Astrochemical Correlations in Molecular Clouds

Abstract

We investigate the spectral correlations between different species used to observe molecular clouds. We use hydrodynamic simulations and a full chemical network to study the abundances of over 150 species in typical Milky Way molecular clouds. We perform synthetic observations in order to produce emission maps of a subset of these tracers. We study the effects of different lines of sight and spatial resolution on the emission distribution and perform a robust quantitative comparison of the species to each other. We use the Spectral Correlation Function (SCF), which quantifies the root mean squared difference between spectra separated by some length scale, to characterize the structure of the simulated cloud in position-position-velocity (PPV) space. We predict the observed SCF for a broad range of observational tracers, and thus, identify homologous species. In particular, we show that the pairs C and CO, C + and CN, NH 3 and H 2 CS have very similar SCFs. We measure the SCF slope variation as a function of beam size for all species and demonstrate that the beam size has a distinct effect on different species emission. However, for beams of up to 10", placing the cloud at 1 kpc, the change is not large enough to move the SCF slopes into different regions of parameter space. The results from this study provide observational guidance for choosing the best tracer to probe various cloud length scales.

An Exploration of the Statistical Signatures of Stellar Feedback

“…Both spectrum slopes are comparable to the SCF slope of -0.29 found by Gaches et al (2015) for simulations of non-magnetized turbulence without feedback. However, Gaches et al (2015) also employ chemical networks to model the abundance distribution of CO, which may account for the better agreement with the W1T2t0.2 slope.…”

“…However, Gaches et al (2015) also employ chemical networks to model the abundance distribution of CO, which may account for the better agreement with the W1T2t0.2 slope. Since the SCF spectrum exhibits no characteristic feature associated with feedback and the SCF slope also depends on resolution, we expect this statistic to be most effective when comparing different subregions within a cloud.…”

“…Any one molecular tracer only samples a limited set of gas densities and scales, so that reconstructing cloud kinematics reliably involves assembling a variety of tracers across different densities and scales (e.g., Gaches et al 2015). Many studies of cloud structure instead rely on a single gas tracer like CO, which is bright and exhibits widespread emission that reflects the underlying H 2 distribution (Bolatto et al 2013;Heyer & Dame 2015).…”

torus-3dpdr: a self-consistent code treating three-dimensional photoionization and photodissociation regions

“…3D-PDR has been fully benchmarked against other PDR codes according to the tests of Röllig et al (2007) and has been used in various applications e.g. Offner et al (2013Offner et al ( , 2014; Bisbas et al (2014); Bisbas, Papadopoulos, & Viti (2015); Gaches et al (2015).…”

TurbuStat: Turbulence Statistics in Python

“…TurbuStat implements the form from Yeremi et al (2014), where the statistic is the normalized root-mean-square difference between the cube and its spatially-shifted self. By iterating over a range of spatial shifts in both spectral dimensions, we create a two-dimensional correlation surface whose azimuthally-averaged index has been shown to be sensitive to changes in turbulent properties Muller et al 2004;Gaches et al 2015). Our implementation can model the correlation surface in either one-or two-dimensions using a sim-ilar approach described for the spatial powerspectrum.…”