adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...635A..48M (acknowledgments)
Acknowledgements The authors wish to thank the anonymous referee for the constructive comments that significantly improved the paper. The authors are grateful to Brett McGuire and Roman Motiyenko for providing the line strengths of CH 3 OCH 2 OH spectroscopic data. The authors acknowledge Gleb Fedoseev for the valuable discussion about the desorption temperatures from the TPD experiments of the O-bearing COMs and Troels C. Petersen for discussions of the statistical methods. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2012.1.00712.S and ADS/JAO.ALMA#2013.1.00278.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. The group of J.K.J. acknowledges support from the H2020 European Research Council (ERC) (grant agreement No 646908) through ERC Consolidator Grant “S4F”. Research at Centre for Star and Planet Formation is funded by the Danish National Research Foundation. A.C. postdoctoral grant is funded by the ERC Starting Grant 3DICE (grant agreement 336474). M.N.D. is supported by the Swiss National Science Foundation (SNSF) Ambizione grant 180079, the Center for Space and Habitability (CSH) Fellowship and the IAU Gruber Foundation Fellowship. This research has made use of NASA’s Astrophysics Data System and VizierR catalogue access tool, CDS, Strasbourg, France ( Ochsenbein et al. 2000 ), as well as community-developed core Python packages for astronomy and scientific computing including Astropy ( Astropy Collaboration 2013 ), Scipy ( Jones et al. 2001 ), Numpy ( van der Walt et al. 2011 ) and Matplotlib ( Hunter 2007 ).
Paper: 2015A&A...578A..34L (acknowledgments)
We acknowledge the referee for valuable comments that have improved the paper a lot. M.L.G. thanks M. Lister and G.-Y Zhao for helpful comments on the model fitting method to the VLBA data. This work is supported by the National Natural Science Foundation of China (No. 11273050); the 973 Program 2015CB857100; Key Laboratory of Radio Astronomy, Chinese Academy of Sciences; and the program of the Light in China’s Western Region (Grant No. YBXM-2014-02). N.M. is funded by an ASI fellowship under contract number I/005/11/0. This research has made use of data from the MOJAVE database that is maintained by the MOJAVE team (Lister et al. 2009 ). This research has made use of data from the University of Michigan Radio Astronomy Observatory which has been supported by the University of Michigan and by a series of grants from the National Science Foundation, most recently AST-0607523.
Paper: 2020ApJS..248...29A (fulltext)
Figure 2 reports the flux measurements in these bands, each split into three distinct bands, 3–7 keV, 7–30 keV, and 30–80 keV for 70 70 The upper edge of the NuSTAR energy range is actually 79 keV, but owing to the negligible impact on the flux values, in this paper, we will use 80 keV for simplicity. NuSTAR and 0.2–0.4 TeV, 0.4–0.8 TeV, and >0.8 TeV for MAGIC and VERITAS. The temporal coverage for the >0.8 TeV band is a about 2 hr longer than for the 0.2–0.4 TeV band because of the increasing analysis energy threshold with the increasing zenith angle of the observations. The exquisite characterization of the multiband flux variations in the X-ray and VHE gamma-ray bands reported in Figure 2 will be used in the sections that follow for the broadband variability and correlation studies. Figure 2. Light curves in various VHE and X-ray energy bands obtained with data from MAGIC, VERITAS, and NuSTAR (split in 15 minute time bins) and Swift-XRT (from several observations with an average duration of about 17 minutes). For the sake of clarity, the 0.3–3 keV fluxes have been scaled by a factor 0.5. The statistical uncertainties are, in most cases, smaller than the size of the marker used to depict the VHE and X-ray fluxes. (The data used to create this figure are available .) 4. Multiband and Multi-timescale Variability 4.1. Fractional Variability The flux variability reported in the multiband light curves can be quantified using the fractional variability parameter F var , as prescribed in Vaughan et al. ( 2003 ): denotes the average photon flux, S denotes the standard deviation of N flux measurements, and denotes the mean squared error, all determined for a given instrument and energy band. The uncertainty on F var is calculated using the prescription from Poutanen et al. ( 2008 ), as described in Aleksić et al. ( 2015a ). This formalism allows one to quantify the variability amplitude, with uncertainties dominated by the flux measurement errors and the number of measurements performed. The systematic uncertainties on the absolute flux measurements 71 71 The systematic uncertainties in the flux measurements at the radio, optical, X-ray, and GeV bands are of the order of 10%–15%, while those of the VHE bands are ∼20%–25%. do not directly add to the uncertainty in F var . The caveats in the usage of F var to quantify the variability in the flux measurements performed with different instruments are described in Aleksić et al. ( 2014 , 2015a , 2015b ). The most important caveat is that the ability to quantify the variability depends on the temporal coverage (observing sampling) and the sensitivity of the instruments used, which are somewhat different across the electromagnetic spectrum. A big advantage of the study presented here is that the temporal coverage of the three bands in X-rays (from NuSTAR) and the three bands in VHE gamma-rays (from MAGIC and VERITAS) is exactly the same, which allows us to make a more direct comparison of the variability
Paper: 2017A&A...601A...2P (acknowledgments)
This work was funded by the French ANR under the Contract No. ANR-13-BS05-0008-02 IMOLABS. This work was also supported by the French program “Physique et Chimie du Milieu Interstellaire” (PCMI) funded by the Conseil National de la Recherche Scientifique (CNRS) and Centre National d’Etudes Spatiales (CNES).
Paper: 2016MNRAS.462L..66Y (acknowledgments)
We thank Thomas Krichbarum for critically reading the manuscript. T. An thanks the grant support from the 973 programme (No. 2013CB837900). The European VLBI Network is a joint facility of independent European, African, Asian, and North American radio astronomy institutes. Scientific results presented in this publication are derived from the following EVN projects: RP017, EY018, EY019, and EY020. The study has received funding from the European Commission Seventh Framework Programme (FP/2007-2013) under grant agreement No. 283393 (RadioNet3). e-VLBI research infrastructure in Europe is supported by the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement number RI-261525 NEXPReS. The WSRT is operated by ASTRON (Netherlands Institute for Radio Astronomy) with support from the Netherlands Organization for Scientific Research. This research has 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: 2018ApJ...869...75M (acknowledgments)
The authors thank David Rodriguez for leading the Cycle 1 ALMA proposal to observe HD 32297, Ian Czekala for helpful discussions regarding the HD 32297 gas detection, Gaspard Duchêne for helpful discussions regarding the inclination of HD 32297, and the anonymous referee for comments that improved the clarity of the manuscript. M.A.M. acknowledges support from a National Science Foundation Astronomy and Astrophysics Postdoctoral Fellowship under award No. AST-1701406. A.M.H. acknowledges support from NSF grant AST-1412647. This paper makes use of the following ALMA data: ADS/JAO.ALMA #2015.1.00633.S and #2012.1.00437.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan) and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This work has also 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, in particular the institutions participating in the Gaia Multilateral Agreements.
Paper: 2020ApJ...901..137S (fulltext)
that models in which the DTD favors long delays. 3. The Stochastic Background The stochastic background is defined as the energy density of gravitational waves per logarithmic frequency interval:where is the critical density of the universe given by . Here H 0 is the Hubble constant, and G is the Newton constant. The spectrum of the SGWB is given by:(Phinney 2001 ), where f and are the observed and rest-frame GW frequencies, respectively. The GW spectrum from a coalescing BNS or BBH is given bywhere is the energy emitted in GWs, is the chirp mass, and f i () and σ are frequencies that characterize the inspiral-merger-ringdown waveforms, are normalization constants chosen to make the waveform continuous, and are the post-Newtonian correction factors (Ajith et al. 2008 ). We model the ensemble distribution of primary black hole masses as a power lawand assume a flat distributionof secondary masses. We set as the lower observed BH mass in low-mass X-ray binaries (Farr et al. 2011 ). We set our fiducial values of α = 1, and ; however, we will explore the effect of varying these two parameters later in the paper. The upper limit on the BH mass is due to the assumption that pair-instability supernovae set an upper limit for the mass of the BHs born from stellar progenitors (Woosley 2017 ). The population analysis using BBH observations from LIGO/Virgo’s first and second observing runs measured the spectral index of the primary BH power law to be at 90% confidence assuming a minimum black hole mass of and a maximum total mass of (Abbott et al. 2018 ). For the BNS systems we assume a uniform distribution between 1.3–1.5 for each of the NSs to be consistent with the range of observed masses of galactic double neutron star systems (Kiziltan et al. 2013 ; Özel Freire 2016 ; Farrow et al. 2019 ), although we note that this choice is in tension with the masses of GW190425 (Abbott et al. 2020 ). 4. Results The results for the two extreme cases of DTDs for both the BBH and BNS systems are shown in Figure 2 . The green shaded region shows the predicted range of for a population of BNS composed of two NSs each with a mass uniformly distributed between 1.3 and 1.5 . The rate of the BNS mergers from LIGO is (Abbott et al. 2020 ). In order to get the full possible range we assign the highest rate to the short DTD model, and the lowest rate to the long DTD model. We do the same for the BBHs where we consider a merger rate between 20 to 110 (Abbott et al. 2020 ). For the BBH systems we considered two models in which (i) the primary BH mass follows bounded between 5 and 50 , and the secondary is uniformly distributed between 5 and m 1 (gray shaded region), and (ii) each BH has a mass of 15 (red shaded region). The average chirp masses of the two BBH population models are the same, , although their predicted differs by a factor of 3 in magnitude. We also show the power-law integrated (PI) curve (Thrane Romano 2013 ) for the detection of the SGWB with an S/N of 3 by
Paper: 2017MNRAS.471..532P (fulltext)
the 14th column. f Experimental lifetime of the upper level used to normalize the oscillator strength and the transition probability given respectively in columns 11 and 12. g Reference of the experimental lifetime used to normalize the oscillator strength and the transition probability given respectively in columns 11 and 12. T = this work; B = Bimont etal. ( 2011 ); W = Wnnstrm etal. ( 1988 ); H = Hannaford etal. ( 1982 ). In Figs 2 4 , the present HFRCPOL oscillator strengths are compared with our previous values (Bimont etal. 2011 ), those of Kurucz (Kurucz 2011 ) and the experimental values of Hannaford etal. ( 1982 ), respectively. Although the latter concerns exclusively the decay transitions of low-lying odd-parity levels (Hannaford etal. 1982 ), they nonetheless provide a good test of the present HFRCPOL model. Figure 2. Comparison between the present HFRCPOL log gf values and those of our previous study (Bimont etal. 2011 ). A straight line of equality has been drawn. Fig. 2 shows a good agreement between our two calculations with no systematic effects, as the core-polarization has been taken into account in both models. However, some discrepancies are seen for the weak transitions due to cancellations such as the transition at 733.295nm with log gf 3.08 and a cancellation factor of CF0.07 in this work, compared to log gf 1.98 obtained with our previous model. In this particular case, it is advisable to use our older published value (Bimont etal. 2011 ), i.e. 1.98, that belongs to a smaller set of calculated strong (log gf 2) decay transitions being not affected by cancellation. Besides, Kurucz ( 2011 ) gives a value of 2.87 for that line. We suspect that this oscillator strength is also affected by cancellation as it is calculated 1 dex weaker than the value of Bimont etal. ( 2011 ), similarly to the present calculation. Unfortunately, CF values are not reported in Kurucz ( 2011 ). Fig. 3 shows that the oscillator strengths computed by Kurucz ( 2011 ) are systematically larger than ours by, on average, 0.07 dex for lines with log gf 0. Furthermore, a significant number (92 transitions out of 357) of the lines with log gf 0 are affected by strong cancellation effects (CF0.1) showing discrepancies of one dex or more. Using our log gf -values with CF0.1 is not recommended as these values could be off by a few dex. Moreover, values of Kurucz ( 2011 ) that are weaker than ours by a few dex should be taken with care as we suspect that they are affected by strong cancellation effects similarly to the case of the line at 733.295nm discussed previously. Figure 3. Comparison between the present HFRCPOL log gf values and those of the Kuruczs data base (Kurucz 2011 ). A straight line of equality has been drawn. In Fig. 4 , it is seen that our HFRCPOL log gf -values agree well with the experimental determinations of Hannaford etal. ( 1982 ), the standard deviation of the differences between the two sets being 0.11 dex. From this comparison, one
Paper: 2021MNRAS.501.5811K (fulltext)
69 Å) per pixel for the G130M (G160M) grating. The resolving power of each individual extraction is quoted as R ∼ 18 000 to 20 000, which corresponds to 15–17 km s −1 for a Gaussian LSF. However, the COS FUV LSF shows the time-dependent non-Gaussianity and the resolving power degraded with time. The spectral resolution can be approximated to ∼19 km s −1 for the COS non-Gaussian LSF at Lifetime Position 1 (see more details in Section 3.2 ). The wavelength regions contaminated by strong Galactic ISM lines are discarded. The typical COS FUV limit is ∼ 13.0. The CalCOS flat-field correction corrects strong wire grid shadow features greater than ∼20 per cent in intensity, but not weak (≤10 per cent in intensity) fixed pattern noise (FPN) produced by the hexagonal pattern of the fiber bundles in the COS FUV micro-channel plate known as MCP Hex (Dashtamirova et al. 2019 ). MPC Hex is supposed to be fixed in the detector pixel space, but not in the wavelength space. In practice, the position of MCP Hex and its intensity change along the pixel space. This sometimes produces false, equally spaced weak absorption-like features in the high-S/N region of the coadded spectrum (Fig. 3 ). It is the most conspicuous when a high-flux Ly α emission region falls on the longer wavelength edge of Segment B of the detector. Due to FPN, the noise is not Gaussian and the conventional way to quote noise as the reciprocal of 1 r.m.s. of the unabsorbed region underestimates true noise (Keeney et al. 2012 ). Since only an individual extraction with 12 shows distinct FPN and the majority of our individual extractions has a lower S/N, we did not correct for MCP Hex (Fitzpatrick Spitzer 1994 ; Savage et al. 2014 ; Wakker et al. 2015 ). Figure 3. Examples of sawtooth-line MCP Hex fixed pattern noise (FPN) in coadded COS spectra. The red vertical ticks spaced at ∼0.4 Å mark the position of Hex FPN. The blue thick tick in the upper panel marks the position of the Milky Way Cl i λ1347.23. When assumed as a H i Ly α, FPN has log N FPN ∼ 12.5. If a weak IGM H i absorption falls on FPN, the apparent increases. 2.6 <italic>FUSE</italic> data Available FUSE spectra (917–1187 Å) were used to obtain a reliable column density of saturated COS FUV H i Ly α lines, since FUSE spectra cover high-order Lyman lines at z ≤ 0.12. The 8th column of Table 3 lists whether the COS AGN has a corresponding FUSE spectrum. The FUSE spectra used in this study are the same ones analysed by Wakker ( 2006 ). They are sampled at ∼0.0066 Å per pixel, weakly dependent on the wavelength. As they are oversampled, we binned the FUSE spectra by 3, 5, or 7 pixels to increase the S/N. The S/N in general increases towards longer wavelengths, i.e. more reliable Ly β profiles than Ly γ profiles. The S/N per resolution element at ∼1050 Å is listed in parenthesis in the 5th column of Table 3 . Since wavelength regions with S/N 5 are not very useful to deblend saturated lines reliably, we excluded these low-S/N
Paper: 2017ApJ...846...47P (acknowledgments)
We would like to thank Mathieu Havel for multiple discussions that helped benchmark our results, Konstantin Batygin for comments on the first draft of this paper, and the anonymous reviewer for her/his insights. This work was funded under grant NSERC RGPIN-2014-06567.
Paper: 2020MNRAS.492.1416L (fulltext)
∼1 month to 27 d, the solar rotation period. An incomplete list of references includes Burlaga Goldstein ( 1984 ), who studied the evolution of plasma flow at 4–5 au and found intermittency in the 5/3 law; they covered a range of 6 months. Marsh Tu ( 1990 ) analysed magnetic field and plasma data from the Helios 1 and Helios 2 spacecrafts between 0.3 and 1 au at the activity minimum of solar cycle 21; they found a −5/3 spectral law, with turbulence better developed in low-speed flows. Espagnet et al. ( 1993 ) computed the power spectra of solar granulation; their energy decreases as a Kolmogorov −5/3 power law in the granulation range (1000–2000 km). Lawrence et al. ( 1995 ) studied ISSN and the 10.7 cm microwave flux; using Fourier and wavelet spectral analysis, they uncovered two regimes of magnetic variability. Cascade (−5/3) behaviour was observed from 2 yr down to 2 d, whereas low dimensional chaotic behaviour was observed at time-scales longer than 8 yr. Abramenko ( 2005 ) worked on images of the photosphere, and extracted the magnetic field of the Sun at times of low flare activity; the study extends over only 60 h. Alexandrova et al. ( 2008 ) computed the power density spectrum using wavelet transforms over a range from 0.1 s to 3 months. McAteer, Gallagher Conlon ( 2010 ) studied turbulence and complexity in order to predict solar flares; using wavelets, these authors found that the PSD of flares is indeed characterized by a −5/3 law. Ashwanden ( 2019 ) recently reviewed models of the solar wind. He noted that, by introducing the reflection of Alfvénic waves, Cranmer van Ballegooijen ( 2005 ) produced ‘a Kolmogorov-like spectrum that does not change dramatically from the photosphere to the solar wind’. The analysis by Plunian et al. ( 2009 ) is the closest to ours and among the most recent ones. As recalled in the Introduction, these authors analysed several kinds of time series of sunspot numbers, concentrations of 14 C in tree rings and 10 Be in polar ice and found two frequency scalings, f − 2/3 when the Sun was active and f − 1 when it was quiescent. These values of exponents are not that expected for Kolmogorov turbulence that should be exactly (within uncertainty) −5/3. Plunian et al. ( 2009 ) suggest that these exponents reflect buoyancy spectra implying values of k − 5/3 and k − 2 for the magnetic energy spectra of the Sun. Plunian et al's ( 2009 ) fig. 3, which displays the PSD of daily and monthly SSN and 10 yr averages of 14 C, ranges from 0.1 to over 10 4 months, shows significant dispersion of the curves, slope changes, and energy peaks at 1 month (27 d) and 10 yr (Schwabe). The −2/3 slope is based on a small range of frequencies (2–20 months). A value of −5/3 (between 1 and 104 months) is not such a bad fit given these uncertainties. But our own results with SSA are much more compelling. 6 CONCLUSION The diverse proxies of solar activity, and in particular solar wind velocity, that have been studied to conclude that
Paper: 2017ApJ...835...64G (acknowledgments)
We thank Brad Cenko, Maria Drout, Or Graur, Atish Kamble, Bob Kirschner, Dan Milisavljevic, Gautham Narayan, Matt Nicholl, Ashley Pagnotta, Ed Shaya, Isaac Shivvers, Jeff Silverman, Alicia Soderberg, and Łukasz Wyrzykowski for constructive suggestions and comments. The authors are especially grateful to Ryan Foley for providing user interface feedback; to Dmitry Tsvetkov and Pavlyuk Nikolay, who donated hundreds of historical light curves; to Pete Challis, Koji Kawabata, Hagai Perets, and Federica Bianco for providing a significant number of spectra; to Matt Nicholl and Lluís Galbany for donating their personal supernova light curve collections; to Carles Badenes, Dave Green, and Pierre Maggi for their SNR contributions; to Dan Milisavljevic for testing the duplicate finder (from data to appear in D. Milisavljevic et al. 2016, in preparation); to Federica Bianco, Matt Nicholl, Maria Pruzhinskaya, and Victor Lebedev (who also pointed us to many missing supernovae), and Ofer Yaron for finding several errors in the catalog; and to David Bishop for his close cooperation and tireless effort to maintain the Latest Supernovae webpage. 37 37 http://www.rochesterastronomy.org/supernova.html The authors would also like to thank the anonymous referee for their constructive comments. This work was supported in part by Einstein grant PF3-140108 (JG). The Open Supernova Catalog made use of NASA’s Astrophysics Data System; the VizieR catalog access tool, CDS, Strasbourg, France; Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013 ); Astroquery (Ginsburg et al. 2016 ), Beautiful Soup 38 38 https://www.crummy.com/software/BeautifulSoup/bs4/doc/ ; DataTables 39 39 https://datatables.net/ ; the Wordpress blogging platform 40 40 http://wordpress.com/ ; the Bokeh interactive visualization library 41 41 http://bokeh.pydata.org/en/latest/ ; and DownloadThemAll, a Firefox add-on. 42 42 http://www.downthemall.net/