Galaxies, Cosmology and the SKA Catherine Cress (UWC) ‏

1. Galaxies, Cosmology and the SKA Catherine Cress (UWC)‏

2. Astrophysics People at UWC: Prof Cress Prof Kilkenny Dr Loubser Dr Johnson Dr Mhlahlo Dr Olivier Prof Koen Dr Faltenbacher Dr Moeketsi Postgraduates: Sean, Faustino, Fidy, Claudio, Ando, Daniel, Daniel, Solohery, Geoffrey Undergraduates with SKA bursaries

3. A. Simulating radio sources: SKA/MeerKAT sources & CMB contamination B. (New) Science for the SKA 1. Dark energy speed of sound measurements using clustering of HI-selected galaxies and HI-galaxy-CMB cross-correlation ‏ ‏2. Cosmology using the Tully-Fisher relation to measure >108 luminosity distances

4. Modelling SKA sources in simulations: radio galaxies, IR-bright galaxies – application to CMB experiments Involved in Atacama Cosmology Telescope project (new CMB experiment from WMAP team) to measure CMB fluctuations to 1 arcminute => can identify 1000's of clusters of galaxies via Sunyaev-Zeldovich effect => w constraints but need to worry about contamination by point sources: starforming galaxies bright in IR and into mm radio galaxies: some bright into mm (most WMAP point sources blazars) => SZ spectral signature more difficult to identify 0 0.5 1 1.5 z

5. Modelling SKA sources in simulations: N-body simulation flavours: Dark Matter only: represent ~105-108 M☉as a single particle allow particles to interact gravitationally gives info on non-linear evolution of density fluctuations Dark Matter+Semi-Analytic Models identify “halos” (clumps) in dark matter simulation trace merger history of halos use basic physics (gas cooling etc.) to “paint” galaxies Dark Matter + Smoothed Particle Hydrodynamics model gas explicitly can add in star formation etc., depending on application ☉

6. Dark Matter Simulation run at CHPC 17 million particles, 50Mpc side for more pictures contact Daniel Cunnama (dcunnama@uwc.ac.za)‏

7. Modelling AGN and Starforming Galaxies in Simulations Dark Matter+Semi-Analytic Models identify “halos” (clumps) in dark matter simulation trace merger history of halos use basic physics (gas cooling etc.) to “paint” galaxies AGN modelled using “radio-mode” and “quasar mode” accretion radio-mode: hot halo gas slowly accretes onto central black hole model by tracking change in black-hole mass of galaxies in sim quasar mode: in mergers of galaxies gas driven to centre: rapid accretion model by tracking major mergers of galaxies Starforming Galaxies: Far-infrared luminosity given be starformation rate (extrapolated to CMB frequencies using known SED's)‏ Radio brightness ~ to FIR in starforming galaxies ☉

8. Modelling radio-loud AGN in Simulations AGN modelled using “radio-mode” and “quasar mode” accretion ☉

9. Modelling radio-loud AGN in Simulations AGN modelled using “radio-mode” and “quasar mode” accretion ☉

10. Modelling Starforming Galaxies in Simulations with Daniel Opolot Far-infrared luminosity given by starformation rate: simulated vs observed densities rho(z): Consider starforming galaxies near clusters of galaxies (potential contaminants to CMB) ☉

11. Modelling Starforming Galaxies in Simulations Far-infrared luminosity given by starformation rate: LFIR= k.SFR CMB contamination: typical thermalSZ ~200μK typical kineticSZ ~20μK => significant contamination from IR-bright sources Sources not randomly distributed in clusters as in Sehgal et al 09 Potential to remove sources using optical/IR/radio data. What do we learn about galaxy evolution?: IR-bright galaxies modelled here consistent with (some) observations => support for galaxy evolution theory used here ☉

12. Science for SKA 1. ‏ What is dark energy? (dark matter = ? with normal gravitational interaction; dark energy=? with weird grav. int.)‏ Aside: How do you test cosmological models? 1. Expansion rate of universe depends on amount of DM and DE → affects how bright objects look and how big they look eg supernovae, BAO 2. Evolution of clustering depends on amount of DM and DE → in universe with no DE, structures form late (counting clusters => contraints)‏ 3. Dark matter affects many observables → eg. rotation curves, velocity dispersions in clusters, gravitational LENSING

13. Science for SKA 1. ‏ What is dark energy? (dark matter = ? with normal gravitational interaction; dark energy=? with weird grav. int.)‏ Cosmological constant? w=-1 in P=wρ Dynamical scalar field? (-1<w<-1/3)‏ * quintessence: sound speed constant: cs=1 * other models like k-essence (with non-standard kinetic term): cs!=1 Can we constrain DE sound speed using SKA? SKA: huge HI survey to z~2 Cross-correlate CMB with HI positions to probe DE through ISW effect Also use clustering of HI galaxies and CMB power spectrum

14. Science for the SKA 1.‏ Integrated Sachs Wolfe anisotropies Primary anisotropies No ISW Matter-dominated Universe CMB ISW curvature or DE-dominated Universe Varying gravitational potential Induces a secondary layer of large-scale anisotropies

15. Science for SKA 1. Clustering to probe dark energy sound speed galaxies anisotropies ISW anisotropies ISW-galaxies cross-correlation Use of CAMB CMB spectrum: ClTT Galaxy autocorrelation

16. Science for the SKA 1‏ Constraints on DE sound speeds possible for low speeds Torres-Rodriguez & Cress MNRAS 2007

17. Science for SKA‏ 1 Fisher matrix analysis with marginalization Uncertainties related to bias questions? Torres-Rodriguez, Cress & Moodley, MNRAS 2008

18. Science for the SKA‏ 2: Cosmology using Tully-Fisher relation Using the Tully-Fisher relation to measure the luminosity distance (dL) to 100's of millions of galaxies. Flux=Luminosity/4π dL2(also, m-M=5log(dL/10pc))‏ and dL=dL(Ωm,Ω,DE,w0,wa etc)‏ Tully-Fisher Relation: intrinsic luminosity of spiral galaxy obtained from rotation speed Rotation speed can be measured by broadening of HI-21cm line an Log V

19. Science for the SKA 2: Cosmology using Tully-Fisher relation Investigate constraints on “standard” cosmological parameters using HI-survey Create simulated catalog of galaxies * start with local HI-mass function, model evolution using absorption system info * assume flux limit=> observable HI-mass (function of z)‏ * HI+cosmology+modelling =>dark matter halo mass ass'd with HI-mass => rotation velocity * random inclinations * assume TFR holds => absolute magnitude in specific band. * Uncertainties: - assume TFR accurate to 10%, allow 5-10% error on photometry - velocity errors given by spectral resolution & redshift in line fitting simulation Torres-Rodriguez, Cress & Moodley , submitted

20. Science for the SKA 2.‏ Instead of trying to measure inclination for every galaxy, rather use the fact that inclinations should be random on large scales. Group simulated galaxies into redshift bins and magnitude bins. Consider distribution of velocities: apparent magnitude vs Vrot at z=0.1,0.5,1 m=24, z=0.2 for 100 sq deg survey

21. Science for the SKA 2.‏ luminosity distance uncertainties:

22. Science for the SKA 2.‏ Constraints on “standard” dark energy parameters for different surveys specs. Fiducial: dv=30km/s, dz=0.01 100 sq deg., dMass=0.01 weff=w0+(1-a)wa 5% Photometric errors: Dark Energy Task Force Stage IV: Torres-Rodriguez, Moodley & Cress submitted

23. Science for the SKA 2: Tully-Fisher‏ 5% Photometric errors: Dark Energy Task Force Stage IV: Torres-Rodriguez, Moodley & Cress submitted

25. Science for the SKA 2.‏ Using TFR: questions Evolution of the TFR? Stellar mass TFR? Baryonic TFR? Combine Vrot & Vdisp? (ala Kassin)‏ Extinction? NB: * can marginalise over evolution parameters. * can calibrate TFR at high-z using SNIa Torres-Rodriguez, Moodley & Cress submitted

26. Summary: IR-bright sources & blazars contaminants for SZ effect Can produce maps of radio sources from simulation: AGN + IR-bright sources (which are also faint radio sources) SKA science: Dark energy sound speed from Cgg, Ctt, Cgt : Tully-Fisher to get luminosity distances => strong constraints on dark energy Future: Publish IR source contamination, more work on radio sources Gas in CHPC hydrosim: insight into HI evolution Tully-Fisher details for SKA science

27. * Summary: IR-bright sources & blazars contaminants for SZ effect Can produce maps of radio sources from simulation: AGN + IR-bright sources (which are also faint radio sources) SKA science: Dark energy sound speed from Cgg, Ctt, Cgt : Tully-Fisher to get luminosity distances => strong constraints on dark energy

28. Angel Torres Rodriguez HI observations with KAT: M31 at z=0.001 with 2km, 500m and 200m baselines Large beams: confusion, even in redshift space

29. SKA site bid preparation(using AIPS) (including HI, protoplanetary disk and reionisation research)‏

30. Clustering of HI-selected galaxies * Sean: Clustering of HI-detected galaxies: Useful for SKA predictions and understanding how luminous matter traces dark matter Also working on radio source luminosity functions with Oxford group (NB for MeerKAT and SKA)

31. Structure Evolution Observations Constrain w structure = galaxies, clusters of galaxies, superclusters etc Structure forms through gravitational collapse of density fluctuations seen in the CMB. How structure evolves and how it appears to us depends on cosmological parameters

32. Effect of varying w on number density of clusters over a given SZ detection threshold Top to bottom at peak: w= -1, -0.6, -0.3, 0 Haiman et al (2001)‏ 0 0.5 1 1.5 z

33. ACT Need to measure redshifts of clusters and velocity dispersions so mass of clusters can be estimated 200 sq. degrees => ~1000 clusters Spectroscopic follow-up on cluster candidates once SZ data available (including deep imaging in cluster region)‏ Deep optical survey in whole strip to allow detection of CMB lensing and kinetic SZ effect? Survey good for other observational projects Xray Optical SZ at 2mm

34. SALT and ACT A photometric survey to detect KSZ? The kinetic SZ-effect: bulk motion of hot gas upscatters photons to higher frequencies => probe of velocities => probe of potentials Constraints on cosmological params: 5-10% errors on w with some info on w(z) (astroph0511061)

35. SALT and ACT A case for a photometric survey in the ACT strip? Weak lensing of CMB: probe dark matter directly at high-z Kinetic SZ: stronger constraints on cosmological parameters direct probe of potentials at high-z Properties of galaxies in outskirts of clusters Faint QSO properties: implications for black hole growth (lensed) QSO's at very high-z Variability studies: SNIa -> cosmology Stars Additional data available: XMM, Galex, Spitzer Additional support: Indians and Rutgers prepared to commit time

36. SALT and ACT HPC Simulations Point source simulations: with Sarah Bryan (PhD student, worked with ACT team in Princeton)‏ and Fidy Ramamonjisoa (NASSP Honours student)‏ * radio & IR galaxies contaminate CMB signal * so far these sources have been randomly distributed in simulations * need to include clustering (possibly preferentially in clusters)‏ * Using galaxies simulated in Millenium simulation * investigating the semi-analytic modelling of IR, mm and radio emission in galaxies * aim to include clustering of contaminant sources in CMB maps and to improve SAMs SALT and PLANCK Spanish Collaboration involving HPC Simulations Xray Optical SZ at 2mm

37. SALT and ACT Summary New generation CMB expt from WMAP team – flucts on smaller scales than WMAP Need to measure redshifts of clusters detected in SZ and and velocity dispersions so mass of clusters can be estimated. 200 sq. degrees => ~1000 clusters Spectroscopic follow-up on cluster candidates once SZ data available (including deep imaging in cluster region)‏ Deep optical survey in whole strip to allow detection of CMB lensing and kinetic SZ effect - good for other observational projects - varibility (SNIa/stars), qso's, clusters GALEX, XMM, Spitzer? data in same field. India, Rutgers prepared to commit time. Point source simulations – include clustering of radio & IR galaxies as could hamper SZ cluster extraction Xray Optical SZ at 2mm

38. Summary CMB/Large-scale structure combination probes cosmological parameters and relationship between dark and luminous matter. ACT: many clusters via SZ effect – spectroscopic follow up with SALT photometric survey for KSZ, weak lensing, qso's etc? simulations to include clustering of point sources SKA: sound speed of dark energy understanding radio source populations HPC: simulations to look at figure rotation of halos => galactic dynamics new opportunities with CHPC: CMB foregrounds, meerKAT science

39. Real Galaxies: Galaxies simulated within underlying dark matter structures: Can reproduce properties of galaxies fairly well in Cold Dark Matter scenario But some problems eg. Angular momentum problem .. Need additions to simple CDM theory?

40. Simulating the Radio Sky: two goals 1. Compare predictions of ΛCDM + galaxy evolution models with observations * HI sources * Radio continuum sources * CMB foregrounds - see talks by Opolot and Ramamonjisoa 2. Make “fake skies” for MeerKAT/SKA Simulations we are working with: 1. DM only simulation on CHPC (2563 particle): test run for CHPC 2. GIMIC simulation on CHPC (with Theuns): Gas + DM, 400*106 particles, 32MPc box 3. Millenium simulation ( already run, with semi-analytic modelling to get galaxy properties, query with SQL)