We introduce the Galaxy Replacement Technique (GRT) that allows us to model tidal stripping of galaxies with very high mass (mstar = 5.4 × 104 M⊙ h−1) and high spatial resolution (10 pc h−1), in a fully cosmological context, using an efficient and fast technique. The technique works by replacing multiple low-resolution dark-matter (DM) halos in the base cosmological simulation with high-resolution models, including a DM halo and stellar disk. We apply the method to follow the hierarchical buildup of a cluster since redshift ∼8 to now, through the hierarchical accretion of galaxies, individually or in substructures such as galaxy groups. We find we can successfully reproduce the observed total stellar masses of observed clusters since redshift ∼1. The high resolution allows us to accurately resolve the tidal stripping process and well describe the formation of ultralow surface brightness features in the cluster (μV < 32 mag arcsec−2) such as the intracluster light (ICL), shells, and tidal streams. We measure the evolution of the fraction of light in the ICL and brightest cluster galaxy using several different methods. While their broad response to the cluster-mass growth history is similar, the methods show systematic differences, meaning we must be careful when comparing studies that use distinct methods. The GRT represents a powerful new tool for studying tidal effects on galaxies and exploring the formation channels of the ICL in a fully cosmological context and with large samples of simulated groups and clusters.
Supernovae classes have been defined phenomenologically, based on spectral features and time series data, since the specific details of the physics of the different explosions remain unrevealed. However, the number of these classes is increasing as objects with new features are observed, and the next generation of large surveys will only bring more variety to our attention. We apply the machine learning technique of multi-label classification to the spectra of supernovae. By measuring the probabilities of specific features or "tags" in the supernova spectra, we can compress the information from a specific object down to that suitable for a human or database scan, without the need to directly assign to a reductive "class". We use logistic regression to assign tag probabilities, and then a feed-forward neural network to filter the objects into the standard set of classes, based solely on the tag probabilities. We present STag, a software package that can compute these tag probabilities and make spectral classifications.