RESUMEN
Anomaly detection is challenging, especially for large datasets in high dimensions. Here, we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. DRAMA is released as a general python package that implements the general framework with a wide range of built-in options. This approach identifies the primary prototypes in the data with anomalies detected by their large distances from the prototypes, either in the latent space or in the original, high-dimensional space. DRAMA is tested on a wide variety of simulated and real datasets, in up to 3000 dimensions, and is found to be robust and highly competitive with commonly used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning, and highly unbalanced datasets. Besides, DRAMA naturally provides clustering of outliers for subsequent analysis.
RESUMEN
We show that the redshift-space quadrupole will be a powerful tool for constraining dark energy even if the baryon oscillations are missing from the monopole power spectrum and bias is scale and time dependent. We calculate the accuracy with which next-generation galaxy surveys such as KAOS will measure the quadrupole power spectrum, which gives the leading anisotropies in the power spectrum in redshift space due to linear velocity, and the so-called "Finger of God" and Alcock-Paczynski effects. Combining the monopole and quadrupole power spectra, in the complete absence of baryon oscillations (Omegab=0), leads to a roughly 500% improvement in constraints on dark energy compared with those from the monopole spectrum alone.
