adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...869...82R (fulltext)
12 of the exposures do not approach the “saturation” threshold (blue dots), while 14 of the exposures have one pixel with a significant nonlinearity correction applied (red dots). Aperture photometry of the individual HST -calibrated FLT files shows a a bimodal pattern with a systematic difference of 0.2 mag. The bimodality is a result of the dither pattern: in the exposures with a correction applied, the PSF of the star is centered perfectly on a single pixel while in the other exposures the center falls in a gap between pixels, lowering the peak flux in any one pixel below the nonlinearity threshold. Figure 1. Aperture photometry of the SNAP observations of XZ Cyg. Each point corresponds to a single frame, points are color-coded based on their dither position. The ∼0.2 mag difference caused by the dither pattern is a result of an overcorrection for nonlinearity in a single bright pixel at dither point a. Although the stars are well isolated, the presence of significantly nonlinear pixels without a proper correction means that we cannot apply simple aperture photometry to our direct SNAP and GO observations. Our solution to this situation is to mask pixels in frames with poor nonlinearity corrections and then undertake PSF photometry. 3.1. Direct Imaging Photometry Photometry was carried out with DAOPHOT (Stetson 1987 , 1994 ) on all individual direct imaging “FLT” frames with the HST Pixel Area Map correction applied. Because we cannot generate a unique PSF for each frame, we used DAOPHOT to generate and apply one PSF for use in the 64 × 64 images and then generated a separate PSF for the 128 × 128 images. To generate the PSF, we used stars that are “clean” (i.e., images that do not have pixels that approach the nonlinear regime): 156 images for the 64 × 64 subarray and 208 for the 128 × 128 subarray or 26 images of six and eight stars each, respectively. To determine which parameters to assign to our model PSF, we generated several thousand individual PSF models with PSF radius and fitting radius varied in integer pixel increments from 2 to 17 and the seven different PSF functional forms available in DAOPHOT. We then compared the magnitude derived from each model PSF for each star with aperture photometry and chose PSF parameters that, on average, best reproduced the aperture magnitudes. We assigned a PSF radius of 14 pixels to the 64 × 64 PSF model and 17 pixels for the 128 × 128 PSF model and used a fitting radius of 12 pixels and a Moffat function with β = 3.5 for both PSF models. We also use the “clean” images to test the effect of pixel masking on our PSF photometry. We chose the brightest pixel in each image, masked it, and redetermined the magnitude using the same range of PSF parameter values. Regardless of the parameters used, masking pixels did not significantly impact DAOPHOT PSF photometry measurements. When masking stars in our 64 × 64 images, we found a systematic offset of −0.0017 ± 0.0022 mag, and for 128 × 128 images we
Paper: 2019ApJ...877...74M (acknowledgments)
We acknowledge support from INAF under PRIN SKA/CTA FORECaST and PRIN SKA-CTA-INAF ASTRI/CTA Data Challenge. F.S. acknowledges partial support from the H2020 AHEAD project (grant agreement No. 654215) and a Leverhulme Trust Research Fellowship. E.P. acknowledges financial support from ASI and INAF under contract 2017-14-H.0 ASI-INAF. We thank the referee for the constructive comments that significantly helped to improve the paper.
Paper: 2017ApJ...844...98M (fulltext)
to Bolatto et al. ( 2003 ). This ratio of 0.9 is consistent with the massive star-forming region in the MW (e.g., Nishimura et al. 2015 ). For source A, of and of 186 K km s −1 pc 2 yield of cm −2 (K km s −1 ) −1 . In addition, we obtained a similar from source B, cm −2 (K km s −1 ) −1 , from of and of 81 K km s −1 pc 2 . The averaged of cm −2 (K km s −1 ) −1 is four times higher than that in the MW disk, which is in good agreement with that in N83 obtained by Bolatto et al. ( 2003 ), (7.0 ± 3.4) cm −2 (K km s −1 ) −1 . 4.2. Properties of Molecular Gas and Star Formation in N83C We summarize the properties of the molecular gas based on our ALMA observations and discuss their relation to star formation in N83C. The derived gas density of cm −3 is significantly higher than the typical gas density of cm −3 in the Orion A/B molecular clouds (Nishimura et al. 2015 ), even though both gas densities are derived from the same molecular line, 12 CO(). Such a difference in derived gas densities is presumably due to the low metallicity in the SMC. The low metallicity corresponds to the small-dust content in molecular clouds, which causes the reduction of the shielding against far-UV radiation. In this condition, external far-UV radiation from the ii region associated with N83C easily penetrates and photodissociates the outer layer of the 12 CO molecule in the molecular clouds. Thus we can only observe the innermost parts of the molecular clouds, whose gas density is cm −3 , even in 12 CO emission. It is to be noted that theoretical simulations examined by Glover Clark ( 2012 ) support our results: their simulations suggest that diffuse CO emission becomes unobservable, but dense gas continues to produce observable CO emission as metallicity decreases. The observational fact that even 12 CO emission traces the high-density ( cm −3 ) regions in N83C just corresponds to this situation. Then, we consider the meaning of the derived kinetic temperature of molecular gas, 30–50 K. Bolatto et al. ( 2003 ) suggested that warm (∼40 K) clumps exist in some regions of N83 whose 12 CO()/ 12 CO() ratios exceed 2. Although the 12 CO()/ 12 CO() ratio of 0.9 in N83C is not so high (Bolatto et al. 2003 , 2005 ), it is possible that the global gas temperature exceeds 30 K in N83C considering that the bright ii region, which is a likely heating source, spreads over N83. In addition, the kinetic temperature of molecular gas is consistent with the dust temperature of 35 K (Takekoshi et al. 2017 ). Recent observational studies in the MW can also support our results: Nishimura et al. ( 2015 ) reported that the kinetic temperature in Orion A/B molecular clouds is 20–50 K, which is similar to that in the N83C, although the typical temperature of molecular clouds in the MW is 10–20 K. Considering that both N83C and Orion A/B molecular clouds are associated with bright ii regions, it is suggested that their temperature increase is due to the heating by far-UV radiation. However,
Paper: 2016MNRAS.461.2250W (acknowledgments)
The authors thank the anonymous referee for his/her useful comments. JLW acknowledges financial support from the Science and Technology Facilities Council of the UK (STFC) via the PhD studentship programme. This paper made use of information from the Red MSX Source survey data base at http://rms.leeds.ac.uk/cgi-bin/public/RMS_DATABASE.cgi which was constructed with support from STFC. This research has made use of the SIMBAD data base, operated at CDS, Strasbourg, France.
Paper: 2016ApJ...821...38S (acknowledgments)
We thank Alex Heger for the data of his Z9.6 progenitor model, his numerous contributions in developing the KEPLER code,and many insights into the topics of this paper. The help of Feryal zel withcomparing our neutron star masses with observational data and of Gabriel Martnez-Pinedo with our analysis of 19 F production is also appreciated. T.E. and H.-T.J. thank L.Hdepohl, T.Melson, M.Ugliano, and A.Wongwathanarat for numerical support and R.Hix and F.-K. Thielemann for providing the NSE solver introduced into P-HOTB by K.Kifonidis. This work was supported by NASA (NNX14AH34G) and the UC Office of the President (12-LR-237070). At Garching, funding by Deutsche Forschungsgemeinschaft through grant EXC153 Excellence Cluster Universe and the European Research Council through grant ERC-AdG No.341157-COCO2CASA is acknowledged.
Paper: 2017MNRAS.471....2P (acknowledgments)
Acknowledgements This project is funded by DST INSPIRE Faculty [IFA-12/PH-44](Department of Science and Technology (DST), Govt. of India) and DST-SERB Fast track scheme [SR/FTP/PS-118/2011] (Science and Engineering Board (SERB), Govt. of India). We are thankful to the Inter-University Centre for Astronomy and Astrophysics (IUCAA) for providing the HPC facility. RSJ would like to thank IUCAA for providing the research facilities as a visiting student. Computations described in this work were performed using the publicly available enzo code ( http://enzo-project.org ) and data analysis is done with the yt-tools ( http://yt-project.org/ ). Authors would also like to thank the editor and the anonymous referee for their critical comments that have definitely helped in improving the content of this paper.
Paper: 2017MNRAS.469L..36Y (acknowledgments)
Acknowledgments We thank Biping Gong, Wei Xie and Wei Chen for helpful discussions and Likang Zhou and Senlin Yu for language polishing. This work is supported by the National Basic Research Program of China (973 Program, Grant No. 2014CB845800) and by the National Natural Science Foundation of China (Grants No. U1231101 and U1431124).
Paper: 2017ApJ...847...54C (acknowledgments)
A.C. and N.T.P. acknowledge support from the National Science Foundation under CAREER Grant No. 1455090. A.C. and N.T.P. also acknowledge partial support from the Swift Cycle 12 GI program (Grant No. NNX17AF93G). Support for this work was in part provided by the National Aeronautics and Space Administration through Chandra Award No. 19500451 issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of the National Aeronautics Space Administration under contract NAS8-03060. This work was also supported by the GROWTH project funded by the National Science Foundation under grant No. 1545949. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. These results made use of the Discovery Channel Telescope at Lowell Observatory. Lowell is a private, non-profit institution dedicated to astrophysical research and public appreciation of astronomy and operates the DCT in partnership with Boston University, the University of Maryland, the University of Toledo, Northern Arizona University, and Yale University. The upgrade of the DeVeny optical spectrograph has been funded by a generous grant from John and Ginger Giovale. This work made use of data supplied by the UK Swift Science Data Centre at the University of Leicester. This work is partially based on observations collected with the Liverpool Telescope, which is operated on the island of La Palma by Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias with financial support from the UK Science and Technology Facilities Council. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max-Planck Institute for Astronomy, Heidelberg and the Max-Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, Queen’s University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation under grant No. AST-1238877, the University of Maryland, and Eotvos Lorand University (ELTE).
Paper: 2018A&A...620A.123A (fulltext)
region. This demonstrates that additional chemical changes occur at temperatures higher than 120 K during the warm-up, when molecules and radicals become more mobile and could react each other more readily. Both Sample #2 and Sample #3 contain hydrogen, carbon, nitrogen, and oxygen. However, their corresponding organic residues (Res #2 and Res #3) do not display the same functional groups that give rise to the 2200 cm −1 feature. This is evident looking at the infrared spectra of the two samples in the region 2300–2000 cm −1 : both show two multi-peaked bands, but with profiles and components different one another. Thus Res #2 exhibits a 2200 cm −1 feature comparable to those observed for Res #1, Res #4, and Res #5 (see the bottom panel of Fig. 5 ), whereas Res #3 displays a quite sharp feature centred at 2158 cm −1 . We think that the presence of water as component of Sample #3 can affect the chemical rearrangements during proton bombardment at 17 K and especially during the annealing to room temperature, preventing or reducing the formation of molecules containing nitrile and iso-nitrile groups and encouraging the formation of molecules containing amide groups, as already stressed by commenting the XPS spectra in the previous section. Looking at the IR spectrum of Res #3, we are not sure about the chemical groups responsible of the band centred at 2158 cm −1 , even if we hypothesize a contribution of OCN − still trapped in the organic residue (Res #3) at room temperature. OCN − ion is one of the new species formed at 17 K after energetic processing of a frozen icy mixture containing oxygen, carbon and nitrogen bearing species (e.g. van Broekhuizen et al. 2005 ). Indeed it has been detected both in Sample #2 and in Sample #3 after proton bombardment (see the top panel of Fig. 5 ). During the annealing of Sample #3, we have recorded the OCN − peak position as a function of the sample temperature: we recognize a gradual shift towards lower wavenumbers as the sample temperature increases, from 2167 cm −1 at 17 K–2160 cm −1 at 260 K. Therefore the frequency of the peak observed for the refractory residue at room temperature (2158 cm −1 ) could be compatible with the trend of the recorded peak position of the OCN − ion into the ice, as shown in Fig. 6 . In this section, we are mainly focusing on the spectral region 2300 – 2000 cm −1 due to its link with astrobiological studies. In fact the triple CN bond stretching mode of nitriles considered relevant to pre-biotic chemistry falls in this region. The role of nitriles in formation of various pre-biotic compounds has been illustrated by Matthews Minard (2006) . Our experimental results demonstrate that the presence of oxygen in the icy samples is not necessary for the formation, at room temperature, of the multi-component band at 2200 cm −1 that we identify with nitrile (–C≡N) and isonitrile (–N≡C) functional groups. The refractory residues containing nitrogen, such as those analysed in this
Paper: 2018MNRAS.473.3796S (acknowledgments)
Acknowledgements I thank the anonymous referee for valuable comments that helped me in improving the manuscript. I am grateful to Anatoly Zasov for fruitful discussion and his important comments on the manuscript. I thank my family for the support and inspiration. This work is supported by The Russian Science Foundation (RSCF) grant No. 17-72-20119. I acknowledge the usage of the HyperLeda data base ( http://leda.univ-lyon1.fr ).
Paper: 2015A&A...579A.108M (acknowledgments)
We thank the referee, Tony Moffat, for his useful comments that helped improve this paper. J.M.A. and A.S. acknowledge support from [a] the Spanish Government Ministerio de Economía y Competitividad (MINECO) through grants AYA2010-15 081, AYA2010-17 631, and AYA2013-40 611-P and [b] the Consejería de Educación of the Junta de Andalucía through grant P08-TIC-4075. J.M.A. was also supported by the George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy and he is grateful to the Department of Physics and Astronomy at Texas AM University for their hospitality during some of the time this work was carried out. I.N., A.M., J.A., and J.L. acknowledge support from [a] the Spanish Government Ministerio de Econoía y Competitividad (MINECO) through grant AYA2012-39 364-C02-01/02, [b] the European Union, and [c] the Generalitat Valenciana through grant ACOMP/2014/129. R.H.B. acknowledges support from FONDECYT Project 1 140 076. S.S.-D. acknowledges funding by [a] the Spanish Government Ministerio de Economía y Competitividad (MINECO) through grants AYA2010-21 697-C05-04, AYA2012-39 364-C02-01, and Severo Ochoa SEV-2011-0187 and [b] the Canary Islands Government under grant PID2 010 119. J.S.-B. acknowledges support by the JAE-PreDoc program of the Spanish Consejo Superior de Investigaciones Científicas (CSIC). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555.
Paper: 2017ApJ...838..117N (acknowledgments)
P.N. acknowledges support from a Theoretical and Computational Astrophysics Network grant, award number 1332858, from the National Science Foundation. This work was completed at the Aspen Center for Physics, which is supported by National Science Foundation grant PHY-1066293. Useful conversations with Benny Trakhenbrot and Nicholas Stone at the Aspen Black Holes Workshop are gratefully acknowledged. B.A. and A.R. acknowledge support from the TCAN grant for a post-doctoral fellowship and a graduate fellowship, respectively. This research was supported in part (AF) by the National Science Foundation under Grant No. NSF PHY11-25915. E.Z. acknowledges funding from the Swedish Research Council (project 2011-5349). N.C. acknowledges the YCAA Prize Postdoctoral fellowship. N.C. and F.P. acknowledge the SAO Chandra grant AR6-17017B and NASA-ADAP grant MA160009.
Paper: 2016MNRAS.456.3803R (acknowledgments)
AS acknowledges support by the Natural Sciences and Engineering Research Council (NSERC) of Canada. AR is grateful for the Faculty of Science Undergraduate Student Research Award he obtained at the University of Manitoba.
Paper: 2018A&A...620A.134S (acknowledgments)
Acknowledgements D.S. and C.W.O are supported by the Netherlands Organization for Scientific Research (NWO; VIDI project 639.042.422). S.K. acknowledges support from NASA through Hubble Fellowship grant HST-HF2-51394 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. D.S. would like to thank Carsten Dominik for useful discussions and Shigeru Ida for pointing out a typographical error in Schoonenberg Ormel (2017) . We thank Beibei Liu, Joanna Dra̧żkowska, and the anonymous referee for constructive feedback on the manuscript.
Paper: 2016A&A...594A..14P (fulltext)
show the regions which contain 95% of the models. The central blue line is the median line for Planck TT+lowP+BSH. Here Planck indicates Planck TT+lowP. One might wonder whether it is reasonable to stop at first order in . We have therefore tested a generic expansion in powers of the scale factor up to order : We find that all parameters are very stable when allowing higher order polynomials; the parameters are weakly constrained and going from (the linear case) to (quadratic case) to (cubic expansion) does not improve the goodness of fit and stays compatible with , which indicates that a linear parameterization is sufficient. 5.1.2. 1-parameter varying <italic>w</italic> A simple example of a varying model that can be written in terms of one extra parameter only (instead of ) was proposed in Gott Slepian (2011), Slepian et al. (2014) , motivated in connection to a DE minimally-coupled scalar field, slowly rolling down a potential , analogous to the one predicted in chaotic inflation ( Linde 1983 ). More generally, one can fully characterize the background by expanding a varying equation of state , where: at first order in , which is then the only extra parameter. Marginalized posterior contours in the plane – are shown in Fig. 6 . The tightest constraints come from the combination Planck TT+lowP+lensing+BSH that gives at 68% confidence level, which slightly improves constraints found by Aubourg et al. (2015) . Fig. 6 Marginalized posterior contours in the – plane are shown for 1-parameter varying models (see Sect. 5.1.2 ) for different data combinations. Here Planck indicates Planck TT+lowP. 5.1.3. Principal Component Analysis on <italic>w(z)</italic> A complementary way to measure the evolution of the equation of state, which is better able to model rapid variations, proceeds by choosing in fixed bins in redshift and by performing a principal component analysis to uncorrelate the constraints. We consider different bins in and assume that has a constant value in each of them. We then smooth the transition from one bin to the other such that: with , a smoothing scale , and a binning . We have tested also a larger number of bins (up to ) and have found no improvement in the goodness of fit. The constraints on the vector of values that can assume in each bin is difficult to interpret, due to the correlations between bins. To uncorrelate the bins, we perform a principal component analysis ( Huterer Starkman 2003 ; Huterer Cooray 2005 ; Said et al. 2013 ). We first run COSMOMC ( Lewis Bridle 2002 ) on the original binning values ; then extract the covariance matrix that refers to the parameters we want to constrain: where is the vector of parameters and is its transpose. We calculate the Fisher matrix, , and diagonalize it, , where is diagonal and is the orthogonal matrix whose columns are the eigenvectors of the Fisher matrix. We then define (e.g., Huterer Cooray 2005 ) and normalize this such that its rows sum up to unity; this matrix can be
Paper: 2015MNRAS.454..344O (acknowledgments)
Funds from the Proyecto de Excelencia de la Junta de Andalucía, J.A. 2012-FQM1776 and from FEDER are acknowledged.
Paper: 2018ApJ...859...26C (acknowledgments)
Our understanding of the problem of bubble stream RADAR reflectivity has been greatly helped by discussions with Dr. Jason Hofgartner of the Jet Propulsion Laboratory; it is our pleasure to acknowledge our indebtedness to him. We also thank the anonymous reviewer, who led us to significantly clarify our manuscript.