arXiv subjects

Adam Amara

Publications and source records attributed to Adam Amara.

At least 19 recordsLinked to original sources

arXiv2026

Simulation-Based Cosmological Mass Calibration of XXL Galaxy Clusters using HSC Weak Lensing

We present a cosmological analysis of the X-ray-selected galaxy cluster sample from the XXL survey, employing a simulation-based inference (SBI) framework to jointly constrain cosmological parameters and X-ray scaling relations through forward modeling of cluster counts, X-ray observables, and weak-lensing measurements. Our analysis combines X-ray data from the XMM-XXL survey with shear measurements from the three-year shape catalog of the Hyper Suprime-Cam Subaru Strategic Program. The analysis focuses on the XXL C1 sample, comprising 171 clusters for abundance modeling, a subset of 86 clusters located within the XXL-N region for lensing-based mass calibration, and 162 clusters with X-ray temperature and luminosity measurements used to constrain scaling relations. Using the density-estimation likelihood-free inference (DELFI) algorithm, we construct a forward model with 12 parameters that incorporates the XXL selection function and cluster population modeling and accounts for key systematic effects including cluster miscentering, photometric redshift bias, and mass-dependent weak-lensing bias. Our SBI analysis yields a constraint on the cosmological parameter $S_8 \equiv \sigma_8 (\Omega_{m}/0.3)^{0.5} = 0.867 \pm 0.063$, with an additional 3% systematic uncertainty from neural network stochasticity. The result is consistent with Planck and recent cluster-based measurements. The inferred temperature-mass relation is consistent with self-similar expectations within uncertainties, whereas the luminosity-temperature relation exhibits a slope steeper than the self-similar prediction. From the resulting posterior distribution of the forward model, we derive lensing-calibrated mass estimates for all individual XXL clusters with measured X-ray temperatures or luminosities. These results provide a self-consistent mass calibration for future multi-probe cosmological analyses of the XXL sample.

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arXiv2025

Gas Giant and Brown Dwarf Companions: Mass Ratio and Orbital Distributions From A stars to M dwarfs

Understanding demographic properties of planet populations and multiple star systems constrains theories of planet and star formation. Surveys for very low-mass companions to M-A type stars detect brown dwarfs from multiple star formation and planets from circumstellar disks. We fit a composite model describing both very low-mass brown dwarf companions from "multiple-like processes" and gas giants from "planet-like processes" as functions of orbital separation and host star mass. We assemble a database of companion frequency estimates for masses from $< 1$ to $> 75$ Jupiter masses, separations from $< 0.3$ to $> 300$ AU, and host masses from $< 0.3$ to $> 2 M_{\odot}$. Using multinest, we fit these data to various models, performing model selection and deriving probability density functions. We assume companion mass ratio distributions are independent of orbital separation and fit a common log-normal orbital distribution to gas giant populations around M dwarfs, FGK, and A stars. A six-parameter model based on companion mass ratio distributions for planets and brown dwarfs is preferred. The planet CMRD slope is consistent with previous studies ($dN/dq \sim q^{-1.3} \pm 0.03$). Gas giant planets around stars from $< 0.3$ to $> 2.0 M_{\odot}$ follow a log-normal distribution peaking at ln(a) = 1.30 $\pm$ 0.03 (3.8 AU) with dispersion 0.22 $\pm$ 0.04. M dwarf distributions peak at smaller orbital radii than A stars, consistent with iceline considerations. Brown dwarf companion distributions extend stellar binary patterns, with the brown dwarf desert explained by flat-in-q mass functions and limited mass ratios below 0.1.

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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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arXiv2022

Probing the Cosmic Dark Ages with the Lunar Crater Radio Telescope

The Cosmic Dark Ages represent the period in the early evolution of the Universe, starting immediately after the decoupling of CMB photons from matter, and ending with the formation of the first stars and galaxies. The HI signal from the neutral hydrogen atoms is the only mechanism for us to understand this crucial phase in the cosmological history of the Universe and answer fundamental questions about the validity of the standard cosmological model, dark matter physics, and inflation. Due to cosmological redshift, this signal is now only observable in the 3-30~MHz frequency band, which is blocked from reaching the surface of the Earth by the ionosphere. In this paper, we present the design of the Lunar Crater Radio Telescope that intends to carry out unprecedented measurements of this signal by deploying a kilometer-sized parabolic reflector mesh inside a lunar crater on the far side of the Moon and suspending a receiver at its focus.

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arXiv2022

Rubin-Euclid Derived Data Products: Initial Recommendations

This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an initial set of Derived Data products (DDPs) that could realize the science goals enabled by joint processing. All interested Rubin and Euclid data rights holders were invited to contribute via an online discussion forum and a series of virtual meetings. Strong interest in enhancing science with joint DDPs emerged from across a wide range of astrophysical domains: Solar System, the Galaxy, the Local Volume, from the nearby to the primaeval Universe, and cosmology.

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arXiv2021

Quenching and Galaxy Demographics

The open-data revolution in astronomy is forcing the community to develop sophisticated analysis methods that heavily rely on realistic simulations. The phenomenology of the evolution of galaxy demographics can be described by a set of continuity equations invoking two quenching mechanisms: mass quenching and satellite quenching. The combination of these two mechanisms produces a double Schechter function for the quiescent population, as is observed in the low-redshift universe. In this paper we consider these quenching mechanisms, explicitly including satellite galaxies, and add the exact time evolution of the star-forming population. These new features complete the current versions of these continuity equations, and are essential for the realistic simulations required in modern extra-galactic astrophysics. We derive the analytical relation between the quiescent and the active populations, reducing considerably the parameter space for the simulation. In addition, we derive the analytical time dependence of the amplitude of the Schechter function. Finally, we validate our results against the SDSS DR7 galaxy sample. The model will be implemented in the SkyPy library and the main plots sonified using STRAUSS.

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arXiv2021

The Sensitivity of the Redshift Distribution to Galaxy Demographics

Photometric redshifts are commonly used to measure the distribution of galaxies in large surveys. However, the demands of ongoing and future large-scale cosmology surveys place very stringent limits on the redshift performance that are difficult to meet. A new approach to meet this precision need is forward modelling, which is underpinned by realistic simulations. In the work presented here, we use simulations to study the sensitivity of redshift distributions to the underlying galaxy population demographics. We do this by varying the redshift evolving parameters of the Schechter function for two galaxy populations, star-forming and quenched galaxies. Each population is characterised by eight parameters. We find that the redshift distribution of shallow surveys, such as SDSS, is mainly sensitive to the parameters for quenched galaxies. However, for deeper surveys such as DES and HSC, the star-forming parameters have a stronger impact on the redshift distribution. Specifically, the slope of the characteristic magnitude, $a_\mathrm{M}$, for star-forming galaxies has overall the strongest impact on the redshift distribution. Decreasing $a_\mathrm{M}$ by 148 per cent (its given uncertainty) shifts the mean redshift by ${\sim} 45$ per cent. We explore which combination of colour and magnitude measurements are most sensitive to $a_\mathrm{M}$ and we find that each colour-magnitude pair studied is similarly affected by a modification of $a_\mathrm{M}$.

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arXiv2021

The Hydrogen Intensity and Real-time Analysis eXperiment: 256-Element Array Status and Overview

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) is a radio interferometer array currently in development, with an initial 256-element array to be deployed at the South African Radio Astronomy Observatory (SARAO) Square Kilometer Array (SKA) site in South Africa. Each of the 6m, $f/0.23$ dishes will be instrumented with dual-polarisation feeds operating over a frequency range of 400-800 MHz. Through intensity mapping of the 21 cm emission line of neutral hydrogen, HIRAX will provide a cosmological survey of the distribution of large-scale structure over the redshift range of $0.775 < z < 2.55$ over $\sim$15,000 square degrees of the southern sky. The statistical power of such a survey is sufficient to produce $\sim$7 percent constraints on the dark energy equation of state parameter when combined with measurements from the Planck satellite. Additionally, HIRAX will provide a highly competitive platform for radio transient and HI absorber science while enabling a multitude of cross-correlation studies. In this paper, we describe the science goals of the experiment, overview of the design and status of the sub-components of the telescope system, and describe the expected performance of the initial 256-element array as well as the planned future expansion to the final, 1024-element array.

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arXiv2021

SOAR/Goodman Spectroscopic Assessment of Candidate Counterparts of the LIGO-Virgo Event GW190814

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star-black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera (DECam) on the 4m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on 8 separate nights to observe 11 candidates using the 4.1m Southern Astrophysical Research (SOAR) telescope's Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond.

astro-ph.HE↗

arXiv2021

Likelihood-free Forward Modeling for Cluster Weak Lensing and Cosmology

Likelihood-free inference provides a rigorous approach to preform Bayesian analysis using forward simulations only. The main advantage of likelihood-free methods is its ability to account for complex physical processes and observational effects in forward simulations. Here we explore the potential of likelihood-free forward modeling for Bayesian cosmological inference using the redshift evolution of the cluster abundance combined with weak-lensing mass calibration. We use two complementary likelihood-free methods, namely Approximate Bayesian Computation (ABC) and Density-Estimation Likelihood-Free Inference (DELFI), to develop an analysis procedure for inference of the cosmological parameters $(\Omega_\mathrm{m},\sigma_8)$ and the mass scale of the survey sample. Adopting an eROSITA-like selection function and a 10-percent scatter in the observable-mass relation in a flat $\Lambda$CDM cosmology with $\Omega_\mathrm{m}=0.286$ and $\sigma_8=0.82$, we create a synthetic catalog of observable-selected NFW clusters in a survey area of 50 deg$^2$. The stacked tangential shear profile and the number counts in redshift bins are used as summary statistics for both methods. By performing a series of forward simulations, we obtain convergent solutions for the posterior distribution from both methods. We find that ABC recovers broader posteriors than DELFI, especially for the $\Omega_\mathrm{m}$ parameter. For a weak-lensing survey with a source density of $n_\mathrm{g}=20$ arcmin$^{-2}$, we obtain posterior constraints on $S_8=\sigma_8(\Omega_\mathrm{m}/0.3)^{0.3}$ of $0.836 \pm 0.032$ and $0.810 \pm 0.019$ from ABC and DELFI, respectively. The analysis framework developed in this study will be particularly powerful for cosmological inference with ongoing cluster cosmology programs, such as the XMM-XXL survey and the eROSITA all-sky survey, in combination with wide-field weak-lensing surveys.

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arXiv2021

SkyPy: A package for modelling the Universe

SkyPy is an open-source Python package for simulating the astrophysical sky. It comprises a library of physical and empirical models across a range of observables and a command-line script to run end-to-end simulations. The library provides functions that sample realisations of sources and their associated properties from probability distributions. Simulation pipelines are constructed from these models using a YAML-based configuration syntax, while task scheduling and data dependencies are handled internally and the modular design allows users to interface with external software. SkyPy is developed and maintained by a diverse community of domain experts with a focus on software sustainability and interoperability. By fostering development, it provides a framework for correlated simulations of a range of cosmological probes including galaxy populations, large scale structure, the cosmic microwave background, supernovae and gravitational waves. Version 0.4 implements functions that model various properties of galaxies including luminosity functions, redshift distributions and optical photometry from spectral energy distribution templates. Future releases will provide additional modules, for example, to simulate populations of dark matter halos and model the galaxy-halo connection, making use of existing software packages from the astrophysics community where appropriate.

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arXiv2021

lenstronomy II: A gravitational lensing software ecosystem

lenstronomy is an Astropy-affiliated Python package for gravitational lensing simulations and analyses. lenstronomy was introduced by Birrer and Amara (2018) and is based on the linear basis set approach by Birrer et a. (2015). The user and developer base of lenstronomy has substantially grown since then, and the software has become an integral part of a wide range of recent analyses, such as measuring the Hubble constant with time-delay strong lensing or constraining the nature of dark matter from resolved and unresolved small scale lensing distortion statistics. The modular design has allowed the community to incorporate innovative new methods, as well as to develop enhanced software and wrappers with more specific aims on top of the lenstronomy API. Through community engagement and involvement, lenstronomy has become a foundation of an ecosystem of affiliated packages extending the original scope of the software and proving its robustness and applicability at the forefront of the strong gravitational lensing community in an open source and reproducible manner.

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arXiv2021

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 \textsc{CIGALE}. We use CFHTLS broad-band and PAUS narrow-band photometry for a flux-limited ($\mathrm{i}<22.5$) sample of galaxies up to redshift $\mathrm{z \sim 0.8}$. 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.

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arXiv2021

The PAU survey: Estimating galaxy photometry with deep learning

With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos builds on BKGnet, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed Lumos for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, Lumos increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artefacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10% to 2%, comparing to aperture photometry. Furthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with the Deepz machine learning photo-z code and the photo-z outlier rate by 20%. The photo-z improvement is lower than expected from the SNR increment, however currently the photometric calibration and outliers in the photometry seem to be its limiting factor.

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arXiv2020

Combining strong and weak lensing estimates in the Cosmos field

We present a combined cosmic shear analysis of the modeling of line-of-sight distortions on strongly lensed extended arcs and galaxy shape measurements in the COSMOS field. We develop a framework to predict the covariance of strong lensing and galaxy shape measurements of cosmic shear on the basis of the small scale matter power-spectrum. The weak lensing measurement is performed using data from the COSMOS survey calibrated with a cloning scheme using the Ultra Fast Image Generator UFig (Berge 2013). The strong lensing analysis is performed by forward modeling the lensing arcs with a main lensing deflector and external shear components from the same Hubble Space Telescope imaging data set. With a sample of three strong lensing shear measurements we present a 2-sigma detection of the cross-correlation signal between the two complementary measurements of cosmic shear along the identical line of sight. With large samples of lenses available with the next generation ground and space based observatories, the covariance of the signal of the two probes with large samples of lenses allows for systematic checks, cross-calibration of either of the two measurement and the measurement of the small scale shear power-spectrum.

astro-ph.CO↗

arXiv2020

Fast Lightcones for Combined Cosmological Probes

The combination of different cosmological probes offers stringent tests of the $\Lambda$CDM model and enhanced control of systematics. For this purpose, we present an extension of the lightcone generator UFalcon first introduced in Sgier et al. 2019 (arXiv:1801.05745), enabling the simulation of a self-consistent set of maps for different cosmological probes. Each realization is generated from the same underlying simulated density field, and contains full-sky maps of different probes, namely weak lensing shear, galaxy overdensity including RSD, CMB lensing, and CMB temperature anisotropies from the ISW effect. The lightcone generation performed by UFalcon is parallelized and based on the replication of a large periodic volume simulated with the GPU-accelerated $N$-Body code PkdGrav3. The post-processing to construct the lightcones requires only a runtime of about 1 walltime-hour corresponding to about 100 CPU-hours. We use a randomization procedure to increase the number of quasi-independent full-sky UFalcon map-realizations, which enables us to compute an accurate multi-probe covariance matrix. Using this framework, we forecast cosmological parameter constraints by performing a multi-probe likelihood analysis for a combination of simulated future stage-IV-like surveys. We find that the inclusion of the cross-correlations between the probes significantly increases the information gain in the parameter constraints. We also find that the use of a non-Gaussian covariance matrix is increasingly important, as more probes and cross-correlation power spectra are included. A version of the UFalcon package currently including weak gravitational lensing is publicly available.

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arXiv2020

Spectro-Imaging Forward Model of Red and Blue Galaxies

For the next generation of spectroscopic galaxy surveys, it is important to forecast their performances and to accurately interpret their large data sets. For this purpose, it is necessary to consistently simulate different populations of galaxies, in particular Emission Line Galaxies (ELGs), less used in the past for cosmological purposes. In this work, we further the forward modeling approach presented in Fagioli et al. 2018, by extending the spectra simulator Uspec to model galaxies of different kinds with improved parameters from Tortorelli et al. 2020. Furthermore, we improve the modeling of the selection function by using the image simulator Ufig. We apply this to the Sloan Digital Sky Survey (SDSS), and simulate $\sim157,000$ multi-band images. We pre-process and analyse them to apply cuts for target selection, and finally simulate SDSS/BOSS DR14 galaxy spectra. We compute photometric, astrometric and spectroscopic properties for red and blue, real and simulated galaxies, finding very good agreement. We compare the statistical properties of the samples by decomposing them with Principal Component Analysis (PCA). We find very good agreement for red galaxies and a good, but less pronounced one, for blue galaxies, as expected given the known difficulty of simulating those. Finally, we derive stellar population properties, mass-to-light ratios, ages and metallicities, for all samples, finding again very good agreement. This shows how this method can be used not only to forecast cosmology surveys, but it is also able to provide insights into studies of galaxy formation and evolution.

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