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

The Origins of the Circumgalactic Medium in the FIRE Simulations

We use a particle tracking analysis to study the origins of the circumgalactic medium (CGM), separating it into (1) accretion from the intergalactic medium (IGM), (2) wind from the central galaxy, and (3) gas ejected from other galaxies. Our sample consists of 21 FIRE-2 simulations, spanning the halo mass range log(Mh/Msun) ~ 10-12 , and we focus on z=0.25 and z=2. Owing to strong stellar feedback, only ~L* halos retain a baryon mass >~50% of their cosmic budget. Metals are more efficiently retained by halos, with a retention fraction >~50%. Across all masses and redshifts analyzed >~60% of the CGM mass originates as IGM accretion (some of which is associated with infalling halos). Overall, the second most important contribution is wind from the central galaxy, though gas ejected or stripped from satellites can contribute a comparable mass in ~L* halos. Gas can persist in the CGM for billions of years, resulting in well-mixed halo gas. Sight lines through the CGM are therefore likely to intersect gas of multiple origins. For low-redshift ~L* halos, cool gas (T<10^4.7 K) is distributed on average preferentially along the galaxy plane, however with strong halo-to-halo variability. The metallicity of IGM accretion is systematically lower than the metallicity of winds (typically by >~1 dex), although CGM and IGM metallicities depend significantly on the treatment of subgrid metal diffusion. Our results highlight the multiple physical mechanisms that contribute to the CGM and will inform observational efforts to develop a cohesive picture.Comment: 23 pages, 22 figures. Minor revisions from previous version. Online interactive visualizations available at zhafen.github.io/CGM-origins and zhafen.github.io/CGM-origins-pathline

Imprint of Drivers of Galaxy Formation in the Circumgalactic Medium

Galaxie

Neural Networks as Optimal Estimators to Marginalize Over Baryonic Effects

International audienceMany different studies have shown that a wealth of cosmological information resides on small, nonlinear scales. Unfortunately, there are two challenges to overcome to utilize that information. First, we do not know the optimal estimator that will allow us to retrieve the maximum information. Second, baryonic effects impact that regime significantly and in a poorly understood manner. Ideally, we would like to use an estimator that extracts the maximum cosmological information while marginalizing over baryonic effects. In this work we show that neural networks can achieve that when considering some simple scenarios. We made use of data where the maximum amount of cosmological information is known: power spectra and 2D Gaussian density fields. We also contaminate the data with simplified baryonic effects and train neural networks to predict the value of the cosmological parameters. For this data, we show that neural networks can (1) extract the maximum available cosmological information, (2) marginalize over baryonic effects, and (3) extract cosmological information that is buried in the regime dominated by baryonic physics. We also show that neural networks learn the priors of the data they are trained on, affecting their extrapolation properties. We conclude that a promising strategy to maximize the scientific return of cosmological experiments is to train neural networks on state-of-the-art numerical simulations with different strengths and implementations of baryonic effects

The BLAST Survey of the Vela Molecular Cloud: Physical Properties of the Dense Cores in Vela-D

The Balloon-borne Large-Aperture Submillimeter Telescope (BLAST) carried out a 250, 350 and 500 micron survey of the galactic plane encompassing the Vela Molecular Ridge, with the primary goal of identifying the coldest dense cores possibly associated with the earliest stages of star formation. Here we present the results from observations of the Vela-D region, covering about 4 square degrees, in which we find 141 BLAST cores. We exploit existing data taken with the Spitzer MIPS, IRAC and SEST-SIMBA instruments to constrain their (single-temperature) spectral energy distributions, assuming a dust emissivity index beta = 2.0. This combination of data allows us to determine the temperature, luminosity and mass of each BLAST core, and also enables us to separate starless from proto-stellar sources. We also analyze the effects that the uncertainties on the derived physical parameters of the individual sources have on the overall physical properties of starless and proto-stellar cores, and we find that there appear to be a smooth transition from the pre- to the proto-stellar phase. In particular, for proto-stellar cores we find a correlation between the MIPS24 flux, associated with the central protostar, and the temperature of the dust envelope. We also find that the core mass function of the Vela-D cores has a slope consistent with other similar (sub)millimeter surveys