Search

Lessons on supernovae and the birth of neutron stars given at the Postgraduate Program in High Energy Physics, Astrophysics & Cosmology. The programme of the course can be downloaded from here

Supernova (SN) 2017cbv in NGC 5643 is one of a handful of type Ia supernovae (SNe~Ia) reported to have excess blue emission at early times. This paper presents extensive $BVRIYJHK_s$-band light curves of SN 2017cbv, covering the phase from $-16$ to $+125$ days relative to $B$-band maximum light. SN 2017cbv reached a $B$-band maximum of 11.710$\pm$0.006~mag, with a post-maximum magnitude decline $\Delta m_{15}(B)$=0.990$\pm$0.013 mag. The supernova suffered no host reddening based on Phillips intrinsic color, Lira-Phillips relation, and the CMAGIC diagram. By employing the CMAGIC distance modulus $\mu=30.58\pm0.05$~mag and assuming $H_0$=72~$\rm km \ s^{-1} \ Mpc^{-1}$, we found that 0.73~\msun $^{56}$Ni was synthesized during the explosion of SN 2017cbv, which is consistent with estimates using reddening-free and distance-free methods via the phases of the secondary maximum of the NIR-band light curves. We also present 14 near-infrared spectra from $-18$ to $+49$~days relative to the $B$-band maximum light, providing constraints on the amount of swept-up hydrogen from the companion star in the context of the single degenerate progenitor scenario. No $Pa{\beta}$ emission feature was detected from our post-maximum NIR spectra, placing a hydrogen mass upper limit of 0.1 $M_{\odot}$. The overall optical/NIR photometric and NIR spectral evolution of SN 2017cbv is similar to that of a normal SN~Ia, even though its early evolution is marked by a flux excess no seen in most other well-observed normal SNe~Ia. We also compare the exquisite light curves of SN 2017cbv with some $M_{ch}$ DDT models and sub-$M_{ch}$ double detonation models., Comment: 20 figures, Accepted for publication in The Astrophysical Journal

The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) is an open data challenge to classify simulated astronomical time-series data in preparation for observations from the Large Synoptic Survey Telescope (LSST), which will achieve first light in 2019 and commence its 10-year main survey in 2022. LSST will revolutionize our understanding of the changing sky, discovering and measuring millions of time-varying objects. In this challenge, we pose the question: how well can we classify objects in the sky that vary in brightness from simulated LSST time-series data, with all its challenges of non-representativity? In this note we explain the need for a data challenge to help classify such astronomical sources and describe the PLAsTiCC data set and Kaggle data challenge, noting that while the references are provided for context, they are not needed to participate in the challenge., Research note to accompany the https://www.kaggle.com/c/PLAsTiCC-2018 challenge