Thermonuclear Supernova Classification via their Multi-Wavelength Signatures


Thermonuclear, or Type-Ia, supernovae (SNeIa) are deeply connected to topics throughout astrophysics and cosmology. They have synthesized the majority of iron in the universe, have supplied much of the energy input to the interstellar medium, and are one of the premier probes of cosmology. They are, however, an enigma.

The answers to these fundamental questions will have far-reaching impact. These beacons of the Cosmos are governed by nuclear physics processes. The emergent radiation produced by these cataclysmic events encodes details of their structure and dynamics. But the clues that reveal the identity of the progenitor systems, and the physical conditions that govern their explosions, remain elusive.

To date, most observations of these objects have been made at ultraviolet, optical, and infrared (UVOIR) wavelengths. But optical evidence is insufficient due to information loss. In contrast, the escaping nuclear radiation carries with it critical fundamental evidence and can travel great distances unscathed. Only measurements of this radioactivity – the ashes of nuclear burning – will unmask the true identities of thermonuclear supernovae.

Our goal is to combine the UVOIR and nuclear characteristics of SNeIa to gain insights into these enigmatic objects. Classification based on a machine learning approach — or similar — is appropriate, in part because the connections between observables and intrinsic parameters (e.g., progenitor type, mass, explosion dynamics, and distribution of radioactive material) can be complex.


Total project length: 175 hours.

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