Definition of Peculiar Velocity

Black holes present the opportunity to understand the curvature of spacetime. Black holes are difficult to observe due to the inherent strong gravitational effects which not even light can escape. However, observing how blackholes interact with visible matter may provide insight into the detection and property dynamics of massive black holes. Tidal disruption events (TDEs) occur when a star’s path encroaches on the event horizon of a supermassive black hole, such that tidal forces pull the star apart in a process known as spaghettification. As a result, TDEs produce energetic jets that are hypothesized locations of high-energy neutrino production. This project presents a new strategy for efficiently classifying TDEs by employing dual machine learning Random Forest classifiers. Trained on ~11,000 photometric data points and ~20,000 spectroscopic data points, the classifiers analyzed photometric and spectral data independently. The results of the two classifiers were then used to classify the probability that an event is a TDE. The result of this project is a machine learning classifier with a mean accuracy of ~86.5475%. This new classification system will aid in more efficient and accurate classification of TDEs, allowing for greater understanding of supermassive black holes and the creation of galaxies.

There are several considerations to be further investigated. A mean value approach was used to replace missing data points, which theoretically should not affect the data significantly as light curves are continuous. To produce more robust results, a KNN machine learning algorithm or a Random Forest model could be utilized to fill a missing value. While these methods may be computationally expensive, this may be feasible due to the given small dataset. Furthermore, another method may be devised to utilize the outputs of the spectroscopic classifier and the photometric classifier as inputs into another machine learning program to create an even more robust model.