Co-addition and Subtraction of Undersampled Images

In astronomical imaging surveys, repeated observations of the same sky patches are taken in order to obtain deeper images and detect new sources. This is the case in the search for many transient phenomena, such as supernovae, gravitational wave (GW) optical counterparts and other cataclysmic variables. In many such surveys some of the images are undersampled, meaning that the pixel size is too large, and the image suffers from aliasing. For undersampled images, both co-addition of the images and background subtraction are done in a non-optimal manner, which leads to reduced sensitivity and an increased rate of false alarms. We present a new method (named Linear Undersampled Transients \& Addition (LUTRA)) that performs both processes in a mathematically proven optimal way, which allows improved performance for many scientific applications. It also allows easy and direct performance of measurements such as photometry and astrometry in a simple manner, while providing results in super-resolution. We demonstrate the performance of the method on public ZTF data and show $\times 1.25$ higher SNR compared to current methods. We provide an open source Python implementation.

A new approach to template banks of gravitational waves with higher harmonics: reducing matched-filtering cost by over an order of magnitude

Searches for gravitational wave events use models, or templates, for the signals of interest. The templates used in current searches in the LIGO-Virgo-Kagra (LVK) data model the dominant quadrupole mode $(\ell,m)=(2,2)$ of the signals, and omit sub-dominant higher-order modes (HM) such as $(\ell,m)=(3,3)$, $(4,4)$, which are predicted by general relativity. Hence, these searches could lose sensitivity to black hole mergers in interesting parts of parameter space, such as systems with high-masses and asymmetric mass ratios. We develop a new strategy to include HM in template banks that exploits the natural connection between the modes. We use a combination of post-Newtonian formulae and machine learning tools to model aligned-spin $(3,3)$, $(4,4)$ waveforms corresponding to a given $(2,2)$ waveform. Each of these modes can be individually filtered against the data to yield separate timeseries of signal-to-noise ratios (SNR), which can be combined in a relatively inexpensive way to marginalize over extrinsic parameters of the signals. This leads to a HM search pipeline whose matched-filtering cost is just $\approx 3\times$ that of a quadrupole-only search (in contrast to being $\approx\! 100 \times$, as in previously proposed HM search methods). Our method is effectual and is generally applicable for template banks constructed with either stochastic or geometric placement techniques. Additionally, we discuss compression of $(2,2)$-only geometric-placement template banks using machine learning algorithms.