  We perform maximum likelihood estimates (MLEs) for single and double flavor
ultralight dark matter (ULDM) models using the Spitzer Photometry and Accurate
Rotation Curves (SPARC) database. These estimates are compared to MLEs for
several commonly used cold dark matter (CDM) models. By comparing various CDM
models we find, in agreement with previous studies, that the Burkert and
Einasto models tend to perform better than other commonly used CDM models. We
focus on comparisons between the Einasto and ULDM models and analyze cases for
which the ULDM particle masses are: free to vary; and fixed. For each of these
analyses, we perform fits assuming the soliton and halo profiles are: summed
together; and matched at a given radius. When we let the particle masses vary,
we find a negligible preference for any particular range of particle masses,
within $10^{-25}\,\text{eV}\leq m\leq10^{-19}\,\text{eV}$, when assuming the
summed models. For the matched models, however, we find that almost all
galaxies prefer particles masses in the range $10^{-23}\,\text{eV}\lesssim
m\lesssim10^{-20}\,\text{eV}$. For both double flavor models we find that most
galaxies prefer approximately equal particle masses. We find that the summed
models give much larger variances with respect to the soliton-halo (SH)
relation than the matched models. When the particle masses are fixed, the
matched models give median and mean soliton and halo values that fall within
the SH relation bounds, for most masses scanned. When the particle masses are
fixed in the fitting procedure, we find the best fit results for the particle
mass $m=10^{-20.5}\,\text{eV}$ (for the single flavor models) and
$m_1=10^{-20.5}\,\text{eV}$, $m_2=10^{-20.2}\,\text{eV}$ for the double flavor,
matched model. We discuss how our study will be furthered using a reinforcement
learning algorithm.
