X-raying molecular clouds with a short flare: probing statistics of gas density and velocity fields

We take advantage of a set of molecular cloud simulations to demonstrate a possibility to uncover statistical properties of the gas density and velocity fields using reflected emission of a short (with duration much less than the cloud's light-crossing time) X-ray flare. Such a situation is relevant for the Central Molecular Zone (CMZ) of our Galaxy where several clouds get illuminated by an ∼110 yr-old flare from the supermassive black hole Sgr A∗ . Due to shortness of the flare (t 1.6 yr), only a thin slice (z 0.5 pc) of the molecular gas contributes to the X-ray reflection signal at any given moment, and its surface brightness effectively probes the local gas density. This allows reconstructing the density probability distribution function over a broad range of scales with virtually no influence of attenuation, chemo-dynamical biases, and projection effects. Such a measurement is key to understanding the structure and star formation potential of the clouds evolving under extreme conditions in the CMZ. For cloud parameters similar to the currently brightest in X-ray reflection molecular complex Sgr A, the sensitivity level of the best available data is sufficient only for marginal distinction between solenoidal and compressive forcing of turbulence. Future-generation X-ray observatories with large effective area and high spectral resolution will dramatically improve on that by minimizing systematic uncertainties due to contaminating signals. Furthermore, measurement of the iron fluorescent line centroid with sub-eV accuracy in combination with the data on molecular line emission will allow direct investigation of the gas velocity field.IK, EC, and RS acknowledge partial support by the Russian Science Foundation grant 19-12-00369.and the Australia–Germany Joint Research Cooperation Scheme (UADAAD). DS and SW acknowledge support by the German Science Foundation via CRC 956, subprojects C5 and C6. SW further acknowledges support by the ERC Starting Grant RADFEEDBACK (grant no. 679852). We further acknowledge high-performance computing resources provided by the Leibniz Rechenzentrum and the Gauss Centre for Supercomputing (grants pr32lo and pr94du) and by the Australian National Computational Infrastructure (grant ek9)