Inferring properties of dust in supernovae with neural networks

HIGHLIGHTS

• who: Ansari Zoe and collaborators from the Niels Bohr Institute, University of Copenhagen, Jagtvej, Copenhagen, Denmark have published the research work: Inferring properties of dust in supernovae with neural networks, in the Journal: (JOURNAL)
• what: The authors aim to quantify if such methods are suitable to infer quantities and properties of dust from future observations of supernovae. Although the authors trained validated and tested the neural network entirely on simulated SEDs the analysis shows that suite of JWST bandpass filters containing NIRCam F070W F140M F356W and F480M as well as MIRI F560W F770W F1000W F1130W . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.