46 research outputs found
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 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. 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 and 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 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 developersâ approval with respect to program theory and implementation logistics. Tensions between teaching practice and prevention scienceâas well as implications for future research and practice in school-based preventionâare 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 ”m.
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, ÎČ) 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 ”m 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, ÎČ, is ⌠1.7 in the DGS, however metallicity does not make
a strong effect on ÎČ. 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 ”m, 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