I see what you're aiming at, but technically it's not true. It can be similar though.
The point about how we can only infer their existence from a mass of data is similar to how we talk about deep learning. The pretrained neural net works, but we can't pinpoint why, we can only test it on various data and receive results.
There is no "dog that didn't bark", because we clearly can see the graphs and the correlations.
The way we detect gravitational waves is not "the photon haven't arrived", but "the photon has arrived a few nanoseconds earlier than we expected it". (But multiplied by a few thousands of reflections and detected only by interference pattern, not the photon arrival itself).
So, in a way, it's similar to deviations from paths by which we presume dark matter. But also, it's not that: we are almost certain here that these exact deviations are caused by the gravitational waves, because calculations and because multiple unconnected detectors, and because the form of the graph matches.
The point about how we can only infer their existence from a mass of data is similar to how we talk about deep learning. The pretrained neural net works, but we can't pinpoint why, we can only test it on various data and receive results.
There is no "dog that didn't bark", because we clearly can see the graphs and the correlations.
The way we detect gravitational waves is not "the photon haven't arrived", but "the photon has arrived a few nanoseconds earlier than we expected it". (But multiplied by a few thousands of reflections and detected only by interference pattern, not the photon arrival itself).
So, in a way, it's similar to deviations from paths by which we presume dark matter. But also, it's not that: we are almost certain here that these exact deviations are caused by the gravitational waves, because calculations and because multiple unconnected detectors, and because the form of the graph matches.