The accurate separation of stars and galaxies is fundamental to many extragalactic and cosmological studies, yet it remains challenging at faint magnitudes. We present our ongoing investigation of star–galaxy classification in the Vera C. Rubin Observatory’s Data Preview 0 (DP0), emphasizing the correlation between misclassification rates, object brightness, and shape parameters. By cross-referencing the DP0 source catalog with “truth” data provided by the LSST pipeline, we analyzed 750,000 objects spanning AB magnitudes from 23 to 29, grouping 250,000 from 23 to 25, 25 to 27, and 27 to 29 magnitudes, respectively. We find that while classification remains fairly accurate (>80% correct) for sources brighter than around 25 mag, misclassification rates exceed 50% at fainter magnitudes, driven in part by compact galaxies incorrectly flagged as point sources and stars that present a larger morphology. We explore the role of morphology (e.g., semi-major and semi-minor axes) to see whether shape-based cuts can mitigate these errors. The results so far suggest that such criteria offer improvements, though more advanced algorithms may be needed as Rubin scales to deeper imaging. Looking ahead, we will implement a machine-learning pipeline capable of leveraging multi-band photometry and morphology to better separate stars from galaxies in future data releases. This work highlights where and how classification errors arise and underscores the importance of robust star–galaxy separation for the full scientific potential of Rubin Observatory data.

The ground state of a physical system is the lowest energy state, which plays an important role in the determination of the zero temperature phase diagram. In this research, we utilize a Variational Quantum Eigensolver (VQE) Algorithm to numerically estimate the ground state of Ising-type Hamiltonians using the Ising Model as an illustrative example. The research project involves exploiting symmetries of the Hamiltonian to inform a better choice of variational ansatz. The variational principle states any ansatz used to estimate the ground state will always lead to an energy that is higher or equal to that of the ground state. The VQE ansatz, informed by symmetries of the Hamiltonian, provides a systematic approach for estimating the ground state by restricting the ansatz to a tractable subspace. This research aims to overcome the challenges that arise when navigating a large, onerous Hilbert space within which eigenstates of a Hamiltonian live.