相关论文: Emission-line diagnostics of HII regions using con…
Stellar feedback, the energetic interaction between young stars and their birthplace, plays an important role in the star formation history of the universe and the evolution of the interstellar medium (ISM). Correctly interpreting the…
Photometric surveys with the Hubble Space Telescope (HST) allow us to study stellar populations with high resolution and deep coverage, with estimates of the physical parameters of the constituent stars being typically obtained by comparing…
Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional…
We introduce an updated version of our deep learning tool that predicts stellar parameters from the optical spectra of young low-mass stars with intermediate spectral resolution. We adopt a conditional invertible neural network (cINN)…
We present the {\sc warpfield} emission predictor, {\sc warpfield-emp}, which couples the 1D stellar feedback code {\sc warpfield} with the {\sc cloudy} \hii region/PDR code and the {\sc polaris} radiative transfer code, in order to make…
Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine…
Context. Convolutional neural networks (CNNs) have been proven to perform fast classification and detection on natural images and have potential to infer astrophysical parameters on the exponentially increasing amount of sky survey imaging…
The inference of physical parameters from measured distributions constitutes a core task in physics data analyses. Among recent deep learning methods, so-called conditional invertible neural networks provide an elegant approach owing to…
Star clusters interact with the interstellar medium (ISM) in various ways, most importantly in the destruction of molecular star-forming clouds, resulting in inefficient star formation on galactic scales. On cloud scales, ionizing radiation…
Crucial progress in our understanding of star formation and feedback will depend on the ability to obtain spatially resolved spectroscopic observations of \ion{H}{ii} regions, from which reliable instantaneous measurements of their physical…
We present a novel method to model galactic scale star formation and the resulting emission from star clusters and the multi-phase interstellar medium. We combine global parameters, such as SFR and CMF, with {\sc warpfield} which provides a…
We build, as far as theory will permit, self consistent model HII regions around central clusters of aging stars. These produce strong emission line diagnostics applicable to either individual HII regions in galaxies, or to the integrated…
Context. Convolutional neural networks (CNNs) have been established as the go-to method for fast object detection and classification on natural images. This opens the door for astrophysical parameter inference on the exponentially…
The characterization of an exoplanet's interior is an inverse problem, which requires statistical methods such as Bayesian inference in order to be solved. Current methods employ Markov Chain Monte Carlo (MCMC) sampling to infer the…
The advent of space-based observatories such as CoRoT and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial Helium…
The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding…
Star formation is an inefficient process and in general only a small fraction of the gas in a giant molecular cloud (GMC) is turned into stars. This is partly due to the negative effect of stellar feedback from young massive star clusters.…
We combine state-of-the-art models for the production of stellar radiation and its transfer through the interstellar medium (ISM) to investigate ultraviolet-line diagnostics of stars, the ionized and the neutral ISM in star-forming…
We introduce a novel approach to modelling the nebular emission from star-forming galaxies by combining the contributions from many HII regions incorporating loose trends in physical properties, random dust attenuation, a predefined Halpha…
SITELLE is a novel integral field unit spectroscopy instrument that has an impressive spatial (11 by 11 arcmin), spectral coverage, and spectral resolution (R=1-20000). SIGNALS is anticipated to obtain deep observations (down to…