arXiv subjects

Sultan Hassan

Publications and source records attributed to Sultan Hassan.

At least 19 recordsLinked to original sources

arXiv2026

GalSyn I: A Forward-Modeling Code for Synthetic Galaxy Observations from Hydrodynamical Simulations and First Data Release from IllustrisTNG

We present GalSyn (Galaxy Synthesizer), a modular and flexible Python package for generating synthetic observations from hydrodynamical galaxy simulations. GalSyn generates synthetic spectrophotometric data cubes for individual galaxies from simulation cutouts, employing a particle-by-particle spectral modeling approach that enables the rapid production of large synthetic datasets required for statistical population studies, offering a computationally efficient alternative to full radiative transfer codes. Users have full control over the spectral modeling choices, including the stellar population synthesis engine, stellar isochrones, spectral libraries, and initial mass functions. Dust attenuation is modeled at spatially resolved scales using a line-of-sight column-density method, with a comprehensive suite of fixed and adaptive attenuation laws. A decoupled kinematics model independently Doppler-shifts the stellar and nebular components, enabling realistic synthetic integral field unit data cubes. It also provides features to add observational realism, including PSF convolution and noise simulation. Beyond generating imaging and spectroscopic data cubes, GalSyn reconstructs spatially resolved physical property maps and star formation histories (SFHs) of a galaxy. Alongside this paper, we present the first data release of synthetic imaging observations and resolved SFHs generated from the IllustrisTNG simulations. This release includes four mock extragalactic survey fields together with data cubes of the individual galaxies within them, data cubes of 290 local massive galaxies and their progenitors tracked across $0<z<5$, and 259 major-merger systems. Each galaxy data cube contains imaging across 47 filters spanning HST, JWST, Euclid, Rubin/LSST, and the Roman Space Telescope. GalSyn is publicly available at https://github.com/aabdurrouf/GalSyn.

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arXiv2025

Towards Reconciling Reionization with JWST: The Role of Bright Galaxies and Strong Feedback

The elevated UV luminosity functions (UVLF) from recent James Webb Space Telescope (JWST) have challenged the viability of existing theoretical models. To address this, we use a semi-analytical framework -- which couples a physically motivated source model derived from radiative-transfer hydrodynamic simulations of reionization with a Markov Chain Monte Carlo sampler -- to perform a joint calibration to JWST galaxy surveys (UVLF, $\phi_{\rm UV}$ and UV luminosity density, $\rho_{\rm UV}$) and reionization-era observables (ionizing emissivity, $\dot{N}_{\rm ion}$, neutral hydrogen fraction, $x_{\rm HI}$, and Thomson optical depth, $\tau$). We find that models with weak feedback and a higher contribution from faint galaxies reproduce the reionization observables but struggle to match the elevated JWST UVLF at $z > 9$. In contrast, models with stronger feedback (i.e., rapid redshift evolution) and a higher contribution from bright galaxies successfully reproduce JWST UVLF at $z \geq 10$, but over-estimate the bright end at $z < 9$. The strong-feedback model constrained by JWST UVLF predicts a more gradual and extended reionization history, as opposed to the sudden reionization seen in the weak-feedback models. This extended nature of reionization ($z\sim 16$ - $6$) yields an optical depth consistent (at 2-$\sigma$) with the Cosmic Microwave Background (CMB) constraint, thereby alleviating the photon-budget crisis. In both scenarios, reionization is complete by $z \sim 6$, consistent with current data. Our analysis highlights the importance of accurately modeling feedback and ionizing emissivities from different source populations during the first billion years after the Big Bang.

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arXiv2025

Towards Mitigating Systematics in Large-Scale Surveys via Few-Shot Optimal Transport-Based Feature Alignment

Systematics contaminate observables, leading to distribution shifts relative to theoretically simulated signals-posing a major challenge for using pre-trained models to label such observables. Since systematics are often poorly understood and difficult to model, removing them directly and entirely may not be feasible. To address this challenge, we propose a novel method that aligns learned features between in-distribution (ID) and out-of-distribution (OOD) samples by optimizing a feature-alignment loss on the representations extracted from a pre-trained ID model. We first experimentally validate the method on the MNIST dataset using possible alignment losses, including mean squared error and optimal transport, and subsequently apply it to large-scale maps of neutral hydrogen. Our results show that optimal transport is particularly effective at aligning OOD features when parity between ID and OOD samples is unknown, even with limited data-mimicking real-world conditions in extracting information from large-scale surveys. Our code is available at https://github.com/sultan-hassan/feature-alignment-for-OOD-generalization.

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arXiv2025

How does feedback affect the star formation histories of galaxies?

Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the CAMELS suite of cosmological simulations, we study the effects of varying feedback and cosmology on the average star formation histories (SFHs) of galaxies at $z\sim0$ across the IllustrisTNG, SIMBA and ASTRID galaxy formation models. We find that galaxy SFHs in all three models are sensitive to changes in stellar feedback, which affects the efficiency of baryon cycling and the rates at which central black holes grow, while effects of varying AGN feedback depend on model-dependent implementations of black hole seeding, accretion and feedback. We also find strong interaction terms that couple stellar and AGN feedback, usually by regulating the amount of gas available for the central black hole to accrete. Using a double power-law to describe the average SFHs, we derive a general set of equations relating the shape of the SFHs to physical quantities like baryon fraction and black hole mass across all three models. We find that a single set of equations (albeit with different coefficients) can describe the SFHs across all three CAMELS models, with cosmology dominating the SFH at early times, followed by halo accretion, and feedback and baryon cycling at late times. Galaxy SFHs provide a novel, complementary probe to constrain cosmology and feedback, and can connect the observational constraints from current and upcoming galaxy surveys with the physical mechanisms responsible for regulating galaxy growth and quenching.

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arXiv2025

Applying a star formation model calibrated on high-resolution interstellar medium simulations to cosmological simulations of galaxy formation

Modern high-resolution simulations of the interstellar medium (ISM) have shown that key factors in governing star formation are the competing influences of radiative dissipation, pressure support driven by stellar feedback, and the relentless pull of gravity. Cosmological simulations of galaxy formation, such as IllustrisTNG or ASTRID, are however not able to resolve this physics in detail and therefore need to rely on approximate treatments. These have often taken the form of empirical subgrid models of the ISM expressed in terms of an effective equation of state (EOS) that relates the mean ISM pressure to the mean gas density. Here we seek to improve these heuristic models by directly fitting their key ingredients to results of the high-resolution TIGRESS simulations, which have shown that the dynamical equilibrium of the ISM can be understood in terms of a pressure-regulated, feedback modulated (PRFM) model for star formation. Here we explore a simple subgrid model that draws on the PRFM concept but uses only local quantities. It accurately reproduces PRFM for pure gas disks, while it predicts slightly less star formation than PRFM in the presence of an additional thin stellar disk. We compare the properties of this model with the older Springel and Hernquist and TNG prescriptions, and apply all three to isolated simulations of disk galaxies as well as to a set of high-resolution zoom-in simulations carried out with a novel 'multi-zoom' technique that we introduce in this study. The softer EOS implied by TIGRESS produces substantially thinner disk galaxies, which has important ramifications for disk stability and galaxy morphology. The total stellar mass of galaxies is however hardly modified at low redshift, reflecting the dominating influence of large-scale gaseous inflows and outflows to galaxies, which are not sensitive to the EOS itself

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arXiv2024

Towards cosmological inference on unlabeled out-of-distribution HI observational data

We present an approach that can be utilized in order to account for the covariate shift between two datasets of the same observable with different distributions. This helps improve the generalizability of a neural network model trained on in-distribution samples (IDs) when inferring cosmology at the field level on out-of-distribution samples (OODs) of {\it unknown labels}. We make use of HI maps from the two simulation suites in CAMELS, IllustrisTNG and SIMBA. We consider two different techniques, namely adversarial approach and optimal transport, to adapt a target network whose initial weights are those of a source network pre-trained on a labeled dataset. Results show that after adaptation, salient features that are extracted by source and target encoders are well aligned in the embedding space. This indicates that the target encoder has learned the representations of the target domain via the adversarial training and optimal transport. Furthermore, in all scenarios considered in our analyses, the target encoder, which does not have access to any labels ($\Omega_{\rm m}$) during adaptation phase, is able to retrieve the underlying $\Omega_{\rm m}$ from out-of-distribution maps to a great accuracy of $R^{2}$ score $\ge$ 0.9, comparable to the performance of the source encoder trained in a supervised learning setup. We further test the viability of the techniques when only a few out-of-distribution instances are available for training and find that the target encoder still reasonably recovers the matter density. Our approach is critical in extracting information from upcoming large scale surveys.

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arXiv2024

Learning the Universe: GalactISM simulations of resolved star formation and galactic outflows across main sequence and quenched galactic environments

We present a suite of six high-resolution chemo-dynamical simulations of isolated galaxies, spanning observed disk-dominated environments on the star-forming main sequence, as well as quenched, bulge-dominated environments. We compare and contrast the physics driving star formation and stellar feedback amongst the galaxies, with a view to modeling these processes in cosmological simulations. We find that the mass-loading of galactic outflows is coupled to the clustering of supernova explosions, which varies strongly with the rate of galactic rotation $\Omega = v_c/R$ via the Toomre length, leading to smoother gas disks in the bulge-dominated galaxies. This sets an equation of state in the star-forming gas that also varies strongly with $\Omega$, so that the bulge-dominated galaxies have higher mid-plane densities, lower velocity dispersions, and higher molecular gas fractions than their main sequence counterparts. The star formation rate in five out of six galaxies is independent of $\Omega$, and is consistent with regulation by the mid-plane gas pressure alone. In the sixth galaxy, which has the most centrally-concentrated bulge and thus the highest $\Omega$, we reproduce dynamical suppression of the star formation efficiency (SFE) in agreement with observations. This produces a transition away from pressure-regulated star formation.

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arXiv2024

Towards Implementation of the Pressure-Regulated, Feedback-Modulated Model of Star Formation in Cosmological Simulations: Methods and Application to TNG

Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of Springel & Hernquist (2003, hereafter SH03), employ adjustable parameters to satisfy constraints measured in the local Universe. In recent years, however, theory and spatially-resolved simulations of the turbulent, multiphase, star-forming ISM have begun to produce new first-principles models, which when fully developed can replace traditional subgrid prescriptions. This approach has advantages of being physically motivated and predictive rather than empirically tuned, and allowing for varying environmental conditions rather than being tied to local Universe conditions. As a prototype of this new approach, by combining calibrations from the TIGRESS numerical framework with the Pressure-Regulated Feedback-Modulated (PRFM) theory, simple formulae can be obtained for both the gas depletion time and an effective equation of state. Considering galaxies in TNG50, we compare the "native" simulation outputs with post-processed predictions from PRFM. At TNG50 resolution, the total midplane pressure is nearly equal to the total ISM weight, indicating that galaxies in TNG50 are close to satisfying vertical equilibrium. The measured gas scale height is also close to theoretical equilibrium predictions. The slopes of the effective equations of states are similar, but with effective velocity dispersion normalization from SH03 slightly larger than that from current TIGRESS simulations. Because of this and the decrease in PRFM feedback yield at high pressure, the PRFM model predicts shorter gas depletion times than the SH03 model at high densities and redshift. Our results represent a first step towards implementing new, numerically calibrated subgrid algorithms in cosmological galaxy formation simulations.

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arXiv2024

Metallicity Dependence of Pressure-Regulated Feedback-Modulated Star Formation in the TIGRESS-NCR Simulation Suite

We present a new simulation suite for the star-forming interstellar medium (ISM) in galactic disks using the TIGRESS-NCR framework. Distinctive aspects of our simulation suite are: (1) sophisticated and comprehensive numerical treatments of essential physical processes including magnetohydrodynamics, self-gravity, and galactic differential rotation, as well as photochemistry, cooling, and heating coupled with ray-tracing UV radiation transfer and resolved supernova feedback and (2) wide parameter coverage including metallicity over $Z'\equiv Z/Z_\odot\sim0.1-3$, gas surface density $\Sigma_{\rm gas}\sim5-150 M_{\odot}{\rm pc^{-2}}$, and stellar surface density $\Sigma_{\rm star}\sim 1-50 M_{\odot}{\rm pc^{-2}}$. The range of emergent star formation rate surface density is $\Sigma_{\rm SFR}\sim 10^{-4}-0.5 M_{\odot}{\rm kpc^{-2}yr^{-1}}$ and ISM total midplane pressure is $P_{\rm tot}/k_B=10^3-10^6{\rm cm^{-3}K}$, with $P_{\rm tot}$ equal to the ISM weight $W$. For given $\Sigma_{\rm gas}$ and $\Sigma_{\rm star}$, we find $\Sigma_{\rm SFR} \propto Z'^{0.3}$. We provide an interpretation based on the pressure-regulated feedback-modulated (PRFM) star formation theory. We characterize feedback modulation in terms of the yield $\Upsilon$, defined as the ratio of each stress to $\Sigma_{\rm SFR}$. The thermal feedback yield varies sensitively with both weight and metallicity as $\Upsilon_{\rm th}\propto W^{-0.46}Z'^{-0.53}$, while the combined turbulent and magnetic feedback yield shows weaker dependence $\Upsilon_{\rm turb+mag}\propto W^{-0.22}Z'^{-0.18}$. The reduction in $\Sigma_{\rm SFR}$ at low metallicity is due mainly to enhanced thermal feedback yield, resulting from reduced attenuation of UV radiation. With the metallicity-dependent calibrations we provide, PRFM theory can be used for a new subgrid star formation prescription in cosmological simulations where the ISM is unresolved.

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arXiv2024

Cosmological multifield emulator

We demonstrate the use of deep network to learn the distribution of data from state-of-the-art hydrodynamic simulations of the CAMELS project. To this end, we train a generative adversarial network to generate images composed of three different channels that represent gas density (Mgas), neutral hydrogen density (HI), and magnetic field amplitudes (B). We consider an unconstrained model and another scenario where the model is conditioned on the matter density $\Omega_{\rm m}$ and the amplitude of density fluctuations $\sigma_{8}$. We find that the generated images exhibit great quality which is on a par with that of data, visually. Quantitatively, we find that our model generates maps whose statistical properties, quantified by probability distribution function of pixel values and auto-power spectra, agree reasonably well with those of the real maps. Moreover, the cross-correlations between fields in all maps produced by the emulator are in good agreement with those of the real images, which indicates that our model generates instances whose maps in all three channels describe the same physical region. Furthermore, a CNN regressor, which has been trained to extract $\Omega_{\rm m}$ and $\sigma_{8}$ from CAMELS multifield dataset, recovers the cosmology from the maps generated by our conditional model, achieving $R^{2}$ = 0.96 and 0.83 corresponding to $\Omega_{\rm m}$ and $\sigma_{8}$ respectively. This further demonstrates the great capability of the model to mimic CAMELS data. Our model can be useful for generating data that are required to analyze the information from upcoming multi-wavelength cosmological surveys.

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arXiv2023

Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations

The generalization of machine learning (ML) models to out-of-distribution (OOD) examples remains a key challenge in extracting information from upcoming astronomical surveys. Interpretability approaches are a natural way to gain insights into the OOD generalization problem. We use Centered Kernel Alignment (CKA), a similarity measure metric of neural network representations, to examine the relationship between representation similarity and performance of pre-trained Convolutional Neural Networks (CNNs) on the CAMELS Multifield Dataset. We find that when models are robust to a distribution shift, they produce substantially different representations across their layers on OOD data. However, when they fail to generalize, these representations change less from layer to layer on OOD data. We discuss the potential application of similarity representation in guiding model design, training strategy, and mitigating the OOD problem by incorporating CKA as an inductive bias during training.

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arXiv2023

Deep Learning Voigt Profiles I. Single-Cloud Doublets

Voigt profile (VP) decomposition of quasar absorption lines is key to studying intergalactic gas and the baryon cycle governing the formation and evolution of galaxies. The VP velocities, column densities, and Doppler $b$ parameters inform us of the kinematic, chemical, and ionization conditions of these astrophysical environments. A drawback of traditional VP fitting is that it can be human-time intensive. With the coming next generation of large all-sky survey telescopes with multi-object high-resolution spectrographs, the time demands will significantly outstrip our resources. Deep learning pipelines hold the promise to keep pace and deliver science digestible data products. We explore the application of deep learning convolutional neural networks (CNNs) for predicting VP fitted parameters directly from the normalized pixel flux values in quasar absorption line profiles. A CNN was applied to 56 single-component MgII2796, 2803 doublet absorption line systems observed with HIRES and UVES ($R=45,000$). The CNN predictions were statistically indistinct from a traditional VP fitter. The advantage is that once trained, the CNN processes systems $\sim\!10^5$ times faster than a human expert VP fitting profiles by hand. Our pilot study shows that CNNs hold promise to perform bulk analysis of quasar absorption line systems in the future.

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arXiv2023

An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low Redshift Lyman-$α$ Forest

We explore the role of galactic feedback on the low redshift Lyman-$α$ (Ly$α$)~forest ($z \lesssim 2$) statistics and its potential to alter the thermal state of the intergalactic medium. Using the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) suite, we explore variations of the AGN and stellar feedback models in the IllustrisTNG and Simba sub-grid models. We find that both AGN and stellar feedback in Simba play a role in setting the Ly$α$ forest column density distribution function (CDD) and the Doppler width ($b$-value) distribution. The Simba AGN jet feedback mode is able to efficiently transport energy out to the diffuse IGM causing changes in the shape and normalization of the CDD and a broadening of the $b$-value distribution. We find that stellar feedback plays a prominent role in regulating supermassive black hole growth and feedback, highlighting the importance of constraining stellar and AGN feedback simultaneously. In IllustrisTNG, the AGN feedback variations explored in CAMELS do not affect the Ly$α$ forest, but varying the stellar feedback model does produce subtle changes. Our results imply that the low-$z$ Ly$α$ forest can be sensitive to changes in the ultraviolet background (UVB), stellar and black hole feedback, and that AGN jet feedback in particular can have a strong effect on the thermal state of the IGM.

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arXiv2023

Latent space representations of cosmological fields

We investigate the possibility of learning the representations of cosmological multifield dataset from the CAMELS project. We train a very deep variational encoder on images which comprise three channels, namely gas density (Mgas), neutral hydrogen density (HI), and magnetic field amplitudes (B). The clustering of the images in feature space with respect to some cosmological/astrophysical parameters (e.g. $Ω_{\rm m}$) suggests that the generative model has learned latent space representations of the high dimensional inputs. We assess the quality of the latent codes by conducting a linear test on the extracted features, and find that a single dense layer is capable of recovering some of the parameters to a promising level of accuracy, especially the matter density whose prediction corresponds to a coefficient of determination $R^{2}$ = 0.93. Furthermore, results show that the generative model is able to produce images that exhibit statistical properties which are consistent with those of the training data, down to scales of $k\sim 4h/{\rm Mpc}.$

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arXiv2023

HIDM: Emulating Large Scale HI Maps using Score-based Diffusion Models

Efficiently analyzing maps from upcoming large-scale surveys requires gaining direct access to a high-dimensional likelihood and generating large-scale fields with high fidelity, which both represent major challenges. Using CAMELS simulations, we employ the state-of-the-art score-based diffusion models to simultaneously achieve both tasks. We show that our model, HIDM, is able to efficiently generate high fidelity large scale HI maps that are in a good agreement with the CAMELS's power spectrum, probability distribution, and likelihood up to second moments. HIDM represents a step forward towards maximizing the scientific return of future large scale surveys.

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arXiv2023

Bridging the Gap between Cosmic Dawn and Reionization favors Faint Galaxies-dominated Models

It has been claimed that traditional models struggle to explain the tentative detection of the 21\,cm absorption trough centered at $z\sim17$ measured by the EDGES collaboration. On the other hand, it has been shown that the EDGES results are consistent with an extrapolation of a declining UV luminosity density, following a simple power-law of deep Hubble Space Telescope observations of $4 < z < 9$ galaxies. We here explore the conditions by which the EDGES detection is consistent with current reionization and post-reionization observations, including the neutral hydrogen fraction at $z\sim6$--$8$, Thomson scattering optical depth, and ionizing emissivity at $z\sim5$. By coupling a physically motivated source model derived from radiative transfer hydrodynamic simulations of reionization to a Markov Chain Monte Carlo sampler, we find that it is entirely possible to reconcile the high-redshift (cosmic dawn) and low-redshift (reionization) existing constraints. In particular, we find that high contribution from low-mass halos along with high photon escape fractions are required to simultaneously reproduce cosmic dawn and reionization constraints. Our analysis further confirms that low-mass galaxies produce a flatter emissivity evolution, which leads to an earlier onset of reionization with gradual and longer duration, resulting in a higher optical depth. While our faint-galaxies dominated models successfully reproduce the measured globally averaged quantities over the first one billion years, they underestimate the late redshift-instantaneous measurements in efficiently star-forming and massive systems. We show that our (simple) physically-motivated semi-analytical prescription produces consistent results with the (sophisticated) state-of-the-art \thesan radiation-magneto-hydrodynamic simulation of reionization.

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arXiv2023

JWST constraints on the UV luminosity density at cosmic dawn: implications for 21-cm cosmology

An unprecedented array of new observational capabilities are starting to yield key constraints on models of the epoch of first light in the Universe. In this Letter we discuss the implications of the UV radiation background at cosmic dawn inferred by recent JWST observations for radio experiments aimed at detecting the redshifted 21-cm hyperfine transition of diffuse neutral hydrogen. Under the basic assumption that the 21-cm signal is activated by the Ly$α$ photon field produced by metal-poor stellar systems, we show that a detection at the low frequencies of the EDGES and SARAS3 experiments may be expected from a simple extrapolation of the declining UV luminosity density inferred at $z\lesssim 14$ from JWST early galaxy data. Accounting for an early radiation excess above the CMB suggests a shallower or flat evolution to simultaneously reproduce low and high-$z$ current UV luminosity density constraints, which cannot be entirely ruled out, given the large uncertainties from cosmic variance and the faint-end slope of the galaxy luminosity function at cosmic dawn. Our findings raise the intriguing possibility that a high star formation efficiency at early times may trigger the onset of intense Ly$α$ emission at redshift $z\lesssim 20$ and produce a cosmic 21-cm absorption signal 200 Myr after the Big Bang.

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arXiv2023

A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship

Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a normalizing flow (NF) to map the probability of various galaxy and halo properties conditioned on astrophysical and cosmological parameters. By leveraging the learnt conditional relationships we can explore a wide range of interesting questions, whilst enabling simple marginalisation over nuisance parameters. We demonstrate how the model can be used as a generative model for arbitrary values of our conditional parameters; we generate halo masses and matched galaxy properties, and produce realisations of the halo mass function as well as a number of galaxy scaling relations and distribution functions. The model represents a unique and flexible approach to modelling the galaxy-halo relationship.

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