  The existence of the million-degree corona above the cooler photosphere is an
unsolved problem in astrophysics. Detailed study of quiescent corona that
exists regardless of the phase of the solar cycle may provide fruitful hints
towards resolving this conundrum. However, the properties of heating mechanisms
can be obtained only statistically in these regions due to their unresolved
nature. Here, we develop a two-step inversion scheme based on the machine
learning scheme of Upendran & Tripathi (2021a) for the empirical impulsive
heating model of Pauluhn & Solanki (2007), and apply it to disk integrated flux
measurements of the quiet corona as measured by the X-ray solar monitor (XSM)
onboard Chandrayaan - 2. We use data in three energy passbands, viz., 1 - 1.3
keV, 1.3 - 2.3 keV, and 1 - 2.3 keV, and estimate the typical impulsive event
frequencies, timescales, amplitudes, and the distribution of amplitudes. We
find that the impulsive events occur at a frequency of $\approx$25 events per
minute with a typical lifetime of $\approx10$ minutes. They are characterized
by a power law distribution with a slope $\alpha\leq2.0$. The typical
amplitudes of these events lie in an energy range of $10^{21}$ - $10^{24}$
ergs, with a typical radiative loss of about $\approx10^3$ erg cm$^{-2}$
s$^{-1}$ in the energy range of 1 - 2.3 keV. These results provide further
constraints on the properties of sub-pixel impulsive events in maintaining the
quiet solar corona.
