Use a two‑step, probabilistic approach. First run a density‑based method like DBSCAN to pull out the tight blob of “glitch” points. That gives you a clean set of potential outliers without manual tuning. Next fit a Gaussian mixture or a simple Gaussian to the remaining data and compute the likelihood of each point. Anything whose likelihood falls below a small threshold (or whose Mahalanobis distance is too high) is an anomaly. You can also feed the reconstruction error from a lightweight auto‑encoder into the same likelihood check—systematic glitches usually give consistently high errors. Finally, add a penalty term for those low‑likelihood points in your optimization objective so the model learns to ignore or correct for the glitch. This keeps the process routine‑like and fully quantifiable.