  Slow slip events (SSEs) originate from a slow slippage on faults that lasts
from a few days to years. A systematic and complete mapping of SSEs is key to
characterizing the slip spectrum and understanding its link with coeval
seismological signals. Yet, SSE catalogues are sparse and usually remain
limited to the largest events, because the deformation transients are often
concealed in the noise of the geodetic data. Here we present the first
multi-station deep learning SSE detector applied blindly to multiple raw
geodetic time series. Its power lies in an ultra-realistic synthetic training
set, and in the combination of convolutional and attention-based neural
networks. Applied to real data in Cascadia over the period 2007-2022, it
detects 78 SSEs, that compare well to existing independent benchmarks: 87.5% of
previously catalogued SSEs are retrieved, each detection falling within a peak
of tremor activity. Our method also provides useful proxies on the SSE duration
and may help illuminate relationships between tremor chatter and the nucleation
of the slow rupture. We find an average day-long time lag between the slow
deformation and the tremor chatter both at a global- and local-temporal scale,
suggesting that slow slip may drive the rupture of nearby small asperities.
