adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...864..163V (fulltext)
time interval, covering up to 15 ks after the beginning of the prompt emission, and adopt E min = 1 GeV to recover exposure. The condition in Equation ( 20 ) now returns 10,962 s of GTIs in the first 15 ks after t 0 . In this case, six events survive the cuts, among which a ∼10 GeV photon at t 0 + 2239 s. Note that there are still no photons during the prompt emission with this selection. We detect and localize the source producing a TS map for this time interval, with a likelihood model containing the appropriate Galactic and isotropic templates provided by the LAT collaboration as well as all the point sources from the 3FGL catalog (Acero et al. 2015 ). We model GRB 100724B as a point source with a power-law spectrum, and we keep the parameters for all 3FGL sources fixed to their catalog values. No 3FGL source is detected in the small time window of our analysis, thus this choice is irrelevant for our final results. This analysis results in a firm detection of the GRB with a significance of ∼7 σ . The best-fit power law has a photon index of α = −1.8 ± 0.4, a value typical for the high-energy emission of GRBs (Ackermann et al. 2013b ), with an average flux of erg cm −2 s −1 (1–100 GeV). Our localization is shown in Figure 15 . The cross marks the best-fit position, corresponding to R.A. = 12347 and decl. = 7588 (J2000), while the white lines correspond to the 68% and 90% c.l. containment regions. We used this position in the following analysis. The most accurate localization available in the literature before this work was reported in the Fermi /LAT GRB catalog (Ackermann et al. 2013b ), and corresponds to R.A. = 11959, decl. = 7586 (J2000) with a 68% containment radius of 088 (dashed line in Figure 15 ). This localization was based on the detection during the prompt phase, obtained by relaxing the zenith angle cut using a threshold of , and it is compatible with the one found in this work, but it features a much larger containment radius. The excess we find is not related to any known source and lies within the GBM localization region for GRB 100724B. The source is not detected in any other time interval. We therefore identify it as the high-energy counterpart of the burst. Figure 15. Localization of GRB 100724B: the white contours are, respectively, the 68% and 90% containment regions, while the dotted line is the margin of the much larger 68% containment region provided in Ackermann et al. ( 2013b ) based on the prompt interval. The black cross is the best-fit localization. Appendix B Likelihood Ratio Test The Likelihood Ratio Test (LRT) is a procedure for model selection. It is a statistical test that helps to select between two nested models, m 0 and m 1 , the one that best captures all significant features of the data. The model m 0 is the null hypothesis and represents the simpler model, while m 1 is the alternative hypothesis and is more complex than m 0 . The TS is twice the difference in the log-likelihood S between the two
Paper: 2015ApJ...808...76C (acknowledgments)
The authors would like to thank the referee for thoughtful comments which improved the manuscript. This paper is based on observations taken with the NASA/ESA Hubble Space Telescope obtained at the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS5-26555. These observations are associated with the program numbers 9490 and 11147.
Paper: 2015ApJ...800...56J (acknowledgments)
This work is supported by the SOC program (CHINARE2012-02-03), the NSFC grant (NSF11033007), National Basic Research Program of China (973 Program, 2013CB834905, 2015CB857005), and Fundamental Research Funds for the Central Universities (WK 2030220010). P.J. acknowledges support from the National Science Foundation of China with Grants NSFC 11233002 and NSFC11203022, the Fundamental Research Funds for the Central Universities, and the China Postdoctoral Science Foundation. Data from NED, NIST, and SDSS were used. This research uses data obtained through the Telescope Access Program (TAP), which has been funded by the Strategic Priority Research Program “The Emergence of Cosmological Structures” (grant No. XDB09000000). National Astronomical Observatories, Chinese Academy of Sciences, and the Special Fund for Astronomy from the Ministry of Finance. Observations obtained with the Hale Telescope at Palomar Observatory were obtained as part of an agreement between the National Astronomical Observatories, Chinese Academy of Sciences, and the California Institute of Technology.
Paper: 2020MNRAS.495..992L (fulltext)
of relatively pristine gas inflows in the local Universe, given the metallicity cut-off seen for LGRB progenitors. Our discovery of strong H ii region emission located at the explosion site of AT 2018cow appears a strong means to link the progenitor with ongoing star formation, and thus young (massive) stellar populations. This in contrast to inference from observing cold inflows of neutral gas, which provide an even more indirect indicator of current star formation. We note, however, that it is very difficult to make conclusive statements on a single object, given almost any location in a galaxy will have a significant line-of-sight population of old stars. 5.2 Tidal-disruption event Other explanations for AT 2018cow have been proposed that may not involve a massive star at all. One such scenario is the tidal disruption of a main-sequence star or white dwarf by an IMBH (Kuin et al. 2019 ; Perley et al. 2019 ). In the main-sequence TDE scenario (Perley et al. 2019 ), a BH mass of ∼10 4.3 M ⊙ is required. Such an IMBH would be expected to be located within a massive stellar cluster. For the case of a young parent cluster, we do indeed find a star-forming region underlying AT 2018cow, however, the explosion site is significantly offset from the peak of this source in both the white-light image of our MUSE cube and the centroid of the H α emission, where one would expect the massive IMBH to settle towards rapidly. For an older, globular cluster (GC) host, the hosting GC would lie at the upper end of the GC luminosity function (Harris 1996 ). A magnitude of the host of M V , ∼ − 9 to −11 mag follows from both extending the M–σ relation (e.g. Gültekin et al. 2009 ) to IMBH masses, and looking at the luminosities of GCs with signs of IMBHs (e.g. Noyola et al. 2010 ; Feldmeier et al. 2013 ; Lützgendorf et al. 2013 ). At the distance of CGCG 137-068 this would appear as a m V ∼ 23−25 mag source at the explosion site, and should be detectable in high-resolution imaging of the site. An alternate scenario, involving a more massive BH ∼10 5–6 M ⊙ and a low-mass white dwarf was presented by Kuin et al. ( 2019 ). Although white dwarfs may form at a few × 10 7 yr after star formation (Portegies Zwart, McMillan Makino 2007 ), i.e. comparable to the age we infer for the underlying H ii at the explosion site of AT 2018cow), these are the most massive white-dwarfs – low-mass white-dwarfs will form much later. In this case the co-location with star formation is a coincidence in this scenario. This BH required mass is comparable to that of a massive GC itself, and a dwarf satellite galaxy of CGCG 137-068 may be a more promising BH-host system. Notwithstanding the lack of spatial coincidence we find between AT 2018cow and the peak of any underlying source, we searched in our data for existence of a distinct system in the vicinity. In Fig. 6 , we show the results of fitting individual spaxels in a region surrounding the explosion site of AT 2018cow to look for
Paper: 2017ApJ...836L..10H (fulltext)
A study (not shown here) of the possible effect of the geometry of the shock did not demonstrate a significant correlation between the observed spectra and the angle θ nB between the normal of the shock and the interplanetary magnetic field. The histogram shown in Figure 3 (d) indicates that the spectral slopes vary within the interval ∼[−2.2, −0.3] with a peak near −1.2. The bulk of the distribution is significantly shallower than the Kolmogorov scaling f −5/3 (red dashed line in Figure 3 (d)) and that reported in the solar wind (e.g., Smith et al. 2006 ). These observations question the ubiquity of the Kolmogorov scaling in the terrestrial magnetosheath, although a small fraction (17%) of the events showed a Kolmogorov spectrum (here defined as the spectral slope −1.5). Figure 3. 2D distribution of the slopes P α ( X GSE , Y GSE ) at MHD scales. The 2D distribution P α ( X GSE , Y GSE ) of the spectral slopes at MHD (a), and the integrated ones along the Y GSE direction (b) and X GSE direction (c). The blue curves represent the average magnetopause and bow shock positions computed using the paraboloidal bow shock model of Filbert Kellogg ( 1979 ) and the magnetopause model of Sibeck et al. ( 1991 ). The number in each bin represents the number of events. (d) The histogram of the measured spectral slopes where the red dashed line indicates the Kolmogorov slope −5/3. On the contrary, the slopes at the sub-ion scales were found not to depend on the location within the magnetosheath as can be seen in Figure 4 (a). This can be explained by the fact that the kinetic scales (≤100 km) are much smaller than the integral (size) scale of the magnetosheath (>10 4 km), meaning that the large-scale boundaries do not influence the small (kinetic) scale turbulence. Furthermore, the histogram of the slopes (Figure 4 (b)) is found to be similar to those reported in the solar wind (Alexandrova et al. 2012 ; Sahraoui et al. 2013 ) and in the magnetosheath (Huang et al. 2014 ; Hadid et al. 2015 ), with a peak near −2.8. This observation suggests that kinetic scale turbulence has a “universal-like” scaling distribution in the sense that it does not depend either on the explored region (magnetosheath or solar wind) or on the nature of the turbulence at MHD scale ( f −1 energy-containing range or f −5/3 inertial range), which is probably due to that kinetic scale turbulence is generated locally so its nature is not affected by large-scale inhomogeneities/fluctuations. Figure 4. 2D distribution of the slopes P α ( X GSE , Y GSE ) at sub-ion scales. The 2D distribution P α ( X GSE , Y GSE ) of the spectral slopes at sub-ion scales (a); the histogram of the spectral slopes (b). The number in each bin represents the number of events. Hadid et al. ( 2015 ) showed that the f −1 range in the magnetosheath of Saturn was populated by random-like fluctuations. It has been speculated that the interaction of the solar wind with the shock destroys the pre-existing correlations
Paper: 2015ApJ...815...78K (acknowledgments)
H.K. thanks Takeru Suzuki for inputs to develop the Heyney code and Takashi Noguchi for fruitful discussion about double-diffusive convection. This work was supported by Grants-in-Aid from the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) (23244027 and 23103005). H.K. was supported by JSPS KAKENHI Grant Number 15J09448.
Paper: 2020MNRAS.492..177C (fulltext)
( 16 ), the BR emissivity in the classical limit then reads where p min is the minimal electron momentum that is required to produce a photon of energy ω = h ν/ m e c 2 . This is determined by ω = γ min − 1, which yields . In the last step of equation ( 17 ), we used a non-relativistic Maxwell–Boltzmann distribution function, with dimensionless electron temperature θ e = kT e / m e c 2 to carry out the integral. The above expressions explicitly assume p 1 , ω ≪ 1. Even without quantum corrections, to generalize the Kramers approximation to higher temperatures/energies, we shall use the relativistic Maxwell–Boltzmann distribution function where K 2 ( x ) is the modified Bessel function of second kind. 9 We also keep the full relativistic expression for and furthermore realize that at low frequencies the overall BR cross-section scales as towards higher electron energies (see the discussion about Bethe–Heitler limit). Thus, after multiplying the Kramers approximation, equation (2), by and carrying out the thermal average with the above modification, we have the relativistically improved Kramers approximation for the BR emissivity: This shows that in the classical treatment the improved asymptotic scales as for low temperatures. The origin of this correction is not quantum-mechanical but simply due to special relativistic effects. This modification absorbs the leading order corrections towards the BH limit, as we discuss next. We also mention that in equation (19) the temperature-dependent factor, is directly related to the differences in the normalization of the non-relativistic and relativistic Maxwell–Boltzmann distributions. At high temperatures, the corrections can become sizable, giving at and at , and thus should be taken into account for accurate calculations. 4.2 Definition of thermally averaged Gaunt factors The main quantity that enters the BR emission term in the photon Boltzmann equation, as well as the electron temperature evolution equation, is the thermally averaged Gaunt factor. It can be simply obtained by comparing the total plasma emissivity with the emissivity in the Kramers limit and is usually computed as In the last step, we explicitly assumed that the electrons follow a non-degenerate, relativistic Maxwell–Boltzmann distribution. 10 In the non-relativistic limit (γ 1 ≃ 1), equation ( 21 ) is a very good choice. However, for p 1 ≳ 1, the Gaunt factor scales at low frequencies (see Section 2.3 ). It is thus useful to multiply the Kramers cross-section by , which then yields a slightly modified definition for the BR Gaunt factor: This redefinition reduces the dynamic range of the Gaunt factor and is thus very useful for compressing the data in tabulations. The final BR emission term then takes the form where is given by equation ( 19b ) and by equation ( 20 ). Both definitions, of course, give exactly the same answer for the overall BR emission term. Nevertheless, in applications, is beneficial since it does not scale as
Paper: 2019A&A...629A..83A (fulltext)
TiO and FeH lines using the four free model parameters revealed for most stars T mag between 2000 and 2300 K with an average of 2100 K, which is approximately constant across the M1 to M7 spectral classes ( Fig. 4 b). These cool spots are also highly magnetic (see Sect. 5.3 ). Here, the difference between T phot and T mag reduces on average from 1700 K for an M1 dwarf to about 1000 K for an M7 dwarf. Interestingly, this range of spot temperature differences is typical for sunspots of various sizes (e.g., Berdyugina 2005 ). For a young, active M8.5 brown dwarf, Berdyugina et al. (2017) and Kuzmychov et al. (2017) determined T phot = 2800 K and T mag = 2200 K using also atomic and molecular lines. This result agrees with the found trend and extends our finding to ultra-cool dwarfs. The spot temperature difference with respect to the photosphere determined here for M dwarfs differs from that found from broad-band photometric measurements, as summarized by Berdyugina (2005) . This is probably due to a height-dependence of spot parameters as well as the higher sensitivity of molecular lines to the temperature than that of photometry (see Sect. 6 ). In summary, thanks to the variety of our spectral diagnostics, we found two kinds of magnetic regions with clearly different temperatures in M dwarf atmospheres as a function of the spectral class: one is cold (starspots), and the other is warm (network). Fig. 2 Illustration of the high temperature and magnetic sensitivity of FeH lines which is reproduced by our models. Observed (black) and synthetic (red) intensity and circular polarization profiles for FeH at 9897–9913 Å in the considered sample of M dwarfs. The synthetic profiles were calculated using two components: one non-magnetic (“photospheric”) and one magnetic (“spot”) atmosphere model ( Allard et al. 2001 ) with best-fit parameters obtained from fits of Stokes I and V spectra for FeH in the considered wavelength region only. All the following parameters were obtained simultaneously in one χ 2 minimization: the photosphere and spot temperatures, the magnetic field strength, the spot filling factor for the intensity f I , and the circular polarization f V . 5.3 The magnetic field strength The magnetic field strength B was determined during the same fitting procedure as described for T mag in Sect. 5.2 . It corresponds therefore to an average field strength of a given magnetic structure at a given layer of the atmosphere,as defined by the corresponding species. The B values found from atomic and molecular lines are confined to the range of 3–6 kG (there are only three outliers), with an average of about 5 kG ( Fig. 4 d). This average is practically constant throughout the considered spectral class range of M1–M7 dwarfs. Berdyugina et al. (2017) and Kuzmychov et al. (2017) similarly found B ~ 5 kG in their study of a young, active M8.5 brown dwarf. In summary, a cooler starspot atmosphere probed by the mol1 set and a hotter network atmosphere
Paper: 2018MNRAS.480.1457S (acknowledgments)
ACKNOWLEDGEMENTS We thank an anonymous referee for a careful reading of the manuscript and constructive comments. We acknowledge contributions of additional collaborators who helped with imaging observations to discover and monitor light echoes, as well as for discussions and contributions to proposals for telescope time for this and related projects: the Carnegie Supernova Project, Alejandro Clocchiatti, Steve Margheim, Doug Welch, and Nolan Walborn. In particular, Nolan Walborn provided helpful comments on the manuscript just weeks before he passed away, which occurred while this paper was under review. His contributions to massive star research have been tremendous, and his unique insight will be sorely missed. NS’s research on Eta Carinae’s light echoes and related LBV-like eruptions received support from NSF grants AST-1312221 and AST-1515559. Support for JLP is provided in part by FONDECYT through the grant 1151445 and by the Ministry of Economy, Development, and Tourism’s Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics, MAS. DJJ gratefully acknowledges support from the National Science Foundation, award AST-1440254. This paper includes data gathered with the 6.5m Magellan Telescopes located at Las Campanas Observatory, Chile. Based, in part, on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CONICYT (Chile), Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina), and Ministério da Ciência, Tecnologia e Inovação (Brazil) (Program GS-2014B-Q-24).
Paper: 2016MNRAS.459.2964M (fulltext)
this value will be subject to a selection bias as it is easier to obtain unambiguous light curves for principal axis rotators, it is unlikely that a debiased value would reach the 40 per cent that our modelling suggests is required to explain our data. Therefore, we conclude that none of the above mechanisms can currently explain our observed trends with size. 5.2 Comparing results for the inner and outer main belt populations Figs 3 and 4 show that the rate at which the distribution of magnitude variation changes with diameter is greater for the inner belt than the outer belt. This may be due to either a difference in the reorientation of the objects between the two populations, or a difference in shape distribution. Unlike the difference in variability with diameter, the difference with semimajor axis is potentially more straightforward. Reorientation due to YORP will happen more slowly at greater heliocentric distances and thus objects in the outer belt should show systematically less variability, all other factors being equal. This agrees with what we see here. To test this, we assumed that the shape distribution of the outer belt was identical to that obtained for the inner belt. In order to assess the validity of this assumption, a best-fitting truncated Gaussian shape distribution was obtained for the outer main belt independently at 2 D 3 km using the previously outlined method. This gave an average axial ratio of 1 : 0.87 ± 0.09 : 0.74 ± 0.11 corresponding to a population with a shape distribution μ = 0.90, σ = 0.11. This is not significantly different from that found for the inner belt and therefore our assumption holds. The difference between the two populations must therefore be due to a difference in the degree of reorientation. Part of this may be due to the slower rate of YORP in the outer belt. At present our statistical model does not account for these effects and this may offer an avenue for future work. 5.3 Previous constraints on shape and spin A previous study by Szabó Kiss ( 2008 ) used a similar sparse light-curve sampling method, utilizing data from the SDSS Moving Object Catalogue. Assuming all objects to have spin axis parallel to the sky plane, they reported a shape distribution in good agreement with axial ratios from then-published light curves. We compared our findings to an updated distribution of axial ratios from current light curves (Kryszczyńska et al. 2007 ). The light curves given in this data base suggest an average asteroid shape with 1σ spread with axial ratio 1 : 0.76 ± 0.13 : 0.62 ± 0.11. This result is within 1σ of our own findings, albeit both 〈 b / a 〉 and 〈 c / b 〉 are smaller than our results. We then used the truncated Gaussian distribution for b / a and the approximated distribution for b / c obtained from the Kryszczyńska et al. ( 2007 ) data set, and varied the upper obliquity limit in an attempt to obtain a good fit from this large diameter shape distribution. It was possible to obtain