arXiv subjects

Jose Schiappacasse-Ulloa

Publications and source records attributed to Jose Schiappacasse-Ulloa.

5 recordsLinked to original sources

arXiv2026

CRIMSONS: An Online Tool for Modeling Chemical Enrichment with Stochastic IMF Sampling

Chemical enrichment is a universal process shaped by several interconnected factors, including stellar feedback, the nature of chemical polluters, and the underlying distribution of stellar masses. Three key ingredients are often difficult to treat simultaneously in chemical-evolution models: the stochastic sampling of the stellar IMF, uncertainties in stellar nucleosynthesis yields, and pre-enrichment from primordial PopIII stars. In low star-formation rate environments, incomplete IMF sampling makes enrichment sensitive to the random presence or absence of massive supernova progenitors. Here, we present CRIMSONS, the first online tool designed to model chemical enrichment while self-consistently incorporating these three ingredients. The framework allows users to explore different IMFs, stellar population masses and metallicities, and multiple stellar yield sets. It follows the evolution of individual stars and tracks 30 elements(H-Zn), including contributions from SN, AGB stars, and TypeIa SNe. The current implementation adopts a closed-box framework, neglecting gas flows and spatial mixing, thus providing a controlled setup to isolate the effects of IMF sampling, stellar yields, and PopIII pre-enrichment. With CRIMSONS, we show that incomplete IMF sampling produces large abundance scatter, particularly in low-mass stellar populations. Different yield prescriptions introduce element-dependent variations, while some abundance ratios remain comparatively robust across models. Finally, comparison between models including primordial enrichment and those adopting only a metallicity floor shows that PopIII pre-enrichment can significantly affect subsequent chemical evolution. CRIMSONS therefore provides a controlled framework for quantifying the relative impact of stochastic IMF sampling, nucleosynthetic uncertainties, and primordial pre-enrichment on predicted abundance patterns

astro-ph.GA↗

arXiv2026

WST instrument Exposure Time Calculator: full simulation of multi-mode spectrograph performance from source to detector

We present a comprehensive Exposure Time Calculator (ETC) developed for the Wide-field Spectroscopic Telescope (WST) concept. The WST, currently in its conceptual phase, is designed as a next-generation large spectroscopic survey facility featuring three complementary observing modes: an Integral Field Spectrograph (IFS) covering 370-930 nm at R of about 4800; a high-resolution Multi-Object Spectrograph (MOS-HR) with four bands at R of about 40000; and a low-resolution Multi-Object Spectrograph (MOS-LR) with four channels at R of about 3800-4900. The ETC simulates the complete photon-propagation path from astronomical source to detector, incorporating wavelength-dependent system throughput (telescope transmission, instrumental optics, detector quantum efficiency), accurate sky background via ESO SkyCalc integration, and a comprehensive noise treatment (photon noise, sky background, read-out noise, dark current). The computational core is implemented as the "pyetc_wst" Python library built on the MPDAF framework, supporting multiple target spectral energy distributions (stellar templates, blackbody, power-law, emission lines, and user-uploaded spectra with arbitrary redshift) and spatial morphologies (point sources and Sersic extended profiles). Four operational modes enable flexible exposure-time optimization. Full spectral outputs include wavelength-dependent signal-to-noise ratio (SNR), source and sky photon counts, noise decomposition by component, and simulated extracted spectra. An interactive web interface, together with a REST API and a command-line tool, complete the user experience and enable batch survey-design workflows.

astro-ph.IM↗

arXiv2026

HRMOS: A High-Resolution Multi-Object Spectrograph for the VLT

This White Paper presents the scientific rationale and instrument concept for HRMOS (High-Resolution Multi-Object Spectrograph), a next-generation instrument proposed for the ESO Very Large Telescope within the VLT 2030 roadmap. Current and planned facilities offer either multi-object spectroscopy or ultra-high spectral resolution, but not both. HRMOS fills this gap by combining very high spectral resolution, multi-object capability, and radial-velocity stability, enabling transformative studies in Galactic and extragalactic astrophysics. The baseline design provides a resolving power of R = 80000, radial-velocity precision of 10 m s-1 (goal: 5 m s-1), simultaneous observations of 50-60 targets, and broad optical coverage down to 385 nm. These capabilities enable precise measurements of elemental abundances, isotopic ratios, line profiles, and radial velocities for large stellar samples, including crowded fields, star clusters, the Galactic bulge, and nearby dwarf galaxies. HRMOS will address key questions on the age of the oldest stellar populations through nucleocosmochronology, the formation and survival of planetary systems, the assembly history of the Milky Way and satellites, the origin of the heaviest elements, stellar evolution, and the chemical and dynamical properties of the interstellar and circumgalactic medium. It will bridge large spectroscopic surveys and the next generation of extremely large telescopes, with strong synergies with 4MOST, Gaia, TESS, PLATO, the proposed Haydn mission, and future ELT instruments. Building on VLT/FLAMES heritage, HRMOS represents a strategic investment for European astronomy in the 2030s.

astro-ph.IM↗

arXiv2025

Barium isotopic ratios in metal-poor stars: calibrating the method with globular clusters

Identifying the nucleosynthesis processes behind heavy-element enrichment in stellar atmospheres is challenging. It typically relies on comparing observed abundance-to-iron ratios with theoretical predictions relative to the Sun, but this method is prone to uncertainty due to limitations of classical 1D hydrostatic models. One promising but still underexplored approach is to measure the isotopic composition of stellar atmospheres by focusing on elements that have both slow (s)-process and rapid (r)-process contributions. While the study of total elemental abundances offers a simplified view, isotopic ratios are directly linked to the underlying nucleosynthesis processes. Our aim is to provide a reliable method for quantifying the contributions of the s- and r-processes to barium in stellar atmospheres. This is achieved by determining barium isotopic ratios using 1D atmospheric models in combination with a carefully calibrated microturbulence, based on the comparison between subordinate and resonance Ba lines. In this initial study, we use member stars of the globular cluster NGC 6752 to calibrate the microturbulence ($v_{mic}$) value for both subordinate and resonance barium lines across different stellar evolutionary stages. This allows us to provide a reliable estimate of $v_{mic}$ that can be applied to accurately determine barium abundances and isotopic ratios in stars ranging from the main sequence to the upper red giant branch. The $v_{mic}$ scale adapted for barium subordinate lines is consistent with that derived from 3D model atmospheres, and thus the $T_{\mathrm{eff}}$-log $g$ dependent relations of the later can be used safely. The $v_{mic}$ for the resonance line at $λ$4934 Angstrom -- for the determination of the isotopic ratio -- is higher, and depends on the equivalent width (EW). We provide calibrated relations between $v_{mic}$ and EW for measuring isotopic ratios.

astro-ph.SR↗

arXiv2023

Quadtree features for machine learning on CMDs

The upcoming facilities like the Vera C. Rubin Observatory will provide extremely deep photometry of thousands of star clusters to the edge of the Galaxy and beyond, which will require adequate tools for automatic analysis, capable of performing tasks such as the characterization of a star cluster through the analysis of color-magnitude diagrams (CMDs). The latter are essentially point clouds in N-dimensional space, with the number of dimensions corresponding to the photometric bands employed. In this context, machine learning techniques suitable for tabular data are not immediately applicable to CMDs because the number of stars included in a given CMD is variable, and equivariance for permutations is required. To address this issue without introducing ad-hoc manipulations that would require human oversight, here we present a new CMD featurization procedure that summarizes a CMD by means of a quadtree-like structure through iterative partitions of the color-magnitude plane, extracting a fixed number of meaningful features of the relevant subregion from any given CMD. The present approach is robust to photometric noise and contamination and it shows that a simple linear regression on our features predicts distance modulus (metallicity) with a scatter of 0.33 dex (0.16 dex) in cross-validation.

astro-ph.IM↗