adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021A&A...652A..13J (fulltext)
Convolutional neural networks take advantage of the typically high correlation of local groups (e.g., adjacent pixels in an image) and hierarchically extract features (edges → motifs → parts → objects; LeCun et al. 2015 ). With this, deep CNNs extract, by layer-wise transformations, the feature representation of an image that serves as a basis to, for example, classify images ( Armstrong Fletcher 2019 ), predict sequences ( Upendran et al. 2020 ), transform images to different domains ( Kim et al. 2019 ; Jarolim et al. 2020 ), or provide segmentation maps ( Long et al. 2015 ). From deep learning applications for semantic segmentation, the concept of multi-scale architectures has proven to be most successful ( Ronneberger et al. 2015 ; Chen et al. 2018 ; Badrinarayanan et al. 2017 ). Here the spatial dimensions are successively reduced, while the depth of the network (number of filters) is being increased. With this, the neural network can correlate features of different scales at each resolution level and can solve more complex tasks with the increased number of filters (greater number of parameters). In order to correlate features across the full image, a sufficient network depth is required. For semantic segmentation, the output is a pixel-wise classification of the input image (segmentation map). Therefore, the feature representation (low spatial resolution) needs to be matched to the size of the input image. The segmentation map can be directly obtained from the deepest layer of the neural network (e.g., Long et al. 2015 ), but with this approach the output typically lacks spatial detail. An alternative approach, which accounts for global relations and spatial resolution simultaneously, is the use of an encoder-decoder architecture ( Ronneberger et al. 2015 ; Chen et al. 2018 ; Badrinarayanan et al. 2017 ). Here, the image is first transferred into a feature representation over multiple scales by the encoder, and then upsampled to the original resolution over the same number of scales by the decoder. At each scale, skip connections are used to combine the upsampled features, which comprise the full context information, with the spatial details from the encoder. A common problem with deep networks is the vanishing gradient problem, where the parameter optimization of the neural network becomes inefficient with increasing network depth ( He et al. 2016 ; Szegedy et al. 2015 ). In addition, when dealing with high resolutions, the training time increases exponentially, which often results in long convergence times for model training. A possible solution to this problem was developed by Karras et al. (2017) for the application of image generation. Here the authors used an approach where the size of the network is progressively increased. The advantage of this method is that the training with low-resolution samples is computationally efficient and allows the deepest layers to be trained until full convergence is reached, before introducing
Paper: 2019A&A...625A..93O (acknowledgments)
Acknowledgements We thank the anonymous referee for useful comments that greatly improved the quality of this paper. This scientific work makes use of the Murchison Radio-astronomy Observatory, operated by CSIRO. We acknowledge the Wajarri Yamatji people as the traditional owners of the Observatory site. Support for the operation of the MWA is provided by the Australian Government (NCRIS), under a contract to Curtin University administered by Astronomy Australia Limited. We acknowledge the Pawsey Supercomputing Centre, which is supported by the Western Australian and Australian Governments. This work is part of the project 176005 “Emission nebulae: structure and evolution” supported by the Ministry of Education, Science, and Technological Development of the Republic of Serbia. T.G.P. would like to thank Dirk Grupe (Morehead State University), Aviv Brokman (University of Kentucky) and Eric Roemmele (University of Kentucky) for useful discussions about Cash statistics.
Paper: 2016ApJ...824...91L (fulltext)
employ the same beta cooling prescription with , and determine the fragmentation boundary, , as a function of the adiabatic index γ . Table 1 shows agreement between and . The match is remarkable, especially with the global 3D simulations of Rice et al. ( 2005 ), since Equation ( 54 ) is derived for 2D disks in the local limit. Table 1 Characteristic Cooling Times as a Function of γ 4.1.2. <inline-formula> <tex-math> </tex-math> <inline-graphic xlink:href="apj523559ieqn106.gif"></inline-graphic> </inline-formula> Standard beta cooling with corresponds to “thermal relaxation”: the temperature is restored to its initial value over the cooling time (Lin Youdin 2015 ; Mohandas Pessah 2015 ). In this case no additional heat source need be invoked to define an inviscid steady state. From Equation ( 47 ), the instability condition is . This is the same as the classic Toomre condition for an isothermal disk (which may be obtained from Equation ( 46 ) by taking ). In this respect, a fully irradiated disk, in which the equilibrium temperature is set externally, behaves isothermally regardless of the cooling time (Gammie 2001 ; Johnson Gammie 2003 ). 4.2. Viscous Disk We now consider a viscous disk with parameters in our adopted viscosity law, Equation ( 15 ). In the 2D case this implies is constant. We check in Appendix B that our dispersion relation reduces to previous results for viscous GI in the isothermal limit (by taking ). It is useful to consider several limiting cases. To see the effect of cooling and irradiation, we simplify the dispersion relation, Equation ( 45 ), by assuming . Then for we findwhich coincides with Gammie's Equation ( 18 ) for vanishing wavenumber. For we findFor a rough measure of the maximum growth rate can be obtained by equating Equations ( 55 ) and ( 56 ). 5 5 If θ is not small and/or Q is large then one may just use Equation ( 55 ) to maximize S over K , see the curve in the bottom panel of Figure 2 . This exercise yields To compute growth rates numerically, we consider a model with , and given by thermal equilibrium (Equation ( 24 )). Furthermore, we relate the strength of self-gravity and viscosity byto mimic a gravito-turbulent basic state, where one might expect the dimensionless stress (Lin Pringle 1987 ). Figure 2 shows growth rates as a function of the wavenumber obtained from the dispersion relation Equation ( 45 ). The limiting behavior for small/large K are well-captured by Equations ( 55 ) and ( 56 ). Comparing the two panels shows that increasing the irradiation level ( θ ) suppresses small-scale perturbations. Figure 2. Growth rates for the 2D viscous problem as a function of the radial wavenumber, k , for a range of cooling times, β . The dashed and dotted lines correspond to asymptotic behaviors for small and large k , respectively, computed from Equations ( 55 ) and ( 56 ). Top: without a floor temperature (); bottom: with a floor temperature set to of the equilibrium temperature. Figure 3 shows the
Paper: 2018MNRAS.479.3155H (acknowledgments)
ACKNOWLEDGEMENTS We thank Liantong Luo, Doron Kushnir, Subo Dong, Andrew Gould, Silvia Toonen, and Hagai Perets for useful discussions. We thank the anonymous referee for useful comments. This research was supported by the ICORE Program (1829/12) and the Beracha Foundation. The computations were performed with a high-performance computing facility which is partly supported by the Israel Atomic Energy Commission – The Council for Higher Education – Pazi Foundation and partly by a research grant from The Abramson Family Center for Young Scientists.
Paper: 2017MNRAS.472.1465P (acknowledgments)
Acknowledgements This work has been partially supported through financial contribution from PRIN INAF 2014. The results are based on observations obtained with XMM–Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA, and on data obtained from the HEASARC archive. PE acknowledges funding in the framework of the NWO Vidi award A.2320.0076. EC acknowledges support from a VIDI grant from the Netherlands Organisation for Scientific Research (NWO). We thank Daniele Viganò who developed part of the software used in this work.
Paper: 2019MNRAS.489.3905S (fulltext)
of 18.5arcsec. This procedure was also applied to the E relic, but since the emission of the NAT is extended, it is impossible to properly separate the contribution of the tail from the relic. Hence, we decided not to subtract the radio galaxy. This choice allows us to study the spectral index behaviour from the core of the NAT to the lobes, and to investigate its connection with the diffuse source. We computed the average flux density and spectral index of the E relic (and of the NAT radio galaxy) and of the W relic and the radio halo emission (sources subtracted) between 1.5 and 3GHz. The values are reported in Table 3 . The uncertainties on the flux density measurements are computed as where is the calibration error, is the rms noise listed in Table 2 and n beam is the number of beams in the sampled region. These uncertainties were then propagated to the spectral index. Table 3. Flux density, spectral index between 1.5 and 3GHz and radio power of extended sources measured from the images used for the spectral index study. We separated the region of the W relic and the halo on the basis of the spectral index profile as described in Section 4.2.2 . The stands for point-like sources subtracted. We extrapolated the radio power at 1.4GHz from the 1.5GHz flux density measurement, considering a luminosity distance D L 1253.3 Mpc (Wright 2006 ) and using the spectral indices for the k -correction. The uncertainties on flux densities were propagated to the radio power. The sum of the radio power of the two relics (sources subtracted) is consistent with the relation found by de Gasperin etal. ( 2014 ) between the radio power of double radio relics and the cluster mass. Finally, we computed the spectral index for each pixel with value 3 in both frequency bands. We show the spectral index map of the E relic in Fig. 5 . We propagated the uncertainties on the flux densities pixel by pixel on the spectral index. The error map of the spectral index for the E relic is shown in the bottom panel of Fig. 5 , while the spectral index image of the western region and its error map are shown in Fig. 6 . Figure 5. Top panel: Spectral index image of the E relic region between 1.5 and 3GHz. The 1.5GHz total intensity contours (BCD configuration) are overlaid in black: the levels start at 3, with 0.04 mJy beam 1 , and are separated by a factor of 2. Bottom panel: Error map of the spectral index image with the same contours overlaid. The restoring beam of the two images is shown in the left-hand corner and its size is 18.5arcsec 18.5arcsec. Figure 6. Top panel: Spectral index image of the W relic and halo region computed between 1.5 and 3GHz. Point-like sources were subtracted. The two dashed red polygons mark the flux density extraction regions for the halo and the West relic. Bottom panel: Error map of the spectral index image. Contours and restoring beam are as in Fig. 5 . Spectral index images obtained from interferometric data should be treated with
Paper: 2018MNRAS.481.1645M (acknowledgments)
ACKNOWLEDGEMENTS We thank the anonymous referee for thoughtful comments. IM acknowledges support by the Deutsche Forschungsgemeinschaft under the grant MI 2009/1-1, as well as support by Observatoire de la Côte d’Azur and Université Côte d’Azur for a one-month visit in 2017, where this project was conceived. ARB and PdL acknowledge financial support from the ANR 14-CE33-014-01. CC acknowledges support from DFG Grant CH1188/2-1 and from the ChETEC COST Action (CA16117), supported by COST (European Cooperation in Science and Technology). VA acknowledges the supported by Fundação para a Ciência e Tecnologia (FCT) through national funds (ref. PTDC/FIS-AST/7073/2014 and ref. PTDC/FIS-AST/1526/2014) and by FEDER through COMPETE2020 (ref. POCI-01-0145-FEDER-016880 and ref. POCI-01-0145-FEDER-016886). VA also acknowledge the support from FCT through Investigador FCT contract of reference IF/00650/2015/CP1273/CT0001. GC acknowledges financial support from the European Union Horizon 2020 research and innovation programme under the Marie Marie Skłodowska-Curie grant agreement no. 664931.
Paper: 2015ApJ...800....8M (acknowledgments)
This research was supported via award numbers GO1–12025A and GO3–14019A to Rochester Institute of Technology and GO3–14019B to Vanderbilt University issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060 (RIT). R.S.'s contribution to the research described here was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, and supported via an award issued by the Chandra X-Ray Observatory Center. This research has made use of data obtained from the Chandra Data Archive and the Chandra Source Catalog, and software provided by the Chandra X-ray Center (CXC) in the application packages CIAO. This research has made use of data and/or software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. E.B. acknowledges support from an IBM-Einstein Fellowship at IAS, and a Simons Foundation Fellowship. J.A.T. and M.A.G. are supported by the Spanish MICINN grant AYA 2011-29754-C03-02 co-funded with FEDER funds.
Paper: 2020MNRAS.497..246G (fulltext)
fit the absorption trough. In Fig. 8 , it is clear that the P Cygni model profile matches the observed spectrum in this region. The model profile is unable to reproduce the emission region of the observed spectrum. This is a result of the feature not being produced purely by scattering; clearly the feature is in net emission, and as a result we underestimate the amount of flux in this region. However, this is not important, as we are only interested in obtaining velocity ranges for the line-forming region. We obtained a minimum ejecta velocity, v phot ∼ 20 000 , and a maximum ejecta velocity, v max ∼ 26 000 . These velocity values are, surprisingly, ∼2 times higher than those obtained from our tardis models. This is quite strong evidence that there is an additional high-velocity component of the ejecta that is not observed in the early, photospheric spectra. The computation for the P Cygni Ca ii line produces a velocity from the absorption trough that is fairly robust, as is the velocity estimates from our tardis models. Therefore, we believe the difference to be a real physical effect, and is an indication of additional high-velocity material, likely of low mass, which has been ejected beyond the emitting photospheric region (which is travelling at ∼12 000–14 500 ). We note that the level of the observed continuum to the red of the P Cygni peak falls significantly below that on the blue side. We attribute this to some oversubtraction of the host galaxy flux model as described in Appendix A , and do not attach any line forming significance to the discrepancy. Our best-fitting model required a Sobolev optical depth, τ ∼ 1.3, which tells us that the P Cygni feature originates from a moderately optically thick region. Even though the +7 d spectrum appears to be nebular, the region forming the P Cygni feature is optically thick, at least for the wavelengths of the Ca ii triplet. The optical depth for these lines in all three of our tardis models is 1. Hence, the envelope is optically thin at these wavelengths in our tardis models. However, the optical depth in the tardis models becomes progressively larger with time. Since a P Cygni line requires an optically thick transition, we have a consistent picture of the Ca ii triplet line being optically thin at early epochs (probably due to high temperature), after which the material develops an optical depth around τ ∼ 1.3, providing the opacity for the absorption trough to form. We will see the next spectrum is consistent with the idea that the opacity in this line has dropped further, due to expansion, and a pure emission component has formed. 4.2 Interpreting the +14 d spectrum In the +14 d spectrum, we observe the Ca ii feature to be purely in emission. We fit a Gaussian profile to the emission feature, to further estimate the velocity of the ejecta material. By assuming the feature is solely produced by the three transitions of the Ca ii triplet, we can estimate the ejecta velocity, and the
Paper: 2016A&A...589A.109C (acknowledgments)
Authors thank the Centre National d’Études Spatiales for its financial support. We thank Laura Portinari for very useful discussions. We thank the referee for his helpful report and his comments.
Paper: 2019MNRAS.486.4304S (fulltext)
object. This is because we need it for the de-blending process, but it is excluded from the rest of the analysis. For the rest of the region, we had to use a different tracer. Good examples are either 24 m (e.g. Marsden et al. 2009 ; Pascale et al. 2009 ; Elbaz et al. 2010 ; Béthermin et al. 2012 ) or radio (e.g. Ivison et al. 2010 ; Magnelli et al. 2010 ; Basu et al. 2015 ; Rujopakarn et al. 2016 ; Delhaize et al. 2017 ). Even though an MIPS 24 m map was available, the beam size is still large (∼6 arcsec) and galaxies will still be blended. Instead we used the 10 cm (3 GHz) JVLA map as our tracer, as it has resolution comparable to that of our ALMA map. We identified 319 significant sources in the radio map (within the same area of the SCUBA-2 maps), and combine these with the 12 ALMA sources. These sources were then passed through XID+ to obtain fluxes for both the PACS and SPIRE maps. A specially modified version of XID+ was used to work with the SCUBA-2 maps, so we also have de-blended fluxes for both 450 and 850 m. 3.3 Association with radio sources We then went about associating each of our SCUBA-2 sources with at least one radio source (and therefore can associate with optical and NIR sources). To determine the most likely radio source we use the standard Poissonian probability of positional match similar to the method outlined in Downes et al. ( 1986 ). This value P gives us the probability of a source not being associated with the SCUBA-2 source and is given by where n is the number density of sources in the radio map, and θ is the separation between the SCUBA-2 source and the radio source. We found all radio sources within 15 arcsec of the SCUBA-2 source, and calculated the P value for all of them. For galaxies that had similar P values we picked the source that was reddest in the IRAC bands, as it has been shown that sub-mm galaxies tend to be redder at these wavelengths (e.g. Ashby et al. 2006 ; Yun et al. 2008 ; Hainline et al. 2009 ). We did not worry about the central source (source 850_1) as we use the ALMA sources from C18 , which have been shown to have association with optical and NIR sources. Using the prescriptions laid out in Ivison et al. ( 2002 ), Chapin et al. ( 2009 ), and Chen et al. ( 2016 ), we considered a reliable match to have a P ≤ 0.05 and a tentative match to have 0.05 ≤ P ≤ 0.1. Anything with a P > 0.1 is considered an untrustworthy match and removed. We found that all but two sources had either reliable or tentative matches, with 850_12 and 850_19 having sources too far to be considered significant. Even though 850_18 and 850_28 had low P values, in the optical and NIR images they were obscured by nearby stars and hence removed from the sample. Finally 850_30 was removed because there was no radio source within 15 arcsec. Applying these cuts we ended up with 37 sources that we can calculate redshifts for, and determine if they are likely to be within the cluster or not. 4 REDSHIFT DETERMINATION To
Paper: 2019ApJ...880..135X (fulltext)
engine half-opening angle θ 0 = 0.2 (denoted as SJ0.2-EH). The jet engine parameter values for these three models are listed in Table 3 . Table 3 Jet Engine Parameters Note. L 0 and τ 0 represent jet engine power and average jet engine duration, respectively. The energy-to-mass ratio η 0 and injected Lorentz factor γ 0 are set to the same value for all of the jet engine models. θ 0 is the half-opening angle of the injected jet engines. To better interpret the role of each fluid component, we use three passive scalars X i to track the mass fraction of each individual component filling the cells. The subscript i labels each individual component: 1 for a stellar progenitor, 2 for ISM material, and 3 for jet engine material. Initially, X 1 ( X 2 ) is set to 1(0) inside the progenitor and 0(1) in the ISM. Three auxiliary equations are solved accordingly (Duffell MacFadyen 2013 ):The conserved mass for each component () is updated based on the density flux through the cell boundary and the addition of source term (for the injection of jet engine material). Dividing the individual conserved mass M i by the total conserved mass () gives us the new primitive passive scalar X i . 4.2. Dynamical Details of the FTD Jet Simulation 4.2.1. Launch of the Jet Engine Figure 6 shows the early evolution of the jet engine flow upon injection. The ram pressure generates a bow shock. Dense stellar material gets pushed to the side, forming a cocoon that confines the engine outflow. A high-density wedge of stellar material develops at the head of the jet. It shreds jet engine materials from the head. These engine materials curl back, forming vortexes. These vortexes then detach from the jet and get swept backward relative to jet propagation. This vortex shedding phenomena is, generally speaking, similar to that found in previous simulations (Scheck et al. 2002 ; Mizuta et al. 2004 ; Morsony et al. 2007 ). The speed of the jet head keeps increasing and soon exceeds the local sound speed (at the time t = 1.5s, the Lorentz factor of the head of the jet is ∼2, as shown in Figure 6 ). The backflow becomes quasi-straight to the main jet (Mizuta et al. 2010 ). At early times, the bow shock has a narrow head and a wide tail. When it approaches the surface, the jet head expands in the low-density envelope, as shown in the next subsection (see also, e.g., Zhang et al. 2003 , 2004b ). We use spherical coordinate to conduct simulations. As jets propagate outward, the width of the cell increases. The features of the inner part of the jet (close to pole) may not be fully resolved. We resolve the relativistic shell in the radial direction via the previously described AMR scheme. As the ultra-relativistic jet shell penetrates the progenitor, the stellar material that lies on top of the jet easily gets pushed aside. No strong “plug” instability has been seen (Lazzati et al. 2010 ; Mizuta Ioka 2013 ; Gottlieb et al. 2018a ; Xie et al. 2018 ). Figure 6. Early time snapshots of jet