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The forward modelling of galaxy surveys has recently gathered interest as one of the primary methods to achieve the required precision on the estimate of the redshift distributions for stage IV surveys. One of the key aspects of forward modelling a galaxy survey is the connection between the physical properties drawn from a galaxy population model and the intrinsic SEDs, achieved through SPS codes (e.g. FSPS). However, SPS requires a large number of detailed assumptions on the constituents of galaxies, for which the model choice or parameter values are currently uncertain. In this work, we perform a sensitivity study of the impact that the variations of the SED modelling choices have on the mean and scatter of the tomographic galaxy redshift distributions. We assumed the Prospector beta model as the fiducial input galaxy population model and used its SPS parameters to build 9 bands magnitudes of a fiducial sample of galaxies. We then built samples of galaxy magnitudes by varying one SED modelling choice at a time. We modelled the colour redshift relation of these galaxy samples using the SOM approach. We placed galaxies in the SOM cells according to their observed frame colours and used their cell assignment to build colour selected tomographic bins. Finally, we compared each variant's binned redshift distributions against the estimates obtained for the fiducial model. We find that the SED components related to the IMF, AGNs, gas physics, and the attenuation law substantially bias the mean and the scatter of the tomographic redshift distributions with respect to those estimated with the fiducial model. Regardless of the applied stellar mass function based re-weighting strategy, the bias in the mean and the scatter of the tomographic redshift distributions are greater than the precision requirements set by next-generation Stage IV galaxy surveys, such as LSST and Euclid.

Large-scale structure surveys measure the shapes and positions of millions of galaxies in order to constrain the cosmological model with high precision. The resulting large data volume poses a challenge for the analysis of the data, from the estimation of photometric redshifts to the calibration of shape measurements. We present GalSBI, a model for the galaxy population, to address these challenges. This phenomenological model is constrained by observational data using simulation-based inference (SBI). The \\(galsbi\\) Python package provides an easy interface to generate catalogs of galaxies based on the GalSBI model, including their photometric properties, and to simulate realistic images of these galaxies using the \\(UFig\\) package.

In today's modern wide-field galaxy surveys, there is the necessity for parametric surface brightness decomposition codes characterised by accuracy, small degree of user intervention, and high degree of parallelisation. We try to address this necessity by introducing \\(\\mathrm{morphofit}\\), a highly parallelisable \\(\\mathrm{Python}\\) package for the estimate of galaxy structural parameters. The package makes use of wide-spread and reliable codes, namely \\(\\mathrm{SExtractor}\\) and \\(\\mathrm{GALFIT}\\). It has been optimised and tested in both low-density and crowded environments, where blending and diffuse light makes the structural parameters estimate particularly challenging. \\(\\mathrm{morphofit}\\) allows the user to fit multiple surface brightness components to each individual galaxy, among those currently implemented in the code. Using simulated images of single Sérsic and bulge plus disk galaxy light profiles with different bulge-to-total luminosity (\\(\\mathrm{B/T}\\)) ratios, we show that \\(\\mathrm{morphofit}\\) is able to recover the input structural parameters of the simulated galaxies with good accuracy. We also compare its estimates against existing literature studies, finding consistency within the errors. We use the package in Tortorelli et al. 2023 to measure the structural parameters of cluster galaxies in order to study the wavelength dependence of the Kormendy relation of early-type galaxies. The package is available on github (https://github.com/torluca/morphofit) and on the Pypi server (https://pypi.org/project/morphofit/).

Next generation photometric and spectroscopic surveys will enable unprecedented tests of the concordance cosmological model and of galaxy formation and evolution. Fully exploiting their potential requires a precise understanding of the selection effects on galaxies and biases on measurements of their properties, required, above all, for accurate estimates of redshift distributions n(z). Forward-modelling offers a powerful framework to simultaneously recover galaxy \\(n(z)\\)s and characterise the observed galaxy population. We present GalSBI-SPS, a new SPS-based galaxy population model that generates realistic galaxy catalogues, which we use to forward-model HSC data in the COSMOS field. GalSBI-SPS samples galaxy physical properties, computes magnitudes with ProSpect, and simulates HSC images in the COSMOS field with UFig. We measure photometric properties consistently in real data and simulations. We compare \\(n(z)\\)s, photometric and physical properties to observations and to GalSBI. GalSBI-SPS reproduces the observed grizy magnitude, colour, and size distributions down to i<23. Median differences in magnitudes and colours remain below 0.14 mag, with the model covering the full colour space spanned by HSC. Galaxy sizes are overestimated by 0.2 arcsec on average and some tension exists in the g-r colour, but the latter is comparable to that seen in GalSBI. \\(n(z)\\)s show a mild positive offset (0.01-0.08) in the mean. GalSBI-SPS qualitatively reproduces the stellar mass-SFR and size-stellar mass relations seen in COSMOS2020. GalSBI-SPS provides a realistic, survey-independent galaxy population description at a Stage-III depth using only literature-based parameters. Its predictive power will improve significantly when constrained against observed data using SBI, thereby providing accurate \\(n(z)\\)s satisfying the stringent requirements set by Stage IV surveys.

With the rise of simulation-based inference (SBI) methods, simulations need to be fast as well as realistic. \\(\\texttt{UFig v1}\\) is a public Python package that simulates astronomical images with exceptional speed, taking approximately the same time as source extraction. This makes it particularly well-suited for SBI methods where computational efficiency is crucial. To render an image, \\(\\texttt{UFig}\\) requires a galaxy catalog, and a description of the point spread function (PSF). It can also add background noise, sample stars using the Besançon model of the Milky Way, and run \\(\\texttt{SExtractor}\\) to extract sources from the rendered image. The extracted sources can be matched to the intrinsic catalog, flagged based on \\(\\texttt{SExtractor}\\) output and survey masks, and emulators can be used to bypass the image simulation and extraction steps. A first version of \\(\\texttt{UFig}\\) was presented in Bergé et al. (2013) and the software has since been used and further developed in a variety of forward modelling applications.

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\\(_limit\\), with blue galaxies residing in smaller halos. For satellite galaxies, environmental quenching is implemented with the parameter t\\(_quench\\), 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 M\\(_limit\\) and t\\(_quench\\) and offers great prospects for the quick generation of galaxy mock catalogues, optimized to agree with observations.

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.

Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majority of its sample of tens of billions of galaxies will lack high-fidelity spectroscopic measurements thereof, instead relying on photometric redshifts (photo-\\(z\\)) subject to systematic imprecision and inaccuracy best encapsulated by photo-\\(z\\) probability density functions (PDFs). We present the version 1 release of Redshift Assessment Infrastructure Layers (RAIL), an open source Python library for at-scale probabilistic photo-\\(z\\) estimation, initiated by the LSST Dark Energy Science Collaboration (DESC) with contributions from the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. RAIL's three subpackages provide modular tools for end-to-end stress-testing, including a forward modeling suite to generate realistically complex photometry, a unified API for estimating per-galaxy and ensemble redshift PDFs by an extensible set of algorithms, and built-in metrics of both photo-\\(z\\) PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo-\\(z\\) data products at scale for a variety of science goals and is not specific to LSST data. We thus describe to the extragalactic science community, including and beyond Rubin the design and functionality of the RAIL software library so that any researcher may have access to its wide array of photo-\\(z\\) characterization and assessment tools.

Galaxies whose images overlap in the focal plane of a telescope, commonly referred to as blends, are often located at different redshifts. Blending introduces a challenge to weak-lensing cosmology probes since such blends are subject to shear signals from multiple redshifts. This effect can be described by joining shear bias and redshift characterisation in the effective redshift distribution, \\(n_(z)\\), which includes the response of apparent shapes of detected objects to shear of galaxies at redshift, \\(z\\). In this work, we propose a novel method to correct \\(n_(z)\\) for redshift-mixed blending by emulating the shear response to neighbouring galaxies. We designed a `half-sky-shearing' simulation with Subaru Hyper Suprime Cam (HSC) wide-like specifications, which allowed us to extract the response of a detected object's measured ellipticity to the shearing of neighbouring galaxies among numerous galaxy pairs. We demonstrate the feasibility of accurately emulating these pairwise responses and validate the robustness of our approach under varying observing conditions and galaxy population uncertainties. We find that the effective redshift of sources at the high-redshift tail of the distribution is about 0.05 lower than expected when the effect is not modelled. Given adequately processed image simulations, our correction method can be readily incorporated into future cosmological analyses to mitigate this source of systematic error.

Galaxies whose images overlap in the focal plane of a telescope, commonly referred to as blends, are often located at different redshifts. Blending introduces a challenge to weak-lensing cosmology probes since such blends are subject to shear signals from multiple redshifts. This effect can be described by joining shear bias and redshift characterisation in the effective redshift distribution, \\(n_{\\gamma}(z)\\), which includes the response of apparent shapes of detected objects to shear of galaxies at redshift, \\(z\\). In this work, we propose a novel method to correct \\(n_{\\gamma}(z)\\) for redshift-mixed blending by emulating the shear response to neighbouring galaxies. We designed a `half-sky-shearing' simulation with Subaru Hyper Suprime Cam (HSC) wide-like specifications, which allowed us to extract the response of a detected object's measured ellipticity to the shearing of neighbouring galaxies among numerous galaxy pairs. We demonstrate the feasibility of accurately emulating these pairwise responses and validate the robustness of our approach under varying observing conditions and galaxy population uncertainties. We find that the effective redshift of sources at the high-redshift tail of the distribution is about 0.05 lower than expected when the effect is not modelled. Given adequately processed image simulations, our correction method can be readily incorporated into future cosmological analyses to mitigate this source of systematic error.