OU-SIM CSWG Interface Mock Catalogues, Monte-Carlo simulations and - PowerPoint PPT Presentation

OU-SIM CSWG Interface Mock Catalogues, Monte-Carlo simulations and analytical models Carlton Baugh Institute for Computational Cosmology Durham University

Mock catalogues INPUTS MOCK CATALOGUE PRODUCTION OUTPUTS

Mock catalogues: Inputs I • N-body simulation INPUTS - large volume, good mass res - many output times 50-100 MOCK CATALOGUE - DM merger trees PRODUCTION - spatial information • Galaxy formation prescription OUTPUTS - physical model e.g. SAM - empirical model e.g. HOD

Mock catalogues: Inputs II • Observational data to INPUTS calibrate model predictions MOCK CATALOGUE e.g. HiZELS/WISP observations PRODUCTION of H-alpha luminosity function and clustering OUTPUTS

Evolution of the LF • Physical model makes predictions • Model parameters adjusted to match observations e.g. z=0 K LF • Can expand these calibration observations to include H-alpha data and new NIR observations • Can force models to match observations exactly Observed LF: HiZELS Sobral et al. 2012

Mock catalogues: Production • Described in Merson et al. INPUTS 2012 arXiv:12064046 MOCK CATALOGUE PRODUCTION OUTPUTS

Mock catalogues: outputs I • Physical properties: INPUTS stellar mass, disk & bulge scale lengths • Broad band photometry MOCK CATALOGUE Euclid NIR, optical PRODUCTION • Line flux EW H-alpha • Images OUTPUTS • No spectra as of now (though possible)

Synthetic images

Mock catalogues: outputs II • Physical properties: INPUTS stellar mass, disk & bulge scale lengths • Broad band photometry MOCK CATALOGUE Euclid NIR, optical PRODUCTION • Line flux EW H-alpha • Images OUTPUTS • No spectra as of now (though possible)

Mock catalogues: outputs III • Data formats – source INPUTS catalogue tables: • HDF5 (compressed format) MOCK CATALOGUE • asciii (biggest, most portable) PRODUCTION • Database: SQL queries • Image files: FITS? OUTPUTS

Mock catalogues: outputs IV • Can provide statistical descriptions INPUTS of model outputs: • E.g. luminosity functions • E.g. Number counts • E.g. Halo Occupation Distributions MOCK CATALOGUE PRODUCTION • Could generate Monte-Carlo realisations of these for testing purposes • Serve as input to empirical OUTPUTS approaches e.g. supply H-alpha emitter HOD for MICE mocks

HOD z=0.84 Euclid flux limit

Inputs: required parameters for Monte-Carlo mock (no clustering) • Cosmological parameters – to give dV/dz, dl(z) • Input statistic to generate realisation of e.g. luminosity function at different z

Inputs: required parameters for empirical mock (with clustering) • Cosmological parameters • N-body simulation • Prescription to associate galaxies with DM: e.g. Halo Occupation Distribution Conditional Luminosity Function Biasing prescription e.g. fn(DM density)

Inputs: required parameters for physical mock • Cosmological parameters • N-body simulation • Physics to include in galaxy formation model - solve differential equations - physics uncertain, so contains parameters - different models will use different implementations

Physical galaxy formation model • Gas cooling - gas density profile • Star formation - mass involved plus timescale • Stellar population synthesis - IMF, yield, recycled fraction • Feedback - SNe (reheat cooled gas) - photo-ionisation, AGN (heat gas that is trying to cool) • Chemical evolution - set by choice of IMF and definition of channels between reservoirs • Galaxy sizes - cons of Ang. Mom; cons of energy, virial theorem in mergers • Galaxy mergers - dynamical friction timescale