Fregean de re thoughts

This papers aims at clarifying some misunderstandings that seem to block an adequate account of de re thoughts within the Fregean framework. It is usually assumed that Fregean senses cannot be de re, or dependent upon objects. Contrary to this assumption, Gareth Evans and John McDowell have claimed that Fregean de re senses are not just possible, but in fact the most promising alternative for accounting for de re thoughts. The reasons blocking this alternative can be traced back to Russellian considerations that contaminated the interpretation of Frege. This contaminated understanding is first detected in Tyler Burge’s distinction between de dicto and de re, then connected to the motivations behind David Kaplan’s notion of character, and finally found in John Searle’s descriptivist account. The difficulty in understanding de re thoughts is, roughly speaking, a side effect of the misunderstanding of the boundaries separating internal and external elements of thoughts, as well as the distinction between mental content and means of representation

The shapes of column density PDFs - The importance of the last closed contour

The probability distribution function of column density (PDF) has become the tool of choice for cloud structure analysis and star formation studies. Its simplicity is attractive, and the PDF could offer access to cloud physical parameters otherwise difficult to measure, but there has been some confusion in the literature on the definition of its completeness limit and shape at the low column density end. In this Letter we use the natural definition of the completeness limit of a column density PDF, the last closed column-density contour inside a surveyed region, and apply it to a set of large-scale maps of nearby molecular clouds. We conclude that there is no observational evidence for log-normal PDFs in these objects. We find that all studied molecular clouds have PDFs well described by power-laws, including the diffuse cloud Polaris. Our results call for a new physical interpretation for the shape of the column density PDFs. We find that the slope of a cloud PDF is invariant to distance but not to the spatial arrangement of cloud material, and as such it is still a useful tool to investigate cloud structure.Comment: A&A Letter, accepted. Comments welcom

Estimating Extinction using Unsupervised Machine Learning

Dust extinction is the most robust tracer of the gas distribution in the interstellar medium, but measuring extinction is limited by the systematic uncertainties involved in estimating the intrinsic colors to background stars. In this paper we present a new technique, PNICER, that estimates intrinsic colors and extinction for individual stars using unsupervised machine learning algorithms. This new method aims to be free from any priors with respect to the column density and intrinsic color distribution. It is applicable to any combination of parameters and works in arbitrary numbers of dimensions. Furthermore, it is not restricted to color space. Extinction towards single sources is determined by fitting Gaussian Mixture Models along the extinction vector to (extinction-free) control field observations. In this way it becomes possible to describe the extinction for observed sources with probability densities. PNICER effectively eliminates known biases found in similar methods and outperforms them in cases of deep observational data where the number of background galaxies is significant, or when a large number of parameters is used to break degeneracies in the intrinsic color distributions. This new method remains computationally competitive, making it possible to correctly de-redden millions of sources within a matter of seconds. With the ever-increasing number of large-scale high-sensitivity imaging surveys, PNICER offers a fast and reliable way to efficiently calculate extinction for arbitrary parameter combinations without prior information on source characteristics. PNICER also offers access to the well-established NICER technique in a simple unified interface and is capable of building extinction maps including the NICEST correction for cloud substructure. PNICER is offered to the community as an open-source software solution and is entirely written in Python.Comment: Accepted for publication in A&A, source code available at http://smeingast.github.io/PNICER

A new method to unveil embedded stellar clusters

In this paper we present a novel method to identify and characterize stellar clusters deeply embedded in a dark molecular cloud. The method is based on measuring stellar surface density in wide-field infrared images using star counting techniques. It takes advantage of the differing $H$-band luminosity functions (HLFs) of field stars and young stellar populations and is able to statistically associate each star in an image as a member of either the background stellar population or a young stellar population projected on or near the cloud. Moreover, the technique corrects for the effects of differential extinction toward each individual star. We have tested this method against simulations as well as observations. In particular, we have applied the method to 2MASS point sources observed in the Orion A and B complexes, and the results obtained compare very well with those obtained from deep Spitzer and Chandra observations where presence of infrared excess or X-ray emission directly determines membership status for every star. Additionally, our method also identifies unobscured clusters and a low resolution version of the Orion stellar surface density map shows clearly the relatively unobscured and diffuse OB 1a and 1b sub-groups and provides useful insights on their spatial distribution.Comment: A&A, in press; 13 pages, multi-layer figures can be displayed with Adobe Acrobat Reade

Molecular clouds have power-law probability distribution functions

In this Letter we investigate the shape of the probability distribution of column densities (PDF) in molecular clouds. Through the use of low-noise, extinction-calibrated \textit{Herschel}/\textit{Planck} emission data for eight molecular clouds, we demonstrate that, contrary to common belief, the PDFs of molecular clouds are not described well by log-normal functions, but are instead power laws with exponents close to two and with breaks between $A_K \simeq 0.1$ and $0.2\,\mathrm{mag}$, so close to the CO self-shielding limit and not far from the transition between molecular and atomic gas. Additionally, we argue that the intrinsic functional form of the PDF cannot be securely determined below $A_K \simeq 0.1\,\mathrm{mag}$, limiting our ability to investigate more complex models for the shape of the cloud PDF.Comment: Letter to the Editor, to appear in A&

On the Star Formation Rates in Molecular Clouds

In this paper we investigate the level of star formation activity within nearby molecular clouds. We employ a uniform set of infrared extinction maps to provide accurate assessments of cloud mass and structure and compare these with inventories of young stellar objects within the clouds. We present evidence indicating that both the yield and rate of star formation can vary considerably in local clouds, independent of their mass and size. We find that the surface density structure of such clouds appears to be important in controlling both these factors. In particular, we find that the star formation rate (SFR) in molecular clouds is linearly proportional to the cloud mass (M_{0.8}) above an extinction threshold of A_K approximately equal to 0.8 magnitudes, corresponding to a gas surface density threshold of approximaely 116 solar masses per square pc. We argue that this surface density threshold corresponds to a gas volume density threshold which we estimate to be n(H_2) approximately equal to 10^4\cc. Specifically we find SFR (solar masses per yr) = 4.6 +/- 2.6 x 10^{-8} M_{0.8} (solar masses) for the clouds in our sample. This relation between the rate of star formation and the amount of dense gas in molecular clouds appears to be in excellent agreement with previous observations of both galactic and extragalactic star forming activity. It is likely the underlying physical relationship or empirical law that most directly connects star formation activity with interstellar gas over many spatial scales within and between individual galaxies. These results suggest that the key to obtaining a predictive understanding of the star formation rates in molecular clouds and galaxies is to understand those physical factors which give rise to the dense components of these clouds.Comment: accepted for publicaton in the Astrophysical Journal; 22 pages, 4 figure

HP2 survey: III The California Molecular Cloud--A Sleeping Giant Revisited

We present new high resolution and dynamic range dust column density and temperature maps of the California Molecular Cloud derived from a combination of Planck and Herschel dust-emission maps, and 2MASS NIR dust-extinction maps. We used these data to determine the ratio of the 2.2 micron extinction coefficient to the 850 micron opacity and found the value to be close to that found in similar studies of the Orion B and Perseus clouds but higher than that characterizing the Orion A cloud, indicating that variations in the fundamental optical properties of dust may exist between local clouds. We show that over a wide range of extinction, the column density probability distribution function (PDF$_N$) of the cloud can be well described by a simple power law with an index that represents a steeper decline with column density than found in similar studies of the Orion and Perseus clouds. Using only the protostellar population of the cloud and our extinction maps we investigate the Schmidt relation within the cloud. We show that the protostellar surface density, $\Sigma_*$, is directly proportional to the ratio of the protostellar and cloud pdfs. We use the cumulative distribution of protostars to infer the functional forms for both $\Sigma_*$ and PDF$_*$. We find that $\Sigma_*$ is best described by two power-law functions with steeper indicies than found in other local GMCs. We find that the protostellar pdf is a declining function of extinction also best described by two power-laws whose behavior mirrors that of $\Sigma_*$. Our observations suggest that variations both in the slope of the Schmidt relation and in the sizes of the protostellar populations between GMCs are largely driven by variations in the slope of the cloud pdf. This confirms earlier studies suggesting that cloud structure plays a major role in setting the global star formation rates in GMCs.Comment: Accepted for publication in Astronomy and Astrophysics. Corrected typos in source coordinates in table A.