adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018A&A...612A..45S (fulltext)
ejects these elements into space where they are then available for the next generation of stars. Common uranium isotopes have half-life decay times of 10 8 to 10 9 years, and had been used to determine the age of the universe by observing ultra-metal-poor stars ( Frebel et al. 2007 ). Gopka et al. (2007) identified Th II 5989.045 Å in the KPNO Arcturus spectrum from Hinkle et al. (2000) . They followed this up by the identification of more such lines from the KPNO atlas ( Gopka et al. 2013 ). The most widely used line is Th II 4019.129 Å. Together with the nearby neodymium line Nd II 4018.8 Å this line is used to form the chronometric ratio Th/Nd, first suggested by Butcher (1987) as an age indicator. However, there is no systematic identification of heavy-element lines in benchmark stars. We have visually searched our library sample for the strong lines of U II at, for example 3859.57 Å and 4241.67 Å as well as Th II at 4019.129 Å and 5989.045 Å, leaning on new oscillator strengths from Nilsson et al. (2002a , b ). Some of these lines are indeed identified, for example the Th II -Nd II line pair at 5989 Å in giant stars like Arcturus, α Ari, or μ Leo or the U II transition at 4241-Å in ε Vir. Figure 7 is a comparison of above three targets with the Sun for the Th 5989-Å region. For many others there is only vague evidence due to blending. Note again that the solar spectrum in this plot was also taken with PEPSI in the same manner as the other stars. It shows a dominant telluric blend on the blue side of the expected Nd II line. Th II 5989.045 may be present in the solar spectrum with an upper limit at the 1 mÅ level. Fig. 7 Th II 5989.045-Å and Nd II 5989.378-Å lines in three RGB stars (full lines, from top to bottom in the Ti I line core; Arcturus, Hamal, Rasalas). The top spectrum shown as dots is a spectrum of the Sun. In connection with chronometry, the s-process elements Ba and Sr (a.o.) are also key elements. The most prominent barium line is the one Ba II transition at 4554.02 Å. Other comparably weaker lines are 3891.78 Å and 4130.64 Å (see, e.g., Siqueira Mello et al. 2014 ).Strontium lines are comparably more prominent than U and Th, for example Sr II at 4077.72 Å or 5215.52 Å. The 4077-Å line is commonly used in the M-K system to classify cool, low-gravity, stellar atmospheres because of its density sensitivity ( Gray Garrison 1989 ). Our library spectra may be used to investigate spectral lines from heavy elements in greater detail than before. 7 Summary We present a library of high-resolution (on average ≈220 000), high-S/N optical spectra of a sample of 48 bright reference stars. The sample includes the northern Gaia benchmark stars and a few well-known M-K standards. Deep spectra are build by average combining individual exposures and reach S/N of many hundreds and, in some cases, even thousands. Continua are set by predetermining synthetic spectra that match the target classification. Continuum adjustments of several tens
Paper: 2018ApJ...855...99C (acknowledgments)
We are thankful to the anonymous referee for providing constructive feedback that improved our manuscript. This research is supported by the National Science Foundation (USA) under grant No. PHY-1430152 (JINA Center for the Evolution of the Elements) and by the ERC Consolidator Grant (Hungary) funding scheme (project RADIOSTAR, G.A. n. 724560). K.B. acknowledges support from the NCN grants Sonata Bis 2 (DEC-2012/07/E/ST9/01360), OPUS (2015/19/B/ST9/01099), and OPUS (2015/19/B/ST9/03188). M.M., N.V., and R.S. are supported by the U.S. Department of Energy under contract No. DE-AC52-07NA27344 for the topical collaboration Fission In r -process Elements (FIRE). This work was also supported in part by the U.S. Department of Energy under grant No. DE-SC0013039 (R.S. and T.S.). A portion of this work was also carried out under the auspices of the National Nuclear Security Administration of the U.S. Department of Energy at Los Alamos National Laboratory under contract No. DE-AC52-06NA25396 (C.F., O.K., R.W., M.M.). This research used resources provided by the Los Alamos National Laboratory Institutional Computing Program (O.K., R.W.). Software: OMEGA (Côté et al. 2017b ), PRISM (Mumpower et al. 2017 ), StarTrack (Belczynski et al. 2002 , 2008 ), Los Alamos suite of statistical Hauser-Feshbach codes (Kawano 2016 ), NumPy (Van Der Walt et al. 2011 ), matplotlib ( https://matplotlib.org ).
Paper: 2020MNRAS.493.4256D (fulltext)
the results to QSANNdRA . Fig. 8 displays the ratio of QSANNdRA ’s relative prediction errors to our calculated relative prediction errors for the original model (Davies et al. 2018a ) and the extended PCA model described above. On average across all target wavelengths, we achieve a 14.2 per cent improvement in (left) and a 11.5 per cent improvement in σ ε (right) on the full test set as compared to Davies et al. ( 2018a ) (shown in grey). When compared to the extended PCA model, we achieve a 6.1 per cent improvement in (left) and a 4.9 per cent improvement in σ ε (right) on the full test set (shown in magenta) with the least improvement being at the Lyα peak. More interestingly, we can compare the performances of QSANNdRA to the extended PCA method on the QSOs in the SDSS data set that are most similar to the z > 7 QSOs that we will use to constrain the high-redshift neutral gas fraction. To do this, we selected the 100 quasars most similar to ULAS J1120+0641 and another 100 most similar to ULAS J1342+0928 based on the red side of the spectra. On these subsets, QSANNdRA ’s performance further improves with respect to the extended PCA method. In particular, on the 100 quasars most similar to ULAS J1120+0641 (at z = 7.085, shown in orange), we achieve a 22.1 per cent improvement in (left) and a 26.2 per cent improvement in σ ε (right). On the 100 nearest neighbours of ULAS J1342+0928 (at z = 7.5413, shown in purple), we achieve a 16.8 per cent improvement in (left) and a 17.5 per cent improvement in σ ε (right). This experiment indicates that overall, the QSANNdRA is more predictive than the model presented in Davies et al. ( 2018a ) as well as its extension and that this is especially true for the two z > 7 QSOs under consideration. Figure 8. Wavelength-dependent distribution of the ratio of QSANNdRA ’s performance to the original PCA model’s performance reported by Davies et al. ( 2018a ) as well as the extended PCA model’s performance in terms of (left) and σ ε (right). The full test set performance improvement as compared to the published model (Davies et al. 2018a ) is shown in grey. When compared to the extended PCA model, the full test set performance ratio (magenta) is compared to the performance ratios on the 100 nearest low-redshift quasar neighbours of ULAS J1120+0641 (orange) and ULAS J1342+0928 (purple) from the test set. 3 APPLICATION TO HIGH-<italic><italic>z</italic></italic> QUASARS Given that we are now confident that our model generalizes to high-redshift objects and performs better than other models in the literature, in this section, we apply QSANNdRA to two high-redshift quasars, namely ULAS J1120+0641 at z = 7.0851 (Mortlock et al. 2011 ; Venemans et al. 2017a ), and ULAS J1342+0928 at z = 7.5413 (Bañados et al. 2018 ) to reconstruct their blue-side spectra and constrain the neutral gas fraction at their corresponding redshifts. 3.1 Reconstructing high-redshift quasar spectra In order to apply our algorithm to the
Paper: 2017MNRAS.470.1881P (fulltext)
of SNe IIn at distances 22Mpc (40Mpc) is () 3 within 8yr in the Northern Sky (top panel in Fig. 9 ). During the period of 20102016, three SNe IIn were detected in the Northern Sky at a mean distance of 29Mpc (Table 3 ). Stacking of their neutrino signal could place stronger constraints on p than the diffuse neutrino flux by a factor of . Table 3. List of SNe IIn detected within 40Mpc over a period of 8yr (20082015). Notes. a Time of maximum optical light. b Available radio light curve. No radio observations exist for the rest of the sources. (1): Arbour Boles ( 2008 ); Botticella etal. ( 2009 ); Brown etal. ( 2014 ); (2): Chandra Soderberg ( 2008 ); (3): Maza etal. ( 2009 ); Fraser etal. ( 2013 ); Potashov etal. ( 2013 ); Mauerhan etal. ( 2013b ); Pastorello etal. ( 2013 ); Graham etal. ( 2014 ); Margutti etal. ( 2014 ); Brown etal. ( 2014 ); Fraser etal. ( 2015 ); (4): Nakano, Yusa Kadota ( 2009 ); (5): de Jaeger etal. ( 2015 ); Stritzinger etal. ( 2011 ); (6): Prieto Seth ( 2011 ); (7): Pastorello etal. ( 2011 ); Humphreys etal. ( 2012 ); Mauerhan etal. ( 2013c ); Brown etal. ( 2014 ); Ofek etal. ( 2014c ); (8): Antezana etal. ( 2013 ); (9): http://www.cbat.eps.harvard.edu/unconf/followups/J14041297-0938168.html ; (10): http://nesssi.cacr.caltech.edu/catalina/current.html ; (11): Terreran etal. ( 2016 ); Denisenko etal. ( 2014 ); (12): Elias-Rosa etal. ( 2015 , 2016 ); (13): Childress etal. ( 2015 ); (14): Zhang Wang ( 2015 ); (15): Masi etal. ( 2015 ); Challis etal. ( 2015 ); (16): Mauerhan etal. ( 2015 , 2017 ); Kankare etal. ( 2015 ). Neutrino production in pp collisions is accompanied by the injection of relativistic electronpositron pairs in the post-shock region and the production of GeV -ray photons via the decay of neutral pions. The first systematic search for -ray emission in Fermi -LATdata from the ensemble of 147 SNe IIn exploding in a dense CSM was recently presented by Ackermann etal. ( 2015 ). No significant excess above the background was found leading to model-independent -ray flux upper limits. These -ray non-detections constrained the ratio of the -ray to optical luminosity in the range 0.011. These can, in principle, be translated to limits on p . However, due to the uncertainty in the escaping fraction of -rays, no stringent limits could be placed on the proton acceleration efficiency. 4.3 IceCube neutrinos in coincidence with SNe IIn? Table 3 shows the detected SNe IIn in the local Universe (i.e. within a distance D L 40Mpc). Out of the 29 high-energy muon neutrinos detected in the Northern Sky above 200 TeV (Aartsen etal. 2016d ), none is found to be in spatial or temporal coincidence with these SNe IIn. However, one cascade-like event 4 from the 4-yr HESE IceCube sample (The IceCube Collaboration etal. 2015 ) was detected on MJD 55798.63, i.e. t 1.13 d after the peak of the SN2011fh optical light curve, with an angular offset of 712. 5 The coincident rate of SNe IIn in the local Universe ( z 0.1) during the time
Paper: 2021MNRAS.501..168L (fulltext)
1 bursts, and the blue-filled triangles indicate the Class 2 bursts. We find that most of the multiple-peaked bursts are located close to the vertex of the CCD. Figure 2. Colour–colour diagram (CCD) of all RXTE observations of 4U 1636-53. The colours of 4U 1636 − 53 are normalized to the colours of Crab. The grey points represent all available observations with one point per observation. The black crosses stand for all X-ray burst in this source. The red-filled circles and the blue-filled triangles represent Class 1 and Class 2 bursts, respectively. We extracted the 64-s interval spectrum prior to a burst as persistent emission. We generated the instrument response matrix using the tool pcarsp and the instrumental background using the tool pcabackest in heasoft for each spectrum. In this work, we fitted the spectrum in the 3.0−20 keV band using xspec version 12.10.1 (Arnaud 1996 ). We added a systematic error to the pre-burst spectra because of calibration uncertainties. During the fitting process, we included the effect of interstellar absorption using the cross-sections of Balucinska-Church McCammon ( 1992 ) and solar abundances from Anders Grevesse ( 1989 ), with a fixed hydrogen column density, N H , of (Pandel, Kaaret Corbel 2008 ). We used all available PCUs during the X-ray bursts to produce time-resolved spectrum. We corrected every spectrum for dead time according to the methods supplied by the RXTE team. Since the light curve of bursts decay is quite smooth, to compensate the lower count rates, we extracted spectrum over longer intervals in the tail of the bursts. For each multiple-peaked burst, we generated one instrument response matrix using the pcarsp and the instrumental background using the pcabackest in heasoft . For the burst spectral analysis, we initially used a single-temperature blackbody model, tbabs*bbodyrad , to fit the net burst spectra, which is well established as a standard procedure in X-ray burst analysis (Kuulkers et al. 2002 ; Galloway et al. 2008 ). The model provides the blackbody colour temperature, kT bb , and the normalization, K bb , proportional to the square of the blackbody radius of the burst emission surface, and it allows us to estimate the bolometric luminosity as a function of time assuming a distance of 5.95 kpc (Fiocchi et al. 2006 ). The bursts bolometric flux are calculated as where , R km is the effective radius of the emitting surface in km, and D 10 is distance to the source in units of 10 kpc. We defined r 1,2 as the ratio of the first peak to the second peak flux. We note that, to reduce the effect of the instrument, we used the bolometric flux (not the net burst count rate ratio in Fig. 1 ) from the standard approach to calculate the ratio r 1,2 . After subtracting the instrumental background for the time-resolved spectra, we used another model that allows us to vary the pre-burst spectrum by a free scaling factor (Worpel et al. 2013 ). We used two models to describe the persistent
Paper: 2016A&A...586A..44C (acknowledgments)
The authors are grateful to Amanda Heiderman and Neal Evans for their useful collaboration and discussion on embedded protostellar environments, and to Mike Dunham for his comments on the paper. Many thanks to the referee, Charles Lada, for his very useful comments and suggestions. Astrochemistry in Leiden is supported by the Netherlands Research School for Astronomy (NOVA), by a Royal Netherlands Academy of Arts and Sciences (KNAW) professor prize, by a Spinoza grant and grant 614.001.008 from the Netherlands Organisation for Scientific Research (NWO). The authors are indebted to the various observers who have collected data for their papers and kindly provided them to be included in the LOMASS database, as well as staff at the JAC. Acknowledgment is given to the following programs and languages for building this database: gildas-class and python for data reduction and format manipulation; python-matplotlib for plotting; MySQL for construction of the database; php and html for the web interface.
Paper: 2018MNRAS.481..509E (fulltext)
be derived. The symbols are the same introduced in the legend of Fig. 8 . In our case, the benefit of using instead of Δα −10, 10 to represent the MFS amplitudes consists of a larger number of useful maps. In Fig. 12 , the versus α 0 plot confirms, based on a larger sample of cases, the trends seen in Fig. 10 . The plot appears less scattered than that of Fig. 10 with regard to positions of Hi-GAL and fBm images, while the positions of the simulations cover a wide range towards larger values. The ‘solenoidal’ and the t 1 , yz maps of both the ‘quasi-hydrodynamical’ and ‘high-magnetization’ scenarios are the closest to the observational points, mostly to the ones corresponding to SPIRE maps of l224 and l217, that is to say that, from the point of view of the MFS amplitude, they show a degree of multifractality similar to that of the most actively star-forming regions in our sample. Figure 12. Plot ot the area A Φ under the MFS and above f = 1.5 versus the peak position α 0 for maps for which A Φ can be derived. The symbols are the same introduced in the legend of Fig. 8 . 4.2.4 Left versus right tail width The diagnostics used in Figs 10 , 11 , and 12 consider the MFS as a whole, neglecting information contained in possible asymmetries. In fact, the left tail of the MFS represents the heterogeneity (broad tail) or uniformity (narrow tail) of the high values distribution, as well as the right tail is related to heterogeneity/uniformity of the low values distribution (e.g. Pavon-Dominguez, Jimenez-Hornero de Rave 2013 ; de Freitas et al. 2017 ). Strong asymmetries are observed in most of the spectra analysed in this paper, with the extreme case represented by PACS 70 maps, discussed above. In order to make use of the MFS asymmetry, single values of α on the two sides of the MFS (for example, α 10 and α −10 , to follow the same approach as above) should be considered (de Freitas et al. 2017 ). In Fig. 13 , the two widths Δα L ≡ α 0 − α 10 and Δα R ≡ α −10 − α 0 , are plotted. First, a strong segregation in abscissa is visible between the group of fBm sets (which are right-skewed and for which the width of the left MFS tail α 10 remains nearly constant, see also Table 3 ) and that of Hi-GAL maps. The latter is internally more composite: the most quiescent, l215, is found on the left while the most active, l224, on the right, as a consequence of the leftward broadening of the MFS in presence of bright compact sources (cf. Fig. 5 ). Along the vertical coordinate, a systematic increase is seen from short to long Herschel wavelengths, while the column density maps appear mixed with SPIRE maps but with no specific trend. Figure 13. Plot ot the MFS left versus right tail amplitude for maps for which it is possible to derive both quantities. The symbols are the same introduced in the legend of Fig. 8 . The dotted line represents the bisector, to facilitate the distinction between left-skewed (below the line) and right-skewed (above the line) MFSs,
Paper: 2015ApJ...804..133A (acknowledgments)
The Telescope Array experiment is supported by the Japan Society for the Promotion of Science through Grants-in-Aids for Scientific Research on Specially Promoted Research (21000002) “Extreme Phenomena in the Universe Explored by Highest Energy Cosmic Rays” and for Scientific Research (19104006), and the Inter-University Research Program of the Institute for Cosmic Ray Research; by the U.S. National Science Foundation awards PHY-0307098, PHY-0601915, PHY-0649681, PHY-0703893, PHY-0758342, PHY-0848320, PHY-1069280, PHY-1069286, PHY-1404495 and PHY-1404502; by the National Research Foundation of Korea (2007-0093860, R32-10130, 2012R1A1A2008381, and 2013004883); by the Russian Academy of Sciences, RFBR grants 11-02-01528a and 13-02-01311a (INR), IISN project No. 4.4509.10, and Belgian Science Policy under IUAP VII/37 (ULB). The foundations of Dr. Ezekiel R. and Edna Wattis Dumke, Willard L. Eccles, and the George S. and Dolores Dore Eccles all helped with generous donations. The State of Utah supported the project through its Economic Development Board, and the University of Utah through the Office of the Vice President for Research. The experimental site became available through the cooperation of the Utah School and Institutional Trust Lands Administration (SITLA), U.S. Bureau of Land Management, and the U.S. Air Force. We also wish to thank the people and the officials of Millard County, Utah for their steadfast and warm support. We gratefully acknowledge the contributions from the technical staffs of our home institutions. An allocation of computer time from the Center for High Performance Computing at the University of Utah is gratefully acknowledged.
Paper: 2018ApJ...855L..27V (fulltext)
well as the narrow range of scales involved, we are only able to claim qualitative similarity with the Mallet et al. ( 2017 ) prediction. Figure 2. Spectral index of the dissipation range binned in the (, β e ) plane. The black square marks the region with the steepest spectral indices in the range of 0.1 ≲ β e ≲ 1 and . To resolve the ambiguous results obtained with Figures 1 (d) and (e), we repeat our analysis with the subset of the data within the black square in Figure 2 , which encloses 41% of the total data and is bounded by 0.1 ≲ β e ≲ 1 and . The spectral indices are significantly steeper in this region, and if this is caused by reconnection, then may have significantly better scaling with in the marked region than with ρ c . In Figure 3 (a), the slope of the power-law fit for as a function of closely agrees with the one based on the full distribution. In contrast, in Figure 3 (b), the slope of the power-law fit for as a function of is remarkably close to 0. Based on this, we suggest that at least at low β e magnetic reconnection may control the ion-scale break of the solar wind turbulence. The intercept of the power-law fit is , implying C D = 4.7 (see Equation ( 1 )). Figure 3. (a) and (b) are identical to Figures 1 (e) and (d); however, they present the subset of measurements, which are within the black square in Figure 2 corresponding to 41% of the overall data points. Both and were close to normally distributed in our data, thus we use an F -test to investigate whether they have equal variance. The ratio of the sample variances is with 95% confidence intervals of [0.8328; 0.8468]. Thus we reject the null hypothesis and conclude that predicts the break with smaller spread than ρ c . Finally, the scaling of as a function of β i , while significant over the whole range of β i present in the data, is not particularly strong. With reference to the results of Chen et al. ( 2014 ), Mallet et al. ( 2017 ) pointed out that correlations between and β i could cause the scaling behavior of d i to mimic that of . Neglecting a nearly constant factor ,and so if were anticorrelated with β i , the ratio would scale less strongly with β i than otherwise expected. Figure 4 shows that empirically, such an anticorrelation does in fact exist in the solar wind (at least at low β i ). Assuming that the break scale (as does appear to be the case: see Figures 1 (d) and 3 (b)), this contributes to the rather shallow scaling of in Figure 1 (b). Similar consideration could contribute to the shallow scaling of with . Figure 4. 2D histogram showing the distribution of the measurements in the () plane. The least-square fit is indicated with a black line and the average and standard deviation of each 5% of the data are marked in white. 4. Conclusion In this Letter, we have presented a statistical study of the break scale between the inertial and dissipation ranges of the solar wind turbulence spectrum, and to what extent agrees with the fundamental ion length
Paper: 2017ApJ...850..144E (acknowledgments)
We thank the anonymous referee for the thorough review that helped us improve this manuscript. We are grateful to Athanasia Tsatsi for useful comments. This research was supported in part by the Polish National Science Centre under grant 2013/10/A/ST9/00023.
Paper: 2018ApJ...853...60W (acknowledgments)
The authors thank Luca Ciotti, Pasquale Londrillo, and Carlo Nipoti for sharing their NMODY code. The authors acknowledge Ortwin Gerhard for sharing the code for generating the self-consistent N -body ICs using Lucy’s method. X.W. thanks for support through NSFC grants 11503025, 11421303, Anhui NSF grant 1708085MA20, and the Fundamental Research Funds for the Central Universities. X.W. thanks for support from the Hundred Talents Project of Anhui Province. This project was partially started when X.W. was an Alexander von Humboldt Fellow at the University of Bonn.