On the shape of the mass-function of dense clumps in the Hi-GAL fields. II. Using Bayesian inference to study the clump mass function

Context. Stars form in dense, dusty clumps of molecular clouds, but little is known about their origin, their evolution and their detailed physical properties. In particular, the relationship between the mass distribution of these clumps (also known as the "clump mass function", or CMF) and the stellar initial mass function (IMF), is still poorly understood. Aims. In order to better understand how the CMF evolve toward the IMF, and to discern the "true" shape of the CMF, large samples of bona-fide pre- and proto-stellar clumps are required. Two such datasets obtained from the Herschel infrared GALactic Plane Survey (Hi-GAL) have been described in paper I. Robust statistical methods are needed in order to infer the parameters describing the models used to fit the CMF, and to compare the competing models themselves. Methods. In this paper we apply Bayesian inference to the analysis of the CMF of the two regions discussed in Paper I. First, we determine the Bayesian posterior probability distribution for each of the fitted parameters. Then, we carry out a quantitative comparison of the models used to fit the CMF. Results. We have compared the results from several methods implementing Bayesian inference, and we have also analyzed the impact of the choice of priors and the influence of various constraints on the statistical conclusions for the preferred values of the parameters. We find that both parameter estimation and model comparison depend on the choice of parameter priors. Conclusions. Our results confirm our earlier conclusion that the CMFs of the two Hi-GAL regions studied here have very similar shapes but different mass scales. Furthermore, the lognormal model appears to better describe the CMF measured in the two Hi-GAL regions studied here. However, this preliminary conclusion is dependent on the choice of parameters priors.Comment: Submitted for publication to A&A on November 12, 2013. This paper contains 11 pages and 7 figure

Theory of Cluster Formation: Effects of Magnetic Fields

Stars form predominantly in clusters inside dense clumps of molecular clouds that are both turbulent and magnetized. The typical size and mass of the cluster-forming clumps are $\sim 1$ pc and $\sim 10^2 -$ 10$^3$ M$_\odot$, respectively. Here, we discuss some recent progress on numerical simulations of clustered star formation in such parsec-scale dense clumps with emphasis on the role of magnetic fields. The simulations have shown that magnetic fields tend to slow down global gravitational collapse and thus star formation, especially in the presence of protostellar outflow feedback. Even a relatively weak can retard star formation significantly, because the field is amplified by supersonic turbulence to an equipartition strength. However, in such a case, the distorted field component dominates the uniform one. In contrast, if the field is moderately strong, the uniform component remains dominant. Such a difference in the magnetic structure is observed in simulated polarization maps of dust thermal emission. Recent polarization measurements show that the field lines in nearby cluster-forming clumps are spatially well-ordered, indicative of a rather strong field. In such strongly-magnetized clumps, star formation should proceed relatively slowly; it continues for at least several global free-fall times of the parent dense clump ($t_{\rm ff}\sim$ a few $\times 10^5$ yr).Comment: 8 pages, proceedings of Computational Star Formation (IAU 270

Clustered Star Formation in Magnetic Clouds: Properties of Dense Cores Formed in Outflow-Driven Turbulence

We investigate the physical properties of dense cores formed in turbulent, magnetized, parsec-scale clumps of molecular clouds, using three-dimensional numerical simulations that include protostellar outflow feedback. The dense cores are identified in the simulated density data cube through a clumpfind algorithm. We find that the core velocity dispersion does not show any clear dependence on the core size, in contrast to Larson's linewidth-size relation, but consistent with recent observations. In the absence of a magnetic field, the majority of the cores have supersonic velocity dispersions. A moderately-strong magnetic field reduces the dispersion to a subsonic or at most transonic value typically. Most of the cores are out of virial equilibrium, with the external pressure dominating the self-gravity. The implication is that the core evolution is largely controlled by the outflow-driven turbulence. Even an initially-weak magnetic field can retard star formation significantly, because the field is amplified by the outflow-driven turbulence to an equipartition strength, with the distorted field component dominating the uniform one. In contrast, for a moderately-strong field, the uniform component remains dominant. Such a difference in the magnetic structure is evident in our simulated polarization maps of dust thermal emission; it provides a handle on the field strength. Recent polarization measurements show that the field lines in cluster-forming clumps are spatially well-ordered. It is indicative of a moderately-strong, dynamically important, field which, in combination with outflow feedback, can keep the rate of star formation in embedded clusters at the observationally-inferred, relatively-slow rate of several percent per free-fall time.Comment: 49 pages, 16 figures accepted by The Astrophysical Journa

Incremental Learning of Nonparametric Bayesian Mixture Models

Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group before learning can begin. Here we explore incremental clustering, in which data can arrive continuously. We present a novel incremental model-based clustering algorithm based on nonparametric Bayesian methods, which we call Memory Bounded Variational Dirichlet Process (MB-VDP). The number of clusters are determined flexibly by the data and the approach can be used to automatically discover object categories. The computational requirements required to produce model updates are bounded and do not grow with the amount of data processed. The technique is well suited to very large datasets, and we show that our approach outperforms existing online alternatives for learning nonparametric Bayesian mixture models

A Case Study of Small Scale Structure Formation in 3D Supernova Simulations

It is suggested in observations of supernova remnants that a number of large- and small-scale structures form at various points in the explosion. Multidimensional modeling of core-collapse supernovae has been undertaken since SN1987A, and both simulations and observations suggest/show that Rayleigh-Taylor instabilities during the explosion is a main driver for the formation of structure in the remnants. We present a case study of structure formation in 3D in a \msol{15} supernova for different parameters. We investigate the effect of moderate asymmetries and different resolutions of the formation and morphology of the RT unstable region, and take first steps at determining typical physical quantities (size, composition) of arising clumps. We find that in this progenitor the major RT unstable region develops at the He/OC interface for all cases considered. The RT instabilities result in clumps that are overdense by 1-2 orders of magnitude with respect to the ambient gas, have size scales on the level of a few % of the remnant diameter, and are not diffused after the first $\sim30$ yrs of the remnant evolution, in the absence of a surrounding medium.Comment: 59 pages, 34 figure

Circumplanetary disks around young giant planets: a comparison between core-accretion and disk instability

Circumplanetary disks can be found around forming giant planets, regardless of whether core accretion or gravitational instability built the planet. We carried out state-of-the-art hydrodynamical simulations of the circumplanetary disks for both formation scenarios, using as similar initial conditions as possible to unveil possible intrinsic differences in the circumplanetary disk mass and temperature between the two formation mechanisms. We found that the circumplanetary disks mass linearly scales with the circumstellar disk mass. Therefore, in an equally massive protoplanetary disk, the circumplanetary disks formed in the disk instability model can be only a factor of eight more massive than their core-accretion counterparts. On the other hand, the bulk circumplanetary disk temperature differs by more than an order of magnitude between the two cases. The subdisks around planets formed by gravitational instability have a characteristic temperature below 100 K, while the core accretion circumplanetary disks are hot, with temperatures even greater than 1000 K when embedded in massive, optically thick protoplanetary disks. We explain how this difference can be understood as the natural result of the different formation mechanisms. We argue that the different temperatures should persist up to the point when a full-fledged gas giant forms via disk instability, hence our result provides a convenient criteria for observations to distinguish between the two main formation scenarios by measuring the bulk temperature in the planet vicinity.Comment: 12 pages, 9 figures, 1 table, accepted for publication at MNRA