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
• large volume, good mass res
• many output times 50-100
• DM merger trees
• spatial information
• Galaxy formation prescription
• physical model e.g. SAM
• empirical model e.g. HOD

INPUTS MOCK CATALOGUE PRODUCTION OUTPUTS

Mock catalogues: Inputs II

• Observational data to

calibrate model predictions e.g. HiZELS/WISP observations

• f H-alpha luminosity function

and clustering

INPUTS MOCK CATALOGUE PRODUCTION OUTPUTS

Evolution of the LF

• Physical model

makes predictions

• Model parameters

adjusted to match

• bservations e.g. z=0

K LF

• Can expand these

calibration

• bservations to

include H-alpha data and new NIR

• bservations
• Can force models to

match observations exactly

Observed LF: HiZELS Sobral et al. 2012

Mock catalogues: Production

• Described in Merson et al.

2012 arXiv:12064046

INPUTS MOCK CATALOGUE PRODUCTION OUTPUTS

Mock catalogues: outputs I

• Physical properties:

stellar mass, disk & bulge scale lengths

• Broad band photometry

Euclid NIR, optical

• Line flux EW

H-alpha

• Images
• No spectra as of now (though

possible)

INPUTS MOCK CATALOGUE PRODUCTION OUTPUTS

Synthetic images

Mock catalogues: outputs II

• Physical properties:

stellar mass, disk & bulge scale lengths

• Broad band photometry

Euclid NIR, optical

• Line flux EW

H-alpha

• Images
• No spectra as of now (though

possible)

INPUTS MOCK CATALOGUE PRODUCTION OUTPUTS

Mock catalogues: outputs III

• Data formats – source

catalogue tables:

• HDF5 (compressed format)
• asciii (biggest, most portable)
• Database: SQL queries
• Image files: FITS?

INPUTS MOCK CATALOGUE PRODUCTION OUTPUTS

Mock catalogues: outputs IV

• Can provide statistical descriptions
• f model outputs:
• E.g. luminosity functions
• E.g. Number counts
• E.g. Halo Occupation Distributions
• Could generate Monte-Carlo

realisations of these for testing purposes

• Serve as input to empirical

approaches e.g. supply H-alpha emitter HOD for MICE mocks

INPUTS MOCK CATALOGUE PRODUCTION OUTPUTS

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