Hintze Centre for Astrophysical Surveys

Abstract:

We present optical observations and analysis of the bright type Iax supernova SN 2020udy hosted by NGC 0812. The evolution of the light curve of SN 2020udy is similar to that of other bright type Iax SNe. Analytical modeling of the quasi-bolometric light curves of SN 2020udy suggests that 0.08 ± 0.01 M⊙ of 56Ni would have been synthesized during the explosion. The spectral features of SN 2020udy are similar to those of the bright members of type Iax class, showing a weak Si ii line. The late-time spectral sequence is mostly dominated by iron group elements with broad emission lines. Abundance tomography modeling of the spectral time series of SN 2020udy using TARDIS indicates stratification in the outer ejecta; however, to confirm this, spectral modeling at a very early phase is required. After maximum light, uniform mixing of chemical elements is sufficient to explain the spectral evolution. Unlike in the case of normal type Ia SNe, the photospheric approximation remains robust until +100 days, requiring an additional continuum source. Overall, the observational features of SN 2020udy are consistent with the deflagration of a carbon–oxygen white dwarf.

Abstract:

The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide-area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from to. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong lens experts to remove false positives which is only set to increase with these surveys. In this work, we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science, and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false-positive rate of 10-3 can be achieved with a completeness of 46 per cent, compared to 34 per cent for the best individual classifier. Given the large number of galaxy-galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses and to help prioritize candidates for follow-up.