In planetary defense, long-period comets are recognized as the potentially most devastating threat. However, any new comet on an impact trajectory would likely only be discovered about one year before impact. The goal of this project is to add years of extra warning time by providing comet searchers directions on where to look for comets when they are still far out. To aid and guide a dedicated search for these dangerous objects, meteor showers may offer a clue. Comets leave debris trails as they travel along their orbits. When our planet intersects such debris trails, we see them as meteors. By detecting rare aperiodic meteor showers from dust clouds, we can estimate the orbit of the parent body and narrow down the search space where to look for long-period comets.
The Cameras for Allsky Meteor Surveillance or CAMS monitors the sky to detect meteors. Until now, processing the images has required time-consuming human input to rule out false positives. Automating this process allows to free the data analyst in CAMS and enable a global expansion and temporal coverage of the camera network that can detect the dust trails of those potentially hazardous long period comets that came close to Earth’s orbit in the past ten millennia.
We developed deep learning tools that allow such automation. Specifically, we developed a Convolutional Neural Network (CNN) that discerns images of meteors vs. other objects in the sky and achieves precision and recall scores of 88.3% and 90.3%, respectively. In addition, we developed a Long-Short Term Memory (LSTM) network that encodes the light curve tracklets into a latent space, and learns to predict whether the tracklet corresponds to a meteor or not. The LSTM achieves a precision of 90.0% and a recall of 89.1%. These methods can now be used by meteor astronomers to automatically analyze sky detections and help guide the search for long-period comets.