adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...880L..23W (fulltext)
and afterglows can be used to constrain the angular structures of energy and Lorentz factor (Beniamini Nakar 2018 ), which are consistent with our results. In Figure 2 , we show the fitting results of the characteristic Lorentz factor (Γ ∼ 2 η 0 γ B ) for GW170817 and 14 short GRBs. GW170817 has Γ ≈ 150, which fits well within the range of short GRBs (Γ = 135 ± 47). For GW170817, the Lorentz factor long the line of sight to the observer is 5.3, which fits the expectation that the early emission is effectively dominated by radiation from mildly relativistic material. Figure 2. Fit results of the characteristic Lorentz factor Γ (left) and the spectral index p (right) for GW170817 (red circle) and 14 short GRBs (blue squares). Markers with error bars indicate median values and symmetric 68% quantiles. In Figure 2 , we show the fitting results of the spectral index p . GW170817 has a tight constraint , as it has good observational data from all three bands. The mean and standard deviation of short GRBs is p = 2.26 ± 0.22. The spectral index of GW170817 is consistent with those of short GRBs. Figures 3 shows the distributions of GW170817 and 14 short GRBs in the ( n 0,0 , E 0,50 ) plane. GW170817 has n 0 ≈ 10 −2 proton cm −3 and E 0 ≈ 6 × 10 49 erg. Considering the jet opening angle θ 0 ≈ 0.11 rad, the corresponding isotropic equivalent energy is E iso ≈ 8 × 10 52 erg. These values are located within the typical ranges of short GRBs. Because n 0,0 and E 0,50 are degenerate parameters, most of the bursts display large error bars. Due to the scaling relations, our model is better at constraining outflow structures rather than estimating accurate values of degenerate parameters. Even though these degenerate parameters are not well constrained, we note that their uncertainties can be marginalized out, and thus will not affect the constraints on non-degenerate parameters. In Wu MacFadyen ( 2018 ), we broke the degeneracies by fixing density as n 0,0 = 10 −3 , and the outflow structure of GW170817 remains consistent. The values of e for the short GRBs is found to be around 0.3, which is consistent with other studies (Cenko et al. 2010 ; Beniamini van der Horst 2017 ). Figure 3. Fit results of explosion energy E 0,50 and ISM density n 0,0 for 14 short GRBs (blue squares) and GW170817 (red circle). Markers with error bars indicate median values and symmetric 68% quantiles. 4.3. On-axis Light Curves We have found that GW170817 and short GRBs share similar outflow structures and other physical parameters, except for the observation angle. Viewed off-axis, GW170817 displayed exceptional behaviors, such as late onset and early brightening. Short GRBs are viewed on-axis and show a general decline shortly after prompt emission. This leads to an interesting question: what would be observed from GW170817 if the observer were located on-axis? In Figure 4 , we show the X-ray afterglow observations for GW170817 (red circles with error bars) and 27 cosmological short
Paper: 2016A&A...587A.105A (fulltext)
computed with the STAREVOL code so far ( Palacios et al. 2003 , 2006 ; Decressin et al. 2009 ; Lagarde et al. 2012 ), while the prescription of Maeder (1997) has been systematically used for the rotating models computed with the Geneva code ( Eggenberger et al. 2012 ; Georgy et al. 2013 ). Meynet et al. (2013) have shown that the choice of the turbulent transport prescriptions may dramatically affect predictions for the structural, chemical, and rotational evolution of massive stars, but they were unable to clearly determine a best combination for fitting the observational chemical constraints. In Sect. 5 we discuss this in a similar way within the framework of the rotational evolution of solar-type stars. Table 3 Parameters and assumptions of the different models computed in this study. Reference. MPZ04 refers to Mathis et al. (2004) , TZ97 to Talon Zahn (1997) ; Matt+15 to Matt et al. (2015) ; Matt+12 to Matt et al. (2012) ; RM 12 to Reiners Mohanty (2012) ; and GB13 to Gallet Bouvier (2013) . SB is for solid-body rotation. 2.4. Transport of chemicals The transport of chemical species in radiative region is computed as a purely diffusive process ( Chaboyer Zahn 1992 ) but accounts for vertical advection and a strong horizontal diffusion. For a chemical species , the concentration follows the equation with the total diffusion coefficient, given bywhere and were defined previously (see Table 2 ). Finally, the term refers to the temporal evolution of the concentration of chemical species due to nuclear burning. 3. Physics of the models For all the models reported in Table 3 , the basic input physics (equation of state, nuclear reaction, opacities) can be found in Lagarde et al. (2012) . The initial abundances and mixing length parameter are calibrated without microscopic diffusion to reproduce a non-rotating Sun with respect to the Asplund et al. (2009) solar mixture with a precision for luminosity and radius at the age of the Sun. We used a mixing length parameter , an initial helium abundance , and an initial metal abundance . These values differ slightly from Lagarde et al. (2012) because we included a non-grey atmosphere treatment based on Krishna Swamy (1966) , which better agrees with observations for Sun-like stars (e.g. Vandenberg et al. 2007 ). In addition to the treatment of transport and loss of angular momentum and mass as described in Sect. 2, we included the modification of the effective gravity by the centrifugal forces and its effect on the stellar structure equations following Endal Sofia (1976) . This effect is slightly visible on the evolutionary track in the Hertzsprung-Russell diagram when the stars rotate fast. Rotation can have a non-negligible impact on the effective temperature for the fastest rotators around the ZAMS. Fig. 2 Evolution of the angular velocity as a function of time for the reference models. Plotted on the left as the rotational periods in days and during the right as the angular velocity in solar
Paper: 2015ApJ...815..102F (acknowledgments)
We acknowledge Matthew Bayliss, Kathy Cooksey, Francesco Di Mille, Steven Elhert, Michael Florian, Tolga Guver, Traci Johnson, Dan Kelson, Michael McDonald, Andy Monson, David Osip, Benjamin Rackham, Nathan Sanders, Anil Seth, Meghin Spencer, Brian Stalder, Tony Stark, Rik Williams, and Amanda Zangari for their assistance in Magellan target-of-opportunity observations. Partial support for this work was provided by NASA through Einstein Postdoctoral Fellowship grant number PF4-150121 and Chandra Award Number G04-15055X, issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under contract NAS8-03060. B.A.Z. acknowledges support from NSF grant AST-1302954, E.B. acknowledges partial support from NSF grant AST-1107973, and R.M. acknowledges support from the James Arthur Fellowship at NYU. Additional support was provided by several NASA/ Swift grants. This work made use of data supplied by the UK Swift Science Data Centre at the University of Leicester. The scientific results reported in this article are in part based on observations made by the Chandra X-ray Observatory and data obtained from the Chandra Data Archive. This paper includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile. Observations reported here were obtained at the MMT Observatory, a joint facility of the University of Arizona and the Smithsonian Institution. Based on observations obtained at the Gemini Observatory acquired through the Gemini Science Archive and processed using the Gemini IRAF package 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), the Australian Research Council (Australia), the Ministèrio da Ciência, Tecnologia e Inovação (Brazil), and the Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina). The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
Paper: 2017MNRAS.472...90D (acknowledgments)
Acknowledgements This work is based in part on observations made with the NASA/ESA Hubble Space Telescope , obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with programs 9744, 12889, 14254. Financial support was provided by NASA through grants from the Space Telescope Science Institute. We are grateful to Vivien Bonvin, Geoff C.-F. Chen, Frederic Courbin, Matthew A. Malkan, Cristian E. Rusu, Jong-Hak Woo and Andreas Schulze for useful comments and suggestions that improved this manuscript. We thank Chien Peng for his help with the estimates of black hole mass. XD is supported by the China Scholarship Council. TT acknowledges support by the Packard Foundations through a Packard Research Fellowship and by the NSF through grants AST-1450141 and AST-1412315. SHS gratefully acknowledges support from the Max Planck Society through the Max Planck Research Group. CER acknowledges support from the NSF grant AST-1312329. DS acknowledges funding support from a Back to Belgium grant from the Belgian Federal Science Policy (BELSPO). KCW and DP are supported by an EACOA Fellowship awarded by the East Asia Core Observatories Association, which consists of the Academia Sinica Institute of Astronomy and Astrophysics, the National Astronomical Observatory of Japan, the National Astronomical Observatories of the Chinese Academy of Sciences, and the Korea Astronomy and Space Science Institute. VNB gratefully acknowledges assistance from a National Science Foundation (NSF) Research at Undergraduate Institutions (RUI) grant AST-1312296. Note that findings and conclusions do not necessarily represent the views of the NSF. VB acknowledges the support of the Swiss National Science Foundation (SNSF).
Paper: 2021MNRAS.505.2913M (fulltext)
explosions, exceeds its binding energy (see Matteucci 1994 ; Bradamante, Matteucci D’Ercole 1998 ; Vincenzo et al. 2014 ): The thermal energy of the gas is produced by SN explosions (of all types) and by stellar winds, while the binding energy of the gas is computed as where W L ( t ) is the potential well due to the luminous matter and W LD ( t ) represents the potential well due to the interaction between dark and luminous matter. This last term mainly depends on the mass of the DM halo ( M DM ) and on the ratio S = R L / R DM between the galaxy effective radius ( R L ) and the radius of the DM core ( R DM ) (see Bertin, Saglia Stiavelli 1992 ). The last term R i ( t ) represents the fraction of matter that is returned by stars into the ISM through stellar winds, SN explosions, and MNS, in the form of the element i . In other words, it represents the rate at which each chemical element is restored into the ISM by all stars dying at the time t . R i ( t ) depends also on the initial mass function (IMF). Here we adopt a Salpeter ( 1955 ) IMF for all galaxies: normalized to unity between 0.1 and 100 M ⊙ . 4 NUCLEOSYNTHESIS PRESCRIPTIONS For all the stars sufficiently massive to die in a Hubble time, the following stellar yields have been adopted. For LIMS we included the metallicity-dependent stellar yields of Karakas ( 2010 ). For massive stars we assumed yields of Kobayashi et al. ( 2006 ). For Type Ia SNe (SNeIa) we included yields of Iwamoto et al. ( 1999 ). We adopted the single-degenerate scenario for SNeIa, in which SNe arise from the explosion via C-deflagration of a C–O white dwarf in a close binary system as it has reached the Chandrasekhar mass due to accretion from its red giant companion. The same stellar yields have been adopted in Vincenzo et al. ( 2015 ). Also, a complete and detailed description of those yields can be found in Romano et al. ( 2010 ). 4.1 Eu and Ba yields For both Eu and the r-process fraction of Ba we considered two different production sites: MNS and MRD-SNe. We assume r-process elements to be produced by (i) only MNS, (ii) only MRD-SNe, (iii) both MNS and MRD-SNe. In our simulations, MNS are systems of two 1.4 M ⊙ neutron stars with progenitors in the 9–50 M ⊙ mass range. In order to include the production of r-process elements from MNS in our chemical evolution code, we need to specify the following parameters (see Matteucci et al. 2014 ): the mass of each elements that is produced per merging event, and ; the time delay between the formation of the double neutron star system and the merging event, τ; the fraction of neutron stars in binaries that produce a MNS, α MNS . For what it concerns the yields of r-process elements from MNS, they have been obtained by assuming that there is a scaling relation between them and those of strontium (Sr). The adopted scaling factors are equal to 0.03 for Eu and to 3.16 for Ba, and have been found from the Solar system r-process contribution, as determined by Simmerer et
Paper: 2017A&A...603A..60F (fulltext)
fact is also illustrated in Fig. 12 , where the contour plots at 75% of the population are depicted. Figure 12 shows the mass range split over four bins for DAE diagram on the top row, for the PCA at the center, and for the RJ NUVR diagram on the bottom row. We note that the overlap is significantly lower for the DAE diagram, compared to the PCA diagram and to the RJ NUVR plane. The data-driven representations present a clear evolution over the axis in terms of mass for both star-forming and quiescent populations, while this evolution is barely observed in the classical RJ NUVR diagram. These distinct behaviors of the stellar mass distributions in the diagrams thus reveal variations of the galaxy SED properties that are again more efficiently extracted with the unsupervised learning techniques than when just taking the NUV and rest-frame colors into consideration. Given that the stellar mass of galaxies tightly correlates with their near-infrared luminosities, the mass distributions observed in Fig. 9 may suggest that the PCA and the DAE results at a given redshift are partially driven by the variations of the near-IR magnitudes that can be probed at this kind of redshift. On the other hand, the stellar mass also correlates with other physical properties such as the dust extinction and the activity of star formation, which both modify the overall shape of galaxy SEDs and necessarily also impact the DAE outputs. For instance, galaxies with enhanced recent star formation display SEDs characterized by a more important contribution of young and massive stars, which produces intense radiations of UV photons boosting the overall galaxy emission at short wavelengths. The star formation rate (SFR) of galaxies also correlates with their stellar mass, but in a non-linear relation such that the SFR per unit of mass mildly decreases in more massive sources ( Speagle et al. 2014 ). The relative level of star-forming activity in galaxies could thus also contribute to the apparent variation of the DAE and PCA distributions with the stellar mass. Fig. 12 Contour plots for different mass bins on the DAE diagram, on the PCA diagram and on the RJ – NUVR plane. The mass range is split over four bins and the contour plots are displayed at of the samples in the different mass bins. The DAE diagram is displayed on the top, PCA diagram at the center, and the RJ NUVR diagram on the bottom. The contour’s colors grow from grey to green for star-forming galaxies, and from yellow to purple for quiescent galaxies, respectively. These plots attest to the relationship with the stellar mass captured by the DAE and PCA diagrams for both star-forming and quiescent populations, while the overlap in the RJ – NUVR diagram is considerably higher. 4.3. Specific star formation rate To further explore the impact of the galaxy star-forming activity in the different diagrams, we conducted a similar analysis, taking into consideration this time the influence of the specific star
Paper: 2018MNRAS.477.1120K (fulltext)
of 0.47, but we do not draw any interpretation from it. Figure 12. Distribution of the peak intensity, duration, fluence and wait time t + for a subsample of flares with peak intensities in the range , durations in the range 2.8 min Δ t ≤ 49 min totalling 1198 flares. This range is chosen to select power-law behaviours of all these quantities, in order to test the SOC hypothesis (see the text). Orange lines represent the power-law fit on each distribution, characterized by a the index α. The fits were made using the integral of the distribution (as in Fig. 10 ). In the case of the fluence and the wait time, fitting interval had to be restricted to the area between the two vertical dashed red lines. We also remark that the power-law index of the Fourier spectrum of the light curve with flares but without orbital modulation is −1.11 ± 0.05 while without flares it is only −0.70 ± 0.05 (see Section 4 ). The pink noise (power-law index of −1) is one of the main characteristics of SOC processes, as this theory was introduced to explain the ubiquitous presence in natural processes of this type of noise (Bak, Tang Wiesenfeld 1987 ). Therefore, the fact that the index is closest to −1 with flares included suggests that these are only the larger-amplitude tail of an overall SOC signal. This is further re-enforced by the previously mentioned fact that the distribution of peak intensities behaves like the upper tail of a broader distribution. 6.3.1 A comparison with low accretion rate CVs The most obvious SOC mechanism to compare J1023 during its accretion phase with is the SOC possibly present in low accretion rate cataclysmic variables (e.g. Dendy, Helander Tagger 1998 ). Low accretion rate non-magnetic CVs often undergo brightening events called dwarf novae during which the systems brightness increases by 2–7 mag. Dwarf novae events are suspected to arise due to a combination of an instability in the accretion disc (disc instability model, or DIM; Osaki 1974 ) and a variable mass accretion rate. At low accretion rates, the discs are cool and stable. Once the discs reach a critical density, the viscosity of the disc changes, and material begins to move rapidly inwards through the disc, increasing the temperature of the disc while rapidly transferring material on to the white dwarf. This continues until the density of the disc drops below the critical value, and the disc becomes stable again. For a review, see Lasota ( 2001 ). While the mechanisms between the outbursts in dwarf novae and the flares in J1023 are very different, future work should be done on investigating the similarities between these systems, especially since the driving force behind dwarf novae and the flares in J1023 is accretion. 7 CONCLUSIONS The Kepler K2 light curve of J1023 has revealed more about the nature of tMSPs during their accreting state. In particular, we have found that the amplitude of the underlying orbital modulation in the accretion state is similar to that in the
Paper: 2019MNRAS.485.1579K (fulltext)
we assume a completeness , relative to CRTS, 7 i.e. 2.4 Models of variability The luminosity of an object in a binary system will not necessarily vary on the orbital period or at all. The premise of photometric identification of MBHBs is that the binary period is somehow imprinted into variations of the observed luminosity. In the particularly convincing example of PG 1302-102 (Graham et al. 2015b ), sinusoidal variations in the light curve can be well explained by Doppler boosting from a mildly relativistic orbital velocity (D’Orazio, Haiman Schiminovich 2015b ; but see also Liu, Gezari Miller 2018 ). Additionally, purely hydrodynamic modulations to accretion rates have been observed in simulations (e.g. Farris et al. 2014 ). Here, we describe models for both types of variability mechanisms. Throughout our analysis, each variability mechanism is considered entirely independently, i.e. we do not consider systems which may be both hydrodynamically and Doppler variable. The physical scenario is depicted schematically in Fig. 5 , assuming thin-disc geometries and a mass ratio q ≳ 10 −2 (see Section 2.4.2 ). On the left, the two MBHs, each with a circum-single disc, are shown within a cavity (‘gap’) of material evacuated by the binary orbit that separates them from the circumbinary disc. The radii of the circum-single discs are determined by the Hill radius of each MBH (see equation 6 ). Despite the presence of the gap, the circumbinary disc continues to transport angular momentum outwards, requiring material to be accreted inwards. Material from the disc overflows across the gap as accretion streams on to each circum-single disc. The orbital period at the inner edge of the circumbinary disc is longer than that of the binary, causing periodic variations in the accretion rate on to each circum-single disc (Fig. 5 , upper right). The secondary MBH is farther from the centre of mass and closer to the circumbinary disc edge, which leads to it receiving a disproportionate fraction of the accreting material [as described by equation ( 1 ) and shown in Fig. 1 ]. Each circum-single disc, in addition to the circumbinary disc (although typically to a lesser extent), will produce AGN-like emission. 2.4.1 Doppler variability Any component of the binary orbital-velocity along the observer’s line of sight will lead to both a relativistic boost and a Doppler shift in the observed spectrum of each circum-single disc. The boost is calculated using the Doppler factor, D ≡ [γ(1 − || / c )] −1 , where the Lorentz factor γ ≡ (1 − 2 / c 2 ) −1/2 . The line-of-sight velocity, || ≡ sin ( i ), depends on the binary inclination i , which we define as zero for face-on systems. The observed flux is calculated as (D’Orazio et al. 2015b ) where the observed frequency ν is related to the rest-frame frequency as, . Assuming a power law of index α ν for the section of the spectrum being observed, the Doppler variation in flux from the source will be (Charisi et al. 2018 ) where
Paper: 2018ApJ...856..171Z (acknowledgments)
We thank Lisa Kewley, Renbin Yan, Shude Mao, Junqiang Ge, Jong-Hak Woo, and Chun Lyu for helpful discussions and suggestions. We thank an anonymous referee for helpful suggestions that improved the paper significantly. The research presented here is partially supported by the 973 Program of China under grants No. 2013CB834905 and No. 2009CB824800, by the National Natural Science Foundation of China under grants No. 11073040, by the Strategic Priority Research Program “The Emergence of Cosmological Structures” of Chinese Academy of Sciences, Grant No. XDB09000000, and by Shanghai Pujiang Talents Program under grant No. 10pj1411800. K.Z. acknowledge the support by the No. 53 China Post-doc general Fund under grants No. 2013M531232. Funding for the Sloan Digital Sky Survey (SDSS) has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Aeronautics and Space Administration, the National Science Foundation, the U.S. Department of Energy, the Japanese Monbukagakusho, and the Max Planck Society. The SDSS is managed by the Astrophysical Research Consortium (ARC) for the Participating Institutions. The SDSS website is http://www.sdss.org/.