Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userKiDS+VIKING-450: A new combined optical & near-IR dataset for cosmology and astrophysics
436
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We present the curation and verification of a new combined optical and near infrared dataset for cosmology and astrophysics, derived from the combination of $ugri$-band imaging from the Kilo Degree Survey (KiDS) and $ZY!J!H!K_{rm s}$-band imaging from the VISTA Kilo degree Infrared Galaxy (VIKING) survey. This dataset is unrivaled in cosmological imaging surveys due to its combination of area ($458$ deg$^2$ before masking), depth ($rle25$), and wavelength coverage ($ugriZY!J!H!K_{rm s}$). The combination of survey depth, area, and (most importantly) wavelength coverage allows significant reductions in systematic uncertainties (i.e. reductions of between 10 and 60% in bias, outlier rate, and scatter) in photometric-to-spectroscopic redshift comparisons, compared to the optical-only case at photo-$z$ above $0.7$. The complementarity between our optical and NIR surveys means that over $80%$ of our sources, across all photo-$z$, have significant detections (i.e. not upper limits) in our $8$ reddest bands. We derive photometry, photo-$z$, and stellar masses for all sources in the survey, and verify these data products against existing spectroscopic galaxy samples. We demonstrate the fidelity of our higher-level data products by constructing the survey stellar mass functions in 8 volume-complete redshift bins. We find that these photometrically derived mass functions provide excellent agreement with previous mass evolution studies derived using spectroscopic surveys. The primary data products presented in this paper are publicly available at http://kids.strw.leidenuniv.nl/.
rate research
Read More
We present a combined tomographic weak gravitational lensing analysis of the Kilo Degree Survey (KV450) and the Dark Energy Survey (DES-Y1). We homogenize the analysis of these two public cosmic shear datasets by adopting consistent priors and modeling of nonlinear scales, and determine new redshift distributions for DES-Y1 based on deep public spectroscopic surveys. Adopting these revised redshifts results in a $0.8sigma$ reduction in the DES-inferred value for $S_8$, which decreases to a $0.5sigma$ reduction when including a systematic redshift calibration error model from mock DES data based on the MICE2 simulation. The combined KV450 + DES-Y1 constraint on $S_8 = 0.762^{+0.025}_{-0.024}$ is in tension with the Planck 2018 constraint from the cosmic microwave background at the level of $2.5sigma$. This result highlights the importance of developing methods to provide accurate redshift calibration for current and future weak lensing surveys.
We present a tomographic cosmic shear analysis of the Kilo-Degree Survey (KiDS) combined with the VISTA Kilo-Degree Infrared Galaxy Survey (VIKING). This is the first time that a full optical to near-infrared data set has been used for a wide-field cosmological weak lensing experiment. This unprecedented data, spanning $450~$deg$^2$, allows us to improve significantly the estimation of photometric redshifts, such that we are able to include robustly higher-redshift sources for the lensing measurement, and - most importantly - solidify our knowledge of the redshift distributions of the sources. Based on a flat $Lambda$CDM model we find $S_8equivsigma_8sqrt{Omega_{rm m}/0.3}=0.737_{-0.036}^{+0.040}$ in a blind analysis from cosmic shear alone. The tension between KiDS cosmic shear and the Planck-Legacy CMB measurements remains in this systematically more robust analysis, with $S_8$ differing by $2.3sigma$. This result is insensitive to changes in the priors on nuisance parameters for intrinsic alignment, baryon feedback, and neutrino mass. KiDS shear measurements are calibrated with a new, more realistic set of image simulations and no significant B-modes are detected in the survey, indicating that systematic errors are under control. When calibrating our redshift distributions by assuming the 30-band COSMOS-2015 photometric redshifts are correct (following the Dark Energy Survey and the Hyper Suprime-Cam Survey), we find the tension with Planck is alleviated. The robust determination of source redshift distributions remains one of the most challenging aspects for future cosmic shear surveys.
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of $(25~h^{-1}{rm Mpc})^3$ volume each: 2,184 state-of-the-art (magneto-)hydrodynamic simulations run with the AREPO and GIZMO codes, employing the same baryonic subgrid physics as the IllustrisTNG and SIMBA simulations, and 2,049 N-body simulations. The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. CAMELS contains thousands of different cosmological and astrophysical models by way of varying $Omega_m$, $sigma_8$, and four parameters controlling stellar and AGN feedback, following the evolution of more than 100 billion particles and fluid elements over a combined volume of $(400~h^{-1}{rm Mpc})^3$. We describe the simulations in detail and characterize the large range of conditions represented in terms of the matter power spectrum, cosmic star formation rate density, galaxy stellar mass function, halo baryon fractions, and several galaxy scaling relations. We show that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum. This emphasizes the need for marginalizing over baryonic effects to extract the maximum amount of information from cosmological surveys. We illustrate the unique potential of CAMELS using several machine learning applications, including non-linear interpolation, parameter estimation, symbolic regression, data generation with Generative Adversarial Networks (GANs), dimensionality reduction, and anomaly detection.
Future Square Kilometre Array (SKA) surveys are expected to generate huge datasets of 21cm maps on cosmological scales from the Epoch of Reionization (EoR). We assess the viability of exploiting machine learning techniques, namely, convolutional neural networks (CNN), to simultaneously estimate the astrophysical and cosmological parameters from 21cm maps from semi-numerical simulations. We further convert the simulated 21cm maps into SKA-like mock maps using the detailed SKA antennae distribution, thermal noise and a recipe for foreground cleaning. We successfully design two CNN architectures (VGGNet-like and ResNet-like) that are both efficiently able to extract simultaneously three astrophysical parameters, namely the photon escape fraction (f$_{rm esc}$), the ionizing emissivity power dependence on halo mass ($C_{rm ion}$) and the ionizing emissivity redshift evolution index ($D_{rm ion}$), and three cosmological parameters, namely the matter density parameter ($Omega_{m}$), the dimensionless Hubble constant ($h$), and the matter fluctuation amplitude ($sigma_{8}$), from 21cm maps at several redshifts. With the presence of noise from SKA, our designed CNNs are still able to recover these astrophysical and cosmological parameters with great accuracy ($R^{2} > 92%$), improving to $R^{2} > 99%$ towards low redshift and low neutral fraction values. Our results show that future 21cm observations can play a key role to break degeneracy between models and tightly constrain the astrophysical and cosmological parameters, using only few frequency channels.
The NGVS-IR project (Next Generation Virgo Survey - Infrared) is a contiguous near-infrared imaging survey of the Virgo cluster of galaxies. It complements the optical wide-field survey of Virgo (NGVS). The current state of NGVS-IR consists of Ks-band imaging of 4 deg^2 centered on M87, and J and Ks-band imaging of 16 deg^2 covering the region between M49 and M87. In this paper, we present the observations of the central 4 deg^2 centered on Virgos core region. The data were acquired with WIRCam on the Canada-France-Hawaii Telescope and the total integration time was 41 hours distributed in 34 contiguous tiles. A survey-specific strategy was designed to account for extended galaxies while still measuring accurate sky brightness within the survey area. The average 5sigma limiting magnitude is Ks=24.4 AB mag and the 50% completeness limit is Ks=23.75 AB mag for point source detections, when using only images with better than 0.7 seeing (median seeing 0.54). Star clusters are marginally resolved in these image stacks, and Virgo galaxies with mu_Ks=24.4 AB mag arcsec^-2 are detected. Combining the Ks data with optical and ultraviolet data, we build the uiK color-color diagram which allows a very clean color-based selection of globular clusters in Virgo. This diagnostic plot will provide reliable globular cluster candidates for spectroscopic follow-up campaigns needed to continue the exploration of Virgos photometric and kinematic sub-structures, and will help the design of future searches for globular clusters in extragalactic systems. Equipped with this powerful new tool, future NGVS-IR investigations based on the uiK diagram will address the mapping and analysis of extended structures and compact stellar systems in and around Virgo galaxies.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
