1. Future modelling efforts Eelco van Kampen ESO evkampen@eso.org

2. Modelling galaxy formation & evolution large-scale environment halo, galaxy & black hole merger histories gas physics star formation stellar population synthesis interstellar radiation field dust feedback processes (linking the above)

3. large-scale environment anything left to do ? Horizon project: thin redshift slice through a full sky cone (Pichon & Teyssier 2008) Millennium-II run: a 100 Mpc/h box (Boylan-Kolchin 2009) Mare Nostrum universe: area around the most massive galaxy cluster (Khalatyan 2005)

4. large-scale environment everything with gas, star formation, feedback, etc. constrained simulations to model specific objects multiple resolutions Dark matter map of the A901/A902 supercluster (Heymans et al. 2008)

5. halo, galaxy & BH merger history extracted from large simulations or analytical formalisms issues:  merger timescales  subhalo heating, stripping, quenching, etc. etc.  environmental effects  multiple mergers (especially at high-z)  black hole mergers and merger history

6. star formation & ISM molecular cooling, interstellar radiation field (ISRF), star formation thresholds,... Differing models for the ISM and star formation, for three different galaxy masses (Robertson & Kravtsov 2008) New atomic & molecular cooling + molecular density SF scaling with ISRF New atomic & molecular cooling + molecular density SF scaling without ISRF “Standard” atomic cooling + total gas density SF scaling + SF threshold model

7. stellar population synthesis models long history, but outstanding issues important for high-z galaxies is the modelling of the Thermally-Pulsing AGB phase of stellar evolution, most important for ages of around a Gyr M05 models compared to others and to data (Maraston 2007)

8. Galaxy geometry used in GRASIL (Silva et al. 1998) Disk: double exponential Bulge: King profile γ=3/2 dust emission for normal galaxies

9. dust emission for other galaxies How to model more complex geometries like merging galaxies, tidal interactions, or multiple mergers ? UDF 2012 (Elmegreen et al. 2009) NGC 6240 (Spitzer image) (Bush et al. 2009) Zw II 96 (Evans et al. 2008)

10. feedback processes Wind recycling appears to be a common phenomenon where galactic superwind feedback is re-accreted on a timescale usually much shorter than a Hubble time. (Oppenheimer & Davé, 2008) An example...

11. Modelling galaxy populations Phenomenological models or numerical simulations ? Phenomenological :  partly numerical, partly analytical, partly heuristic  many assumptions and simplified scaling relations  fast, so parameter space can be explored Numerical:  fully numerical down to resolution scale, below that analytical (‘subgrid physics’)  expensive, limited to a few parameter sets and/or physical assumptions

12. Simulating star formation in disk galaxies: the future - 2015 The ISM is fully resolved This means that the scale height of all ISM components are resolved using at least 10 resolution elements (Romeo 1994). This translates into at least ∆x ∼ 1 pc. If this is not satisfied the true disk stability will not be modelled accurately. At such a resolution, star formation occurs in their natural sites i.e. massive clouds such as GMCs. This treatment is the goal of most simulations but is due to their computational load beyond the capabilities of modern simulations attempting to study the assembly and evolution of large spiral galaxies to z = 0. In addition, as the star formation sites become resolved the codes need to incorporate the radiative feedback in order to accurately treat the life-times of the GMC structures (Murray et al. 2009). Pandora’s box (new small scale physics must be treated)! Computationally impossible in a cosmological context today From Ben Moore’s talk:

13. summary  observed mass assembly history and SFR history reproduced (w/in observational errors) for massive galaxies (M * >few 10 10 M sun )  low mass galaxies form too early, are too passive at all redshifts (z<2), and have stellar pops that are too old at z~0  may indicate that modelling of SN FB in current models needs to be modified  possible dearth of both very rapidly SF galaxies and quenched galaxies at z~2  latest models still fail to reproduce enough bright SMGs  observed mass assembly history and SFR history reproduced (w/in observational errors) for massive galaxies (M * >few 10 10 M sun )  low mass galaxies form too early, are too passive at all redshifts (z<2), and have stellar pops that are too old at z~0  may indicate that modelling of SN FB in current models needs to be modified  possible dearth of both very rapidly SF galaxies and quenched galaxies at z~2  latest models still fail to reproduce enough bright SMGs From Rachel Somerville’s talk:

14. Dec 15th 2009Desika Narayanan Obergurgl Ways Forward: Velocity Fields with ALMA Davé et al 2009: Harassment Narayanan et al 2009b: Major Mergers From Desika Narayanan’s talk:

15. SED and SPH galaxy models: GRASIL3D A.Schurer 09 PhD theses Aim: exploit the spatial information for stars and gas in hydro simulations of galaxy formation and of observed images – requires no symmetries GRASIL->3D: generalised to an arbitrary geometry through a cube grid in which stars and gas particles output by the SPH are distributed Gas in each cell divided in SF molecular clouds and cirrus (if young stars are present and gas density > threshold) Intrinsic stellar SED in each cell, with young stars within MCs Radiation field in each cell due to all other cells 1° Application : P-DEVA (Serna & Dominguez-Tenreiro) + GRASIL3D From Laura Silva’s talk:

16. Modelling galaxies here and there... My current modelling efforts: STAGES: modelling a multi-wavelength dataset for the A901/A902 supercluster SHADES: modelling the sub-mm galaxy population GAMA and Herschel-ATLAS: clusters and clustering My future modelling efforts: SERVS: galaxy evolution vs. environment SCUBA-2 cosmology legacy survey: clustering and proto-clusters proto-cluster galaxies with ALMA, LMT,...

17. SHADES: SCUBA half-degree survey Lockman & SXDF @ 850 micron

18. SHADES: flux vs. photo-z Schael et al. (2010)

19. SHADES vs. phenomenological models model 1: fiducial model 6: more quiescent star formation (in disks) model 7: more bursting star formation (merger-driven) Black lines: total SFR density (blue: bursting, red: quiescent)

20. Flux versus photo-z

21. Flux versus stellar mass

22. Stellar mass components

23. Simulations and simulators  science simulations (sky images)  telescope simulators (simulates corruption of sky images by the telescope, detectors, etc.) For ground-based telescopes (JCMT, ALMA, etc.) one needs to simulate the atmosphere as well. Most useful for the more complex observing modes.

24. Interferometer simulators The simdata interferometer simulator (part of the CASA package) produces synthetic visibilities a synthesized deconvolved image some analysis tools (image fidelity etc.) The simdata simulator models: thermal noise cross-polarization leakage, gain drift atmospheric phase delay (using a mock phase screen)

25. ALMA, last week

26. An example: M51 A continuum subtracted H alpha image of the nearby galaxy M51 (NGC 5194 -- provided by D. Thilker at NRAO). This H alpha image should be a reasonable representation of the atomic FIR lines and other tracers of massive star formation.

27. M51 ‘observed’ with ALMA

28. M51 at lower redshift (and using a larger mosaic)

29. Sub-mm map-making from model galaxies to a mock sub-mm map...

30. A blank field (850 micron at the JCMT)

31. With a proto-cluster at z=3.8

32. TAMASIS TAMASIS is an ASTRONET funded project aiming at enhancing the science that can be produced from submillimeter surveys The aim is to:  provide reliable, easy-to-use, and optimal map-making tools for Herschel and future generation of sub-mm instruments, so that the ambitious science objectives that are set for these facilities can be met.  aid the interpretation of future sub-mm datasets by generating observation- like mock maps from the theoretical models, and improving the models themselves  develop along the way specific data analysis tools requested for map- making performance assessments. Deconvolution and source detection are two areas where the ability to take in multi-wavelength data will make a significant difference.

33. TAMASIS Specifically, TAMASIS aims to achieve the following goals: 1. Produce simulations of the sub-mm flux distribution on the sky, in cosmology, star formation and Galactic structure. These models will be "observed" with the instrument simulators to provide controlled input samples to aid in algorithmic development and evaluation. In a further stage, these simulations will produce mock maps that can be directly compared to observed maps. 2. Improve and develop techniques to produce sub-mm maps, focusing on scan maps because they represent the observing mode of choice. 3. Improve and develop techniques to analyze scan maps, from source detection to fluctuation analysis methods. 4. Produce realistic mock maps for those legacy surveys which are of particular interest to the ASTRONET partner countries, for example Herschel ATLAS and HI-GAL 5. Deliver over the course of the project to the astronomical community tools and test maps that will have been thoroughly validated to allow others to benefit from our efforts.

34. TAMASIS Saclay: Marc Savage, Pierre Chanial Orsay: Alain Abergel + post-doc Leiden: Paul van der Werf, Rowan Meijerink ESO: Eelco van Kampen, Juan Gonzalez

35. ASTROSIM network This programme aims to bring together European computational astrophysicists working on a broad range of topics from the stability of the solar system to the formation of stars and galaxies. AstroSim provides funding for conferences, workshops, training schools, exchange visits and collaborative travel. It’s easy to apply, decisions are made quickly with little bureaucracy. For more information talk to Ben Moore, or goto http://www.astrosim.net/

36. Summary modelling of galaxies is getting more and more sophisticated in all parts galaxy populations still need to be simulated using phenomenological models of galaxy formation such models have variable success still instrument simulators should be used to predict data for more complex observing modes, eg. mapping collaborative networks exist to assist the community, eg. TAMASIS and ASTROSIM