Dark Energy Survey year 1 results: galaxy sample for BAO measurement

ABSTRACT What are the mass and galaxy profiles of cosmic voids? In this paper, we use two methods to extract voids in the Dark Energy Survey (DES) Year 1 redMaGiC galaxy sample to address this question. We use either 2D slices in projection, or the 3D distribution of galaxies based on photometric redshifts to identify voids. For the mass profile, we measure the tangential shear profiles of background galaxies to infer the excess surface mass density. The signal-to-noise ratio for our lensing measurement ranges between 10.7 and 14.0 for the two void samples. We infer their 3D density profiles by fitting models based on N-body simulations and find good agreement for void radii in the range 15–85 Mpc. Comparison with their galaxy profiles then allows us to test the relation between mass and light at the 10 per cent level, the most stringent test to date. We find very similar shapes for the two profiles, consistent with a linear relationship between mass and light both within and outside the void radius. We validate our analysis with the help of simulated mock catalogues and estimate the impact of photometric redshift uncertainties on the measurement. Our methodology can be used for cosmological applications, including tests of gravity with voids. This is especially promising when the lensing profiles are combined with spectroscopic measurements of void dynamics via redshift-space distortions.

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x -cut Cosmic shear: Optimally removing sensitivity to baryonic and nonlinear physics with an application to the Dark Energy Survey year 1 shear data

Physical Review D ◽

10.1103/physrevd.103.043531 ◽

2021 ◽

Vol 103 (4) ◽

Author(s):

Peter L. Taylor ◽

Francis Bernardeau ◽

Eric Huff

Keyword(s):

Dark Energy ◽

Dark Energy Survey ◽

Cosmic Shear ◽

Energy Survey

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A machine learning approach to the detection of ghosting and scattered light artifacts in dark energy survey images

Astronomy and Computing ◽

10.1016/j.ascom.2021.100474 ◽

2021 ◽

pp. 100474

Author(s):

C. Chang ◽

A. Drlica-Wagner ◽

S.M. Kent ◽

B. Nord ◽

D.M. Wang ◽

...

Keyword(s):

Machine Learning ◽

Dark Energy ◽

Scattered Light ◽

Learning Approach ◽

Dark Energy Survey ◽

Machine Learning Approach ◽

Energy Survey

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Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab526 ◽

2021 ◽

Vol 503 (2) ◽

pp. 2688-2705

Author(s):

C Doux ◽

E Baxter ◽

P Lemos ◽

C Chang ◽

A Alarcon ◽

...

Keyword(s):

Dark Energy ◽

Internal Consistency ◽

Goodness Of Fit ◽

Small Scale ◽

P Value ◽

Posterior Predictive Distribution ◽

Data Set ◽

Dark Energy Survey ◽

Data Vector ◽

Energy Survey

ABSTRACT Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. We present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

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KiDS+VIKING-450 and DES-Y1 combined: Cosmology with cosmic shear

Astronomy and Astrophysics ◽

10.1051/0004-6361/201936154 ◽

2020 ◽

Vol 638 ◽

pp. L1 ◽

Cited By ~ 13

Author(s):

S. Joudaki ◽

H. Hildebrandt ◽

D. Traykova ◽

N. E. Chisari ◽

C. Heymans ◽

...

Keyword(s):

Dark Energy ◽

Cosmic Microwave Background ◽

Gravitational Lensing ◽

Error Model ◽

Calibration Error ◽

Microwave Background ◽

Weak Gravitational Lensing ◽

Dark Energy Survey ◽

Cosmic Shear ◽

Energy Survey

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 0.8σ reduction in the DES-inferred value for S8, which decreases to a 0.5σ 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 S8 = 0.762−0.024+0.025 is in tension with the Planck 2018 constraint from the cosmic microwave background at the level of 2.5σ. This result highlights the importance of developing methods to provide accurate redshift calibration for current and future weak-lensing surveys.

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Dark Energy Survey

Astronomy & Geophysics ◽

10.1111/j.1468-4004.2008.49408_3.x ◽

2008 ◽

Vol 49 (4) ◽

pp. 4.08-4.08

Keyword(s):

Dark Energy ◽

Dark Energy Survey ◽

Energy Survey

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Dark Energy Survey Year 1 Results: Wide-field mass maps via forward fitting in harmonic space

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa565 ◽

2020 ◽

Vol 493 (4) ◽

pp. 5662-5679 ◽

Cited By ~ 1

Author(s):

B Mawdsley ◽

D Bacon ◽

C Chang ◽

P Melchior ◽

E Rozo ◽

...

Keyword(s):

Dark Energy ◽

Mapping Technique ◽

Harmonic Space ◽

Wide Field ◽

Direct Inversion ◽

Dark Energy Survey ◽

Central Regions ◽

Fitting In ◽

Energy Survey ◽

Good Agreement

ABSTRACT We present new wide-field weak lensing mass maps for the Year 1 Dark Energy Survey (DES) data, generated via a forward fitting approach. This method of producing maps does not impose any prior constraints on the mass distribution to be reconstructed. The technique is found to improve the map reconstruction on the edges of the field compared to the conventional Kaiser–Squires method, which applies a direct inversion on the data; our approach is in good agreement with the previous direct approach in the central regions of the footprint. The mapping technique is assessed and verified with tests on simulations; together with the Kaiser–Squires method, the technique is then applied to data from the DES Year 1 data and the differences between the two methods are compared. We also produce the first DES measurements of the convergence Minkowski functionals and compare them to those measured in simulations.

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