arXiv subjects

Luca Tortorelli

Publications and source records attributed to Luca Tortorelli.

At least 19 recordsLinked to original sources

arXiv2026

GalSBI: Forward Modelling Galaxy Clustering and Population

Forward modelling is a powerful approach for analyzing large-scale structure surveys. For this purpose, we extend the GalSBI framework to jointly model the galaxy population and clustering using an efficient subhalo abundance matching scheme based on optimal transport. We use simulation-based inference to constrain the model parameters by comparing UFig image simulations with DES Y3 imaging data. As a validation, we find that galaxy photometry and morphology agree well with multi-band imaging data of different depths, namely DES and HSC deep fields. Galaxy clustering for simulation and data is also in good agreement when comparing the angular power spectrum for different magnitude and color cuts. We further compare simulated redshift distributions against high-precision photometric redshifts in HSC deep field imaging of the COSMOS field. We find the redshift distributions across magnitude cuts to be similar to previous work, however with more realistic uncertainty modelling due to the addition of clustering contribution to sample variance. The agreement of the mean redshifts with data is very good, between $0.2\sigma$ and $1.6\sigma$ for different magnitude cuts, with sample variance being the dominant uncertainty contributor in bright samples ($<24$ mag) and subdominant compared to galaxy population model uncertainty in fainter samples. As a byproduct we measure the galaxy luminosity function and galaxy-halo connection, which are broadly consistent with existing literature. The updated GalSBI code and galaxy population model are publicly available. They enable accurate forward-modelled image simulations with realistic clustering, which can be used to model the effect of sample variance, source clustering, redshift distributions, and blending in large-scale-structure surveys. This makes GalSBI a powerful tool for the analysis of current and next-generation cosmological galaxy surveys.

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arXiv2026

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts

The cosmological redshift of a galaxy's light is inferable from its observable properties in images. Because imaging is much easier to acquire than spectroscopic observations that would allow the identification of distinct line features, this motivates the technique of photometric redshift estimation (photo-$z$). Photo-$z$ has been an early and sustained driver for the utilization of artificial intelligence (AI) in astrophysics, and conversely AI methods are underlying most of the recent advances in photo-$z$. Here we review the diversity of AI methods applied to the photo-$z$ problem over the years in a discriminative way, that is, to regress redshift from photometric observables. We argue that, besides optimization suiting specific applications, this approach has effectively converged. It is limited not by the AI methodology but by the size and substantial systematic uncertainties and selection effects in spectroscopic training samples. In order to progress, either an unobtainable quantity and quality of training data or a more principled approach in using it is required. We thus outline ongoing research of integrating AI in a Bayesian modeling of galaxy data. This comes in the form of generative models for representing the distribution of intrinsic properties and outcomes of telescope observations of the galaxy population.

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arXiv2025

ProMage: fast galaxy magnitudes emulation combining SED forward-modelling and machine learning

We present ProMage, a feed-forward neural network that emulates the computation of observer- and rest-frame magnitudes from the generative galaxy SED package ProSpect. The network predicts magnitudes conditioned on input galaxy physical properties, including redshift, star formation history, gas and dust parameters. ProMage accelerates magnitude computation by a factor of $10^4$ compared to ProSpect, while achieving per-mille relative accuracy for $99\%$ of sources in the test set across the $g,r,i,z,y$ Hyper Suprime-Cam bands. This acceleration is key to enabling fast inference of galaxy physical properties in next-generation Stage IV surveys and to generating large catalogue realisations in forward-modelling frameworks such as GalSBI-SPS.

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arXiv2025

Emulating redshift mixing due to blending in weak gravitational lensing

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.

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arXiv2025

GalSBI-SPS: a stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications

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.

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arXiv2025

Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production

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.

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arXiv2024

GalSBI: Phenomenological galaxy population model for cosmology using simulation-based inference

We present GalSBI, a phenomenological model of the galaxy population for cosmological applications using simulation-based inference. The model is based on analytical parametrizations of galaxy luminosity functions, morphologies and spectral energy distributions. Model constraints are derived through iterative Approximate Bayesian Computation, by comparing Hyper Suprime-Cam deep field images with simulations which include a forward model of instrumental, observational and source extraction effects. We developed an emulator trained on image simulations using a normalizing flow. We use it to accelerate the inference by predicting detection probabilities, including blending effects and photometric properties of each object, while accounting for background and PSF variations. This enables robustness tests for all elements of the forward model and the inference. The model demonstrates excellent performance when comparing photometric properties from simulations with observed imaging data for key parameters such as magnitudes, colors and sizes. The redshift distribution of simulated galaxies agrees well with high-precision photometric redshifts in the COSMOS field within $1.5\sigma$ for all magnitude cuts. Additionally, we demonstrate how GalSBI's redshifts can be utilized for splitting galaxy catalogs into tomographic bins, highlighting its potential for current and upcoming surveys. GalSBI is fully open-source, with the accompanying Python package, $\texttt{galsbi}$, offering an easy interface to quickly generate realistic, survey-independent galaxy catalogs.

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arXiv2024

UFig v1: The ultra-fast image generator

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 Besan\c{c}on 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 Berg\'e et al. (2013) and the software has since been used and further developed in a variety of forward modelling applications.

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arXiv2024

galsbi: A Python package for the GalSBI galaxy population model

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 $\texttt{galsbi}$ 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 $\texttt{UFig}$ package.

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arXiv2024

The PAU Survey: galaxy stellar population properties estimates with narrowband data

Narrowband galaxy surveys have recently gained interest as a promising method to achieve the necessary accuracy on the photometric redshift estimate of individual galaxies for stage-IV cosmological surveys. One key advantage is the ability to provide higher spectral resolution information about galaxies that should allow a more accurate and precise estimation of galaxy stellar population properties. However, the impact of adding narrow-band photometry on the stellar population properties estimate is largely unexplored. The scope of this work is two-fold: on one side, leveraging the predictive power of broad-band and narrow-band data to infer galaxy physical properties such as stellar masses, ages, star formation rates and metallicities. On the other hand, evaluating the improvement of performance in estimating galaxy properties when we use narrow-band data instead of broad-band. In this work we measure the stellar population properties of a sample of galaxies in the COSMOS field for which both narrowband and broadband data are available. In particular, we employ narrowband data from PAUS and broad-band data from CFHTLS. We use two different spectral energy distribution fitting codes to measure galaxy properties, namely CIGALE and Prospector. We find that the increased spectral resolution of narrow-band photometry does not yield a substantial improvement on constraining galaxy properties using spectral energy distribution fitting. Still we find that we obtain a more diverse distribution of metallicities and dust optical depths with cigale when employing the narrowband data. The effect is not as prominent as expected, which we relate this to the low narrowband SNR of a majority of the galaxies, the respective drawbacks of both codes as well as the coverage only in the optical regime. The measured properties are afterwards compared to the COSMOS2020 catalogue, showing good agreement.

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arXiv2024

Impact of stellar population synthesis choices on forward modelling-based redshift distribution estimates

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.

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arXiv2024

Emission Line Predictions for Mock Galaxy Catalogues: a New Differentiable and Empirical Mapping from DESI

We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically-determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust predictions for galaxy photometry, but reliably predicting the strengths of emission lines has remained challenging. Our new mapping is a simple neural network implemented using the JAX Python automatic differentiation library. It is trained on Dark Energy Spectroscopic Instrument Early Release data to predict the equivalent widths (EWs) of the eight brightest optical emission lines (including H$\alpha$, H$\beta$, [O II], and [O III]) from a galaxy's rest-frame optical continuum. The predicted EW distributions are consistent with the observed ones when noise is accounted for, and we find Spearman's rank correlation coefficient $\rho_s > 0.87$ between predictions and observations for most lines. Using a non-linear dimensionality reduction technique (UMAP), we show that this is true for galaxies across the full range of observed spectral energy distributions. In addition, we find that adding measurement uncertainties to the predicted line strengths is essential for reproducing the distribution of observed line-ratios in the BPT diagram. Our trained network can easily be incorporated into a differentiable stellar population synthesis pipeline without hindering differentiability or scalability with GPUs. A synthetic catalogue generated with such a pipeline can be used to characterise and account for biases in the spectroscopic training sets used for training and calibration of photo-$z$'s, improving the modelling of systematic incompleteness for the Rubin Observatory LSST and other surveys.

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arXiv2024

The powerful lens galaxy cluster PLCK G287.0+32.9 (${\theta}_E \sim 43''$)

We present a new high-precision strong lensing model of PLCK G287.0$+$32.9, a massive lens galaxy cluster at $z=0.383$, with the aim to get an accurate estimation of its effective Einstein radius and total mass distribution. We also present a spectroscopic catalog containing accurate redshift measurements for 490 objects, including multiply-lensed sources and cluster member galaxies. We exploit high-quality spectroscopic data from three pointings of the VLT Multi Unit Spectroscopic Explorer, covering a central $3~\rm{arcmin}^2$ region of the cluster. We complete the spectroscopic catalog by including redshift measurements from VLT-VIMOS and KECK-DEIMOS. We identify 129 spectroscopic cluster member galaxies, with redshift values $0.360 \leq z \leq 0.405$ and $m_{\rm{F160W}} \leq 21$, and 24 photometric ones identified with a Convolutional Neural Network from ancillary HST imaging. We also identify 114 multiple images from 28 background sources, of which 84 images from 16 sources are new and the remaining ones were identified in previous work. The best-fitting lens model shows a root mean square separation value between the predicted and observed positions of the multiple images of $0.75''$, corresponding to an improvement in reconstructing the observed positions of the multiple images of a factor of $2.5$ with respect to previous models. Using the predictive power of our new lens model we find 3 new multiple images and we confirm the configuration of three systems of multiple images that were not used for the optimization of the model. The derived total mass distribution confirms this cluster to be a very prominent gravitational lens with an effective Einstein $\theta_{E} = 43.4'' \pm 0.1''$, that is in agreement with previous estimates and corresponds to a total mass enclosed in the critical curve of $M_E = {3.33}_{-0.07}^{+0.02} \times{ 10^{14} M_\odot}$.

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arXiv2024

Simulation-based inference of deep fields: galaxy population model and redshift distributions

Accurate redshift calibration is required to obtain unbiased cosmological information from large-scale galaxy surveys. In a forward modelling approach, the redshift distribution n(z) of a galaxy sample is measured using a parametric galaxy population model constrained by observations. We use a model that captures the redshift evolution of the galaxy luminosity functions, colours, and morphology, for red and blue samples. We constrain this model via simulation-based inference, using factorized Approximate Bayesian Computation (ABC) at the image level. We apply this framework to HSC deep field images, complemented with photometric redshifts from COSMOS2020. The simulated telescope images include realistic observational and instrumental effects. By applying the same processing and selection to real data and simulations, we obtain a sample of n(z) distributions from the ABC posterior. The photometric properties of the simulated galaxies are in good agreement with those from the real data, including magnitude, colour and redshift joint distributions. We compare the posterior n(z) from our simulations to the COSMOS2020 redshift distributions obtained via template fitting photometric data spanning the wavelength range from UV to IR. We mitigate sample variance in COSMOS by applying a reweighting technique. We thus obtain a good agreement between the simulated and observed redshift distributions, with a difference in the mean at the 1$\sigma$ level up to a magnitude of 24 in the i band. We discuss how our forward model can be applied to current and future surveys and be further extended. The ABC posterior and further material will be made publicly available at https://cosmology.ethz.ch/research/software-lab/ufig.html.

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arXiv2023

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$_{\mathrm{limit}}$, with blue galaxies residing in smaller halos. For satellite galaxies, environmental quenching is implemented with the parameter t$_{\mathrm{quench}}$, 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$_{\mathrm{limit}}$ and t$_{\mathrm{quench}}$ and offers great prospects for the quick generation of galaxy mock catalogues, optimized to agree with observations.

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arXiv2023

Exploring the low-mass regime of galaxy-scale strong lensing: Insights into the mass structure of cluster galaxies

We aim at a direct measurement of the compactness of three galaxy-scale lenses in massive clusters, testing the accuracy of the scaling laws that describe the members in strong lensing (SL) models of galaxy clusters. We selected the multiply imaged sources MACS J0416.1$-$2403 ID14 ($z=3.221$), MACS J0416.1$-$2403 ID16 ($z=2.095$), and MACS J1206.2$-$0847 ID14 ($z=3.753$). Eight images were observed for the first SL system, and six for the latter two. We focused on the main deflector of each galaxy-scale SL system (identified as members 8971, 8785, and 3910, respectively), and modelled its total mass distribution with a truncated isothermal sphere. We accounted for the lensing effects of the remaining cluster components, and included the uncertainty on the cluster-scale mass distribution through a bootstrapping procedure. We measured a truncation radius value of $6.1^{+2.3}_{-1.1} \, \mathrm{kpc}$, $4.0^{+0.6}_{-0.4} \, \mathrm{kpc}$, and $5.2^{+1.3}_{-1.1} \, \mathrm{kpc}$ for members 8971, 8785, and 3910, respectively. Alternative non-truncated models with a higher number of free parameters do not lead to an improved description of the SL system. We measured the stellar-to-total mass fraction within the effective radius $R_e$ for the three members, finding $0.51\pm0.21$, $1.0\pm0.4$, and $0.39\pm0.16$, respectively. We find that a parameterisation of the properties of cluster galaxies in SL models based on power-law scaling relations with respect to the total luminosity cannot accurately describe their compactness over their full total mass range. Our results agree with modelling of the cluster members based on the Fundamental Plane relation. Finally, we report good agreement between our values of the stellar-to-total mass fraction within $R_e$ and those of early-type galaxies from the SLACS Survey. Our work significantly extends the regime of the current samples of lens galaxies.

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arXiv2023

$\mathrm{morphofit}$: An automated galaxy structural parameters fitting 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 S\'ersic 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/).

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arXiv2022

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.

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