We developed a deep convolutional neural network (CNN), used as a classifier, to estimate photometric redshifts and associated probability distribution functions (PDF) for galaxies in the Main Galaxy ...Sample of the Sloan Digital Sky Survey at z < 0.4. Our method exploits all the information present in the images without any feature extraction. The input data consist of 64 × 64 pixel ugriz images centered on the spectroscopic targets, plus the galactic reddening value on the line-of-sight. For training sets of 100k objects or more (≥20% of the database), we reach a dispersion σMAD < 0.01, significantly lower than the current best one obtained from another machine learning technique on the same sample. The bias is lower than 10−4, independent of photometric redshift. The PDFs are shown to have very good predictive power. We also find that the CNN redshifts are unbiased with respect to galaxy inclination, and that σMAD decreases with the signal-to-noise ratio (S/N), achieving values below 0.007 for S/N > 100, as in the deep stacked region of Stripe 82. We argue that for most galaxies the precision is limited by the S/N of SDSS images rather than by the method. The success of this experiment at low redshift opens promising perspectives for upcoming surveys.
In this work, we propose two convolutional neural network classifiers for detecting contaminants in astronomical images. Once trained, our classifiers are able to identify various contaminants, such ...as cosmic rays, hot and bad pixels, persistence effects, satellite or plane trails, residual fringe patterns, nebulous features, saturated pixels, diffraction spikes, and tracking errors in images. They encompass a broad range of ambient conditions, such as seeing, image sampling, detector type, optics, and stellar density. The first classifier, M
AXI
M
ASK
, performs semantic segmentation and generates bad pixel maps for each contaminant, based on the probability that each pixel belongs to a given contaminant class. The second classifier, M
AXI
T
RACK
, classifies entire images and mosaics, by computing the probability for the focal plane to be affected by tracking errors. We gathered training and testing data from real data originating from various modern charged-coupled devices and near-infrared cameras, that are augmented with image simulations. We quantified the performance of both classifiers and show that M
AXI
M
ASK
achieves state-of-the-art performance for the identification of cosmic ray hits. Thanks to a built-in Bayesian update mechanism, both classifiers can be tuned to meet specific science goals in various observational contexts.
A large-scale hydrodynamical cosmological simulation, Horizon-AGN, is used to investigate the alignment between the spin of galaxies and the cosmic filaments above redshift 1.2. The analysis of more ...than 150 000 galaxies per time step in the redshift range 1.2 < z < 1.8 with morphological diversity shows that the spin of low-mass blue galaxies is preferentially aligned with their neighbouring filaments, while high-mass red galaxies tend to have a perpendicular spin. The reorientation of the spin of massive galaxies is provided by galaxy mergers, which are significant in their mass build-up. We find that the stellar mass transition from alignment to misalignment happens around 3 × 1010 M⊙. Galaxies form in the vorticity-rich neighbourhood of filaments, and migrate towards the nodes of the cosmic web as they convert their orbital angular momentum into spin. The signature of this process can be traced to the properties of galaxies, as measured relative to the cosmic web. We argue that a strong source of feedback such as active galactic nuclei is mandatory to quench in situ star formation in massive galaxies and promote various morphologies. It allows mergers to play their key role by reducing post-merger gas inflows and, therefore, keeping spins misaligned with cosmic filaments.
Abstract
We describe the Dark Energy Survey (DES) photometric data set assembled from the first three years of science operations to support DES Year 3 cosmologic analyses, and provide usage notes ...aimed at the broad astrophysics community.
Y3
GOLD
improves on previous releases from DES,
Y1
GOLD
, and Data Release 1 (DES DR1), presenting an expanded and curated data set that incorporates algorithmic developments in image detrending and processing, photometric calibration, and object classification.
Y3
GOLD
comprises nearly 5000 deg
2
of
grizY
imaging in the south Galactic cap, including nearly 390 million objects, with depth reaching a signal-to-noise ratio ∼10 for extended objects up to
i
AB
∼ 23.0, and top-of-the-atmosphere photometric uniformity <3 mmag. Compared to DR1, photometric residuals with respect to Gaia are reduced by 50%, and per-object chromatic corrections are introduced.
Y3
GOLD
augments DES DR1 with simultaneous fits to multi-epoch photometry for more robust galactic color measurements and corresponding photometric redshift estimates.
Y3
GOLD
features improved morphological star–galaxy classification with efficiency >98% and purity >99% for galaxies with 19 <
i
AB
< 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used to select the cosmologic analysis samples.
We combine Dark Energy Survey Year 1 clustering and weak lensing data with baryon acoustic oscillations and Big Bang nucleosynthesis experiments to constrain the Hubble constant. Assuming a flat ΛCDM ...model with minimal neutrino mass (∑m_ν = 0.06 eV), we find |$H_0=67.4^{+1.1}_{-1.2}\ \rm {km\,\rm s^{-1}\,\rm Mpc^{-1}}$| (68 per cent CL). This result is completely independent of Hubble constant measurements based on the distance ladder, cosmic microwave background anisotropies (both temperature and polarization), and strong lensing constraints. There are now five data sets that: (a) have no shared observational systematics; and (b) each constrains the Hubble constant with fractional uncertainty at the few-per cent level. We compare these five independent estimates, and find that, as a set, the differences between them are significant at the 2.5σ level (χ^2/dof = 24/11, probability to exceed = 1.1 per cent). Having set the threshold for consistency at 3σ, we combine all five data sets to arrive at |$H_0=69.3^{+0.4}_{-0.6}\ \rm {km\,\mathrm{ s}^{-1}\,\mathrm{ Mpc}^{-1}}$|.
We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 ...spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe Ia is based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat ΛCDM model we find a matter density . For a flat wCDM model, and combining our SN Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state , and . For a flat w0waCDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find and . These results are in agreement with a cosmological constant and with previous constraints using SNe Ia (Pantheon, JLA).
Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will ...require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.
ABSTRACT
Determining the distribution of redshifts of galaxies observed by wide-field photometric experiments like the Dark Energy Survey (DES) is an essential component to mapping the matter density ...field with gravitational lensing. In this work we describe the methods used to assign individual weak lensing source galaxies from the DES Year 3 Weak Lensing Source Catalogue to four tomographic bins and to estimate the redshift distributions in these bins. As the first application of these methods to data, we validate that the assumptions made apply to the DES Y3 weak lensing source galaxies and develop a full treatment of systematic uncertainties. Our method consists of combining information from three independent likelihood functions: self-organizing map p(z) (sompz), a method for constraining redshifts from galaxy photometry; clustering redshifts (WZ), constraints on redshifts from cross-correlations of galaxy density functions; and shear ratios (SRs), which provide constraints on redshifts from the ratios of the galaxy-shear correlation functions at small scales. Finally, we describe how these independent probes are combined to yield an ensemble of redshift distributions encapsulating our full uncertainty. We calibrate redshifts with combined effective uncertainties of σ〈z〉 ∼ 0.01 on the mean redshift in each tomographic bin.
We report the results of a systematic search for ultra-faint Milky Way satellite galaxies using data from the Dark Energy Survey (DES) and Pan-STARRS1 (PS1). Together, DES and PS1 provide multi-band ...photometry in optical/near-infrared wavelengths over ∼80% of the sky. Our search for satellite galaxies targets ∼25,000 deg2 of the high-Galactic-latitude sky reaching a 10 point-source depth of 22.5 mag in the g and r bands. While satellite galaxy searches have been performed independently on DES and PS1 before, this is the first time that a self-consistent search is performed across both data sets. We do not detect any new high-significance satellite galaxy candidates, recovering the majority of satellites previously detected in surveys of comparable depth. We characterize the sensitivity of our search using a large set of simulated satellites injected into the survey data. We use these simulations to derive both analytic and machine-learning models that accurately predict the detectability of Milky Way satellites as a function of their distance, size, luminosity, and location on the sky. To demonstrate the utility of this observational selection function, we calculate the luminosity function of Milky Way satellite galaxies, assuming that the known population of satellite galaxies is representative of the underlying distribution. We provide access to our observational selection function to facilitate comparisons with cosmological models of galaxy formation and evolution.
We describe the creation, content, and validation of the Dark Energy Survey (DES) internal year-one cosmology data set, Y1A1 GOLD, in support of upcoming cosmological analyses. The Y1A1 GOLD data set ...is assembled from multiple epochs of DES imaging and consists of calibrated photometric zero-points, object catalogs, and ancillary data products-e.g., maps of survey depth and observing conditions, star-galaxy classification, and photometric redshift estimates-that are necessary for accurate cosmological analyses. The Y1A1 GOLD wide-area object catalog consists of million objects detected in co-added images covering in the DES grizY filters. The 10 limiting magnitude for galaxies is , , , , and . Photometric calibration of Y1A1 GOLD was performed by combining nightly zero-point solutions with stellar locus regression, and the absolute calibration accuracy is better than 2% over the survey area. DES Y1A1 GOLD is the largest photometric data set at the achieved depth to date, enabling precise measurements of cosmic acceleration at z 1.
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