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Register a new userRidges in the Dark Energy Survey for cosmic trough identification
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Cosmic voids and their corresponding redshift-aggregated projections of mass densities, known as troughs, play an important role in our attempt to model the large-scale structure of the Universe. Understanding these structures leads to tests comparing the standard model with alternative cosmologies, constraints on the dark energy equation of state, and provides evidence to differentiate among gravitational theories. In this paper, we extend the subspace-constrained mean shift algorithm, a recently introduced method to estimate density ridges, and apply it to 2D weak-lensing mass density maps from the Dark Energy Survey Y1 data release to identify curvilinear filamentary structures. We compare the obtained ridges with previous approaches to extract trough structure in the same data, and apply curvelets as an alternative wavelet-based method to constrain densities. We then invoke the Wasserstein distance between noisy and noiseless simulations to validate the denoising capabilities of our method. Our results demonstrate the viability of ridge estimation as a precursor for denoising weak lensing quantities to recover the large-scale structure, paving the way for a more versatile and effective search for troughs.
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The 21-cm absorption feature reported by the EDGES collaboration is several times stronger than that predicted by traditional astrophysical models. If genuine, a deeper absorption may lead to stronger fluctuations on the 21-cm signal on degree scales (up to 1~Kelvin in rms), allowing these fluctuations to be detectable in nearly 50~times shorter integration times compared to previous predictions. We commenced the AARTFAAC Cosmic Explorer (ACE) program, that employs the AARTFAAC wide-field imager, to measure or set limits on the power spectrum of the 21-cm fluctuations in the redshift range $z = 17.9-18.6$ ($Delta u = 72.36-75.09$~MHz) corresponding to the deep part of the EDGES absorption feature. Here, we present first results from two LST bins: 23.5-23.75h and 23.5-23.75h, each with 2~h of data, recorded in `semi drift-scan mode. We demonstrate the application of the new ACE data-processing pipeline (adapted from the LOFAR-EoR pipeline) on the AARTFAAC data. We observe that noise estimates from the channel and time-differenced Stokes~$V$ visibilities agree with each other. After 2~h of integration and subtraction of bright foregrounds, we obtain $2sigma$ upper limits on the 21-cm power spectrum of $Delta_{21}^2 < (8139~textrm{mK})^2$ and $Delta_{21}^2 < (8549~textrm{mK})^2$ at $k = 0.144~h,textrm{cMpc}^{-1}$ for the two LST bins. Incoherently averaging the noise bias-corrected power spectra for the two LST bins yields an upper limit of $Delta_{21}^2 < (7388~textrm{mK})^2$ at $k = 0.144~h,textrm{cMpc}^{-1}$. These are the deepest upper limits thus far at these redshifts.
This work determines the degree to which a standard Lambda-CDM analysis based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-dependent dark energy equation of state w(z). We introduce and apply a novel random curve generator to simulate instances of w(z) from constraint families with increasing distinction from a cosmological constant. After producing a series of mock catalogs of binned type Ia supernovae corresponding to each w(z) curve, we perform a standard Lambda-CDM analysis to estimate the corresponding posterior densities of the absolute magnitude of type Ia supernovae, the present-day matter density, and the equation of state parameter. Using the Kullback-Leibler divergence between posterior densities as a difference measure, we demonstrate that a standard type Ia supernova cosmology analysis has limited sensitivity to extensive redshift dependencies of the dark energy equation of state. In addition, we report that larger redshift-dependent departures from a cosmological constant do not necessarily manifest easier-detectable incompatibilities with the Lambda-CDM model. Our results suggest that physics beyond the standard model may simply be hidden in plain sight.
We describe an initiative to build and use the Dark Energy Spectrometer (DESpec), a wide-field spectroscopic survey instrument for the Blanco 4 meter telescope at Cerro Tololo InterAmerican Observatory (CTIO) in Chile. A new system with about 4000 robotically positioned optical fibers will be interchangeable with the CCD imager of the existing Dark Energy Camera (DECam), accessing a field of view of 3.8 square degrees in a single exposure. The proposed instrument will be operated by CTIO and available for use by the astronomy community. Our collaboration proposes to use DESpec to conduct a wide, deep spectroscopic survey to study Dark Energy. In a survey of about 350 nights, the DESpec collaboration proposes to obtain spectroscopic redshifts for about 8 million galaxies over 5000 square degrees selected from the Dark Energy Survey (DES). This Dark Energy Spectroscopic Survey will advance our knowledge of cosmic expansion and structure growth significantly beyond that obtainable with imaging-only surveys. Since it adds a spectroscopic third dimension to the same sky as DES, DESpec will enable increasingly precise techniques to discriminate among alternative explanations of cosmic acceleration, such as Dark Energy and Modified Gravity.
We describe an updated calibration and diagnostic framework, Balrog, used to directly sample the selection and photometric biases of Dark Energy Surveys (DES) Year 3 (Y3) dataset. We systematically inject onto the single-epoch images of a random 20% subset of the DES footprint an ensemble of nearly 30 million realistic galaxy models derived from DES Deep Field observations. These augmented images are analyzed in parallel with the original data to automatically inherit measurement systematics that are often too difficult to capture with traditional generative models. The resulting object catalog is a Monte Carlo sampling of the DES transfer function and is used as a powerful diagnostic and calibration tool for a variety of DES Y3 science, particularly for the calibration of the photometric redshifts of distant source galaxies and magnification biases of nearer lens galaxies. The recovered Balrog injections are shown to closely match the photometric property distributions of the Y3 GOLD catalog, particularly in color, and capture the number density fluctuations from observing conditions of the real data within 1% for a typical galaxy sample. We find that Y3 colors are extremely well calibrated, typically within ~1-8 millimagnitudes, but for a small subset of objects we detect significant magnitude biases correlated with large overestimates of the injected object size due to proximity effects and blending. We discuss approaches to extend the current methodology to capture more aspects of the transfer function and reach full coverage of the survey footprint for future analyses.
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 zeropoints, 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 ~137 million objects detected in coadded images covering ~1800 deg$^2$ in the DES grizY filters. The 10{sigma} limiting magnitude for galaxies is g = 23.4, r = 23.2, i = 22.5, z = 21.8, and Y = 20.1. Photometric calibration of Y1A1 GOLD was performed by combining nightly zeropoint 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 $lesssim$ 1.
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