T CrB Pre-Eruption Dip Analysis and Eruption Time Prediction Using Machine Learning Time Series Models

The recurrent nova T CrB (T Coronae Borealis) is on the verge of its next dramatic eruption. Using decades of observations from the American Association of Variable Star Observers (AAVSO) Database, we set out to build an "eruption alarm" based on machine learning to predict the eruption. We focused primarily on the photometric behavior preceding the 1946 eruption, and emphasized the "pre-eruption dip" in 1945. The time series models, including Gaussian process regression (GPR) and long short-term memory (LSTM) networks, are fed with both current data and historical records to forecast the next eruption. It is likely to occur near JD 2461231.8 (July 10, 2026), with an estimated uncertainty window of ≈30 days. A timely prediction of the eruption epoch can enable coordinated follow-up campaigns across multiple messengers and wavelengths, thus providing a continuous spectral energy distribution (SED). Such coverage will offer new insights into the details of the mass accretion and evolution of this intriguing binary.