adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...603A..72B (fulltext)
and in quiescence, along with their rates of change. We have also refined the supercycle length (and possible changes in superoutburst occurrence rate). The structure of this paper is the following: Sect. 2 presents details about observations, data reduction, and photometry analysis. Section 3 contains information about the photometric behaviour of the system and a thorough study of positive and negative superhumps. We discuss this information in Sect. 4 and present a summary of our campaign dedicated to MN Dra in Sect. 5 . 2. Observations and data reduction The observational data of MN Dra presented here were collected throughout three observation campaigns. The first data were obtained during 6 nights from 2009 October 12 to October 22, in the Skinakas Observatory, Greece. At that time, the star was in a quiescent state. The second campaign was conducted during 15 nights from 2013 July 09 to September 10. On that occasion, data were gathered in Poland, at the Borowiec station of the Poznań Astronomical Observatory, at J. Kochanowski University in Kielce, and at the Ostrowik station of the Warsaw University Observatory. During the 2013 campaign we detected two superoutbursts of MN Dra. The longest campaign was organized in 2015, from June 4 to December 18. Data covering 84 nights of observations were collected at the Borowiec Station, in Pisa in Italy, at Antelope Hills Observatory and at the MDM Observatory in the USA. Once again, two superoutbursts in MN Dra were observed. In Table 1 we present the journal of CCD observations of MN Dra. In total, the star was observed 374.37 h, and 8717 useful exposures were obtained. To summarize, eight telescopes with diameters ranging from 0.25 to 1.30 m were used to collect data during the 2009, 2013, and 2015 campaigns. All data of MN Dra were gathered in a clear filter (“white light”). This approach allowed us to obtain reliable photometry around 18–20 mag when the object was in quiescence. Bias, dark, and flat-field correction of raw files was made in a standard way. For the data reduction we used the IRAF package. The profile (point-spread function) photometry was obtained with the DAOPHOTII package ( Stetson 1987 ). Relative unfiltered magnitudes of MN Dra were derived from the difference between the magnitude of the star and the mean magnitude of two comparison stars marked as C1 and C2 in Fig. 1 , where the map of the observed region is shown. The equatorial coordinates of comparison stars C1 (, , mag in filter) and C2 (, , mag in filter) were taken from the 2MASS All-Sky Catalog ( Cutri et al. 2003 ). Instrumental magnitudes were transformed into the passband, which is very close to observations made in white light. Fig. 1 Finding chart with the position of MN Dra marked as V1 and two comparison stars C1 and C2. The field of view is about . 3. Light curves 3.1. Global photometric behaviour Fig. 2 Global photometric behaviour of MN Dra. For convenience, we use only a day number HJD-2 450 000 [d]
Paper: 2017MNRAS.471.3200M (fulltext)
so the shell velocity equals the ratio of the swept-up momentum and the swept-up mass M sh ( t ) ≈ f Ω M ej + M w,sh ( t ), where the swept-up mass of the pre-dynamical wind is given by and t end = t run ( v w / v sh ) is the time required for shock to reach the outer edge of the pre-dynamical outflow. Although the time evolution of v sh is complicated by the implicit dependence of M w,sh on v sh , we find that for fiducial parameters its value varies by less than a factor of 2 from its asymptotic value of where in the final line we have taken M w ≲ M ej , f Ω = 0.3 and . In our analytic estimates that follow we assume a temporarily constant value of , where ξ ≈ 0.5–1. Emission escapes on the photon diffusion time-scale through the fast ejecta, which at the shock radius R sh ≈ v sh t is given by where we have used equation ( 3 ) for δ o = 5 and δ i = 2. If the opacity remains roughly constant in time, then equating this to the expansion time t gives the time-scale for the shock-powered emission to rise to its peak (Arnett 1982 ) where κ 0.3 ≡ κ/(0.3 cm 2 g −1 ) is normalized to a typical value (Fig. 4 ). In the second line, we have normalized the ejecta mass to a fixed fraction of the total stellar mass; the fast ejecta velocity to the binary escape speed; and the semimajor axis of the binary to 10 times the main-sequence stellar radius R ms ≈ R ⊙ ( M / M ⊙ ) 0.8 . All else being equal, equation ( 21 ) shows that mergers between more massive stars, or evolved stars with radii much larger than their main-sequence values, will result in later peaking light curves, consistent with our full numerical solutions (Fig. 5 ). However, overall the dependence on binary parameter is relatively weak, consistent with the factor of ≲5 range of observed values t pk ≈ 30–150 d for a ∼ 2–100 R ms and M ∼ 1.5–30 M ⊙ (Fig. 1 , Table 1 ). Also note that, by assuming a fixed, relatively high value of the opacity in deriving equation ( 21 ), we cannot account for cases when the shock heating is insufficient to keep the ejecta ionized (e.g. the 2 M ⊙ model in Fig. 5 ). Table 1. Properties of LRN and their stellar progenitors. References: (1) Mason et al. ( 2010 ), (2) Munari et al. ( 2002 ), (3) Tylenda et al. ( 2005 ), Afşar Bond ( 2007 ) but see Munari et al. ( 2005 ), (4) Blagorodnova et al. ( 2017 ), (5) Smith et al. ( 2016 ), (6) Mauerhan et al. ( 2015 ), (7) Mauerhan et al. ( 2017 ), (8) MacLeod et al. ( 2017a ). a Ejecta velocity inferred from optical spectra (note that this may not always represent the velocity of the bulk of the ejecta, but instead the fastest components). b Approximate peak luminosity of second peak. c Assuming the second peak was bolometrically about 1 mag fainter than the first. If the forward shock dominates the shock power (equation 7 ), then the peak luminosity of the shock-powered second light-curve peak is given by (in the limit v sh ≫ v w ) where we have used equations ( 1 ) and ( 2 ). Normalizing again to the
Paper: 2018MNRAS.474.3451X (fulltext)
Rincon, Ogilvie Proctor ( 2007 ), where a subcritical dynamo generated by the interaction between (fluctuating) non-axisymmetric and (mean) axisymmetric toroidal magnetic field leads to ultimate saturation in a bounded domain with zero initial magnetic field. Indeed, in our theory the domain-integrated helicities of the flow and the magnetic field are both zero, while the domain-integrated cross-helicity vanishes because and are out of phase. The theory also allows us to investigate the astrophysically relevant weakly dissipative regime. We do this by showing that the inviscid limit of the theory, Re ≫ 1, is well behaved, and then examine the further limit of nearly ideal MHD by considering large values of Re m . Although the zonal magnetic field fluctuation becomes very large in this regime the angular momentum flux remains well behaved for all wavenumbers and decreases as | m | −1 (equation 34 ). A simple prediction for the magnitude of this flux follows from the assumption that the dominant wavenumber m corresponds to the optimum wavenumber from linear theory that yields the fastest MRI growth rate. The resulting expression for the angular momentum flux, , resembles the prescription of the α-model of Shakura Sunyaev ( 1973 ) but with the sound speed replaced by the Alfvén speed. A similar dependence of the momentum flux on the square of the magnetic field was observed in the simulations of a compressible but magnetic field dominated disc by Hawley et al. ( 1995 ), although subsequent computations by Sano et al. ( 2004 ) and Pessah, Chan Psaltis ( 2007 ) suggest a weaker dependence on v A . We emphasize that throughout this paper the symbols Re and Re m refer to scaled values of the Reynolds number and the magnetic Reynolds number, i.e. the condition Re m ≫ 1, for example, refers to Re m * ≫ ε −1 while the condition Re m ≪ 1 refers to Re m * ≪ ε −1 . The latter condition can therefore be satisfied for the large Re m * expected of astrophysical systems provided only that these are small compared to ε −1 , a quantity defined here in terms of the shear rate. Our approach has several attractive features, one of which is the fact that angular momentum is inevitably transported outward. In this connection we mention that Lesur Longaretti ( 2007 ) find that the transport coefficient F v /|σ| in a turbulent disc increases with the magnetic Prandtl number Re m / Re . In our (non-turbulent but fully non-linear) study we find a similar tendency when m is fixed (cf. Fig. 7 a): in the supercritical regime, above the marginal stability curve, F v decreases as Re increases for fixed Re m ; when Re is fixed, F v increases as Re m increases. Possible concerns include neglecting the possibility that saturation is accompanied by net outflow in the vertical direction, as measured by the quantity although this is a consistent assumption that follows from the relation obtained from equation ( 1a ). This relation is identically satisfied by . A further concern is
Paper: 2016MNRAS.458.4354O (fulltext)
that the mass point C is under the action of an arbitrary additional force F (per unit mass) that is not gravitational and acts on the gas component of the supercloud. In our case, F will represent an hydrodynamic force due to the friction between the gas supercloud and the surrounding interstellar medium or due to the pass of a shock wave that affects the gas supercloud. Then, the motion equations in the reference system ( x, y ) are given by Figure 1. Illustration of the stationary coordinate system ( x, y ) centred at the Galactic centre and the rotating coordinate system (η, ξ) of origin O moving on a circular orbit of radius R 0 . The figure plane represents the Galactic plane and the rotation sense is clockwise. The angle θ, between the y -axis and ξ-axis, increases in the sense of the Galactic rotation. where F x and F y are the components of F and subscript c refers to the coordinates of the point C. Because the force K ( R c ) is equal to the centripetal force at R c , we will write K ( R c ) = R c ω( R c ) 2 , where ω( R c ) refers to the Galactic angular velocity at R c and for short will be written as ω c . Since we are interested in referring the motions to the rotating system, we should convert equations ( 1 ) into ones depending on (η, ξ). The formulae that govern this transformation are which are obtained from simple geometrical considerations of Fig. 1 . Besides, we should have into account that F x = F ξ sin θ + F η cos θ and F y = F ξ cos θ − F η sin θ, and that θ is a function of time t given by θ = θ 0 + ω 0 t . We adopt θ 0 = 0, i.e. the y -axis and ξ-axis agree at t = 0. Differentiating twice equations ( 2 ) with respect to the time t, we obtain where and . From the second members of equations ( 1 ), with the corresponding replacements, we get that where and . By equating equations ( 3 ) to the corresponding equations ( 4 ), we have The system of equations ( 5 ) is equivalent to the system a 1 = c 1 and a 2 = c 2 . Thus, the exact epicyclic equations of motion for the mass point C are If we adopt a flat rotation curve for the Galaxy, with a rotation velocity V 0 = 220 km s −1 , in approximate agreement with the measurements in a large range of Galactocentric distances (e.g. Sofue, Honma Omodaka 2009 ; Bovy et al. 2012 ), and . Since (see Fig. 1 ), we have For the study of the orbit of the local supercloud, the origin of the system (η, ξ) should be located in the solar neighbourhood at the Galactocentric distance of the Sun (i.e. R 0 ≈ 8 kpc). Replacing equation ( 7 ) into equations ( 6 ), this equation system can be solved numerically. The initial conditions needed to solve the differential equation system ( 6 ), i.e. η c (0), ξ c (0), , , will be calculated from the LSR velocity and position of the supercloud's gravitational centre at t = 0. With this purpose, we use the relations ( 2 ) to obtain η c = x c cos θ − y c sin θ and ξ c = x c sin θ + y c cos θ − R 0 . Hence, and . Since θ = ω 0 t = 0 at t = 0, the former
Paper: 2019MNRAS.489.2038I (fulltext)
Figure 8. The dependence of dimensionless critical accretion rate on IBHs dimensionless mass m M /(10M ). Solid, dashed, and dotted curves correspond to JWST detection limit, that of MM without the confusion effect being taken into account, and that of MM with this effect being accounted, respectively. Figure 9. The dependence of the parameter on m M /(10M ). Curves of different styles describe the same cases as in Fig. 8 . Figure 10. Same as Fig. 9 , but now the dependence of parameter eff is shown. Figure 11. The minimal number of black holes within CMZ needed for observations with JWST and MM, for fixed values of the efficiency parameter , as a function of mass. Solid, dashed, and dotted curves corresponding to observations with JWST and MM without and with the confusion limit being taken into account. Curves with progressively larger values of N bh for a given value of m correspond to progressively smaller values of . The accretion rate given by expression ( 1 ) should be smaller than its critical value for 1. This requirement sets a limit of the possibility of observations of IBHs by a given instrument and for a given value of the density of the interstellar medium, IBH mass, and velocity. In order to compare ( 1 ) with , we express the accretion rate given by equation ( 1 ) in terms of our characteristic unit: , set c s 0 in equation ( 1 ), substitute there our chosen value of density of interstellar medium 3.34 10 22 gcm 3 and assume that a typical IBH velocity 225kms 1 . We obtain where . To begin with, let us consider a black hole travelling at the typical velocity and set in equation( 17 ). In this case from the requirement that for 1, we get lower limits on black hole masses, M crit , available for observations. For larger black hole masses, the condition gives a lower limit on the efficiency parameter , which is shown in Fig. 9 . One can see from this figure that JWST and MM without taking into account the confusion effect could be able to observe IBHs with masses larger than M crit 30M , while allowing for the confusion effect MM is able to detect IBHs only when M 90M , respectively. The range of available values of is rather limited to 0.1 even for the largest considered black hole mass 100M when the confusion effect is neglected and 0.8 in the opposite case. That should be relatively large may be a problem for observations since some works suggest significantly smaller values, see e.g. Igumenshchev Narayan ( 2002 ). On the other hand, assuming that high accretion efficiency is sustained and taking into account that the available volume of CMZ 10 6 pc 3 , it could be possible to observe IBHs with number density as small as 10 6 pc 3 . Available values of and M crit can be smaller in a situation when there are many IBHs within CMZ, with their number N bh 1 and, accordingly, their number density 10 6 pc 3 . In this case, assuming the Maxwellian statistics of distribution of IBHs over velocities we can estimate the number of IBHs
Paper: 2016ApJ...821...28B (fulltext)
circles mark the mass coordinate . The last few profiles show the envelope oscillating due to mass transfer rates falling below the steady He-burning regime. On the low mass donor end, however, carbon may not ignite at all. The model with initial donor mass stays at lower accretion rates and falls far enough below the steady helum burning range that the mild helium oscillations become powerful enough to blow off mass. The blue dashed line shows how the maximum temperature in the shell will oscillate between the flashing helium burning shell and the hot, compressed C/O ashes below the burning layer. At these accretion rates, however, the accretion efficiency stays above 70%, allowing the WD to gain enough mass to trigger carbon ignition in the core. We compare our accreted mass retention efficiency calculated in the final stages of this model using super-Eddington winds to the analytical estimates of Kato Hachisu ( 2004 ) and Wang et al. ( 2015 ) in Figure 7 . Wang et al. ( 2015 ) used MESA (r3661) for their study, but used the default super-Eddington wind settings, whereas we triggered super-Eddington winds while the WD is still in a compact configuration in order to speed the computation. Systems with donor mass , therefore, will fall to lower accretion rates and efficiencies, and ultimately lose contact before a core ignition occurs. The components, now both C/O WDs, will spiral in due to GWR and contribute to the double-degenerate channel for SNe Ia, as discussed in Section 6 . Figure 7. The top panel shows the accreted mass retention efficiency during the last stages of the system with the donor stars. The black line shows what we calculated by taking the ratio of the growth rate of the accretor and the mass loss rate of the donor. The red short-dashed lines show the retention efficiency given analytically by Kato Hachisu ( 2004 ) for 1.3 and cores. The blue long-dashed lines show the retention efficiency given analytically by Wang et al. ( 2015 ) for 1.3 and cores. The bottom panel shows the mass transfer rate being donated to the WD. Between the high and low mass donor limits is the region where core carbon ignitions occur. This is where the donor mass is high enough that the accretion rates do not fall too far below the stability boundary before is reached, but also low enough that it avoids a carbon shell ignition from long sustained high accretion rates. A clear example of this is shown in Figure 8 , with the trajectories of the core and envelope shown with profiles in space for a system with . In Figure 9 we show the composition and temperature profile when this model has grown to , the same profile as that in Figure 8 . As the C/O envelope is , this shows that the base is at a temperature minimum, and the temperature maximum in the envelope is only deep. The core ignition in these models indicates that they are likely SN Ia progenitors. Figure 8. Profiles of of the WD in the binary system with . Markings have the same meaning as
Paper: 2017A&A...602A.123L (fulltext)
unobscured sources, to dex for faint and highly obscured sources. The average value of the error is dex, with standard deviation . We show the average errors with a black cross in the left panel of Fig. 4 , while the specific value for each source is used in the following analysis. In order to look for intrinsic correlations between these two quantities, one possibility is to compute the partial Spearman rank correlation between two variables in the presence of a third, and to assess the statistical significance of such correlation (e.g., Macklin 1982 ). To derive the correlation coefficient between and , conditioned by the distance, (, , ), we evaluate the Spearman coefficient related to each pair of parameters and then combine them according to the expression (Conover 1980), which returns the partial correlation between and , corrected for the dependency on . The resulting is 0.15, and the associated confidence level, in terms of standard deviations, that the first two variables are correlated, independently of the influence of the third, is , following Eq. (6) of Macklin ( 1982 ). Therefore, the two quantities appear to be significantly correlated, after the effect of redshift on both of them is taken into account. 3.2. Redshift bins The second approach, often used in the literature, is to define redshift bins that are as narrow as possible to minimize the distance effect, and to look for correlations between the two quantities. Thanks to the large sample collected in this work, we can divide the sample into five redshift bins. For every redshift bin, a large distribution in both luminosities can be observed, with the typical luminosity increasing with redshift (Fig. 4 ). Most of the observational works mentioned in Sect. 1 looked for the distribution of average in bins, or average in bins (but see Gruppioni et al. 2016 ). Both hydrodynamical simulations (e.g., Volonteri et al. 2015 a,b) and semi-analytic models (e.g., Neistein Netzer 2014 ) show that, in the - plane, there may be the superimposition of a weak correlation for the bulk of the population, and a strong correlation only for the most extreme merger phases, corresponding to the highest and . If the underlying distribution shows such a complex shape, the results of the two approaches (average in bins or average in bins) may be very different. In Fig. 5 (left) we show the result of plotting average in bin of (both in log scale) in five redshift bins. As can be seen, there is no correlation at all and, as expected, there are no sources below the relation computed for a pure AGN template in Mullaney et al. ( 2011 ), similar to the one used in Netzer et al. (2009). Following this approach, we are therefore able to reproduce the results of Shao et al. ( 2010 ), Rosario et al. ( 2012 ), and others that claim no correlation between AGN activity and SF over several orders of magnitude in luminosity. On the other hand, computing average in bins (in log scale) from the same bivariate
Paper: 2017ApJ...839...17S (fulltext)
by a Gaussian probability density function (PDF) with mean and standard deviation . For the flux limits, we used the 1 σ upper limits as as well as σ for the PDFs. All PDFs are set to zero for negative fluxes. Each of these Gaussian PDF representations of the measurements are corrected by the lens magnifications listed in Table 2 , using the intrinsic flux as simply the observed flux divided by the magnification and that the variance on the intrinsic flux is given by:Here is the estimated lensing magnification for each image. The uncertainties on the magnification factors quoted in Table 2 are not symmetric, and we therefore, conservatively, use the largest absolute value of the upper/lower magnification ranges as . The product of the individual magnification-corrected PDFs for each image gives us a representation of the intrinsic flux of the background source including information from both the detections and the upper limits. If any of the detections and/or upper flux limits were in disagreement such a product would be driven to 0. Within 3 σ, all of our measurements are in agreement with each other. Sampling the distribution resulting from taking the product of the individual PDFs, we obtain the combined intrinsic magnification-corrected line fluxes or line flux limits. In Table 3 , we quote the median value and 68% (∼1 σ ) confidence intervals for the final distribution. Comparing these values with the measurements performed directly on the stack also given in Table 3 scaled by the average HFF lens magnification of images A–E (), the de-magnified Ly α and C iv fluxes agree within the errors (especially if the large uncertainties on the magnification are folded into the error budget). The intrinsic flux limits appear to be roughly a factor of three tighter from the stack than from the Gaussian PDF if the uncertainties on the lens magnification are ignored. The tentative detection of the O iii ]-doublet in the stacks agree with the magnification-corrected flux limit set by the Gaussian PDF combination. 5.2. Interpreting UV Line Fluxes and EWs As mentioned in Section 1 , EWs and flux ratios of rest-frame UV lines are strongly correlated with the ionization strength and the gas-phase metallicity of the stellar populations dominating the SEDs of the emitters (e.g., Stark et al. 2015b , 2017 ; Feltre et al. 2016 ; Gutkin et al. 2016 ; Jaskot Ravindranath 2016 ; Mainali et al. 2017 ; Pérez-Montero Amorín 2017 ). Hence, the obtained flux ratios (limits) from the GLASS spectra can be used as an indicator of the physical properties of the multiply imaged source behind RXC J2248.7–4431 as described in the following. The C iii ] EW and the flux ratio between C iv and C iii ] are sensitive to the gas-phase metallicity and the ionization parameter of the stellar populations (Feltre et al. 2016 ; Gutkin et al. 2016 ; Jaskot Ravindranath 2016 ; Pérez-Montero Amorín 2017 ). From the GLASS spectra, we get rest-frame (1 σ ). As shown by Rigby et al. (