Query : 2022A&A...666A.176A

2022A&A...666A.176A - Astronomy and Astrophysics, volume 666A, 176 (2022/10-1)

Inferring properties of dust in supernovae with neural networks.


Abstract (from CDS):

Context. Determining properties of dust that 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 and temperature of dust as well as its composition from a large set of simulated data. We aim to quantify if such methods are suitable to infer quantities and properties of dust from future observations of supernovae.
Methods. We developed a neural network consisting of eight fully connected layers and an output layer with specified activation functions that allowed us to predict the dust mass, temperature, and composition as well as their respective uncertainties for each single supernova of a large set of simulated supernova spectral energy distributions (SEDs). We produced the large set of supernova SEDs for a wide range of different supernovae and dust properties using the advanced, fully three-dimensional radiative transfer code MOCASSIN. We then convolved each SED with the entire suite of James Webb Space Telescope (JWST) bandpass filters to synthesise a photometric data set. We split this data set into three subsets which were used to train, validate, and test the neural network. To find out how accurately the neural network can predict the dust mass, temperature, and composition from the simulated data, we considered three different scenarios. First, we adopted a uniform distance of ∼0.43 Mpc for all simulated SEDs. Next we uniformly distributed all simulated SEDs within a volume of 0.43-65 Mpc and, finally, we artificially added random noise corresponding to a photometric uncertainty of 0.1 mag. Lastly, we conducted a feature importance analysis via SHapley Additive explanations (SHAP) to find the minimum set of JWST bandpass filters required to predict the selected dust quantities with an accuracy that is comparable to standard methods in the literature.
Results. We find that our neural network performs best for the scenario in which all SEDs are at the same distance and for a minimum subset of seven JWST bandpass filters within a wavelength range 3-25 µm. This results in rather small root-mean-square errors (RMSEs) of ∼0.08 dex and ∼42 K for the most reliable predicted dust masses and temperatures, respectively. For the scenario in which SEDs are distributed out to 65 Mpc and contain synthetic noise, the most reliable predicted dust masses and temperatures achieve an RMSE of ∼0.12 dex and ∼38 K, respectively. Thus, in all scenarios, both predicted dust quantities have smaller predicted uncertainties compared to those in the literature achieved with common SED fitting methods of actual observations of supernovae. 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. Although we trained, validated, and tested our neural network entirely on simulated SEDs, our analysis shows that a suite of JWST bandpass filters containing NIRCam F070W, F140M, F356W and F480M as well as MIRI F560W, F770W, F1000W, F1130W, F1500W, and F1800W filters are likely the most important filters needed to derive the quantities and determine the properties of dust that 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.

Abstract Copyright: © Z. Ansari et al. 2022

Journal keyword(s): galaxies: star formation - methods: statistical - supernovae: general

Simbad objects: 11

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Number of rows : 11
N Identifier Otype ICRS (J2000)
ICRS (J2000)
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2024
1 V* S And No* 00 42 43.12368 +41 16 03.2124           ~ 184 2
2 SNR J052559-660453 SNR 05 26 00.4 -66 05 02     12.71     ~ 438 2
3 SN 1987A SN* 05 35 28.020 -69 16 11.07           SNIIpec 4945 2
4 SN 2006jc SN* 09 17 20.78 +41 54 32.7     13.8     SNIbn 343 1
5 SN 2010jl SN* 09 42 53.33 +09 29 41.8           SNIIn 293 1
6 0FGL J1746.0-2900 gam 17 45 41 -29 00.8           ~ 494 2
7 SNR G011.2-00.3 SNR 18 11 28.9 -19 25 29           ~ 287 1
8 SNR G021.5-00.9 SNR 18 33 33.612 -10 34 07.69           ~ 461 1
9 SNR G029.7-00.2 SNR 18 46 22.8 -02 59 47           ~ 351 2
10 SN 2004et SN* 20 35 25.33 +60 07 17.7   12.88       SNIIP 489 1
11 3C 461 BL? 23 23 24.000 +58 48 54.00     14.30     ~ 2797 1

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