Dark Energy Survey: Year 1 results summary

Dark Energy Survey: Year 1 results summary The DES Collabora:on Ignacio Sevilla- Noarbe (CIEMAT) V Fundamental Cosmology Mee:ng Teruel 2017

The Dark Energy Survey has recently released various results for Year 1 Photometric Data Set for Cosmology (1708.01531) Redshift Distributions of the Weak Lensing Source Galaxies (1708.01532) Weak Lensing Shape Catalogues (1708.01533) Galaxy Clustering for Combined Probes (1708.01536) Cosmological Constraints from Cosmic Shear (1708.01538) Galaxy-galaxy Lensing (1708.01537) Cosmological Constraints from Galaxy Clustering and Weak Lensing (1708.01530)

What this talk is about (SPOILERS) DES Collabora+on 2017 DES-Y1 on its own is compatible with high-z inferred cosmological parameters wcdm is not favored Y3 catalogs available in December Come work with us on the next batch of exciting results!

What this talk is about (SPOILERS) DES-Y1 on its own is compatible with high-z inferred cosmological parameters wcdm is not favored Y3 catalogs available in December Come work with us on the next batch of exciting results! DES Collabora+on 2017

What this talk is about (SPOILERS) Skip Ad >> DES-Y1 on its own is compatible with high-z inferred cosmological parameters wcdm is not favored Y3 catalogs available in December Come work with us on the next batch of exciting results! DES Collabora+on 2017

The Dark Energy Survey is a 1 resolu:on map of 1/8 of the southern sky up to depths of i_ab = 24 in 5 photometric bands (g, r,i, z, Y) to explore dark energy using four probes: Cluster counts Large Scale Structure (BAOs) Supernovae Weak Lensing during 525 nights in 5 years (2013-2018)

The inevitable collaboration slide Currently led by Josh Frieman (Fermilab). Fermilab, UIUC/NCSA, University of Chicago, LBNL, NOAO, University of Michigan, University of Pennsylvania, Argonne Na:onal Laboratory, Ohio State University, Santa- Cruz/SLAC/Stanford Consor:um, Texas A&M University UK Consor:um UCL, Cambridge, Edinburgh, Portsmouth, Sussex, Nodngham ETH - Zurich Ludwig- Maximilians Universität 200+ scien:sts 25+ ins:tu:ons CTIO Spain Consor:um CIEMAT, ICE (IEEC/CSIC), IFAE OzDES Consor:um Brazil Consor:um Observatorio Nacional, CBPF,Universidade Federal do Rio de Janeiro, Universidade Federal do Rio Grande do Sul

Photometric Data Set for Cosmology redmagic (lens) sample source sample Redshift Distributions [ ] [ ]Shape Catalogs Galaxy Clustering [ ] Galaxy-Galaxy Lensing [ ] Cosmic Shear Cosmological Constraints from Galaxy Clustering and Weak Lensing

DES Data Management converts raw data into galaxy catalogs used for cosmological inference SV ~250 sq.deg.; 10 exposures Y1 ~1800 sq.deg; 3-4 exposures Y3, Y5 ~5000 sq.deg; 6, 10 exp. SINGLE EXPOSURES (10 X 5 BANDS) COMBINATION (COADD) MULTI EPOCH OBJECT FIT GOLD CATALOG A.Drlica- Wagner, I.S.- N. et al. 2017 9

Survey conditions and depth for each exposure are registered and combined in maps Band g r i z Y Depth (10s) (Sext/MOF) 23.4/ 23.7 23.2/ 23.5 22.5/ 22.9 21.8/ 22.2 20.1 PSF FWHM 1.25 1.07 0.97 0.89 1.07 10 A.Drlica- Wagner, A.Drlica-Wagner, I.S.- N. I.S. et al. et al. 2017 2017, B.Leistedt et al. 2016 Y1 Astrometric precision: ~0.3 arcsecs Y1 Photometric precision: 1-2 %

From the Gold catalog, red- sequence galaxies are selected as lenses and for clustering 660,000 redmagic galaxies with excellent photo-z Measure angular clustering in 5 redshift bins and lenses (0.15-0.9) ~unbiased E.Rozo et al. 2016 σ z /(1+z) = 0.014 1% 4σ outliers

Density fluctua:ons of lenses can correlate with observing condi:ons and induce a fake signal Discard pixels with large correlations Re-weight for small dependencies J.Elvin- Poole, M.Crocce et al. 2017

Redshin and luminosity dependence of red galaxies behave as expected 10 100 Corrections affect large scales Shaded regions not included (8 Mpc h -1 ) b(z=z 1 ) b(z=z 2 ) b(z=z 3 ) b(z=z 4 ) b(z=z 5 ) 1.40±0.08 1.61±0.05 1.60±0.05 1.93±0.05 1.99±0.07 J.Elvin- Poole, M.Crocce et al. 2017

From the Gold catalog, galaxies are selected as sources 18M/26M galaxies used as sheared sources Positions and shapes, more galaxies, less accurate redshifts 4 redshift bins (0.2-1.3) A.Drlica- Wagner, I.S.- N. et al. 2017

For source galaxies redshins are determined from BPZ and validated Mean of template based method BPZ for binning N(z) from pdfs for cosmo parameter inference Bias of N(z) calibrated with COSMOS field multi-band and via cross-correlations with redmagic (WZ) Uncertainty in bias is a subdominant error, more important in Y3,Y5 B.Hoyle, D.Gruen et al. 2017 M. Ga1 et al. 2017

Two shear catalogs have been produced Metacalibration (26M sources) New method measuring estimator shear response internally by deconvolving, shearing, convolving. E.Huff & R.Mandelbaum (2017); E.Sheldon & E.Huff (2017) im3shape (18M sources) Best-fit bulge & disc models, calibrated with simulations. J.Zuntz et al. (2013) J.Zuntz, E.Sheldon et al. 2017

The Dark Energy Survey has detected the strongest cosmic shear signal to date M.Troxel, N.MacRann et al. 2017

Results are compa:ble with CMB S 8 Strongest constraints on intrinsic alignments to date. Neutrino masses are not constrained further. Photo-z and shear systematics are not limiting. IA model marginalization degrades constraints. Robust vs changes in shape of N(z), ~IA modelling, scales, shear catalogs Ω m M.Troxel, N.MacRann et al. 2017

The source catalog can be cross- correlated with the lens catalog J.Prat, C.Sánchez et al. 2017

The source catalog can be cross- correlated with redmagic J.Prat, C.Sánchez et al. 2017

Galaxy bias is consistent between galaxy- galaxy lensing and galaxy clustering J.Prat, C.Sánchez et al. 2017

With these ingredients we proceed to infer cosmology for ΛCDM and wcdm Data vector (3x2pt stats): w(θ), γ t (θ), ξ ± (θ) for 4x5 bins 20 nuisance parameters (photo-z biases, multiplicative shear biases, lens galaxy bias, IA parameters). 6/7 cosmo parameters (A S! S 8, n S, Ω m, Ω b, Ω ν, h, (w)) Redundancy rule of twos: 2 shear catalogs 2 photo-z validations 2 cosmological estimator codes 2 levels of blinding We use evidence ratio to check compatibility Covariance is computed theoretically, validated with sims. (E.Krause et al. 2017)

Individual constraints in ΛCDM are compa:ble so we can unblind

Evidence ra:o shows substan:al compa:bility with Planck R=4.2 so combination is possible S 8 Still compatible after changing assumptions on priors Adding low redshift probes increases compatibility Ω m

In ΛCDM, we constrain S 8 and Ω m to a similar level as Planck S 8 Ω m DES Collabora+on 2017

wcdm is not par:cularly favored or disfavored, w compa:ble with - 1 S 8 Ω m w = 0.36 DES Collabora+on 2017

Bayesian evidence ra:o points to compa:bility with Planck S 8 Strong evidence for IA with photometric survey. Lens bias constrained to 10% DES Collabora+on 2017 Ω m

DES- Y1 in combina:on with previous measurements provide strongest constaints to ΛCDM to date DES-Y1 on its own is compatible with high-z inferred cosmological parameters wcdm is not favored S 8 DES+Planck shift h towards local measurements Neutrino mass bounds slightly loosened DES Collabora+on 2017 Ω m

The Dark Energy Survey has detected the strongest cosmic shear signal to date M.Troxel, N.MacRann et al. 2017

We detect objects in coadded images, and measure them in multiple ways Y1 Astrometric precision: ~0.3 arcsecs Y1 Photometric precision: 1-2 % Detect on r+i+z combined image Coadded photometry (SExtractor): measure on coadded images in each band Multi-object fitting photometry: measure using multi-epoch, multi-object, multiband fit and subtract neighbors light. 37

This process produces 139 million objects. We have to weed out the bad objects and enhance/ complement their measurements Objects can have incongruent colors, inconsistent magnitudes or positions, problems arising during their processing. Faulty regions with large amounts of reflections, satellite trails or else. Foreground object areas. Photometric calibration is enhanced via SLR corrections. Stars and galaxies have to be separated for different analyses. A. Drlica-Wagner, I.S et al. 2017 38

((combina:on))

Using these galaxies, a mass map is derived from convergence es:mates C.Chang, A.Pujol et al. 2017