adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018A&A...620A.127M (acknowledgments)
Acknowledgements This research received funding from the Hungarian Academy of Sciences through the Lendület grants LP2014-17 and LP2018-7, and the János Bolyai Research Scholarship (L.M. and E.P.); the Hungarian National Research, Development, and Innovation Office through the NKFIH grants K-115709, PD-116175, and PD-121203; and the ÚNKP-17-3 program of the Ministry of Human Capacities of Hungary (Á.L.J.). L.M. has been supported by the Premium Postdoctoral Research Program of the Hungarian Academy of Sciences. This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, some of which participate in the Gaia Multilateral Agreement, which include, for Switzerland, the Swiss State Secretariat for Education, Research and Innovation through the ESA Prodex program, the “Mesures d’accompagnement”, the “Activités Nationales Complémentaires”, and the Swiss National Science Foundation. This paper includes data collected by the Kepler mission. Funding for the Kepler and K2 missions are provided by the NASA Science Mission Directorate. The Kepler/ K2 data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for MAST for non-HST data is provided by the NASA Office of Space Science via grant NNX09AF08G and by other grants and contracts. We thank Carolyn Doherty for proofreading the manuscript, and Benjamin Montet for the fruitful discussions about the f3 code.
Paper: 2018ApJ...855..121V (fulltext)
β is examined. 3.1. Analysis of 100 minute Intervals Some examples of the spectra calculated with Blackman–Tukey and Morlet wavelet methods are shown in Figure 1 . The position of the peak in the signature toward higher frequencies is consistent with both methods. The Morlet method yields a smoother time-averaged spectrum than does the Blackman–Tukey method. Figure 1 (a) is a case where the two methods closely follow one another through the signature. About 30% of all cases have signature peaks within 20% of each other. More cases show a greater difference in magnitudes of the peak value such as those in Figures 1 (b) and (c). The rare case is shown in Figure 1 (d). Only about 4% of cases have a relative difference in peaks that are more than 80%. In no case did the sign of the signatures disagree. Figure 1. Sample magnetic helicity spectra σ m calculated with the Blackman–Tukey (black line) and Morlet wavelet (magenta, gray line) methods. Wind spacecraft data are employed in this figure and all subsequent figures. Intervals evaluated come from 2004 December 1 to 2005 December 31. In this figure, panel (a) shows an interval starting on 2005 January 10 17:47 UTC, (b) 2005 January 3 22:21, (c) 2004 December 29 17:25, and (d) 2005 January 16 18:25. The positions of the peaks in the signatures ν MW and ν BT are close for each interval. The corresponding scatter plot in Figure 2 shows that the data points cluster tightly along the line of equal values. In 3 of 289 intervals, the signature was absent from the Morlet wavelet spectrum (e.g., Figure 1 (d)), so the peak position could not be obtained. Markovskii et al. ( 2016 ) had established for the peak positions determined by the Blackman–Tukey method that the position in wavenumber based on the solar wind speed convecting the structure correlates with an inverse scale based on β 1/2 . Since the Blackman–Tukey method agrees with the Morlet wavelet method with respect to signature position, the correlation with this inverse scale is obtained with either method. Figure 2. Scatter plot of the magnetic helicity signature position calculated with the Blackman–Tukey ( ν BT ) and Morlet wavelet ( ν MW ) methods. The values of the signatures were not equal to each other in each interval. Figure 3 displays the scatter plot of the signature magnitude . The average absolute difference between σ BT and σ MW is 0.07, which can be regarded as an uncertainty in the measured peak value. Nevertheless, statistically, the results of the two methods are not much different. Figure 3. Scatter plot of the magnetic helicity signature magnitude calculated with the Blackman–Tukey ( σ BT ) and Morlet wavelet ( σ MW ) methods. Figure 4 plots the distribution functions of for each method. The distributions show comparable maxima and spreads. A student’s T-test (Trumpler Weaver 1953 ) calculation found that the means of each distribution are very likely the same (probability equals 0.82). The Blackman–Tukey distribution extends
Paper: 2017ApJ...846...85M (acknowledgments)
Comments of an anonymous referee on an earlier version of this paper are gratefully acknowledged. We are indebted to Rocío Márquez from the Scientific Instrumentation center of the University of Granada for providing the SEM images. This work been supported by the Plan Nacional de Astronomía y Astrofísica contracts AYA2015-67152-R and AYA2015-71975-REDT.
Paper: 2019ApJ...872...45S (acknowledgments)
L.S. is indebted to Jiang-Tao Li for the valuable advice and thanks Ning-Xiao Zhang for the technical help. This work is supported by the 973 Program under grants 2017YFA0402600 and 2015CB857100 and the NSFC under grants 11773014, 11633007, and 11851305. Facility: Chandra (ACIS-S). Software: CIAO (Fruscione et al. 2006 ), WVT binning (Cappellari Copin 2003 ; Diehl Statler 2006 ), Xspec (Arnaud 1996 ), SPEX (Kaastra et al. 1996 ), DS9. 12 12 http://ds9.si.edu/site/Home.html
Paper: 2017MNRAS.466.3674N (fulltext)
we note that the results for -ray spectra present a similar feature they are too soft as compared to observations. We compute the -ray spectral index in the energy range 0.87GeV at different Galactic coordinates ( l , b ) and, for each position in the sky, we estimated the difference between the spectral slope of simulated and observed -ray spectra (two examples of observed and simulated -ray spectra can be found in Fig. 7 , dashed and solid lines, respectively). For the observed spectra, the slope has been computed in the same energy range, from Selig etal. ( 2015 ). The results are shown in Fig. 2 , right-hand panel. The open histogram shows the distribution of the difference between the simulated and observed slopes. We limit this investigation to the region of the map at small latitudes, 5 b 5. The average value is around 0.08, indicating that the simulated -ray spectra are on average softer than what inferred from observations. To solve the inconsistency between observations and both the simulated CR spectrum at Earth and the -ray spectra, we consider a model with a harder injection spectrum . The shaded histograms in Fig. 2 show that in this case predictions of the CR slope around the location of the Earth (left-hand panel) and of the map of -ray radiation (right-hand panel) are in better agreement with observational constraints. For this reason, from now on we present and discuss in detail only the results obtained with 2.25. Fig. 2 (left-hand panel) also shows that the predicted CR spectrum at the location of the Earth (thick vertical line) is not representative of the average CR spectral slope (peak of the distribution). While in these examples the difference between the local and average spectral slopes is only 0.05, larger differences can be obtained by slightly changing the location of the arm as compared to the Earth. In this sense, the model is not predictive, and there is degeneracy among the simulation parameters. Although a complete study of the parameter space is beyond the scope of this paper, we outline that the proposed model gives a natural explanation for the increasing evidence that the local CR spectrum might not be representative of the average Galactic one (Ackermann etal. 2012 ; Neronov Malyshev 2015 ). Figure 2. Left-hand panel: the distribution of the simulated CR spectral index, estimated in the range 103000GeV, in the plane Z 0. The open histogram shows the results assuming an injection spectral index 2.3, while the shaded histogram is obtained for a harder injection index, 2.25. In both cases, the diffusion coefficient is proportional to E 0.4 . The vertical blue and grey lines depict the observed spectrum on the Earth, respectively. Right-hand panel: the distribution of the difference between the -ray spectral index in the plane of the sky obtained from simulations and from observations. A small latitude region has been selected: 5 b 5. The two different histograms respectively refer to the two different
Paper: 2019ApJ...871..103G (acknowledgments)
We thank the anonymous referee for helpful comments. We thank the staff of the Keck Observatory for their help in obtaining the observations presented herein. Support for this work at UCLA was provided by the Kaplan Family Student Support Fund and the W. M. Keck Foundation. The data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. Facility: Keck:II (NIRC2). Software: NumPy (van der Walt et al. 2011 ), Astropy (Astropy Collaboration et al. 2013 ), emcee (Foreman-Mackey et al. 2013 ), SciPy (Jones et al. 2001 ), Matplotlib (Hunter 2007 ).
Paper: 2019MNRAS.485.5831L (fulltext)
channels) with a 1 × 1 kernel. 3.3 Architectures The layers of a neural network make up its architecture. Designing a good architecture is difficult, time consuming, and requires expert knowledge. The hyperparameters, those that need to be defined before training, include the number of convolutional filters, filter size (typically 3 × 3 pixels), padding (which defines the margin used when the convolutions are applied), stride (which determines if any pixels are skipped during the convolution), pooling layer size and dropout (a random probability of a neuron being ignored) to name a few. Whilst much research has been done to choose the best hyperparameters (e.g. Simonyan Zisserman 2014 ; Szegedy et al. 2015 ; Murugan 2017 ), in practice it is trial and error, and complexity is added slowly. We use Google’s Tensorflow library 4 and three different architecture models (Table 1 ). Table 1. CNN architecture versions used in this paper. Note . a Taken from Google’s internal training on the ILSVRC-2012-CLS http://www.image-net.org/challenges/LSVRC/2012/ data set using single image crop and may differ from values listed elsewhere. Inception (Szegedy et al. 2015 ) is a state of the art CNN. The performance is often defined by the percentage of correct classifications featuring in the top-1 and top-5 predicted categories of each image. In 2015, Inception_v3 was the first CNN to bypass the average human top-5 error rate of 5 , obtaining a top-5 error of 3.5 on the ILSVRC-2012 data set of 1000 categories. The original Inception-v1 deep convolutional architecture was named GoogLeNet . It was inspired by Lin, Chen Yan ( 2013 )’s Network In Network , and was one of the first CNN architectures to implement modules of parallel operations (inception modules) instead of the traditional sequential stacking. We implement the latest version Inception_v4 (Szegedy et al. 2016 ) which has since been refined to include batch-normalization, additional factorization ideas, more inception modules, and a more simplified uniform architecture. Another CNN we use is NASNet-A-large (Zoph et al. 2017 ). Nasnet is an architecture that was created by a controller neural network Neural Architecture Search (NAS) . With a small data set, NAS uses a recurrent neural network (RNN; Zoph Le 2016 ) and reinforcement learning to continuously propose improvements to the architecture. Since the use of NAS on larger data sets would be computationally expensive, it was applied to CIFAR-10, a data set of 8 × 10 7 small images in 10 classes to search for an optimal but scalable convolutional cell. Using a larger data set Imagenet 2012 5 which consists of ∼1.4 × 10 7 images and 1000 classes, NASNet-A-large retrained the weights of its own repetitions of these cells and filters in the penultimate layer. The final architecture consists of 18 repeated cells with 168 convolutional filters per cell. NASNet-a-large exceeds the performance of any human-designed model to date. The last architecture we
Paper: 2015ApJ...807..167T (acknowledgments)
We acknowledge financial support from the ASI-INAF grant I/037/12/0. This work was supported under NASA Contract No. NNG08FD60C and made use of data from the NuSTAR mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by NASA. We thank the NuSTAR Operations, Software and Calibration teams for support with the execution and analysis of these observations. We also thank the Swift team for quickly approving and executing the requested ToO observations. This research has made use of the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (Caltech, USA). The Fermi -LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT, as well as scientific data analysis. These include the National Aeronautics and Space Administration and the Department of Energy in the United States; the Commissariat à l’Energie Atomique and the Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules in France; the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy; the Ministry of Education, Culture, Sports, Science and Technology (MEXT), High Energy Accelerator Research Organization (KEK), and Japan Aerospace Exploration Agency (JAXA) in Japan; and the K. A. Wallenberg Foundation, the Swedish Research Council, and the Swedish National Space Board in Sweden. Additional support for science analysis during the operations phase is gratefully acknowledged from the Istituto Nazionale di Astrofisica in Italy and the Centre National d’Études Spatiales in France. Part of this work is based on archival data, software, or online services provided by the ASI Data Center (ASDC).
Paper: 2015A&A...580A.103G (acknowledgments)
The authors acknowledge financial support from the Programme National de Physique Stellaire (PNPS) of CNRS/ INSU and from the Agence Nationale pour la Recherche (ANR) through the IMAGINE project. T.G. is supported by the Special Priority Program 1488 PlanetMag of the German Science Foundation. The authors wish to thank Sébastien Deheuvels for providing us with a density profile from a stellar evolutionary model.
Paper: 2016MNRAS.460L.104L (fulltext)
CMB temperature maps for the low multipole l from other independent observations? If the answer is yes, then the reconstructed temperature map may be used to determine whether the anomalies are primordial or systematic. CMB polarization is one of the candidates. Due to the poor correlation coefficients (less than 0.5 for multipole l = 2 and l = 3), however, the reconstruction from only CMB polarization cannot achieve our goal. The scattering of CMB photons in clusters of galaxies may shed light on this attempt. The scattering of CMB photons in clusters of galaxies induces a polarization signal, which is determined by the quadrupole anisotropy in the photon distribution at the cluster location (Sazonov Sunyaev 1999 ). Therefore, the remote quadrupole in distant clusters, in principle, is investigable by the measurements of this induced polarization signal. The measurements of these signals were originally proposed to suppress the cosmic variance uncertainty (Kamionkowski Loeb 1997 ; Portsmouth 2004 ; Bunn 2006 ). Moreover, the magnitude of these signals gives some clues about the evolution of the CMB quadrupole to probe the dark energy. Of particular interest here is that it probes three-dimensional information of potential fluctuations around our last scattering surface (Seto Sasaki 2000 ). Even though this induced polarization in galaxy clusters has not been observed, its detection contains rich information for the study of cosmology. In this Letter, we propose an independent observation for the study of the CMB anomalies. We explore a practically useful cosmological probe from the measurements of remote quadrupole: the reconstruction of CMB temperature anisotropies for low multipole. Provided by the strong correlation of the remote quadrupole in low redshifts with the local CMB (Hall Challinor 2014 ), the reconstructed CMB temperature anisotropies are accurate enough for distinguishing the sources of low-quadrupole and axis-of-evil problems. 2 SIMULATIONS OF CMB SKY AND CMB INDUCED POLARIZATION IN DISTANT GALAXY CLUSTERS The CMB temperature anisotropies and E -mode polarization from a single plane wave can be written as (Ma Bertschinger 1995 ) where P l is Legendre polynomial, Δ Xl is the transfer function with X = T, E for temperature anisotropies and E -mode polarization, respectively. To compute the spherical harmonics coefficients of the temperature anisotropy and E -mode polarization a X,lm and some derivations later, we need the additional theorem of the spin-weighted spherical harmonics (Ng Liu 1999 ) where α, β and γ are the Euler angles as being composed of a rotation α around , followed by β around the new and finally γ around . Then, the spherical harmonics coefficients are On the other hand, the polarization effect in the distant clusters of galaxies arises from the presence of the quadrupole component of the CMB in the rest frame of a cluster. For a cluster located in the direction, the primordial CMB quadrupole induced
Paper: 2017MNRAS.464..231R (acknowledgments)
This work has made use of data obtained at the Thai National Observatory on Doi Inthanon, operated by NARIT. We are grateful to Dr W.J. Tango for providing the data base of limb-darkening corrections and an anonymous referee for valuable comments and references. AR acknowledges support from the ESO Scientific Visitor Programme .
Paper: 2016A&A...589A..75M (acknowledgments)
We thank Flavien Kiefer, who worked with us at the beginning of the project. The invaluable advice of Shay Zucker about the analysis and the careful reading of the manuscript and very thoughtful comments of Arieh Konigl, Titos Matsakos, and Daniel C. Fabrycky are much appreciated. This research has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant-agreement numbers 291352 (ERC).
Paper: 2015A&A...579A..95K (acknowledgments)
We thank the anonymous referee for useful comments that substantially improved this paper. Support for H.K., S.G.G., M.H., T.d.J., and C.P.G. is provided by the Ministry of Economy, Development, and Tourism’s Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics, MAS. H.K. and S.G.G. acknowledge support by CONICYT through FONDECYT grants 3140563 and 3130680, respectively. H.K. also acknowledges helpful support from the staffs of Cerro Pachón and NOAO South during the observing run. K.M. acknowledges financial support by Grant-in-Aid for Scientific Research (No. 23740141 and 26800100) from the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT). The work by K.M. is partly supported by WPI Initiative, MEXT, Japan. E.Y.H. acknowledges the generous support provided by the Danish Agency for Science and Technology and Innovation through a Sapere Aude Level 2 grant. This research made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Paper: 2015A&A...581A.101M (acknowledgments)
We are grateful to the anonymous referee for his/her careful reading and helpful and constructive comments that improved the paper. N.M. thanks the École Normale Supérieure de Cachan and the Laboratoire AstroParticle et Cosmologie for financial support in the early stage of this work. B.S. acknowledges financial support from MIUR PRIN2010-2011 (J91J12000450001), from the PRIN-MIUR 201278X4FL grant, from a PRIN-INAF/2012 Grant, from the inDark INFN Grant and from the Consorzio per la Fisica di Trieste. A portion of the research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
Paper: 2019MNRAS.487.1729L (fulltext)
and FRI classes. However, it performs worse overall for the FRIIs, as shown in Table 9 . This may be due to the more complex decoder confusing radio emission from the FRIIs with noise. Table 9. The average metrics (in percentages) across each of the classes in the original LOFAR data set, for the inc. decoder CapsNet model. Five runs were done in total, using 600 samples in the test set. As the default CapsNet model performs better overall compared to the other two CapsNet models, it is chosen as the basis of comparison against the two ConvNet models across the original fits and masked 4rms sigma-clipped data sets. The default CapsNet model still performs significantly worse compared to the two ConvNets, as it is beyond both their 95 per cent confidence intervals, across all metrics. The variability in metrics is higher for the original data set compared to that of the two ConvNets, as is evident in the generally increased confidence intervals of the CapsNet model, in Table 7 , particularly for the FRIIs. Fig. 8 shows the Receiver Operating Characteristic (ROC) curves across the default capsule network and ConvNet-4. ROC curves plot the true positive rate (recall) against the false positive rate. Figure 8. ROC curves for both a single run with the default CapsNet model and the ConvNet-4 model. The curves show that ConvNet-4 outperforms the default CapsNet across all the classes. In a first attempt to use the default CapsNet model (containing 58M free parameters), we observed a clear overfitting, owing to the large number of free parameters compared to the number of training images. Despite this, the model still achieved very similar results to the models using many fewer parameters quoted in this work. Table 10 shows the labels and prediction vector for some sources that the Capsule network could not reliably classify , as probabilities higher than 0.5 across two classes were attained. Table 10. The labels and corresponding probability vector of the default CapsNet network predictions, using four examples of sources shown in Fig. 10 , having probabilities greater than 0.5 across two classes. 5.2 LOFAR original and augmented images We augmented the images with translation, rotation, and flipping as outlined in Section 3.2 , keeping the distribution of FRI and FRII sources the same as in the original data set. Table 1 gives the number of original and augmented images. There are again 79 per cent and 21 per cent of the original samples used in training and testing, respectively. 5.2.1 ConvNet-4 and ConvNet-8 We applied both ConvNet-4 and ConvNet-8 models to the original and augmented data set, with the results shown in Tables 5 and 6 . The overall metrics are significantly better (Avg. Recall = 93.4 and 94.3) than was observed when the same model was used on the original images (Avg. Recall = 88.7 and 91.2 for ConvNet-4 and ConvNet-8, respectively), therefore both models benefit from data augmentation. The confidence intervals are also