Baryonic and Dark Matter Next Generation Surveys: Scientific, Observational and Instrumental Challenges Andy Taylor Institute for Astronomy, School of Physics University of Edinburgh, UK Gray & Taylor et al 2005 11/13/2018 IoP-RAS Meeting

11/13/2018 IoP-RAS Meeting

Outline Scientific aims of future surveys Overview of future surveys Challenges for future surveys Summary 11/13/2018 IoP-RAS Meeting

Outline Scientific aims of future surveys Overview of future surveys Challenges for future surveys Summary 11/13/2018 IoP-RAS Meeting

Aims of Lensing Surveys What are the scientific challenges for lensing? Astrophysical: Galaxy halo properties (galaxy-galaxy, galaxy-quasar) Clusters & filaments (mass mapping vs Xray & starlight) High-redshift Universe (gravitational telescopes) Fundamental: Dark Matter properties ( DM mass & interactions, neutrino mass) Dark Energy properties (EoS, evolution) Initial conditions (s8, ns, dns/dlnk) Testing Einstein Gravity 11/13/2018 IoP-RAS Meeting

Properties of Dark Matter Cold Dark Matter: Mass – break in matter power spectrum Thermal properties – resolve smallest halos with shear and flexion. Neutrinos Mass – another scale length in matter power spectrum from free-streaming. 11/13/2018 IoP-RAS Meeting

Residual systematics (“B modes”) Baryonic & Dark Matter in COSMOS Residual systematics (“B modes”) Blue: stellar mass Yellow: galaxy number Red: hot gas B-mode map 11/13/2018 IoP-RAS Meeting Massey, et al, Nature, 2007

COSMOS 3-D Dark Matter Maps Photon equation of motion: Right Ascension Redshift 0.0 0.2 0.4 0.6 0.8 Declination Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes). Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect. 11/13/2018 IoP-RAS Meeting Massey, et al, Nature (2007)

Observable Effects of Dark Energy Geometry: DE changes the photon distance-redshift relation: r(z) Angular diameter distance DA Luminosity Distance DL Dynamics: Alters the growth of density perturbations, d(t). r(z) z w = 0 w = -1 d r 11/13/2018 IoP-RAS Meeting

Constraining w from the CMB + Supernova Energy-density scales with expansion as Close to a Cosmological Constant. (assumes flat Universe) Spergel et al ApJ 2006 11/13/2018 IoP-RAS Meeting

Constraining w from the CMB + Supernova + Lensing from CFHT Energy-density scales with expansion as Close to a Cosmological Constant. (assumes flat Universe) Spergel et al ApJ 2006, Tereno et al 2006 11/13/2018 IoP-RAS Meeting

Initial Conditions - Inflation Spergel et al. ApJ, 2006 11/13/2018 IoP-RAS Meeting

Testing Einstein Gravity Three tests of gravity: Model testing. Eg, DGP, TeVeS, braneworld. Generalized Einstein metric Consistency Relations: Geometric wG versus Dynamic wD. 11/13/2018 IoP-RAS Meeting

Outline Scientific aims of future surveys Overview of future surveys Challenges for future surveys Summary 11/13/2018 IoP-RAS Meeting

Timeline Lensing (+z) Spec IR Space 2004 SDSS/AAOmega CFHTLS DEEP2 2006 LAMOST Pan-STARRS-1 UKIDSS VST-KIDS VIKING/VHS Planck 2009 Pan-STARRS-4 DES WFMOS JWST JWST 2011 HSC/Subaru VIRUS 2012 2013 SNAP/JEDI/ADEPT/Destiny? 2014 LSST 2015 2016 DUNE DUNE DUNE 2018 2019 SKA (Radio) SKA (Radio) 2025 ELT 11/13/2018 IoP-RAS Meeting

2003-2008: CFHTLS Canada-France-Hawaii 3.6m Telescope Mauna Kea 40 CCD, 340 Mpixels 1 sq deg MegaCam Surveys: Wide: 170 sq deg u*g’r’i’z’, i’=24.5 Deep: 4 sq deg, r’=28 11/13/2018 IoP-RAS Meeting

2007-2010: Pan-STARRS-1 Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). Hawaii, MPIA, Taiwan, Harvard, Johns Hopkins, UK (Edinburgh, Belfast, Durham), 1.8 meter primary 1.4Gpixel camera. 7 sq deg fov. Medium Deep Survey 3p Survey g, r, i, z, y (r=24.5) PS4 – 4xPS1 (2009). 11/13/2018 IoP-RAS Meeting

2008-2013: VST-KIDS & VIKING ESO’s Kilo-Degree Survey 2m primary 184Mpixels 1sqdeg fov OmegaCAM 1,500 sq deg u’g’r’i’z’ VIKING (VISTA Kilo-degree INfrared Galaxy survey) 1500 sq deg in parallel on VISTA Z,Y,J,H,Ks 11/13/2018 IoP-RAS Meeting

2010-2015: DES The Dark Energy Survey. 4-metre Blanco at CTIO (South) 500 Megapixel, 3 sqdeg fov camera 5 yr survey (30% of time). g,r,i,z over 5000 sq deg r = 24.1 (10sig) 4 dark energy probes: WL, BAOs, SN & Clusters 11/13/2018 IoP-RAS Meeting

2011-2016: Subaru-HyperSuprimeCam 8.3m Primary 3.14 sq deg fov 1.4 Gpixel camera HyperSuprimeCam 3500 sq deg/year ~17,500 sq deg (5 yrs) ugriz? ? 11/13/2018 IoP-RAS Meeting

2014-2024: LSST Large Synoptic Survey Telescope (LSST) 8.4m (effectively 6.5m) Primary 3.2 Gpixel, 9.6 sq deg fov camera ugrizY Cerro Pachon, Chile 30Tbyte per night 11/13/2018 IoP-RAS Meeting

2017-2021: DUNE – Dark UNiverse Explorer Proposal to ESA Cosmic Visions programme. 1.2m satellite telescope r-i-z + Y,J,H 0.5 sq deg fov 3-year weak lensing survey: 20,000 sq deg AB=24.5 (10sig), zm=0.9 n0=35/sq arcmin Ground-based optical complement needed for photo-z’s. 11/13/2018 IoP-RAS Meeting

2020-2025: SKA Square Kilometre Array (SKA) Radio interferometer. Frequency range 100 MHz - 25 GHz 1 sq deg fov (1.4GHz) - 200 sq deg (0.7GHz) 20,000 sqdeg zm~1.0 sz=0 (spec) n0=10/sqarcmin (useable HI sources) 11/13/2018 IoP-RAS Meeting

Grasp for optical surveys Grasp vs. Start Date SKA~108 103 102 10 1 LSST HSC PS4 DES Grasp (D2*fov) Dark Energy Survey Pan-STARRS-1 PS1 Grasp for optical surveys doubles every ~2.5 yrs CFHT VST-KIDS 170 sq deg DUNE 1700 sq deg 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 11/13/2018 IoP-RAS Meeting Start Date

Survey Area vs. End Date 104 103 102 PS1 PS4 DUNE LSST SKA HSC DES [sqdeg] Dark Energy Survey Pan-STARRS-1 VST-KIDS 170 sq deg CFHT-W 1700 sq deg 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 End Date 11/13/2018 IoP-RAS Meeting

Depth (Median Redshift) Survey Depth vs. Area 104 103 102 PS1 DUNE PS4/ SKA LSST HSC DES Area [sqdeg] VST-KIDS Constant Time CFHT-W 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 Depth (Median Redshift) 11/13/2018 IoP-RAS Meeting

Dark Energy Figure of Merit (FoM) Dark Energy Task Force Figure of Merit: Define pivot redshift, zp: wa w(z) wp Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes). Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect. Dw0 w = -1 zp z 11/13/2018 IoP-RAS Meeting

3-D Shear Power Background sources Dark matter halos Observer (e.g. Heavens 2003, Kitching, Heavens & Taylor 2005) Observer Dark matter halos Background sources Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes). Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect. 11/13/2018 IoP-RAS Meeting

3-D Shear Ratios (Jain & Taylor 2003, Taylor, Kitching, Bacon, Heavens 2005) Background sources Dark matter halos Observer Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes). Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect. Signal depends on (Wm, Wv, w0, wa) and is insensitive to clustering. 11/13/2018 IoP-RAS Meeting

FoM for Dark Energy from Lensing 3-D shear power and shear-ratios combined with Planck Explorer CMB survey (2008) Current limit 1/FoM CFHT KIDS PS1 SKA DES HSC PS4 Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes). Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect. FoM doubles every 2.5 yrs DUNE LSST Saturation 11/13/2018 IoP-RAS Meeting 2023 (with Tom Kitching) End Date

Outline Scientific aims of future surveys Overview of future surveys Challenges for future surveys Summary 11/13/2018 IoP-RAS Meeting

(Taylor, Kitching & Heavens, 2006) Effect of Systematics What is the effect of systematics in results? Can estimate effect using Fisher Matrix formalism: Eg for a straight line zero-point fit: y b db 11/13/2018 IoP-RAS Meeting x (Taylor, Kitching & Heavens, 2006)

Image Distortions Image distortions: (Kitching, Taylor & Heavens 2007) calibration rotation bias. 11/13/2018 IoP-RAS Meeting

Image Distortions Image distortions: calibration, rotation, bias. Effect of these on constant w: Shear Power: no 1st order effect from gbias: Shear-ratios: No bias to 1st order. Require: 11/13/2018 IoP-RAS Meeting

Observational Challenges Photometric redshifts: calibration, bias, outliers zphot zspec Abdalla et al (2007) 11/13/2018 (Abdalla et al, 2007; Kitching, Taylor & Heavens 2007) IoP-RAS Meeting

Observational Challenges Photometric redshifts: calibration, bias, outliers Can estimate bias effect from Fisher analysis: Shear Power: Shear-ratios: Shear-ratio: 11/13/2018 IoP-RAS Meeting

Photometric Redshift Challenges 5-optical + 3-IR? VST-KIDS/VIKING. Do we need U-band? VST-KIDS Calibration with spectroscopic surveys How many? 105? VLT, WFMOS, ELTs? Need synergy with IR & spectroscopic surveys. Abdalla et al (2007) 11/13/2018 IoP-RAS Meeting

Intrinsic Alignment Challenges Two alignment effects: Intrinsic-Intrinsic alignments Galaxy-Intrinsic alignment 11/13/2018 IoP-RAS Meeting (Bridle & King, 2007; Kitching, Taylor & Heavens 2007)

Intrinsic Alignment Challenges Model using Heymans et al (2006). Find no effect on shear-ratio signal (averaged out), but enters noise. Minimal effect on shear-power (but see Bridle & King 2007). Using signal where alignment contribution is small. 11/13/2018 IoP-RAS Meeting

Marginalize over Nuisance Parameters Use data to estimate these parameters (self-calibration). Marginalisation over uncertainties will increase error: w Dwmarg Dwcond gbias 11/13/2018 IoP-RAS Meeting

Marginalize over Nuisance Parameters Effect of marginalisation over image distortion uncertainties, for Shear + Ratios + Planck: For a DUNE mission FoM (Dwp): Shear Power Shear-Ratio Combined Baseline 500 (0.015) 150 (0.024) 915 (0.012) Pz+IA+g 116 (0.03) 70 (0.03) 670 (0.014) 0.1% prior 440 (0.02) 100 (0.028) 900 (0.012) Mostly photo-z’s Mostly Image Distortions (Kitching, Taylor & Heavens 2007) 11/13/2018 IoP-RAS Meeting

Nonlinear Matter Distribution Non-linear matter power spectrum. Fitting functions not accurate Need MC N-body sims Baryons? Non-Gaussian corrections to the shear field. Covariance of power (4pt-fn) Higher-order correlations Non-Gaussian likelihoods log Clgg log l P(k) k 11/13/2018 IoP-RAS Meeting

Data Analysis Challenges Tera/Pico-Bytes of data to push through pipeline. (eg. LSST raw=1500GB & Cats=400GB) 4 layers: Data acquisition, book keeping Raw Data Reduction (registration) Shape analysis (KSB++, shapelets, K2K, automated) Science analysis (map making, power spectra, etc) How do we simulate large dynamic range? And Monte-Carlo surveys ~1000 times? c.f. CMB temperature & polarisation experiments (see A. Challinor’s Talk). 11/13/2018 IoP-RAS Meeting

Organizational Challenges How to coordinate the effort? EU Research-Training Network. DUEL (Dark Universe with Extragalactic Lensing) Exploit Cosmological Lensing from CFHTLS, Pan-STARRS, VST-KIDS Plan for future surveys (DUNE…) 8 Network Partners: Edinburgh, Paris, Bonn, Heidelberg, Munich, Leiden, Naples, British Columbia 7 Postdocs & 7 PhD students across network. Training & exchange of methods & data. 11/13/2018 IoP-RAS Meeting

Conclusions Map dark matter in 3-D over all sky to z=1. Expect DE FoM to double every 2.5 years. Dark Energy probes saturate beyond z=1. Bias in nuisance parameters biases w. Self-calibration leads to doubling of errors. Can add extra priors, or combine WL methods. Optical/IR, Photo-z/Spec-z, Ground-Space synergies Major challenges in data analysis lie ahead! 11/13/2018 IoP-RAS Meeting