  Core-collapse supernovae (CCSNe) are a potential source for ground-based
gravitational wave detectors, as their predicted emission peaks in the
detectors' frequency band. Typical searches for gravitational wave bursts
reconstruct signals using wavelets. However, as CCSN signals contain multiple
complex features in the time-frequency domain, these techniques often struggle
to reconstruct the entire signal. An alternative method developed in recent
years involves applying principal component analysis (PCA) to a set of
simulated CCSN models. This technique enables model selection between
astrophysical CCSN models as well as waveform reconstruction. However, PCA
faces its own difficulties, such as being unable to reconstruct signals longer
than the simulations; many CCSN simulations are stopped before the emission
peaks due to insufficient computational resources. In this study, we show how
combining PCA with dynamic time warping (DTW) improves the reconstruction of
CCSN gravitational wave signals in Gaussian noise characteristic of Advanced
LIGO at design sensitivity. For the waveforms used in this analysis, we find
that the number of PCs needed to represent 90% of the data is reduced from nine
to four by applying DTW, and that the match between the original and
reconstructed waveforms improves for signal-to-noise ratios in the range
[0,50].
