arXiv subjects

Eirini Angeloudi

Publications and source records attributed to Eirini Angeloudi.

9 recordsLinked to original sources

arXiv2026

Semiresolved Stellar Populations as Distance Indicators

Galaxy distances are central to our understanding of the Universe. Despite the success of existing approaches, independent and complementary methods remain valuable for testing systematic effects and extending the applicability range of different distance metrics. Here we demonstrate that the spectrum of an individual semi-resolved stellar population encodes direct information about its distance and that valuable distance constraints can, in principle, be obtained by combining multiple independent measurements. When applied to optical spectra of bulge of the Andromeda galaxy (M\,31), we derive a stellar population-based distance of 756$\pm$57 kpc ($\mu_0 = 24.39 \pm 0.15$), in agreement with state-of-the-art measurements. Crucially, semi-resolved stellar population distances does not require secondary calibrations, although its absolute scale remains conditional on the adopted stellar population models. Our findings provide a first assessment of the feasibility of deriving stellar population-based distance estimates from simple stellar population models, motivating further tests of the broader applicability and precision of the method.

astro-ph.GA↗

arXiv2026

FASTAR -- II. Semi-resolved evolutionary stellar population models

Standard evolutionary synthesis models rely on the assumption of a fully sampled stellar initial mass function (IMF). Under this assumption, the age, chemical composition, and IMF uniquely define the predicted absorption spectra. However, with current instrumentation pushing observations towards higher spatial resolutions and lower surface brightnesses, the assumption of a fully sampled IMF does not always hold true. Here we present the semi-resolved version of the FASTAR models, a comprehensive set of evolutionary synthesis predictions able to reproduce the stochastic behavior of discretely-sampled IMFs. Semi-resolved FASTAR predictions share the same evolutionary principles, ingredients, and features of the integral (fully sampled IMF) version of the FASTAR models, expanding a range of ages from 20 Myr to 14 Gyr, metallicities between -2.5 < [M/H] < +0.3, and several IMF functional forms. Detailed spectroscopic measurements can be carried out within the 3,540-7,400 A wavelength range, and low-resolution spectral energy distributions can also be synthesized over a wider 2,000-to-12,000 A coverage. Semi-resolved FASTAR models also depend on the number of stars contributing to the observed spectra, which determines the effective sampling of the different stellar evolutionary phases along the isochrones. This incomplete sampling implies that semi-resolved FASTAR models are inevitably stochastic. On top of the inherent stochasticity of the models, derived quantities such as equivalent widths, colors, or mass-to-light ratios might present strong deviations compared to standard fully sampled simple stellar population models. This stochasticity dilutes the boundary between model predictions and data, promoting new sampling-based inference approaches. FASTAR semi-resolved models allow for the effective exploration of the parameter space thanks to their optimized, JAX-based computation.

astro-ph.GA↗

arXiv2026

FASTAR -- I. Continuous and differentiable evolutionary stellar population models

The development of evolutionary stellar population models is central to interpreting observations of galaxies in terms of astrophysical quantities. Stellar population models must therefore be both accurate and compatible with inversion algorithms in order to extract meaningful information from the observed data. Here we present FASTAR, a fully differentiable stellar population synthesis code. Contrary to traditional, grid-based single stellar population models, FASTAR can be continuously evaluated at any age (between 20 Myr and 14 Gyr), metallicity (-2.5 < [M/H] < +0.3), and initial mass function (IMF). Changes in the IMF parameterization are straightforward, allowing for consistent conversions of colors, magnitudes, and mass-to-light ratios, as well as the synthesis of models under the assumption of arbitrary IMF functional forms. FASTAR provides detailed spectroscopic predictions over the MILES wavelength range (3,540-7,400 A) as well as more coarsely sampled spectral energy distributions across a wider 2,000-to-12,000 A, which can be directly convolved with any arbitrary set of photometric filters. FASTAR performs at the same level of state-of-the-art simple stellar population models benchmarked against observations of globular clusters and high signal-to-noise spectra of early-type galaxies, but it is faster, lighter, and more flexible. Moreover, its differentiable nature allows for a quantitative understanding of model behavior and uncertainties, as well as a natural framework for gradient descent inference algorithms.

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arXiv2025

The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies

Understanding the origin of stars within a galaxy - whether formed in-situ or accreted from other galaxies (ex-situ) - is key to constraining its evolution. Spatially resolving these components provides crucial insights into a galaxy's mass assembly history. We aim to predict the spatial distribution of ex-situ stellar mass fraction in MaNGA galaxies, and to identify distinct assembly histories based on the radial gradients of these predictions in the central regions. We employ a diffusion model trained on mock MaNGA analogs (MaNGIA), derived from the TNG50 cosmological simulation. The model learns to predict the posterior distribution of resolved ex-situ stellar mass fraction maps, conditioned on stellar mass density, velocity, and velocity dispersion gradient maps. After validating the model on an unseen test set from MaNGIA, we apply it to MaNGA galaxies to infer the spatially-resolved distribution of their ex-situ stellar mass fractions - i.e. the fraction of stellar mass in each spaxel originating from mergers. We identify four broad categories of ex-situ mass distributions: flat gradient, in-situ dominated; flat gradient, ex-situ dominated; positive gradient; and negative gradient. The vast majority of MaNGA galaxies fall in the first category - flat gradients with low ex-situ fractions - confirming that in-situ star formation is the main assembly driver for low- to intermediate-mass galaxies. At high stellar masses, the ex-situ maps are more diverse, highlighting the key role of mergers in building the most massive systems. Ex-situ mass distributions correlate with morphology, star-formation activity, stellar kinematics, and environment, indicating that accretion history is a primary factor shaping massive galaxies. Finally, by tracing their assembly histories in TNG50, we link each class to distinct merger scenarios, ranging from secular evolution to merger-dominated growth.

astro-ph.GA↗

arXiv2024

Simulation-based inference of the 2D ex-situ stellar mass fraction distribution of galaxies using variational autoencoders

Galaxies grow through star formation (in-situ) and accretion (ex-situ) of other galaxies. Reconstructing the relative contribution of these two growth channels is crucial for constraining the processes of galaxy formation in a cosmological context. In this on-going work, we utilize a conditional variational autoencoder along with a normalizing flow - trained on a state-of-the-art cosmological simulation - in an attempt to infer the posterior distribution of the 2D ex-situ stellar mass distribution of galaxies solely from observable two-dimensional maps of their stellar mass, kinematics, age and metallicity. Such maps are typically obtained from large Integral Field Unit Surveys such as MaNGA. We find that the average posterior provides an estimate of the resolved accretion histories of galaxies with a mean ~10% error per pixel. We show that the use of a normalizing flow to conditionally sample the latent space results in a smaller reconstruction error. Due to the probabilistic nature of our architecture, the uncertainty of our predictions can also be quantified. To our knowledge, this is the first attempt to infer the 2D ex-situ fraction maps from observable maps.

astro-ph.GA↗

arXiv2024

Constraints on the in-situ and ex-situ stellar masses in nearby galaxies with Artificial Intelligence

The hierarchical model of galaxy evolution suggests that the impact of mergers is substantial on the intricate processes that drive stellar assembly within a galaxy. However, accurately measuring the contribution of accretion to a galaxy's total stellar mass and its balance with in-situ star formation poses a persistent challenge, as it is neither directly observable nor easily inferred from observational properties. Here, we present theory-motivated predictions for the fraction of stellar mass originating from mergers in a statistically significant sample of nearby galaxies, using data from MaNGA. Employing a robust machine learning model trained on mock MaNGA analogs (MaNGIA) in turn obtained from a cosmological simulation (TNG50), we unveil that in-situ stellar mass dominates almost across the entire stellar mass spectrum (1e9Msun < M* < 1e12Msun). Only in more massive galaxies (M* > 1e11Msun) does accreted mass become a substantial contributor, reaching up to 35-40% of the total stellar mass. Notably, the ex-situ stellar mass in the nearby universe exhibits significant dependence on galaxy characteristics, with higher accreted fractions favored by elliptical, quenched galaxies and slow rotators, as well as galaxies at the center of more massive dark matter halos.

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arXiv2024

AstroPT: Scaling Large Observation Models for Astronomy

This work presents AstroPT, an autoregressive pretrained transformer developed with astronomical use-cases in mind. The AstroPT models presented here have been pretrained on 8.6 million $512 \times 512$ pixel $grz$-band galaxy postage stamp observations from the DESI Legacy Survey DR8. We train a selection of foundation models of increasing size from 1 million to 2.1 billion parameters, and find that AstroPT follows a similar saturating log-log scaling law to textual models. We also find that the models' performances on downstream tasks as measured by linear probing improves with model size up to the model parameter saturation point. We believe that collaborative community development paves the best route towards realising an open source `Large Observation Model' -- a model trained on data taken from the observational sciences at the scale seen in natural language processing. To this end, we release the source code, weights, and dataset for AstroPT under the MIT license, and invite potential collaborators to join us in collectively building and researching these models.

astro-ph.IM↗

arXiv2024

ERGO-ML: Comparing IllustrisTNG and HSC galaxy images via contrastive learning

Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to the real one is difficult. In this paper of the ERGO-ML series (Extracting Reality from Galaxy Observables with Machine Learning), we utilize contrastive learning to directly compare a large sample of simulated and observed galaxies based on their stellar-light images. This eliminates the need to specify summary statistics and allows to exploit the whole information content of the observations. We produce survey-realistic galaxy mock datasets resembling real Hyper Suprime-Cam (HSC) observations using the cosmological simulations TNG50 and TNG100. Our focus is on galaxies with stellar masses between $10^9$ and $10^{12} M_\odot$ at $z=0.1-0.4$. This allows us to evaluate the realism of the simulated TNG galaxies in comparison to actual HSC observations. We apply the self-supervised contrastive learning method NNCLR to the images from both simulated and observed datasets (g, r, i - bands). This results in a 256-dimensional representation space, encoding all relevant observable galaxy properties. Firstly, this allows us to identify simulated galaxies that closely resemble real ones by seeking similar images in this multi-dimensional space. Even more powerful, we quantify the alignment between the representations of these two image sets, finding that the majority ($\gtrsim 70$ per cent) of the TNG galaxies align well with observed HSC images. However, a subset of simulated galaxies with larger sizes, steeper Sersic profiles, smaller Sersic ellipticities, and larger asymmetries appears unrealistic. We also demonstrate the utility of our derived image representations by inferring properties of real HSC galaxies using simulated TNG galaxies as the ground truth.

astro-ph.GA↗

arXiv2023

ERGO-ML: Towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps

Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not trivial: cosmological galaxy simulations are the only tools we have for calibration. We study the robustness of a simulation-based inference of the ex-situ stellar mass fraction of nearby galaxies to different observables -- integrated and spatially-resolved -- and to different galaxy formation models -- IllustrisTNG and EAGLE -- with Machine Learning. We find that at fixed simulation, the fraction of accreted stars can be inferred with very high accuracy, with an error $\sim5$ per cent (10 per cent) from 2D integral-field spectroscopic maps (integrated quantities) throughout the considered stellar mass range. A bias (> 5 per cent) and an increase in scatter by a factor of 2 are introduced when testing with a different simulation, revealing a lack of generalization to distinct galaxy-formation models. Interestingly, upon using only stellar mass and kinematics maps in the central galactic regions for training, we find that this bias is removed and the ex-situ stellar mass fraction can be recovered in both simulations with < 15 per cent scatter, independently of the training set's origin. This opens up the door to a potential robust inference of the accretion histories of galaxies from existing Integral Field Unit surveys, such as MaNGA, covering a similar field of view (FOV) and containing spatially-resolved spectra for tens of thousands of nearby galaxies.

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