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Abstract:

The Zwicky Transient Facility SN Ia Data Release 2 provides a perfect opportunity to perform a thorough search for and subsequent analysis of Si  II λ 6355 high-velocity features (HVFs) in the pre-peak regime. The source of such features remains unclear, but potential origins include circumstellar material, as well as enhancements to the abundances or densities intrinsic to the supernova (SN) ejecta. Therefore, they may provide clues to the elusive progenitor and explosion scenarios of Type Ia SNe (SNe Ia). We employed a Markov chain Monte Carlo fitting method followed by Bayesian information criterion testing to classify single and double Si  II λ 6355 components in the DR2. The detection efficiency of our classification method was investigated through the fitting of simulated features, which allowed us to place cuts on the spectral quality required for reliable classification. These simulations were also used to perform an analysis of the recovered parameter uncertainties and potential biases in the measurements. Within the 329 spectra sample we investigated, we identified 85 spectra exhibiting Si  II λ 6355 HVFs. We find that HVFs decrease in strength with phase relative to their photospheric counterparts; however, this decrease can occur at different phases for different objects. HVFs with larger velocity separations from the photosphere were observed to fade earlier, leaving only the double components with smaller separations as we moved towards maximum light. Our findings suggest that around three quarters of SN Ia spectra before −11 d show high-velocity components in the Si  II λ 6355, with this dropping to around one third in the six days before maximum light. We observed no difference between the populations of SNe Ia that do and do not form Si  II λ 6355 HVFs in terms of the SALT2 light curve parameter x 1 , peak magnitude, decline rate, host mass, or host colour, supporting the idea that these features are ubiquitous across the SN Ia population.

Abstract:

In this work, we explore the applicability of unsupervised machine learning algorithms to finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide huge volumes of data in which to detect rare transients; the challenge for astronomers is how to find them. We demonstrate the effectiveness of anomaly detection algorithms using 1.3 GHz light curves from the SKA precursor MeerKAT. We make use of three sets of descriptive parameters (‘feature sets’) as applied to two anomaly detection techniques in the astronomaly package and analyse our performance by comparison with citizen science labels on the same data set. Using transients found by volunteers as our ground truth, we demonstrate that anomaly detection techniques can recall over half of the radio transients in the 10 per cent of the data with the highest anomaly scores. We find that the choice of anomaly detection algorithm makes a minor difference, but that feature set choice is crucial, especially when considering available resources for human inspection and/or follow-up. Active learning, where human labels are given for just 2 per cent of the data, improves recall by up to 20 percentage points, depending on the combination of features and model used. The best-performing results produce a factor of 5 times fewer sources requiring vetting by experts. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other data sets, and as a real-time transient detection system for upcoming large surveys.