Thermonuclear Supernova Classification via their Multi-Wavelength Signatures

Description

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.

Duration

Total project length: 175 hours.

Task ideas

Expected results

Test

Please use this link to access the test and relative data set for this project.

Requirements

Mentors

Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch and jamesgsoc2@gmail.com with Project Title and include your CV and test results. The mentors will then get in touch with you.

Corresponding Project

Participating Organizations