Hintze Centre for Astrophysical Surveys

Abstract:

Despite their cosmological utility, the progenitors of Type Ia supernovae (SNe Ia) are still unknown, with many efforts focused on whether accretion from a nondegenerate companion can grow a carbon–oxygen white dwarf to near the Chandrasekhar mass. The association of SNe Ia resembling SN 1991T ("91T-like") with circumstellar interaction may be evidence for this "single-degenerate" channel. However, the observed circumstellar medium (CSM) in these interacting systems is unlike a stellar wind—of particular interest, it is sometimes detached from the stellar surface, residing at ~1016 cm. A Hubble Space Telescope (HST) program to discover detached CSM around 91T-like SNe Ia successfully discovered interaction nearly two years after explosion in SN 2015cp (Graham et al. 2018). In this work, we present radio and X-ray follow-up observations of SN 2015cp and analyze them in the framework of Harris et al. (2016) to limit the properties of a constant-density CSM shell in this system. Assuming the HST detection took place shortly after the shock crossed the CSM, we constrain the total CSM mass in this system to be <0.5 ${M}_{\odot }$. This limit is comparable to the CSM mass of supernova PTF11kx, but does not rule out lower masses predicted for recurrent novae. From lessons learned modeling PTF11kx and SN 2015cp, we suggest a strategy for future observations of these events to increase the sample of known interacting SNe Ia.

Abstract:

The Langton Ultimate Cosmic ray Intensity Detector (LUCID) is a payload onboard the satellite TechDemoSat-1, used to study the radiation environment in Low Earth Orbit (635 km). LUCID operated from 2014 to 2017, collecting over 2.1 million frames of radiation data from its five Timepix detectors on board. LUCID is one of the first uses of the Timepix detector technology in open space, with the data providing useful insight into the performance of this technology in new environments. It provides high-sensitivity imaging measurements of the mixed radiation field, with a wide dynamic range in terms of spectral response, particle type and direction. The data has been analysed using computing resources provided by GridPP, with a new machine learning algorithm that uses the Tensorflow framework. This algorithm provides a new approach to processing Medipix data, using a training set of human labelled tracks, providing greater particle classification accuracy than other algorithms. For managing the LUCID data, we have developed an online platform called Timepix Analysis Platform at School (TAPAS). This provides a swift and simple way for users to analyse data that they collect using Timepix detectors from both LUCID and other experiments. We also present some possible future uses of the LUCID data and Medipix detectors in space.