adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJS..246...42W (fulltext)
right edges of the distribution correspond to the points at which particles just enter and exit the E ′ detector, L1, and are caused by anticorrelated variations of Δ E and E ′, which can occur as a result of variations in the particle path lengths through the Δ E detector (e.g., caused by differences in incidence angle) or Bohr/Landau fluctuations in Δ E . Simulations of the instrument response can be used to take these effects into account. Particles with E / M values beyond the high-energy end of the distribution are detected and identified using detectors deeper in the telescope. The relatively small number of He nuclei reaching the L2 detector in this weak event were not used in this study. Figure 6 shows mass histograms that result from projecting the data shown in Figure 5 on the mass axis. The middle panel corresponds to the region of the IS⊙IS/EPI-Hi plot to the left of the vertical red line and the right panel to the region to the right of this line. The red curves show Gaussian fits to the central regions of the peaks, which result in similar standard deviations of σ M = 0.18 and 0.20 amu for the low- and high- E / M regions, respectively. However, the low-energy mass peak has more prominent non-Gaussian tails. Figure 6. Mass histograms for the time period 2019-109 12:00 to 2019-113 12:00 from ACE /ULEIS and IS⊙IS/EPI-Hi. The IS⊙IS/EPI-Hi histograms are shown for two subsets of the data selected based on whether the E / M value is less than or greater than 1.2 MeV nucleon –1 , where E / M is calculated from the total energy and the assumption that M = 4. Red curves show Gaussian fits to the central regions of the 4 He peaks in the two IS⊙IS/EPI-Hi panels. Figure 7 shows the derived masses of the detected He events as a function of the time of detection at both PSP /IS⊙IS/EPI-Hi (top panel) and ACE /ULEIS (bottom panel). In order to obtain good separation between and , only those He events from PSP /IS⊙IS/EPI-Hi that have MeV nucleon –1 were used. ACE /ULEIS measured the highest intensity of both He isotopes on 2019-110 before midday. This was during one of the intervals during which IS⊙IS/EPI-Hi was turned off (gray bands). After midday on 2019-111, when IS⊙IS/EPI-Hi observed the highest intensity of both isotopes, ACE /ULEIS was still observing , but intensities of both He isotopes were lower than they had been during the first half of the previous day. The event at PSP /IS⊙IS/EPI-Hi continued through much of 2019-112, being cut off when the instrument was again turned off around 18:30 on 2019-112. At ACE /ULEIS, no He was observed after midday on 2019-112. Figure 7. Calculated He mass vs. time from PSP /IS⊙IS/EPI-Hi and ACE /ULEIS. The IS⊙IS/EPI-Hi data are restricted to events from the >1.2 MeV nucleon –1 subset shown in the right panel of Figure 6 . Gray regions indicate times when IS⊙IS/EPI-Hi was turned off. To derive abundance ratios between and , we have used IS⊙IS/EPI-Hi data from the E / M interval 1.35–1.75 MeV nucleon
Paper: 2019MNRAS.489.5326L (fulltext)
that for the initial core as a whole (β core = 1.9), is much smaller than what is typically adopted in MHD simulations of protoplanetary discs, as mentioned earlier. Interestingly, the magnetic energy is dominated by the poloidal component of the magnetic field rather than the toroidal component, as shown by the values of β t and β p in Fig. 15 for each disc, which are the ratios of the thermal to magnetic energy due to the toroidal and poloidal field component, respectively. The disc remains significantly magnetized at later epochs. The values of λ d and β are formally 9.2 and 2.5, respectively, at the epoch when , smaller than their counterparts at the earlier epoch of , but they are affected by a relatively low-density region that satisfies the disc criteria but is detached from the main body of the disc (see panel v of Fig. 15 ). By the last epoch shown (panel aa, ), the disc has λ d = 23 and β = 7.7, with the magnetic energy strongly dominated by the poloidal field component. As the AD coefficient increases, the disc starts to form earlier. For example, by the epoch when , a disc is already well-developed in the most magnetically diffusive model (M0.0AD10.0, panel l), clearly visible although underdeveloped in the second most diffusive model (M0.0AD3.0, panel k), but barely discernible in the standard AD case (panel j). Another trend is that, as the AD coefficient increases, the disc is somewhat bigger (compare the discs in the last three columns of Fig. 15 ) and more massive, with the disc mass increasing from for Model M0.0AD1.0 to for M0.0AD3.0 to for M0.0AD10.0. The disc remains significantly magnetized, with λ d ∼ 10−20 and β ∼ 10−20 typically, and the magnetic energy dominated by the poloidal field component in most cases. The main exception is the last epoch of the most diffusive model (M0.0AD10.0, ), when the disc is significantly less magnetized, with λ d = 32, β = 59, and the magnetic energy dominated by the toroidal field component instead. Turbulence changes the formation and properties of discs substantially, as illustrated by Fig. 16 , which is the same as Fig. 15 but for all models with sonic turbulence () and different levels of AD. The change is the most pronounced at the earliest epoch (), when the disc is well developed in all AD models and is clearly visible (although underdeveloped) even for the ideal MHD case (panel a). The ideal MHD disc is strongly magnetized, with λ d = 3.8 that is comparable to that of its parental core (especially the central part that is less magnetized compared to the core as a whole) and a plasma-β that is much less than unity (β d = 0.1). The strong magnetization, we believe, is the reason that the disc enabled by turbulence early in the ideal MHD case is transient; the magnetic braking is simply too efficient to allow the disc to persist for a long time. The same is broadly true for the early discs formed in the two weakest AD cases (Model M1.0AD0.1 and M1.0AD0.3), even though their masses
Paper: 2020MNRAS.492L..17S (acknowledgments)
ACKNOWLEDGEMENTS The authors thank to an anonymous referee for his/her valuable comments that helped to improve the article. This project has been supported by the Hungarian NKFI Grant K-119517 and the GINOP 2.3.2-15-2016-00003 of the Hungarian National Research, Development and Innovation Office, and the Lendület LP2018-7/2019 grants of the Hungarian Academy of Sciences, and by the Slovak Research and Development Agency under the contract No. APVV-15-0458. AD was supported by the ÚNKP-19-4 New National Excellence Program of the Ministry of Human Capacities and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. AD and GyMS would like to thank the City of Szombathely for support under Agreement No. 67.177-21/2016. This paper includes data collected with the TESS and Kepler missions, obtained from the MAST data archive at the Space Telescope Science Institute (STScI). Funding for the TESS mission is provided by the NASA Explorer Program. Funding for the Kepler mission is provided by the NASA Science Mission Directorate. STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 526555.
Paper: 2016ApJ...819..119L (acknowledgments)
Y.T.L. dedicates this work to the loving memory of his mother, Ms. Chun-Chih Kang. We are grateful to Keiichi Umetsu, David Spergel, Jim Gunn, Cheng Li, Rita Tojeiro, Zheng Zheng, Frank van den Bosch, Masamune Oguri, Uros Seljak, Lihwai Lin, Chung-Pei Ma, and Ying Zu for helpful comments, to Gerard Lemson for help with the Millennium Simulation database, to Surhud More for providing the data used in Figure 4 in electronic form, and to Bau-Ching Hsieh for help with parallel computation. We thank the anonymous referee for a constructive report. Y.T.L. thanks I.H. for constant encouragement and support. Y.T.L. acknowledges support from the Ministry of Science and Technology grants MOST 102-2112-M-001-001-MY3 and MOST 104-2112-M-001-047. R.M. is supported by the Department of Energy Early Career Award Program and by a Sloan Fellowship. This work was completed in part using the computing resources provided by the University of Chicago Research Computing Center. B.D. and A.K. were supported by the NASA ATP grant NNH12ZDA001N and by the Kavli Institute for Cosmological Physics (KICP) at the University of Chicago through grants NSF PHY-0551142 and PHY-1125897. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The SDSS Web Site is http://www.sdss.org/ . The Millennium Simulation databases used in this paper were constructed as part of the activities of the German Astrophysical Virtual Observatory (GAVO).
Paper: 2021MNRAS.505..215R (fulltext)
stage. From our observations of SDSS 1137 we also see that the binary mostly emits in blue bands as occurs in other disc accreting AM CVns. The X-ray emission is an order of magnitude smaller than the UV one, and the X-ray spectrum is also soft (consistent with the one of SDSS 1411 within errors). This suggests that the X-ray component is less important in AM CVns with discs than the UV component. The main uncertainty in the light curve of Fig. 1 is the duration of the outburst. This is due to the poor photometric coverage at early epochs (between 2017 December 21 and 2018 March 25), as well as the instrumental fluctuations and relatively large photometric uncertainties of the ZTF data, especially in the g band. However, an upper limit can be established considering the observation date of the XMM – Newton data (2017 December 21) and the beginning of the first rebrightening, which then imply an outburst duration of 380 d. For comparison purposes we have plotted that value in the outburst duration versus orbital period relation for AM CVns (Fig. 4 ). This duration is comparable to that of the recently reported value for SDSS 0807 (Rivera Sandoval et al. 2020 ), for which the first detected superoutburst lasted 390 d. It is clear that these two systems do not follow expectations from the DIM. They exceed by several times their expected duration, suggesting that there are additional mechanisms at work causing the outbursts of (long-period) AM CVn binaries. Figure 4. Orbital period ( P orb ) versus outburst duration (τ dur ) for AM CVns. The orange line indicates the relation obtained from the DIM (Cannizzo Ramsay 2019 ) and the blue line the empirical relation (Levitan et al. 2015 ) derived for systems with P orb up to 37 min. Upper limits are marked in cyan colour. We superimpose the results for SDSS 1137 from this paper, for SDSS 0807 (Rivera Sandoval et al. 2020 ) and for SDSS 1411 (Rivera Sandoval Maccarone 2019 ). The first two systems clearly exceed the expected τ dur from the DIM by several times suggesting the presence of additional (or even different) mechanisms in the origin of the outbursts, while SDSS 1411 still fits the predictions from the model. Note that the duration of the outburst in SDSS 1137 is an upper limit, where the yellow circle denotes the duration between the first ZTF measurement and the chosen date for the end of the outburst (See Section 4 ). The dashed line indicates the AM CVn with the longest P orb identified so far (SDSS J1505+0659, P orb = 67.8 min) as reported by Green et al. ( 2020 ) and which up to date has not shown outbursts. Other data in the plot were taken from Cannizzo Ramsay ( 2019 ) and references therein. A error for the outburst duration for all systems that do not have an upper limit was considered, as done by Levitan et al. ( 2015 ). 4.1 Colour evolution during outbursts of AM CVns: evidence of enhanced mass-transfer In order to investigate the mechanism(s) that drive outbursts in long-period
Paper: 2015MNRAS.447..392M (acknowledgments)
We are grateful to the referee for thoughtful comments that improved the content and clarity of the paper. We thank Andrew Siemion for assistance observing remotely with the GBT, and Carl Heiles and Leo Blitz for useful discussions. JM received support from a National Science Foundation Graduate Research Fellowship. KA is supported by funding through Herschel , a European Space Agency Cornerstone Mission with significant participation by NASA, through an award issued by JPL/Caltech. This research used NASA Astrophysics Data System Bibliographic Services, the SIMBAD data base, operated at CDS, Strasbourg, France, and the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA.
Paper: 2017MNRAS.465.1672S (acknowledgments)
Acknowledgments SS and MS acknowledge support from Higher Education Commission (HEC), Pakistan. MNS Qureshi acknowledges the Higher Education Commission (HEC), Pakistan grant 20-1886/RD10. PHY acknowledges NSF grant AGS1550566 to the University of Maryland, and the BK21 plus program from the National Research Foundation (NRF), Korea, to Kyung Hee University. He also acknowledges the Science Award Grant from the GFT Foundation to the University of Maryland.
Paper: 2016MNRAS.460.4282W (acknowledgments)
Based on observations obtained with XMM–Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA. Based on observations made with the WHT operated on the island of La Palma by the Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias. We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research.
Paper: 2015A&A...583A.135B (acknowledgments)
This Italian GAPS project acknowledges support from INAF through the “Progetti Premiali” funding scheme of the Italian Ministry of Education, University, and Research. We thank the TNG staff for help with the observations. K.B. also thanks V. D’Orazi and C. Sneden for fruitful discussions of heavy elements.
Paper: 2016A&A...592A..73R (fulltext)
linearly by the axial mean magnetic field vector , then only the formulationis possible if pseudoscalars, such as or , are not available. Without global rotation, an effect in the formulation does not occur in the linear relation ( 16 ). The parameter describes the turbulence-induced eddy diffusivity in the fluid, while the vector gives a velocity which transports (“pumps”) the large-scale magnetic field downward for positive . If, however, terms that are nonlinear in are included in the heuristic expression ( 16 ), then the extra termsoccur within the second-order-correlation approximation (Kitchatinov et al. 1994 ). The first term of the right hand side represents the known magnetic influence on the eddy diffusivity, while the second one forms a magnetic-induced pumping effect. There is no term representing an effect with the large-scale current helicity of the background field as (pseudoscalar) coefficient. For nonrotating and nonhelically driven turbulence, the large-scale current helicity does not create an effect in this approximation. Indeed, we do not find an effect in the numerical simulations below. There is instead the – sometimes ignored – magnetic-induced new pumping term, which for positive advects the magnetic field towards the maximum magnetic field. This effect should appear in all numerical models with helical large-scale field and nonhelically driven flow (Yousef et al. 2003 ; Bhat et al. 2014 ). Equations ( 16 ) and ( 17 ) yieldThe nonlinear terms magnetically affect both the eddy diffusivity and the advection velocity, which are basically reduced if the factor is positive. The possesses the dimension of a time and can be estimated as in the high-conductivity limit. One easily finds for important cases. For , the coefficient is always positive for all spectral functions that do not grow with growing frequency. Moreover, for all the full expression ( 19 ) is positive for which represents long correlation times as well as for , i.e.very short correlation times (so-called white noise). If compared with the magnetic-diffusion time , the delta-like spectral line represents the low-conductivity limit, while the white-noise spectrum represents the high-conductivity limit. In both limits, the is positive and, therefore, both eddy diffusivity and diamagnetic pumping are magnetically quenched rather than amplified (“anti-quenched”). 3.1. Numerical results Fig. 5 Two horizontal components ( top : , bottom : ) of the turbulence-induced electromotive force for background fields without helicity. Left panels : , (only homogeneous magnetic field in direction); right panels : , (only homogeneous electric current in direction). The averages over all snapshots are noted in the plots. The two horizontal components of the electromotive force are calculated for two different cases. In the first simulation, the applied magnetic fields are assumed to be free of helicity, i.e. . The other possibility works with . If the results of both setups
Paper: 2018MNRAS.477..964K (fulltext)
overly optimistic. 9 In addition to uncertainties in the most representative noise model, even choosing an accurate number of pulsars is non-trivial. PTA continue to expand by adding new systems, which are a very important part of detection forecasts (e.g. Taylor et al. 2016b ), but have different frequency sensitivities and thus different leverages on SNR. Finally, the GWF detection statistics we use are suboptimal in at least two respects. First, the statistics in Rosado et al. ( 2015 ) do not account for eccentric effects, and we treat the GW energy from single sources that is split across multiple frequency bins as entirely independent. Secondly, the excess power statistics may not perform as well on data where noise processes are harder to distinguish from single GW sources. The presence of line-like noise, due to uncertainties in planetary ephemerides (and their harmonics) for example; or instrumental effects, could introduce significant interference. At the very least, it is likely that foreground sources with periods comparable to the observing duration will be hard to distinguish from red noise when using single pulsar searches. This may necessitate correlated searches with lower recovery efficiencies. 4.2 Conclusions Single GW sources resolvable above the background, which we refer to as GWF sources, tend to be most detectable at frequencies near ∼0.1–0.3 yr −1 . At higher frequencies there are fewer sources which also tend to be less loud. At lower frequencies the GWB from all other sources is more likely to drown out the singles. According to most of our models, we would expect there to be a foreground source with a characteristic strain of about 10 −15 (or timing residual of ∼30 ns) after 10 yr of observations. These values are roughly a factor of 2 higher than those predicted by Sesana et al. ( 2009 , e.g. Fig. 3 ) and Ravi et al. ( 2014 , e.g. Fig. 4 ). When taking eccentricity into account, the primary effect of non-circular evolution is to shift GW energy from lower to higher frequencies. Thus, increasing eccentricity decreases the occurrence rate of GWF sources, especially at lower frequencies, both by increasing the GWB amplitude and by diffusing the single-source strains themselves. Changes in the effectiveness of stellar scattering, and more generally the rate of environmental hardening, has a small effect on the properties and occurrence rates of the GWF. Measurements of the number of GWF sources can thus provide strong constraints on the eccentricity distribution of the underlying MBHB population. Even in the absence of foreground detections, measuring the number of sources contributing to the background could provide the same information. This could be done either by directly resolving numerous loud sources in particular frequency bins (i.e. in frequency space), or indirectly by inference from the degree of anisotropy of the GWB (i.e. in angular space). DPs are usually higher for the GWF than for the GWB – indicating
Paper: 2021A&A...646L..13L (fulltext)
the observed acceleration g obs to that due to baryons g bar , where , , and g † is the only free parameter. This relation was established by analyzing the data points from 153 galaxies in a statistical sense. We wanted to test the constancy of G N , so we fit Eq. ( 2 ) to rotation curves fixing g † = 1.2 × 10 −10 m s −2 and varying G N from galaxy to galaxy. We also tried to vary g † and G N simultaneously, and obtained similar results. We show as an example the case that has the same number of fitting parameters as in Rodrigues et al. (2018a) . We set the likelihood function as with χ 2 given by where g RAR is the expected centripetal acceleration from Eq. ( 2 ) and is the uncertainty on the observed acceleration. We impose a flat prior on G N with G N > 0 as Rodrigues et al. (2018a) did for g † . Following the procedure of Rodrigues et al. (2018a) , we also impose flat priors on Υ ⋆ with a tolerance of a factor of two, though this is not consistent with stellar population synthesis models ( Schombert et al. 2019 ). Galaxy distance affects the fit quality as it is directly related to the contributions of the baryonic components. When the distance D is changed to D ′, the galaxy radius R and the contribution of each component will transform according to where k denotes gas, disk, or bulge. Rodrigues et al. (2018a) allow galaxy distance to vary freely within 20% of their observational values. This prior does not reflect actual observational errors. Distances measured through Cepheids or the tip magnitude of the red giant branch are highly accurate, so a 20% variation could be larger than 4 σ . The Hubble flow method is much less accurate; distances measured using this method could have errors up to 30%, so the imposed 20% free range is even within the 1 σ region. Rodrigues et al. (2018a) also ignored the uncertainty on disk inclination. Possible outer asymmetries and warps in the gas disk could mislead the determination of inclinations, as reflected in their uncertainties, which would in turn affect the observed rotation velocities. Despite the shortcomings of the methodology of Rodrigues et al. (2018a) , we choose to fit the same parameters (except G N instead of g † ) and impose the same priors to achieve the most direct comparison. We map the posterior distributions of the fitting parameters using the standard affine-invariant ensemble sampler in the Python package e m c e e ( Foreman-Mackey et al. 2013 ). The Markov chains are initialized with 200 random walkers and iterated for 2000 steps after 1000 burnt-in iterations. We record the best-fit parameters that maximize the probability function for every galaxy. Figure 1 shows the posterior probability distribution of G N . Though we analyze all the SPARC galaxies, we only include 101 of them in Fig. 1 based on the following quality cuts: we removed galaxies with quality flag Q = 3 (see Lelli et al. 2016 ), inclination smaller than 30°, or reduced . This is a quality cut similar to that
Paper: 2016MNRAS.456.1723L (acknowledgments)
The authors would like to thank Basu A., Heesen V., Calzetti D., and the anonymous referee for very helpful discussions and comments. LJT acknowledges the financial support from NASA through the grants NNX13AE87G, NNH14ZDA001N, NNX15AM93G, and NNX15AV24G. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The work at Ruhr-University Bochum has been supported by DFG through FOR1048. CJV acknowledges support for this work from the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1144468.