Theoretical predictions for IMF diagnostics in UV spectroscopy of star clusters
Greg Ashworth (1), Michele Fumagalli (1), Angela Adamo (2), Mark R., Krumholz (3) ((1) Durham University, (2) Stockholm University, (3) Australian, National University)

TL;DR
This paper investigates UV spectroscopy combined with photometry as a method to determine the IMF slope in unresolved star clusters, extending a spectral synthesis code and applying Bayesian inference to improve parameter recovery.
Contribution
It extends the slug code with UV spectral synthesis and equivalent width calculations, and demonstrates improved IMF slope estimation using combined UV spectral features and photometry.
Findings
Bayesian inference improves IMF slope recovery by 32% with UV data.
Including UV spectral features enhances IMF slope constraints.
Sensitivity to modest equivalent widths significantly improves parameter estimation.
Abstract
We explore the possibility of using UV spectroscopy in combination with broad-band photometry as diagnostic tools for understanding the shape of the Initial Mass Function (IMF) in unresolved stellar populations. Building on our previous work, we extend the Stochastically Lighting Up Galaxies code (slug) to include a high-resolution UV spectral synthesiser and equivalent width calculation capabilities. We first gain a qualitative understanding of how UV spectral features behave as the parameters that define a star cluster in slug (mass, age, extinction, and IMF slope alpha) are changed. We then exploit Bayesian inference techniques to recover the alpha values for clusters simulated with slug, using mock observations of these clusters comprised of broad-band photometry and equivalent width measurements of a selection of UV spectral features. We find some improvement when compared to…
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