survey with an Imaging Telescope Array), successfully occurred on 2019 July 13, we are facing the challenge of computing reliable photometric redshifts for 3 million of active galactic nuclei (AGNs) over the entire sky, having available only patchy and inhomogeneous ancillary data. While we have a good understanding of the photo-z quality obtainable for AGN using spectral energy distribution (SED)-fitting technique, we tested the capability of machine learning (ML), usually reliable in computing photo-z for QSO in wide and shallow areas with rich spectroscopic samples. Using MLPQNA as example of ML, we computed photo-z for the X-ray-selected sources in Stripe 82X, using the publicly available photometric and spectroscopic catalogues. Stripe 82X is at least as deep as eROSITA will be and wide enough to include also rare and bright AGNs. In addition, the availability of ancillary data mimics what can be available in the whole sky. We found that when optical, and near- and mid-infrared data are available, ML and SED fitting perform comparably well in terms of overall accuracy, realistic redshift probability density functions, and fraction of outliers, although they are not the same for the two methods. The results could further improve if the photometry available is accurate and including morphological information. Assuming that we can gather sufficient spectroscopy to build a representative training sample, with the current photometry coverage we can obtain reliable photo-z for a large fraction of sources in the Southern hemisphere well before the spectroscopic follow-up, thus timely enabling the eROSITA science return. The photo-z catalogue is released here.

astro-ph.IM↗

arXiv2019

Star Formation Rates for photometric samples of galaxies using machine learning methods

Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised Machine Learning models. We demonstrate that, with such approach, accurate multi-band photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky survey Data Release 7. The catalogue is available through the Vizier facility at the following link ftp://cdsarc.u-strasbg.fr/pub/cats/J/MNRAS/486/1377.

astro-ph.IM↗

arXiv2019

Catalog of quasars from the Kilo-Degree Survey Data Release 3

We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multi-dimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r<22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190,000 quasar candidates. Accuracy of 97%, purity of 91%, and completeness of 87%, as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from WISE. An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of p(QSO)>0.8 is optimal for purity, whereas p(QSO)>0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey.

astro-ph.IM↗

arXiv2019

A Comparison of Photometric Redshift Techniques for Large Radio Surveys

Future radio surveys will generate catalogues of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multi-wavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best with high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts.

astro-ph.IM↗

arXiv2018

The first sample of spectroscopically confirmed ultra-compact massive galaxies in the Kilo Degree Survey

We present results from an ongoing investigation using the Kilo Degree Survey (KiDS) on the VLT Survey Telescope (VST) to provide a census of ultra-compact massive galaxies (UCMGs), defined as galaxies with stellar masses $M_{\rm \star} > 8 \times 10^{10} \rm M_{\odot}$ and effective radii $R_{\rm e} < 1.5\,\rm kpc$. UCMGs, which are expected to have undergone very few merger events, provide a unique view on the accretion history of the most massive galaxies in the Universe. Over an effective sky area of nearly 330 square degrees, we select UCMG candidates from KiDS multi-colour images, which provide high quality structural parameters, photometric redshifts and stellar masses. Our sample of $\sim 1000$ photometrically selected UCMGs at $z < 0.5$ represents the largest sample of UCMG candidates assembled to date over the largest sky area. In this paper we present the first effort to obtain their redshifts using different facilities, starting with first results for 28 candidates with redshifts $z < 0.5$, obtained at NTT and TNG telescopes. We confirmed, as bona fide UCMGs, 19 out of the 28 candidates with new redshifts. A further 46 UCMG candidates are confirmed with literature spectroscopic redshifts (35 at $z < 0.5$), bringing the final cumulative sample of spectroscopically-confirmed lower-z UCMGs to 54 galaxies, which is the largest sample at redshifts below $0.5$. We use these spectroscopic redshifts to quantify systematic errors in our photometric selection, and use these to correct our UCMG number counts. We finally compare the results to independent datasets and simulations.

astro-ph.GA↗

arXiv2018

Vialactea Visual Analytics tool for Star Formation studies of the Galactic Plane

We present a visual analytics tool, based on the VisIVO suite, to exploit a combination of all new-generation surveys of the Galactic Plane to study the star formation process of the Milky Way. The tool has been developed within the VIALACTEA project, founded by the 7th Framework Programme of the European Union, that creates a common forum for the major new-generation surveys of the Milky Way Galactic Plane from the near infrared to the radio, both in thermal continuum and molecular lines. Massive volumes of data are produced by space missions and ground-based facilities and the ability to collect and store them is increasing at a higher pace than the ability to analyze them. This gap leads to new challenges in the analysis pipeline to discover information contained in the data. Visual analytics focuses on handling these massive, heterogeneous, and dynamic volumes of information accessing the data previously processed by data mining algorithms and advanced analysis techniques with highly interactive visual interfaces offering scientists the opportunity for in-depth understanding of massive, noisy, and high-dimensional data.

astro-ph.IM↗

arXiv2018

Evolution of galaxy size--stellar mass relation from the Kilo Degree Survey

We have obtained structural parameters of about 340,000 galaxies from the Kilo Degree Survey (KiDS) in 153 square degrees of data release 1, 2 and 3. We have performed a seeing convolved 2D single Sérsic fit to the galaxy images in the 4 photometric bands (u, g, r, i) observed by KiDS, by selecting high signal-to-noise ratio (S/N > 50) systems in every bands. We have classified galaxies as spheroids and disc-dominated by combining their spectral energy distribution properties and their Sérsic index. Using photometric redshifts derived from a machine learning technique, we have determined the evolution of the effective radius, \Re\ and stellar mass, \mst, versus redshift, for both mass complete samples of spheroids and disc-dominated galaxies up to z ~ 0.6. Our results show a significant evolution of the structural quantities at intermediate redshift for the massive spheroids ($\mbox{Log}\ M_*/M_\odot>11$, Chabrier IMF), while almost no evolution has found for less massive ones ($\mbox{Log}\ M_*/M_\odot < 11$). On the other hand, disc dominated systems show a milder evolution in the less massive systems ($\mbox{Log}\ M_*/M_\odot < 11$) and possibly no evolution of the more massive systems. These trends are generally consistent with predictions from hydrodynamical simulations and independent datasets out to redshift z ~ 0.6, although in some cases the scatter of the data is large to drive final conclusions. These results, based on 1/10 of the expected KiDS area, reinforce precedent finding based on smaller statistical samples and show the route toward more accurate results, expected with the the next survey releases.

astro-ph.GA↗