16 research outputs found
Rapid Simulations of Halo and Subhalo Clustering
The analysis of cosmological galaxy surveys requires realistic simulations
for their interpretation. Forward modelling is a powerful method to simulate
galaxy clustering without the need for an underlying complex model. This
approach requires fast cosmological simulations with a high resolution and
large volume, to resolve small dark matter halos associated to single galaxies.
In this work, we present fast halo and subhalo clustering simulations based on
the Lagrangian perturbation theory code PINOCCHIO, which generates halos and
merger trees. The subhalo progenitors are extracted from the merger history and
the survival of subhalos is modelled. We introduce a new fitting function for
the subhalo merger time, which includes a redshift dependence of the fitting
parameters. The spatial distribution of subhalos within their hosts is modelled
using a number density profile. We compare our simulations with the halo finder
ROCKSTAR applied to the full N-body code GADGET-2. The subhalo velocity
function and the correlation function of halos and subhalos are in good
agreement. We investigate the effect of the chosen number density profile on
the resulting subhalo clustering. Our simulation is approximate yet realistic
and significantly faster compared to a full N-body simulation combined with a
halo finder. The fast halo and subhalo clustering simulations offer good
prospects for galaxy forward models using subhalo abundance matching.Comment: 28 pages, 10 figures, Accepted for publication in JCA
Fast Forward Modelling of Galaxy Spatial and Statistical Distributions
A forward modelling approach provides simple, fast and realistic simulations
of galaxy surveys, without a complex underlying model. For this purpose, galaxy
clustering needs to be simulated accurately, both for the usage of clustering
as its own probe and to control systematics. We present a forward model to
simulate galaxy surveys, where we extend the Ultra-Fast Image Generator to
include galaxy clustering. We use the distribution functions of the galaxy
properties, derived from a forward model adjusted to observations. This
population model jointly describes the luminosity functions, sizes,
ellipticities, SEDs and apparent magnitudes. To simulate the positions of
galaxies, we then use a two-parameter relation between galaxies and halos with
Subhalo Abundance Matching (SHAM). We simulate the halos and subhalos using the
fast PINOCCHIO code, and a method to extract the surviving subhalos from the
merger history. Our simulations contain a red and a blue galaxy population, for
which we build a SHAM model based on star formation quenching. For central
galaxies, mass quenching is controlled with the parameter M,
with blue galaxies residing in smaller halos. For satellite galaxies,
environmental quenching is implemented with the parameter
t, where blue galaxies occupy only recently merged
subhalos. We build and test our model by comparing to imaging data from the
Dark Energy Survey Year 1. To ensure completeness in our simulations, we
consider the brightest galaxies with . We find statistical agreement
between our simulations and the data for two-point correlation functions on
medium to large scales. Our model provides constraints on the two SHAM
parameters M and t and offers great
prospects for the quick generation of galaxy mock catalogues, optimized to
agree with observations.Comment: Prepared for submission to JCAP. 28 pages, 15 figure
Fast forward modelling of galaxy spatial and statistical distributions
A forward modelling approach provides simple, fast and realistic simulations of galaxy surveys, without a complex underlying model. For this purpose, galaxy clustering needs to be simulated accurately, both for the usage of clustering as its own probe and to control systematics. We present a forward model to simulate galaxy surveys, where we extend the Ultra-Fast Image Generator to include galaxy clustering. We use the distribution functions of the galaxy properties, derived from a forward model adjusted to observations. This population model jointly describes the luminosity functions, sizes, ellipticities, SEDs and apparent magnitudes. To simulate the positions of galaxies, we then use a two-parameter relation between galaxies and halos with Subhalo Abundance Matching (SHAM). We simulate the halos and subhalos using the fast PINOCCHIO code, and a method to extract the surviving subhalos from the merger history. Our simulations contain a red and a blue galaxy population, for which we build a SHAM model based on star formation quenching. For central galaxies, mass quenching is controlled with the parameter Mlimit, with blue galaxies residing in smaller halos. For satellite galaxies, environmental quenching is implemented with the parameter tquench, where blue galaxies occupy only recently merged subhalos. We build and test our model by comparing to imaging data from the Dark Energy Survey Year 1. To ensure completeness in our simulations, we consider the brightest galaxies with i < 20. We find statistical agreement between our simulations and the data for two-point correlation functions on medium to large scales. Our model provides constraints on the two SHAM parameters Mlimit and tquench and offers great prospects for the quick generation of galaxy mock catalogues, optimized to agree with observations.ISSN:1475-751
The PAU survey: measurement of narrow-band galaxy properties with approximate bayesian computation
Narrow-band imaging surveys allow the study of the spectral characteristics of galaxies without the need of performing their spectroscopic follow-up. In this work, we forward-model the Physics of the Accelerating Universe Survey (PAUS) narrow-band data. The aim is to improve the constraints on the spectral coefficients used to create the galaxy spectral energy distributions (SED) of the galaxy population model in Tortorelli et al. 2020. In that work, the model parameters were inferred from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) data using Approximate Bayesian Computation (ABC). This led to stringent constraints on the B-band galaxy luminosity function parameters, but left the spectral coefficients only broadly constrained. To address that, we perform an ABC inference using CFHTLS and PAUS data. This is the first time our approach combining forward-modelling and ABC is applied simultaneously to multiple datasets. We test the results of the ABC inference by comparing the narrow-band magnitudes of the observed and simulated galaxies using Principal Component Analysis, finding a very good agreement. Furthermore, we prove the scientific potential of the constrained galaxy population model to provide realistic stellar population properties by measuring them with the SED fitting code CIGALE. We use CFHTLS broad-band and PAUS narrow-band photometry for a flux-limited (i < 22.5) sample of galaxies up to redshift z similar to 0.8. We find that properties like stellar masses, star-formation rates, mass-weighted stellar ages and metallicities are in agreement within errors between observations and simulations. Overall, this work shows the ability of our galaxy population model to correctly forward-model a complex dataset such as PAUS and the ability to reproduce the diversity of galaxy properties at the redshift range spanned by CFHTLS and PAUS.ISSN:1475-751
Dissolved inorganic carbon budgets in the eastern subpolar North Atlantic in the 2000s from in situ data
The subpolar North Atlantic (SPNA) is important in the global carbon cycle because of the deep water ventilation processes that lead to both high uptake of atmospheric CO2 and large inventories of anthropogenic CO2 (C-ant). Thus, it is crucial to understand its response to increasing anthropogenic pressures. In this work, the budgets of dissolved inorganic carbon (DIC), C-ant and natural DIC (DICnat) in the eastern SPNA in the 2000s, are jointly analyzed using in situ data. The DICnat budget is found to be in steady state, confirming a long-standing hypothesis from in situ data for the first time. The biological activity is driving the uptake of natural CO2 from the atmosphere. The C-ant increase in the ocean is solely responsible of the DIC storage rate which is explained by advection of C-ant from the subtropics (65%) and C-ant air-sea flux (35%). These results demonstrate that the C-ant is accumulating in the SPNA without affecting the natural carbon cycle