Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques fitting parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieves an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

The first direct detection of gravitational waves by the Laser Interferometer Gravitational-Wave Observatory (LIGO) in September 2015 proved their existence, as predicted by Einstein's General Theory of Relativity, and ushered in the era of gravitational-wave interferometry. In this article, we present a set of lab course experiments at different levels of advancement, which give students insight into the basic LIGO operating principle and advanced detection techniques. Starting with methods for folding an optical cavity, we advance to analogy experiments with sound waves that can be detected with a Michelson interferometer with an optical cavity arm. In that experiment, students also learn how the sensitivity of the device can be tuned. In a last step, we show how optical heterodyne detection (the mixing of a signal with a reference oscillator) was used in Initial LIGO. We hope these experiments not only give students an understanding of some LIGO techniques but also awaken a fascination for how unimaginably tiny signals, created by powerful cosmic events a billion years ago or earlier, can be detected today here on Earth.