Person:

Pineda, Jaime

We present the chemistry, temperature, and dynamical state of a sample of 193 dense cores or core candidates in the Perseus Molecular cloud and compare the properties of cores associated with young stars and clusters with those which are not. The combination of our NH3 and CCS observations with previous millimeter, submillimeter, and Spitzer data available for this cloud enables us both to determine core properties precisely and to accurately classify cores as starless or protostellar. The properties of cores in different cluster environments and before-and-after star formation provide important constraints on simulations of star formation, particularly under the paradigm that the essence of star formation is set by the turbulent formation of prestellar cores. We separate the influence of stellar content from that of the cluster environment and find that cores within clusters have (1) higher kinetic temperatures (12.9 K versus 10.8 K) and, (2) lower fractional abundances of CCS ((0.6 × 10^{–9}) versus (2.0 × 10^{–9})) and (NH_3 (1.2 × 10^{–8}) versus (2.9 × 10^{–8})). Cores associated with protostars have (1) slightly higher kinetic temperatures (11.9 K versus 10.6 K), (2) higher NH3 excitation temperatures (7.4 K versus 6.1 K), (3) are at higher column density ((1.2 × 10^{22} cm^{–2}) versus (0.6 × 10^{22} cm^{–2})), have (4) slightly more nonthermal/turbulent (NH_3) line widths ((0.14 km \ s^{–1}) versus (0.11 km \ s^{–1} FWHM)), have (5) higher masses ((1.5 M \odot) versus (1.0 M \odot)), and have (6) lower fractional abundance of CCS ((1.4 × 10^{–9}) versus (2.4 × 10^{–9})). All values are medians. We find that neither cluster environment nor protostellar content makes a significant difference to the dynamical state of cores as estimated by the virial parameter—most cores in each category are gravitationally bound. Only the high precision of our measurements and the size of our sample make such distinctions possible. Overall, cluster environment and protostellar content have a smaller influence on the properties of the cores than is typically assumed, and the variation within categories is larger than the differences between categories.

We use the COMPLETE Survey's observations of the Perseus star-forming region to assess and intercompare the three methods used for measuring column density in molecular clouds: near-infrared (NIR) extinction mapping; thermal emission mapping in the far-IR; and mapping the intensity of CO isotopologues. Overall, the structures shown by all three tracers are morphologically similar, but important differences exist among the tracers. We find that the dust-based measures (NIR extinction and thermal emission) give similar, log-normal, distributions for the full ((\sim20 \ pc \ scale)) Perseus region, once careful calibration corrections are made. We also compare dust- and gas-based column density distributions for physically meaningful subregions of Perseus, and we find significant variations in the distributions for those ((smaller, \sim few \ pc \ scale)) regions. Even though we have used (^{12}CO) data to estimate excitation temperatures, and we have corrected for opacity, the (^{13}CO) maps seem unable to give column distributions that consistently resemble those from dust measures. We have edited out the effects of the shell around the B-star HD 278942 from the column density distribution comparisons. In that shell's interior and in the parts where it overlaps the molecular cloud, there appears to be a dearth of (^{13}CO), which is likely due either to (^{13}CO) not yet having had time to form in this young structure and/or destruction of (^{13}CO) in the molecular cloud by the HD 278942's wind and/or radiation. We conclude that the use of either dust or gas measures of column density without extreme attention to calibration (e.g., of thermal emission zero-levels) and artifacts (e.g., the shell) is more perilous than even experts might normally admit. And, the use of (^{13}CO) data to trace total column density in detail, even after proper calibration, is unavoidably limited in utility due to threshold, depletion, and opacity effects. If one's main aim is to map column density (rather than temperature or kinematics), then dust extinction seems the best probe, up to a limiting extinction caused by a dearth of sufficient background sources. Linear fits among all three tracers' estimates of column density are given, allowing us to quantify the inherent uncertainties

We study the mass spectrum of substructures in the Perseus Molecular Cloud Complex traced by (^{13}CO(1–0)), finding that (dN/dM \ \alpha \ M^{−2.4}) for the standard Clumpfind parameters. This result does not agree with the classical dN/dM (dN/dM \ \alpha \ M^{−1.6}). To understand this discrepancy, we study the robustness of the mass spectrum derived using the Clumpfind algorithm. Both two- and three-dimensional Clumpfind versions are tested, using 850 μm dust emission and (^{13}CO) spectral-line observations of Perseus, respectively. The effect of varying threshold is not important, but varying stepsize produces a different effect for two- and three-dimensional cases. In the two-dimensional case, where emission is relatively isolated (associated with only the densest peaks in the cloud), the mass spectrum variability is negligible compared to the mass function fit uncertainties. In the three-dimensional case, however, where the (^{13}CO) emission traces the bulk of the molecular cloud (MC), the number of clumps and the derived mass spectrum are highly correlated with the stepsize used. The distinction between “two dimension” and “three dimension” here ismore importantly also a distinction between “sparse” and “crowded” emission. In any “crowded” case, Clumpfind should not be used blindly to derive mass functions. Clumpfind’s output in the “crowded” case can still offer a statistical description of emission useful in intercomparisons, but the clump-list should not be treated as a robust region decomposition suitable to generate a physically meaningful mass function. We conclude that the (^{13}CO) mass spectrum depends on the observations resolution, due to the hierarchical structure of the MC.