Discovery of Crystallized Water Ice in a Silhouette Disk in the M43 Region

We present the 1.9--4.2um spectra of the five bright (L<11.2) young stars associated with silhouette disks with moderate to high inclination angle of 39--80deg in the M42 and M43 regions. The water ice absorption is seen toward d121-1925 and d216-0939, while the spectra of d182-316, d183-405, and d218-354 show no water ice feature around 3.1um within the detection limits. By comparing the water ice features toward nearby stars, we find that the water ice absorption toward d121-1925 and d216-0939 most likely originates from the foreground material and the surrounding disk, respectively. The angle of the disk inclination is found to be mainly responsible for the difference of the optical depth of the water ice among the five young stars. Our results suggest that there is a critical inclination angle between 65deg and 75deg for the circumstellar disk where the water ice absorption becomes strong. The average density at the disk surface of d216-0939 was found to be 6.38x10^(-18) g cm^(-3). The water ice absorption band in the d216-0939 disk is remarkable in that the maximum optical depth of the water ice band is at a longer wavelength than detected before. It indicates that the primary carrier of the feature is purely crystallized water ice at the surface of the d216-0939 disk with characteristic size of ~0.8um, which suggests grain growth. This is the first direct detection of purely crystallized water ice in a silhouette disk.Comment: 28 pages, 8 figures, Accepted by Ap

The MUSE-Wide Survey: A first catalogue of 831 emission line galaxies

We present a first instalment of the MUSE-Wide survey, covering an area of 22.2 arcmin$^2$ (corresponding to $\sim$20% of the final survey) in the CANDELS/Deep area of the Chandra Deep Field South. We use the MUSE integral field spectrograph at the ESO VLT to conduct a full-area spectroscopic mapping at a depth of 1h exposure time per 1 arcmin$^2$ pointing. We searched for compact emission line objects using our newly developed LSDCat software based on a 3-D matched filtering approach, followed by interactive classification and redshift measurement of the sources. Our catalogue contains 831 distinct emission line galaxies with redshifts ranging from 0.04 to 6. Roughly one third (237) of the emission line sources are Lyman $\alpha$ emitting galaxies with $3 < z < 6$, only four of which had previously measured spectroscopic redshifts. At lower redshifts 351 galaxies are detected primarily by their [OII] emission line ($0.3 \lesssim z \lesssim 1.5$), 189 by their [OIII] line ($0.21 \lesssim z \lesssim 0.85$), and 46 by their H$\alpha$ line ($0.04 \lesssim z \lesssim 0.42$). Comparing our spectroscopic redshifts to photometric redshift estimates from the literature, we find excellent agreement for $z<1.5$ with a median $\Delta z$ of only $\sim 4 \times 10^{-4}$ and an outlier rate of 6%, however a significant systematic offset of $\Delta z = 0.26$ and an outlier rate of 23% for Ly$\alpha$ emitters at $z>3$. Together with the catalogue we also release 1D PSF-weighted extracted spectra and small 3D datacubes centred on each of the 831 sources.Comment: 24 pages, 14 figures, accepted for publication in A&A, data products are available for download from http://muse-vlt.eu/science/muse-wide-survey/ and later via the CD

Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy

We present a novel nonparametric Bayesian approach based on L\'{e}vy Adaptive Regression Kernels (LARK) to model spectral data arising from MALDI-TOF (Matrix Assisted Laser Desorption Ionization Time-of-Flight) mass spectrometry. This model-based approach provides identification and quantification of proteins through model parameters that are directly interpretable as the number of proteins, mass and abundance of proteins and peak resolution, while having the ability to adapt to unknown smoothness as in wavelet based methods. Informative prior distributions on resolution are key to distinguishing true peaks from background noise and resolving broad peaks into individual peaks for multiple protein species. Posterior distributions are obtained using a reversible jump Markov chain Monte Carlo algorithm and provide inference about the number of peaks (proteins), their masses and abundance. We show through simulation studies that the procedure has desirable true-positive and false-discovery rates. Finally, we illustrate the method on five example spectra: a blank spectrum, a spectrum with only the matrix of a low-molecular-weight substance used to embed target proteins, a spectrum with known proteins, and a single spectrum and average of ten spectra from an individual lung cancer patient.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS450 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Kinematic classifications of local interacting galaxies: implications for the merger/disk classifications at high-z

The classification of galaxy mergers and isolated disks is key for understanding the relative importance of galaxy interactions and secular evolution during the assembly of galaxies. The kinematic properties of galaxies as traced by emission lines have been used to suggest the existence of a significant population of high-z star-forming galaxies consistent with isolated rotating disks. However, recent studies have cautioned that post-coalescence mergers may also display disk-like kinematics. To further investigate the robustness of merger/disk classifications based on kinematic properties, we carry out a systematic classification of 24 local (U)LIRGs spanning a range of galaxy morphologies: from isolated spiral galaxies, ongoing interacting systems, to fully merged remnants. We artificially redshift the WiFeS observations of these local (U)LIRGs to z=1.5 to make a realistic comparison with observations at high-z, and also to ensure that all galaxies have the same spatial sampling of ~900 pc. Using both kinemetry-based and visual classifications, we find that the reliability of kinematic classification shows a strong trend with the interaction stage of galaxies. Mergers with two nuclei and tidal tails have the most distinct kinematic properties compared to isolated disks, whereas a significant population of the interacting disks and merger remnants are indistinguishable from isolated disks. The high fraction of late-stage mergers showing disk-like kinematics reflects the complexity of the dynamics during galaxy interactions. However, the exact fractions of misidentified disks and mergers depend on the definition of kinematic asymmetries and the classification threshold when using kinemetry-based classifications. Our results suggest that additional indicators such as morphologies traced by stars or molecular gas are required to further constrain the merger/disk classifications at high-z.Comment: 16 pages, 5 figures, ApJ accepte

Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks

The Fermi Large Area Telescope (LAT) is currently the most important facility for investigating the GeV $\gamma$-ray sky. With Fermi LAT more than three thousand $\gamma$-ray sources have been discovered so far. 1144 ($\sim40\%$) of the sources are active galaxies of the blazar class, and 573 ($\sim20\%$) are listed as Blazar Candidate of Uncertain type (BCU), or sources without a conclusive classification. We use the Empirical Cumulative Distribution Functions (ECDF) and the Artificial Neural Networks (ANN) for a fast method of screening and classification for BCUs based on data collected at $\gamma$-ray energies only, when rigorous multiwavelength analysis is not available. Based on our method, we classify 342 BCUs as BL Lacs and 154 as FSRQs, while 77 objects remain uncertain. Moreover, radio analysis and direct observations in ground-based optical observatories are used as counterparts to the statistical classifications to validate the method. This approach is of interest because of the increasing number of unclassified sources in Fermi catalogs and because blazars and in particular their subclass High Synchrotron Peak (HSP) objects are the main targets of atmospheric Cherenkov telescopes.Comment: 18 pages, 17 figures, accepted for publication on MNRA

An information adaptive system study report and development plan

The purpose of the information adaptive system (IAS) study was to determine how some selected Earth resource applications may be processed onboard a spacecraft and to provide a detailed preliminary IAS design for these applications. Detailed investigations of a number of applications were conducted with regard to IAS and three were selected for further analysis. Areas of future research and development include algorithmic specifications, system design specifications, and IAS recommended time lines

The complex galaxy cluster Abell 514: New results obtained with the XMM - Newton satellite

We study the X-ray morphology and dynamics of the galaxy cluster Abell 514. Also, the relation between the X-ray properties and Faraday Rotation measures of this cluster are investigated in order to study the connection of magnetic fields and the intra-cluster medium. We use two combined XMM - Newton pointings that are split into three distinct observations. The data allow us to evaluate the overall cluster properties like temperature and metallicity with high accuracy. Additionally, a temperature map and the metallicity distribution are computed, which are used to study the dynamical state of the cluster in detail. Abell 514 represents an interesting merger cluster with many substructures visible in the X-ray image and in the temperature and abundance distributions. The new XMM - Newton data of Abell 514 confirm the relation between the X-ray brightness and the sigma of the Rotation Measure (S_X - sigma_RM relation) proposed by Dolag et al. (2001).Comment: 9 pages, 13 figures, accepted for publication in A&

Signal estimation in cognitive satellite networks for satellite-based industrial internet of things

Satellite industrial Internet of Things (IIoT) plays an important role in industrial manufactures without requiring the support of terrestrial infrastructures. However, due to the scarcity of spectrum resources, existing satellite frequency bands cannot satisfy the demand of IIoT, which have to explore other available spectrum resources. Cognitive satellite networks are promising technologies and have the potential to alleviate the shortage of spectrum resources and enhance spectrum efficiency by sharing both spectral and spatial degrees of freedom. For effective signal estimations, multiple features of wireless signals are needed at receivers, the transmissions of which may cause considerable overhead. To mitigate the overhead, part of parameters, such as modulation order, constellation type, and signal to noise ratio (SNR), could be obtained at receivers through signal estimation rather than transmissions from transmitters to receivers. In this article, a grid method is utilized to process the constellation map to obtain its equivalent probability density function. Then, binary feature matrix of the probability density function is employed to construct a cost function to estimate the modulation order and constellation type for multiple quadrature amplitude modulation (MQAM) signal. Finally, an improved M 2 M ∞ method is adopted to realize the SNR estimation of MQAM. Simulation results show that the proposed method is able to accurately estimate the modulation order, constellation type, and SNR of MQAM signal, and these features are extremely useful in satellite-based IIoT