adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...863..175G (fulltext)
becomes more and more noisy toward higher frequencies and therefore the de-convolved power spectrum will be more and more uncertain toward higher and higher frequencies. Figure 15. (a) Simulated red-noise light curve ( a = 2), containing 1000 data points with a sampling interval of one day (blue open circles), along with 30% of the time series selected at random times to mimic an unevenly sampled data set (magenta stars). (b) The corresponding power spectra of the unevenly sampled light curve derived using the LSP method (resulting in a = 0.3; magenta dashed curve), and the FT of the ACF (returning a = −0.1; cyan dashed curve). (c) Spectral window of power spectrum estimated from FT of the ACF (see the text of Appendix A for details). Appendix B Selection of (<italic>p</italic>, <italic>q</italic>) Parameters for the CARMA Process The order of the CARMA( p , q ) process is chosen based on how close the model is to the data using the minimum AICc criterion adopted in the present study. It has been argued that the models for various pairs of ( p , q ) values for which the minimum AICc is within 10 are not statistically indistinguishable from each other. Figure 16 shows power spectra corresponding to a few sets of ( p , q ) parameters of the analyzed Fermi -LAT light curve. As these overlap substantially, we choose the lowest-order model—CARMA(1, 0)—as the best-fitting model to describe the high-energy γ –ray variability in OJ 287. Figure 16. Power spectra corresponding to a few sets of ( p , q ) parameters of the analyzed Fermi -LAT light curve of OJ 287. The corresponding 1 σ confidence intervals are denoted by shaded areas. See the text of Appendix B for details. Appendix C Lack of ∼12 Year QPO in the Optical Light Curve Here, we discuss in more detail the lack of the claimed ∼12 year quasi-periodicity in the analyzed optical light curve. To demonstrate the robustness of the CARMA modeling in detecting QPO features against the background of red noise in the power spectrum for a finite time series covering only a few periods, an artificial light curve is generated with three components: (1) a pure red-noise ( β = 2) power spectrum using the method of Emmanoulopoulos et al. ( 2013 ), (2) a sinusoidal component with a 12 year period, and (3) a Gaussian white noise with mean 0 and standard deviation 0.1 representing measurement uncertainty. The data points are evenly sampled with a sampling interval of 1 day, and the total duration of the simulated time series is 14,600 days (40 years, corresponding to our relatively better sampled optical light curve starting from 1970; see Table 2 ). Next, we kept 20% of the data selected at random times to mimic an unevenly sampled data set. Figures 17 (a)–(e) present the results of CARMA modeling on our simulated light curve, while Figure 17 (f) shows the computed power spectrum. Since the simulated light curve covers only ∼3 periods, we do not detect a clear peak on the ∼12 year timescale against the
Paper: 2020A&A...644A..75L (fulltext)
are quite discrepant (0.2 dex and 0.8 dex in our two stars, respectively). The 4215.5 Å line of the star S11–97 is affected by the CN molecular band in this region. Spectral synthesis taking into account the carbon abundance derived in the CH band led to an abundance that agrees better with the 4077.7 Å line. Yttrium. Two very faint lines ( 15 mÅ) of yttrium were detected in our spectra, but we were only able to place upper limits on the Y abundance in our stars. Barium. Four lines of barium are present in our wavelength ranges. One is very faint ( λ 5853 Å) and detected for only one star, and the other three lines are strong. Two of them are blended with weak iron lines ( λ 4934 Å and λ 6141 Å). Therefore we proceeded by spectral synthesis, taking into account all blends and the Ba HFS components. Barium has five isotopes; different fractions of even-A and odd-A (A=atomic mass) nuclei ( 134 Ba + 136 Ba + 138 Ba) : ( 135 Ba + 137 Ba) were tested: the 82:18 solar fraction, and the r -process fractions of 54:46 and 28:72. The Ba λ 4934 Å resonance line is more sensitive than the three subordinate lines to the adopted fraction. The solar 82:18 fraction led to the best agreement between the resonance and the subordinates lines. We refer to Jablonka et al. (2015) , Mashonkina et al. (2017b) for a more detailed investigation of the possible cause. 5. Discussion 5.1. Carbon Figure 3 shows that none of our stars can be considered as carbon-enhanced based on the Aoki et al. (2007) criterion. Nonetheless, our stars are evolved enough to have converted C into N by the CNO cycle, as they are above log( L ⋆ / L ⊙ ) = 2.3, that is, the limit above which a metal-poor 0.8 M ⊙ star is thought to undergo additional mixing between the bottom of the stellar convective envelope and the outer layer of the advancing hydrogen shell (see Placco et al. 2014 , and references therein for a discussion). Fig. 3. [C/Fe] as a function of log( L / L ⊙ ) for Galactic dwarf satellite and halo red giants with metallicities [Fe/H] –2.5. The Sextans stars we analyzed are represented by large red circles. Red squares are Sextans stars from Tafelmeyer et al. (2010) , the red triangle is the Sextans carbon-rich star from Honda et al. (2011) . Gray dots denote the [C/Fe] abundances of MW halo stars from Yong et al. (2013) . RGB stars in Sculptor ( Jablonka et al. 2015 ; Simon et al. 2015 ; Tafelmeyer et al. 2010 ), Fornax ( Tafelmeyer et al. 2010 ), and Draco ( Shetrone et al. 2013 ; Cohen Huang 2009 ) are shown in orange, blue, and green; respectively. The dotted line is the Aoki et al. (2007) dividing line for carbon enhancement, which takes into account the depletion of carbon with evolution along the RGB. Placco et al. (2014) developed a procedure for correcting the measured carbon abundances based on stellar model evolution and depending on the log(g) of the stars. They showed that when these corrections were applied to their dataset, the fraction of carbon-rich stars [C/Fe] >
Paper: 2019A&A...630A..83Z (acknowledgments)
We are grateful to the referee, Brent Groves, for very constructive comments that helped to improve the manuscript. We thank Stefanie Komossa (MPIfR), Pavel Kroupa (University of Bonn), Thomas Krichbaum (MPIfR), Madeleine Yttergren (University of Cologne), Bozena Czerny (CFT PAN), Mary Loli Martinez-Aldama (CFT PAN), and Swayamtrupta Panda (CFT PAN) for very helpful discussions and input. This work was done with the financial support of SFB956 – “Conditions and Impact of Star Formation: Astrophysics, Instrumentation and Laboratory Research” at the Universities of Cologne and Bonn and MPIfR, in which M.Z., G.B., A.E., N.F., S.B., J.-A.Z., K.H. are members of sub-group A2 – “Conditions for Star Formation in Nearby AGN and QSO Hosts” and A1 – “Understanding Galaxy Assembly”, with M.V. and L.F. being its former members. Michal Zajaček acknowledges the financial support by National Science Centre, Poland, grant No. 2017/26/A/ST9/00756 (Maestro 9).
Paper: 2021MNRAS.502.3329G (fulltext)
to account for the radiative recombination continuum and lines, a cutoff power-law model to account for the off-nuclear X-ray sources, in addition to the AGN-related emission, which is parametrized by two different MYTorus scattered and line components, in addition to the transmitted one described by the zeroth-order component of that model. In our fitting, in addition to our baseline model, we added the Bremsstrahlung and cutoff power law with all parameters fixed at the values provided by Bauer et al. ( 2015 ), and a Gaussian line to roughly model the excess around 6.5 keV. To account for the multiple absorption components, we also added a second Borus model, whose best-fitting parameters are , CFtor = 83 per cent, and A Fe = 1. Our best-fitting parameters are broadly consistent with the results presented by Bauer et al. ( 2015 ). The observed flux in the 2–10 keV energy band is 5.4 × 10 −12 erg cm −2 s −1 , and the intrinsic one (i.e. corrected for absorption) 1.3 × 10 −10 erg cm −2 s −1 . A0.2 NGC 1194 The starting model for the spectral fit of this source is provided by the work of Masini et al. ( 2016 ), who fitted the NuSTAR spectrum with the MYTorus model in the decoupled mode, with the addition of a Gaussian line at 6.8 keV, and a scattering fraction of the primary continuum of . In our fitting, we used our baseline model and found the main parameters (Γ, N H , and f s ) to be fully consistent with their best-fit results. The 2–10 keV observed flux is 1.2 × 10 −12 erg cm −2 s −1 , and the intrinsic one 1.0 × 10 −11 erg cm −2 s −1 . A0.3 NGC 2273 The starting spectral model for this source is again provided by the work of Masini et al. ( 2016 ), who fitted the NuSTAR spectrum with the Torus model that favoured a heavily absorbed scenario with . In our fitting, we used our baseline model, which yielded a best fit broadly consistent with their results. The 2–10 keV observed flux is 9.2 × 10 −13 erg cm −2 s −1 , and the intrinsic one 3.6 × 10 −10 erg cm −2 s −1 . A0.4 NGC 3079 The starting spectral model for this source is again provided by the work of Masini et al. ( 2016 ), who fitted the NuSTAR spectrum with the MYTorus model in a coupled mode. The results obtained with our baseline model are consistent within the respective uncertainties with their results. The 2–10 keV observed flux is 6.4 × 10 −13 erg cm −2 s −1 , and the intrinsic one 1.2 × 10 −10 erg cm −2 s −1 . A0.5 NGC 3393 The starting spectral model for this source is provided by the work of Koss et al. ( 2015 ) and Masini et al. ( 2016 ), who fitted the NuSTAR spectrum with both MYTorus and Torus models. The results obtained with our baseline model are broadly consistent with the results presented by these authors with a slightly larger value of N H (10 25 versus ). The 2–10 keV observed flux is 4.4 × 10 −13 erg cm −2 s −1 , and the intrinsic one 8.3 × 10 −11 erg cm −2 s −1 . A0.6 NGC 4388 The starting spectral model for this source is once more provided by the work of
Paper: 2015A&A...578A.122M (acknowledgments)
We are grateful to the STScI staff members for help in scheduling the intricate STIS observations. We thank the Paranal Observatory for conducting the UVES observations. Stony Brook’s continuing involvement in the SMARTS Consortium is made possible by generous support from the Provost of Stony Brook University. A.M. was co-funded under the Marie Curie Actions of the European Commission (FP7-COFUND). We thank Jose Groh for valuable discussions and comments on the paper draft. We thank the anonymous referee for insisting that we distinguish the two lines of sights described in this paper more clearly.
Paper: 2016ApJ...831..201L (fulltext)
component of Fe K α emission (log ξ = 0). A very similar result is reached by performing the same test on the 2014 mean EPIC-pn + NuSTAR spectrum (Δ χ 2 = 52), resulting in a modest reflection strength, R ∼ 0.6, while the photon index and Fe abundance remain unchanged: Γ ∼ 1.8; A Fe ∼ 3. The remaining key parameters in the relxill model are largely consistent with those reported in Table 2 ; i.e., log ξ ∼ 1.2, q ∼ 3, θ ∼ 40, r in 6. The black hole spin, a *, is unconstrained although we find a lower-limit on the high-energy cutoff of E cut > 130 keV. Finally, we return to check for the presence of highly-ionized absorption in the contemporaneous NuSTAR data. In terms of the EPIC-pn analysis, the two absorption grids modeling the highly-ionized two-velocity outflow are highly significant, with a combined contribution to the fit statistic of Δ χ 2 = 42 (for six additional parameters; meanwhile, the highly-ionized emission and soft-band absorption grids improve the fit by Δ χ 2 = 39 and Δ χ 2 = 44, respectively). Zoghbi et al. ( 2015 ) detected no significant discrete absorption features in the NuSTAR spectrum. We now find, in direct comparison with XMM-Newton , including the same two xstar absorption zones to the same baseline model fitted to just the contemporaneous NuSTAR observations offers a far less significant improvement to the fit statistic, with Δ χ 2 = 9. As such, even though the highly-ionized outflow is now well established thanks to XMM-Newton , we also confirm the main result from Zoghbi et al. ( 2015 ) that it was not detectable by NuSTAR alone. This demonstrates the importance of long-look observations with XMM-Newton in addition to the broad-band coverage of NuSTAR . 4. DISCUSSION AND CONCLUSIONS An extended XMM-Newton observation of PG 1211+143 has revealed spectral structure in the Fe K band, unseen in previous (shorter) observations. Utilizing the high throughput of the EPIC-pn, Pounds et al. ( 2016a ) showed that highly-ionized outflow resolved two velocity components ( and ), while an analysis of the high-resolution RGS data in Pounds et al. ( 2016b ) found lower-ionization co-moving counterparts of that dual-velocity flow. Here, we have extended our modeling over a wider energy range, incorporating contemporaneous NuSTAR data. The power of this approach—i.e., combining XMM-Newton and NuSTAR data—has previously been demonstrated in a precise measurement of the higher-energy continuum and thereby helping to disentangle absorption and reflection in a number of AGNs—e.g., NGC 1365 (Risaliti et al. 2013 ; Walton et al. 2014 ; Rivers et al. 2015 ), NGC 5548 (Kaastra et al. 2014 ; Ursini et al. 2015 ; Cappi et al. 2016 ) and PDS 456 (Nardini et al. 2015 ). In Section 3.1 , we fitted the hard X-ray spectrum with relativistic reflection, following the recent analysis of Zoghbi et al. ( 2015 ). Using the NuSTAR data alone, we confirm their solution, finding a steep photon index (Γ ∼ 2.4) and high reflection fraction ( R ∼
Paper: 2017MNRAS.470.4307F (fulltext)
other words, the PDFs are equal, because the system self-adjusts, not because glitch sizes are correlated with forward waiting times, as in the α ≪ α c (β) regime in Section 4 . The explanation for the qualitative behaviour seen in Fig. 20 is the same as for Fig. 13 . Figure 20. Correlation between sizes and forward/backward waiting times: Pearson coefficient, r , as a function of the control parameter α, for separable η with φ( x ) = x δ . For each value of α, we sample Δ t and Δ X over the course of a single simulation of N = 10 6 glitches, after the system reaches stationarity. We then calculate the pairwise correlation between the two sets for each α. Parameter: δ = 7. 6.5 Lag-dependent spin-down For the sake of completeness, we extend the results of Section 6 to the case of a general lag-dependent spin-down torque in Appendix E . 7 SAMPLING SIZE BIAS We now consider more carefully how we sample the different random variables used throughout this paper: X , N , Δ t , ΔΩ c (∝Δ X ), X e , and X s . Both X and N differ fundamentally from the other variables, because they are defined continuously for all t . The other variables are defined at discrete instants, when N ( t ) jumps by one due to a glitch. We therefore sample X ( t ) and N ( t ) by running an ensemble of simulations and observe each simulation at some time t 0 . The other variables are sampled at every glitch in a single simulation once it reaches stationarity. As an example of the subtle consequences of sampling bias, consider the ordering of the first moments 〈 X 〉, 〈 X s 〉, and 〈 X e 〉. Fig. 21 shows p ( x ), p e ( x ), and p s ( x ) for the non-separable power-law η ( 18 ) and ( 19 ) from Section 4 . Note that p s ( x ) is skewed towards larger values than p ( x ). By integrating the curves, we find 〈 X s 〉 = 0.600, 〈 X 〉 = 0.579, 〈 X e 〉 = 0.667, and hence 〈 X 〉 〈 X s 〉 〈 X e 〉. This seems wrong: X s , the value of X immediately after a glitch, ought to be smaller, on average, than X measured at some arbitrary time t . However, X is sampled from an ensemble at fixed time t 0 , whereas X s and X e are drawn from a different ensemble (every glitch event in a single simulation). If we observe the system at time t , we are more likely to land in a longer inter-glitch time interval than a smaller one, in which the (biased) X s and X e are spaced farther apart. Figure 21. Stationary PDFs p s ( x ), p e ( x ), and p ( x ) for the λ and power-law η used throughout Section 4 . Both p e ( x ) and p s ( x ) are sampled from a single simulation of N = 10 8 glitches after stationarity. We obtain p ( x ) by sampling an ensemble of S = 10 6 simulations at t = 8. Parameters: β = 10 −2 and α = 5. Sampling bias has an important observable consequence, even though p s and p e cannot be observed. We can obtain an ensemble distribution of waiting times by choosing a time t 0 ≫ 1 and measuring the length of the interval T ( i + 1) − T ( i ) for every simulation in the ensemble, where T ( i ) is
Paper: 2019A&A...628A..37B (fulltext)
where the rise speed of the flux tube at a depth of 20 Mm is more than about 150 m s −1 produce preemergence diverging flows that are not consistent with the observations. Norton et al. (2017) found from HMI vector magnetograms that the surface magnetic flux increases during the emergence process more slowly than predicted by simulations of flux emergence with rise speeds of 500 m s −1 at 20 Mm. Norton et al. (2017) suggested that these observations imply that the rise speed is too high in the simulations. There are, however, case studies that suggest very high rise speeds for some individual active regions. As one example, Kosovichev et al. (2018) measured an upward pattern motion of the horizontal divergence (inferred from time-distance helioseismology) of about 1.3 km s −1 at 20 Mm for AR11726, which emerged in April 2013. This particular active region was not in the sample used by Schunker et al. (2016) , which covered the time period May 2010 to November 2012. Here we extend the work of Birch et al. (2016) and measure the spatial variations and temporal evolution of near-surface flows before and during the emergence of the active regions described by Schunker et al. (2016) . Our goal is to determine whether statistically significant flow patterns are associated with the emergence of active regions. We expect that these flows will be useful for constraining models for the origin and formation of active regions. 2. Data reduction The catalog of Schunker et al. (2016) describes 105 active regions that emerge on the visible disk. It also contains an associated set of quiet-Sun control regions that have the same latitude and distance from the central meridian. Schunker et al. (2016) assigned each active region a P -factor that describes the amount of preexisting flux near the emergence location. The P -factors are determined by examining line-of-sight magnetograms by eye. A P -factor larger than two indicates emergence into an area with preexisting magnetic field. Here we only consider emerging active regions with a P -factor of two or lower. These emerging active regions and their partner quiet-Sun control regions form the basis for the data analysis carried our here. We refer to the emerging active regions as “AR” and to the quiet-Sun control regions as “QS” regions. For each AR and associated QS region there is a set of SDO/HMI Dopplergrams, continuum intensity images, and line-of-sight magnetic field observations as described by Schunker et al. (2016) . The data cubes of Dopplergrams were used as input for the helioseismic holography (Sect. 2.1 ). The data cubes of continuum intensity images were used as input to a local correlation tracking algorithm (Appendix B ). The magnetograms were used to determine the emergence location and emergence time, and to classify the complexity of the emergence (as described in Schunker et al. 2016 ). 2.1. Helioseismic holography We used surface-focusing holography as described in Birch et al. (2016) . The
Paper: 2021A&A...650A..69C (fulltext)
This is well in the range of those measured for UCXBs. 4.2. The nature of the accretor Determining the nature of the compact object in an unclassified LMXB is often challenging. It can be assessed based either on a dynamical measurement of the mass of the compact object (via optical and near infrared spectroscopy during quiescence; see e.g. Casares et al. 1992 ), or on the detection of X-ray coherent pulsations and/or thermonuclear X-ray bursts, which would provide a straightforward observational evidence for a NS accretor. To-date, all confirmed UCXBs are known to contain a NS accretor, although there are a number of BH candidates (see e.g. Bahramian et al. 2017 ). Assuming that our identification of J0840 as a UCXB is correct, it is then tempting to conclude that this system hosts a NS as well. However, our non-detection of coherent pulsations or thermonuclear X-ray bursts leaves this as an open question. 4.2.1. On the lack of coherent X-ray pulsations Coherent pulsations are thought to be formed in NS systems where the magnetic field is large enough to truncate the accretion disk and channel part of the disk material onto a small region on the NS surface, close to the magnetic poles. The radiation emitted from the heated impact region (hot spot) or from a slab of shocked plasma that forms above it (accretion columns) appears modulated at the NS spin period, producing a pulsed emission component. About 30 accreting millisecond X-ray pulsars have been detected to date (for reviews, see Di Salvo Sanna 2020 ; Patruno et al. 2021 ) and some of them were seen to exhibit pulsed fractions as small as a few percent in outburst (e.g. Strohmayer et al. 2018 ), that is, below the upper limits estimated for J0840 (Sect. 3.2 ). Besides, three accreting NS–LMXBs have shown coherent X-ray pulsations only sporadically during their outbursts, with the most extreme case being represented by Aql X-1, which showed pulsations over a ∼150 s interval out of a total observing time of ∼1.5 Ms ( Casella et al. 2008 ). However, most NS–LMXBs are non-pulsating systems, and a systematic analysis by Patruno et al. (2018) suggested that weak pulsations might not form at all in (most) non-pulsating LMXBs. Our non-detection of pulsations from J0840 is thus not sufficient to conclusively rule out a NS accretor and might still point either to an accreting millisecond pulsar characterised by a very small X-ray pulsed fraction and/or intermittent pulsations, or a non-pulsating NS–LMXB. 4.2.2. On the lack of thermonuclear X-ray bursts Many NS–LMXBs exhibit thermonuclear (type-I) X-ray bursts due to unstable ignition of hydrogen and/or helium freshly accreted onto the NS surface (for a review, see Degenaar et al. 2018 ). Bursts triggering all-sky monitors (including Swift /BAT) have been detected from several NS–LMXBs and in some cases they even led to the discovery of new transient systems (e.g. Wijnands et al. 2009 ; Degenaar et al. 2012 ). These episodes typically last a few
Paper: 2018MNRAS.475L..49M (acknowledgments)
Acknowledgements I would like to thank the anonymous referee for valuable comments that improved this work. The author is supported by the Grants-in-Aid for Scientific Research of the Japan Society for the Promotion of Science (16H07413, 17H02864).