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We present a comparison between independent computer codes, modeling the physics and chemistry of interstellar photon dominated regions (PDRs). Our goal was to understand the mutual differences in the PDR codes and their effects on the physical and chemical structure of the model clouds, and to converge the output of different codes to a common solution. A number of benchmark models have been created, covering low and high gas densities and far ultraviolet intensities. The benchmark models were computed in two ways: one set assuming constant temperatures, thus testing the consistency of the chemical network and photo-processes, and a second set determining the temperature selfconsistently. We investigated the impact of PDR geometry and agreed on the comparison of results from spherical and plane-parallel PDR models. We identified a number of key processes governing the chemical network which have been treated differently in the various codes, and defined a proper common treatment. We established a comprehensive set of reference models for ongoing and future PDR model bench-marking and were able to increase the agreement in model predictions for all benchmark models significantly.
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Recent Herschel and ALMA observations of Photodissociation Regions (PDRs) have revealed the presence of a high thermal pressure (P ~ 10^7-10^8 K cm-3) thin compressed layer at the PDR surface where warm molecular tracer emission (e.g. CH+, SH+, high-J CO, H2,...) originate. These high pressures (unbalanced by the surrounding environment) and a correlation between pressure and incident FUV field (G0) seem to indicate a dynamical origin with the radiation field playing an important role in driving the dynamics. We investigate whether photoevaporation of the illuminated edge of a molecular cloud could explain these high pressures and pressure-UV field correlation. We developed a 1D hydrodynamical PDR code coupling hydrodynamics, EUV and FUV radiative transfer and time-dependent thermo-chemical evolution. We applied it to a 1D plane-parallel photoevaporation scenario where a UV-illuminated molecular cloud can freely evaporate in a surrounding low-pressure medium. We find that photoevaporation can produce high thermal pressures and the observed P-G0 correlation, almost independently from the initial gas density. In addition, we find that constant-pressure PDR models are a better approximation to the structure of photoevaporating PDRs than constant-density PDR models, although moderate pressure gradients are present. Strong density gradients from the molecular to the neutral atomic region are found, which naturally explain the large density contrasts (1-2 orders of magnitude) derived from observations of different tracers. The photoevaporating PDR is preceded by a low velocity shock (a few km/s) propagating into the molecular cloud. Photoevaporating PDR models offer a promising explanation to the recent observational evidence of dynamical effects in PDRs.
We present the revised ``Meudon model of Photon Dominated Region (PDR code), presently available on the web under the Gnu Public Licence at: http://aristote.obspm.fr/MIS. General organisation of the code is described down to a level that should allow most observers to use it as an interpretation tool with minimal help from our part. Two grids of models, one for low excitation diffuse clouds and one for dense highly illuminated clouds, are discussed, and some new results on PDR modelisation highlighted.
Program representation learning is a fundamental task in software engineering applications. With the availability of big code and the development of deep learning techniques, various program representation learning models have been proposed to understand the semantic properties of programs and applied on different software engineering tasks. However, no previous study has comprehensively assessed the generalizability of these deep models on different tasks, so that the pros and cons of the models are unclear. In this experience paper, we try to bridge this gap by systemically evaluating the performance of eight program representation learning models on three common tasks, where six models are based on abstract syntax trees and two models are based on plain text of source code. We kindly explain the criteria for selecting the models and tasks, as well as the method for enabling end-to-end learning in each task. The results of performance evaluation show that they perform diversely in each task and the performance of the AST-based models is generally unstable over different tasks. In order to further explain the results, we apply a prediction attribution technique to find what elements are captured by the models and responsible for the predictions in each task. Based on the findings, we discuss some general principles for better capturing the information in the source code, and hope to inspire researchers to improve program representation learning methods for software engineering tasks.
We revised the treatment of interstellar dust in the KOSMA-tau PDR model code to achieve a consistent description of the dust-related physics in the code. The detailed knowledge of the dust properties is then used to compute the dust continuum emission together with the line emission of chemical species. We coupled the KOSMA-tau PDR code with the MCDRT (multi component dust radiative transfer) code to solve the frequency-dependent radiative transfer equations and the thermal balance equation in a dusty clump under the assumption of spherical symmetry, assuming thermal equilibrium in calculating the dust temperatures, neglecting non-equilibrium effects. We updated the calculation of the photoelectric heating and extended the parametrization range for the photoelectric heating toward high densities and UV fields. We revised the computation of the H2 formation on grain surfaces to include the Eley-Rideal effect, thus allowing for high-temperature H2 formation. We demonstrate how the different optical properties, temperatures, and heating and cooling capabilities of the grains influence the physical and chemical structure of a model cloud. The most influential modification is the treatment of H2 formation on grain surfaces that allows for chemisorption. This increases the total H2 formation significantly and the connected H2 formation heating provides a profound heating contribution in the outer layers of the model clumps. The contribution of PAH surfaces to the photoelectric heating and H2 formation provides a boost to the temperature of outer cloud layers, which is clearly traced by high-J CO lines. Increasing the fraction of small grains in the dust size distribution results in hotter gas in the outer cloud layers caused by more efficient heating and cooler cloud centers, which is in turn caused by the more efficient FUV extinction.
Blazar hadronic models have been developed in the past decades as an alternative to leptonic ones. In hadronic models the gamma-ray emission is associated with synchrotron emission by protons, and/or secondary leptons produced in proton-photon interactions. Together with photons, hadronic emission models predict the emission of neutrinos that are therefore the smoking gun for acceleration of relativistic hadrons in blazar jets. The simulation of proton-photon interactions and all associated radiative processes is a complex numerical task, and different approaches to the problem have been adopted in the literature. So far, no systematic comparison between the different codes has been performed, preventing a clear understanding of the underlying uncertainties in the numerical simulations. To fill this gap, we have undertaken the first comprehensive comparison of blazar hadronic codes, and the results from this effort will be presented in this contribution.
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