Related papers: Understanding Molecular Abundances in Star-Forming…
Chemical abundance determinations from stellar spectra are challenged by observational noise, limitations in stellar models, and departures from simplifying assumptions. While traditional and supervised machine learning methods have made…
(abridged) Elemental abundances of FGK stars can be derived routinely from high-resolution optical spectra, but this remains considerably more difficult for cooler stars. Machine-learning methods offer a practical route to infer otherwise…
Chemical evolution models are useful for understanding the formation and evolution of stars and galaxies. Model predictions will be more robust as more observational constraints are used. We present chemical evolution models for the dwarf…
Chemical evolution models are powerful tools for interpreting stellar abundance surveys and understanding galaxy evolution. However, their predictions depend heavily on the treatment of inflow, outflow, star formation efficiency (SFE), the…
We aim to investigate the chemistry and gas phase abundance of HNCO and the variation of the HNCO/CS abundance ratio as a diagnostic of the physics and chemistry in regions of massive star formation. A numerical-chemical model has been…
Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle…
The star forming processes strongly influence the ISM chemistry. Nowadays, there are available many high-quality databases at millimeter wavelengths. Using them, it is possible to carry out studies that review and deepen previous results.…
Our unified chemical and spectrophotometric evolution code allows to simultaneously study the ISM abundances of a series of elements and the spectral properties of the stellar population in our model galaxies. We use stellar evolutionary…
Industrial processes generate complex data that challenge fault detection systems, often yielding opaque or underwhelming results despite advanced machine learning techniques. This study tackles such difficulties using the Tennessee Eastman…
Since molecules are ubiquitous in space, the study of the 'Molecular Universe' could unfold the mystery of the existing Interstellar medium. Star formation is linked to the chemical evolution processes. Thus, an analysis of the formation of…
The detection and subsequent characterisation of exoplanets are intimately linked to the characteristics of their host star. Therefore, it is necessary to study the star in detail in order to understand the formation history and…
Machine Learning (ML) is becoming increasingly popular in fluid dynamics. Powerful ML algorithms such as neural networks or ensemble methods are notoriously difficult to interpret. Here, we introduce the novel Shapley Additive Explanations…
Context: Molecular data of extreme environments, such as Arp 220, but also NGC 253, show evidence for extremely high cosmic ray (CR) rates (10^3-10^4 * Milky Way) and mechanical heating from supernova driven turbulence. Aims: The…
We develop a model to implement metal enrichment in a cosmological context based on the hydrodynamical AP3MSPH code described by Tissera, Lambas and Abadi (1997).The star formation model is based on the Schmidt law and has been modified in…
Supervised machine learning models are trained with various molecular descriptors to predict infrared emission spectra of interstellar polycyclic aromatic hydrocarbons. We demonstrate that a feature importance analysis based on the random…
I will present predictions from chemical evolution model aimed at a self-consistent study of both optical (i.e. stellar) and X-ray (i.e.gas) properties of present-day elliptical galaxies. Detailed cooling and heating processes in the…
We analyze the stellar abundances of massive galaxies ($\log M_\ast/M_\odot>10.5$) at $z=2$ in the IllustrisTNG simulation with the goal of guiding the interpretation of current and future observations, particularly from the James Webb…
Astrochemical models are essential to bridge the gap between the timescales of reactions, experiments, and observations. Ice chemistry in these models experiences a large computational complexity as a result of the many parameters required…
SHAP (SHapley Additive exPlanations) has become a popular method to attribute the prediction of a machine learning model on an input to its features. One main challenge of SHAP is the computation time. An exact computation of Shapley values…
Accurate short-term electricity load forecasting is a cornerstone of U.S. grid reliability; however, prevailing deep learning models remain opaque, limiting operator trust during extreme weather. A unified, interpretable, physics-informed…