Dr. Yannick Roehlly Profile

Dr. Yannick Roehlly

at Lab d'Astrophysique de Marseille

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Proceedings Article | 25 July 2024 Paper

CIGALE: a modeling and SED plus spectral fitting code for very large galaxy samples

Denis Burgarella, Véronique Buat, Médéric Boquien, Yannick Roehlly, Jean-Charles Lambert

Proceedings Volume 13098, 130980N (2024) https://doi.org/10.1117/12.3019399

KEYWORDS: Galactic astronomy, Surface conduction electron emitter displays, Spectroscopy, Signal to noise ratio, Stars, Machine learning, Signal attenuation, Photometry, Modeling, James Webb Space Telescope

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CIGALE∗ means Code Investigating GALaxy Emission. This open-source code is developed in Python 3 and parallelized. It is built to study the evolution of galaxies by statistically comparing modelled galaxy Spectral Energy Distributions (SEDs) to observed ones. CIGALE can handle data from the X-rays and far ultraviolet to the far infrared and radio through several modules that model the emission of stars, gas, dust and Active Galactic Nuclei (AGNs). In a recent evolution of CIGALE, we added the possibility to use both and simultaneously photometric and spectroscopic data (line fluxes, equivalent widths). In the most recent version of CIGALE, we modified the code to make it compatible with the James Webb Space Telescope (JWST) NIRSpec instrument. More instrumental setups are being added for other space-borne (e.g. JWST/MIRI and JWST/NIRISS) and ground-based instruments (e.g. SUBARU/PFS, VLT/MOONS or ELT/MOSAIC). This now allows to analyze the resolved emission of local galaxies to the emission of the most distant ones at z ⪆10 in a very efficient way: a simple fitting analysis can be performed for about 1200 NIRSpec spectra in three hours. CIGALE is also able to create model spectra and to predict flux densities and emission line fluxes. We show that CIGALE might be one of the fastest fitting codes on the market. It is often written in papers that SED fitting codes are much slower than machine learning. Beyond this kind of naive and not fully tested claim on the variety of fitting codes, we show that CIGALE can compete with some machine learning methods, with an inference time which is about the same order (and without any learning phase) than convolutional or deep-learning methods on very large galaxy samples.

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