The molecular content of the nearby galaxy from IRAS and HI observations

Because infrared emission is a very good tracer of mass at high latitudes, by combining it with HI observations it provides a convenient though indirect way of observing the spatial distribution of molecular material. Moreover, these observations will premit placing limits on the fraction of total infrared luminosity emitted by dust associated with molecular and atomic hydrogen clouds. A preliminary result from the study of the correlation between HI column density and 100 micron infrared flux density as measured by the IRAS satellite is reported. The ratio F100/W(HI) = R has an average value of roughty 17 KJy/sr/(K km/s) over the whole sky. Bright regions in the FIR such as the Galactic plane and HII regions are excluded from the data. The histogram of the number of pixels vs R has a strong peak near 17 (same units as before) and is asymmetric about this mean value, having a tail at higher values of R. This basic shape is fairly independent of the region of the sky we observe. The peak confirms the general correlation between infrared emission and HI column density reported previously. One way to explain the shape of the distribution is to assume a constant dust to gas mass ratio and a constant interstellar radiation field and associate points in the tail with molecular clouds. In this case the ratio R is higher for points in the tail because it does not account for the column density of molecular hydrogen

A neural network prototyping package within IRAF

We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do

A Naive Bayes Source Classifier for X-ray Sources

The Chandra Carina Complex Project (CCCP) provides a sensitive X-ray survey of a nearby starburst region over >1 square degree in extent. Thousands of faint X-ray sources are found, many concentrated into rich young stellar clusters. However, significant contamination from unrelated Galactic and extragalactic sources is present in the X-ray catalog. We describe the use of a naive Bayes classifier to assign membership probabilities to individual sources, based on source location, X-ray properties, and visual/infrared properties. For the particular membership decision rule adopted, 75% of CCCP sources are classified as members, 11% are classified as contaminants, and 14% remain unclassified. The resulting sample of stars likely to be Carina members is used in several other studies, which appear in a Special Issue of the ApJS devoted to the CCCP.Comment: Accepted for the ApJS Special Issue on the Chandra Carina Complex Project (CCCP), scheduled for publication in May 2011. All 16 CCCP Special Issue papers are available at http://cochise.astro.psu.edu/Carina_public/special_issue.html through 2011 at least. 19 pages, 7 figure

Class Discovery in Galaxy Classification

In recent years, automated, supervised classification techniques have been fruitfully applied to labeling and organizing large astronomical databases. These methods require off-line classifier training, based on labeled examples from each of the (known) object classes. In practice, only a small batch of labeled examples, hand-labeled by a human expert, may be available for training. Moreover, there may be no labeled examples for some classes present in the data, i.e. the database may contain several unknown classes. Unknown classes may be present due to 1) uncertainty in or lack of knowledge of the measurement process, 2) an inability to adequately ``survey'' a massive database to assess its content (classes), and/or 3) an incomplete scientific hypothesis. In recent work, new class discovery in mixed labeled/unlabeled data was formally posed, with a proposed solution based on mixture models. In this work we investigate this approach, propose a competing technique suitable for class discovery in neural networks, and evaluate both methods for classification and class discovery on several astronomical data sets. Our results demonstrate up to a 57% reduction in classification error compared to a standard neural network classifier that uses only labeled data

Applying Machine Learning to Catalogue Matching in Astrophysics

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the HI Parkes All Sky Survey (HIPASS), and SuperCOSMOS optical survey. Previous work had matched 44% (1887 objects) of HIPASS to the SuperCOSMOS catalogue. A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12% correct). Applying this model, to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72% matched.Comment: 8 Pages, 5 Figure

Dust Emissivity Variations In the Milky Way

Dust properties appear to vary according to the environment in which the dust evolves. Previous observational indications of these variations in the FIR and submm spectral range are scarce and limited to specific regions of the sky. To determine whether these results can be generalised to larger scales, we study the evolution in dust emissivities from the FIR to mm wavelengths, in the atomic and molecular ISM, along the Galactic plane towards the outer Galaxy. We correlate the dust FIR to mm emission with the HI and CO emission. The study is carried out using the DIRBE data from 100 to 240 mic, the Archeops data from 550 mic to 2.1 mm, and the WMAP data at 3.2 mm (W band), in regions with Galactic latitude |b| < 30 deg, over the Galactic longitude range (75 deg < l < 198 deg) observed with Archeops. In all regions studied, the emissivity spectra in both the atomic and molecular phases are steeper in the FIR (beta = 2.4) than in the submm and mm (beta = 1.5). We find significant variations in the spectral shape of the dust emissivity as a function of the dust temperature in the molecular phase. Regions of similar dust temperature in the molecular and atomic gas exhibit similar emissivity spectra. Regions where the dust is significantly colder in the molecular phase show a significant increase in emissivity for the range 100 - 550 mic. This result supports the hypothesis of grain coagulation in these regions, confirming results obtained over small fractions of the sky in previous studies and allowing us to expand these results to the cold molecular environments in general of the outer MW. We note that it is the first time that these effects have been demonstrated by direct measurement of the emissivity, while previous studies were based only on thermal arguments.Comment: 16 pages, 6 figures, accepted in A&

ISO far infrared observations of the high latitude cloud L1642. II. Correlated variations of far-infrared emissivity and temperature of "classical large" dust particles

Our aim is to compare the infrared properties of big, ``classical'' dust grains with visual extinction in the cloud L1642. In particular, we study the differences of grain emissivity between diffuse and dense regions in the cloud. The far-infrared properties of dust are based on large-scale 100um and 200um maps. Extinction through the cloud has been derived by using the star count method at B- and I-bands, and color excess method at J, H and Ks bands. Radiative transfer calculations have been used to study the effects of increasing absorption cross-section on the far-infrared emission and dust temperature. Dust emissivity, measured by the ratio of far-infrared optical depth to visual extinction, tau(far-IR)/A(V), increases with decreasing dust temperature in L1642. There is about two-fold increase of emissivity over the dust temperature range of 19K-14K. Radiative transfer calculations show that in order to explain the observed decrease of dust temperature towards the centre of L1642 an increase of absorption cross-section of dust at far-IR is necessary.This temperature decrease cannot be explained solely by the attenuation of interstellar radiation field. Increased absorption cross-section manifests itself also as an increased emissivity. We find that, due to temperature effects, the apparent value of optical depth tau(far-IR), derived from 100um and 200um intensities, is always lower than the true optical depth.Comment: 11 pages, 9 figures. Accepted for publication in A&

Infrared Emission from the Composite Grains: Effects of Inclusions and Porosities on the 10 and 18 μm\mu m Features

In this paper we study the effects of inclusions and porosities on the emission properties of silicate grains and compare the model curves with the observed infrared emission from circumstellar dust. We calculate the absorption efficiency of the composite grain, made up of a host silicate oblate spheroid and inclusions of ice/graphite/or voids, in the spectral region 5.0-25.0$\mu m$. The absorption efficiencies of the composite spheroidal oblate grains for three axial ratios are computed using the discrete dipole approximation (DDA). We study the absorption as a function of the volume fraction of the inclusions and porosity. In particular, we study the variation in the $10\mu m$ and $18\mu m$ emission features with the volume fraction of the inclusions and porosities. We then calculate the infrared fluxes for these composite grains at several dust temperatures (T=200-350K) and compare the model curves with the average observed IRAS-LRS curve, obtained for circumstellar dust shells around oxygen rich M-type stars. The model curves are also compared with two other individual stars. The results on the composite grains clearly indicate that the silicate feature at 10$\mu m$ shifts with the volume fraction of graphite inclusions. The feature does not shift with the porosity. Both the features do not show any broadening with the inclusions or porosity. The absorption efficiencies of the composite grains calculated using DDA and Effective Medium Approximation (EMA) do not agree. The composite grain models presented in this study need to be compared with the observed IR emission from the circumstellar dust around a few more stars.Comment: 12 pages, 12 figures, 7 tables; To appear in A & A, 201

Diffusion of School-Based Prevention Programs in Two Urban Districts: Adaptations, Rationales, and Suggestions for Change

The diffusion of school-based preventive interventions involves the balancing of high-fidelity implementation of empirically-supported programs with flexibility to permit local stakeholders to target the specific needs of their youth. There has been little systematic research that directly seeks to integrate research- and community-driven approaches to diffusion. The present study provides a primarily qualitative investigation of the initial roll-out of two empirically-supported substance and violence prevention programs in two urban school districts that serve a high proportion of low-income, ethnic minority youth. The predominant ethnic group in most of our study schools was Asian American, followed by smaller numbers of Latinos, African Americans, and European Americans. We examined the adaptations made by experienced health teachers as they implemented the programs, the elicitation of suggested adaptations to the curricula from student and teacher stakeholders, and the evaluation of the consistency of these suggested adaptations with the core components of the programs. Data sources include extensive classroom observations of curricula delivery and interviews with students, teachers, and program developers. All health teachers made adaptations, primarily with respect to instructional format, integration of real-life experiences into the curriculum, and supplementation with additional resources; pedagogical and class management issues were cited as the rationale for these changes. Students and teachers were equally likely to propose adaptations that met with the program developers’ approval with respect to program theory and implementation logistics. Tensions between teaching practice and prevention science—as well as implications for future research and practice in school-based prevention—are considered

Revealing the cold dust in low-metallicity environments: I. Photometry analysis of the Dwarf Galaxy Survey with Herschel

Context. We present new photometric data from our Herschel Guaranteed Time Key Programme, the Dwarf Galaxy Survey (DGS), dedicated to the observation of the gas and dust in low-metallicity environments. A total of 48 dwarf galaxies were observed with the PACS and SPIRE instruments onboard the Herschel Space Observatory at 70, 100, 160, 250, 350, and 500 µm. Aims. The goal of this paper is to provide reliable far infrared (FIR) photometry for the DGS sample and to analyse the FIR/submillimetre (submm) behaviour of the DGS galaxies. We focus on a systematic comparison of the derived FIR properties (FIR luminosity, LFIR, dust mass, Mdust , dust temperature, T, emissivity index, β) with more metal-rich galaxies and investigate the detection of a potential submm excess. Methods. The data reduction method is adapted for each galaxy in order to derive the most reliable photometry from the final maps. The derived PACS flux densities are compared with the Spitzer MIPS 70 and 160 µm bands. We use colour-colour diagrams to analyse the FIR/submm behaviour of the DGS galaxies and modified blackbody fitting procedures to determine their dust properties. To study the variation in these dust properties with metallicity, we also include galaxies from the Herschel KINGFISH sample, which contains more metal-rich environments, totalling 109 galaxies. Results. The location of the DGS galaxies on Herschel colour-colour diagrams highlights the differences in dust grain properties and/or global environments of low-metallicity dwarf galaxies. The dust in DGS galaxies is generally warmer than in KINGFISH galaxies (TDGS ∼ 32 K and TKINGFIS H ∼ 23 K). The emissivity index, β, is ∼ 1.7 in the DGS, however metallicity does not make a strong effect on β. The proportion of dust mass relative to stellar mass is lower in low-metallicity galaxies: Mdust /Mstar ∼ 0.02% for the DGS versus 0.1% for KINGFISH. However, per unit dust mass, dwarf galaxies emit about six times more in the FIR/submm than higher metallicity galaxies. Out of the 22 DGS galaxies detected at 500 µm, about 41% present an excess in the submm beyond the explanation of our dust SED model, and this excess can go up to 150% above the prediction from the model. The excess mainly appears in lower metallicity galaxies (12+log(O/H) ;S 8.3), and the strongest excesses are detected in the most metal-poor galaxies. However, we so stress the need for observations longwards of the Herschel wavelengths to detect any submm excess appearing beyond 500 .Norwegian Lis