学术文献库

Context. Determining properties of dust formed in and around supernovae from observations remains challenging. This may be due to either incomplete coverage of data in wavelength or time but also due to often inconspicuous signatures of dust in the observed data. Aims. Here we address this challenge using modern machine learning methods to determine the amount, composition and temperature of dust from a large set of simulated data. We aim to determine whether such methods are suitable to infer these properties from future observations of supernovae. Methods. We calculate spectral energy distributions (SEDs) of dusty shells around supernovae. We develop a neural network consisting of eight fully connected layers and an output layer with specified activation functions that allow us to predict the dust mass, temperature and composition and their respective uncertainties from each SED. We conduct a feature importance analysis via SHapley Additive exPlanations (SHAP) to find the minimum set of JWST filters required to accurately predict these properties. Results. We find that our neural network predicts dust masses and temperatures with a root-mean-square error (RMSE) of $\sim$ 0.12 dex and $\sim$ 38 K, respectively. Moreover, our neural network can well distinguish between the different dust species included in our work, reaching a classification accuracy of up to 95\% for carbon and 99\% for silicate dust. Conclusions. Our analysis shows that the JWST filters NIRCam F070W, F140M, F356W, F480M and MIRI F560W, F770W, F1000W, F1130W, F1500W, F1800W are likely the most important needed to determine the properties of dust formed in and around supernovae from future observations. We tested this on selected optical to infrared data of SN 1987A at 615 days past explosion and find good agreement with dust masses and temperatures inferred with standard fitting methods in the literature.