adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.466.3293P (fulltext)
to M 0 , the total mass at t 0. The drop of the total mass over time is due to accretion of gas by sink particles. The vertical dashed line marks the onset of star formation. Interestingly, the evolution of the atomic hydrogen mass fraction is almost identical in runs FSN and FWSN, with a value around 40percent. Simulation FRWSN, however, has a mass fraction of atomic hydrogen around 70percent. This is due to the effect of photodissociation, which converts molecular into atomic gas. The mass fraction of ionized hydrogen starts to grow monotonically 10Myr after the onset of star formation from 1percent to 7percent in run FSN. This is the result of the supernova explosions, which produce an increasing amount of ionized gas. Run FWSN has a roughly constant ionized hydrogen mass fraction of 2percent. Due to the reduced SFR in this simulation, many fewer supernovae explode compared to run FSN, and the additional collisional ionization by stellar wind feedback cannot compensate for this effect. The ionized hydrogen mass fraction of run FRWSN oscillates between 3percent and 10percent as a function of time. These oscillations are very closely correlated with the variability in the radiative output from the star clusters (compare Fig. 11 ). This clearly demonstrates that photoionization from stellar radiation is the primary source of ionized gas in this simulation, with supernovae and winds contributing only a negligible amount. The molecular hydrogen mass fraction of run FSN drops from 60percent to 40percent from 40Myr onwards. The delay of 10Myr between the onset of star formation and the reduction of the molecular hydrogen mass fraction suggests that it is due to supernova feedback, since the evolution of the atomic and ionized hydrogen mass fractions show a similar behaviour. In contrast, if this reduction was primarily due to accretion of molecular gas on to the sink particles, one would not expect such a delay. For run FWSN, the molecular hydrogen mass fraction stays at around 60percent. In run FRWSN, the molecular hydrogen mass fraction oscillates around a value of 20percent. These oscillations are again indicative of photoevaporation processes caused by the stellar irradiation of the molecular clouds. 8 DEPLETION TIME Since we dynamically form H 2 in the simulations, it is interesting to check whether the relation between the molecular gas surface density H 2 and the SFR surface density SFR obtained in the simulations is in agreement with observations. The ratio of these two quantities is the depletion time . Using SFR from Fig. 7 and the instantaneous H 2 measured in face-on projections of the disc, we can plot t dep as a function of time for the three simulations. The result is shown in Fig. 14 . Figure 14. Top: depletion time for the three simulations as a function of time t . Middle: plot of SFR versus H 2 for the three simulations for all snaphots after t 40Myr. The dashed lines show constant depletion times of 0.1, 1 and 10Gyr (from
Paper: 2016ApJ...829...68Y (fulltext)
from observed frequencies and ratios r 01 and r 10 by using Equations ( 3 ) and ( 6 ). Appourchaux et al. ( 2012 ) extracted the frequencies of low- l p -modes of KIC 9812850. Using the ratio r 10 computed from the frequencies and the observed frequencies ν n ,1 , the values of A , ν 0 , and B 0 in Equation ( 3 ) are estimated to be 264 ± 62 π , 2309 ± 141 μ Hz, and 0.043 ± 0.005, respectively, for KIC 9812850. Thus, the radius r c of KIC 9812850 is predicted to be about 0.14 by Equation ( 6 ). 3. THE RADIUS OF THE CONVECTIVE CORE OF THE BEST MODEL OF KIC 9812850 3.1. Evolutionary Models In order to compare the value of r c estimated by Equations ( 3 ) and ( 6 ) for KIC 9812850 with that of the evolutionary model of KIC 9812850, we searched for the best model of KIC 9812850 that matches both nonseismic constraints and seismic characteristics in a grid of evolutionary models. We used the Yale Rotation Evolution Code (YREC; Pinsonneault et al. 1989 ; Yang Bi 2007a ; Yang et al. 2015 ) to construct the models. For the microphysics, the OPAL equation-of-state table EOS2005 (Rogers Nayfonov 2002 ) and OPAL opacity table GN93 (Iglesias Rogers 1996 ) were adopted, supplemented by the Alexander Ferguson ( 1994 ) opacity tables at low temperature. The models with a mass less than 1.30 take into account the diffusion and settling of both helium and heavy elements by using the diffusion coefficients of Thoul et al. ( 1994 ). The standard mixing-length theory is adopted to treat convection. The mixing-length parameter α is a free parameter in this work. For the Sun, the value of the α for the YREC is 1.74. The distance of the overshooting of the convective core is defined as H p , where is a free parameter and H p is the local pressure scale height. The full mixing of material is assumed in the overshooting region. The initial helium mass fraction is fixed at the standard big bang nucleosynthesis value 0.248 (Spergel et al. 2007 ) and 0.295. All models are evolved from zero-age MS to the end of MS. The values of the input parameters, mass, α , , and Z i , for the calculations are summarized in Table 1 . Table 1 Range of the Input Parameters for the Evolutionary Tracks The adiabatic oscillation frequencies ν n , l of models were computed by using the pulsation code jig7 of Guenther ( 1994 ). The effects of the near-surface effects of a model on the frequencies were calculated by using the method of Kjeldsen et al. ( 2008 ). 3.2. Observational Constraints on Models KIC 9812850 is an F8 star (Wright et al. 2003 ). The value of [Fe/H] given by Ammons et al. ( 2006 ) is , but that given by Bruntt et al. ( 2012 ) is −0.16 ± 0.06 for KIC 9812850. Combining the value of 0.023 (Grevesse Sauval 1998 ) of ( Z / X ) ⊙ of the Sun, the value of ( Z / X ) s of KIC 9812850 is estimated to be between 0.016 and 0.033 for the [Fe/H] of Ammons et al. ( 2006 ), or in the range of 0.014–0.018 for the [Fe/H] of Bruntt et al. ( 2012 ). The effective temperature of KIC
Paper: 2017MNRAS.464.4977K (fulltext)
al. ( 2013 ), blue circles of Fig. 2 and Magnelli et al. ( 2011 ), red circles of Fig. 2 . The parameters of the analytic expression (dark green dot–dashed line) we obtain from dust correcting the Schechter ( 1976 ) fit given by Cucciati et al. ( 2012 ) are φ ⋆ = 0.0033 Mpc −3 , SFR ⋆ = 16.7 M ⊙ yr −1 and α = −1.07. 3.7 The star formation rate function at <italic>z</italic> ∼ 1.15 To obtain an estimate of the SFRF at redshift z ∼ 1.15, we use the IR LFs from Gruppioni et al. ( 2013 , z ∼ 1.0–1.2) and Magnelli et al. ( 2013 , z ∼ 1.0–1.3) and the FUV LF of Cucciati et al. ( 2012 , z ∼ 1.0–1.2). Even at these intermediate redshifts, the IR surveys are unable to probe low star-forming objects. The red dashed line is the analytical SFRF obtained from the double power-law fit of the LF of Magnelli et al. ( 2013 ). The results from the FUV LF of Cucciati et al. ( 2012 ) imply a much shallower SFRF with a much lower characteristic SFR. Once again we see that the SFRFs from the extrapolations of IR studies typically have higher characteristic SFRs and are steeper for faint objects than those found by UV data. Our results for the SFRF at redshift z ∼ 1.15 are in the bottom left panel of Fig. 2 and Table 8 . Table 8. Stepwise SFR functions at z ∼ 1.15 using the data from Gruppioni et al. ( 2013 , red diamonds of Fig. 2 ) and Magnelli et al. ( 2013 , red circles in the bottom left panel of Fig. 2 ). The parameters of the analytic expression (dark green dot–dashed line) we obtain from dust correcting the Schechter ( 1976 ) fit given by Cucciati et al. ( 2012 ) are φ ⋆ = 0.006 Mpc −3 , SFR ⋆ = 8.3 M ⊙ yr −1 and α = −0.93. 3.8 The star formation rate function at <italic>z</italic> ∼ 0.9 To retrieve the SFRF at redshift z ∼ 0.9, we use the LFs of Ly et al. ( 2011 , z ∼ 0.84), Gruppioni et al. ( 2013 , ∼0.8–1.0), Cucciati et al. ( 2012 , ∼0.8–1.0), Magnelli et al. ( 2013 , z ∼ 0.7–1.0) and Sobral et al. ( 2013 , z ∼ 0.84). As discussed in Section 2.2 , the LF of Ly et al. ( 2011 ) is an Hα LF that is corrected for incompleteness and dust attenuation effects with the Hopkins et al. ( 2001 ) dust correction law. This dust correction law implies a luminosity/SFR dependent correction with the observed (non-intrinsic) luminosity/SFR. To obtain the SFRF from this Hα LF we use equation ( 2 ). The magenta filled diamonds of Fig. 2 are the stepwise determination of the SFRF for z ∼ 0.84 from the Hα LF of Ly et al. ( 2011 ). From Fig. 2 , we see that the bright end of the SFRF from Ly et al. ( 2011 ) is in excellent agreement with the results from the IR samples and this suggests that the SFR dependent dust corrections suggested by Hopkins et al. ( 2001 ) are robust. For the low star-forming objects, we see that the SFRFs derived from Ly et al. ( 2011 ) and Sobral et al. ( 2013 ) are in very good agreement. However, the SFRFs that rely on the results of Sobral et al. ( 2013 ) are highly uncertain for luminous star-forming objects and is not consistent with the SFRs of
Paper: 2018ApJ...857...83J (fulltext)
closer, due to an attractive Lorentz force, in the same way as in CsTsB above. The loops then merge and seem to cross each other, because the positive toroidal field of the loop located at lower latitudes ends up appearing at higher latitudes at t = 4 days and later. From the temporal evolution of the energies that were shown in Figure 6 , it is clear that this case is the one producing the largest release of magnetic energy—because of the multiple reconnections—and thus the largest increase in kinetic energy. However, the loops do not seem to fully reconnect; the two main initial structures are still visible after eight days of evolution. However, it is still clear that the loops underwent a strong loss of magnetic intensity. Indeed, after four days, only 30% of the initial magnetic intensity remains in the loops, and only 20% remains after 5.4 days. As we shall see in the next section, even if the magnetic structures still seem to be well-confined in latitude and radius, the morphology of the emerging radial field is very different from the other cases. Figure 14. Same as Figure 7 , but for CoToBw. In this case, a strong interaction occurs between the structures: the loop of positive B φ passes through the loop of negative B φ after three to four days of evolution. 4. Consequences for Emerging Regions One of the main motivations to study interacting Ω-loops was to investigate the consequences of such interactions on emerging magnetic regions at the solar surface. We note here that, in our calculations, the top of the domain is located at r = 0.96 R ⊙ , i.e., still quite far from the solar surface—but we can get some insight on the possible implication for solar active regions by looking at the radial magnetic field close to the top of our domain in each case. In this section, we begin by focusing on the top panels of Figures 15 – 19 ; we will discuss the bottom panels (mainly the radial current maps) in the next section. Figure 15. Case CoTsBw. Top panel: zoom on the emerging radial magnetic field at r = 0.93 R ⊙ at three different times. Bottom panel: sketch of the initial configuration, with an emerging radial field at r = 0.93 R ⊙ shown on the whole Northern hemisphere and a zoomed map of the radial current J r at the last time t = 10 days. On this last panel, we superimpose contours of the radial magnetic field at 80% (thicker) and 2% (thinner) of its min and max values. The PIL, indicated by the magenta arrows, is then visible when the contours at 2% of opposite sign meet. Two emerging regions of different handedness are clearly visible here. Figure 16. Same as Figure 15 , but for case CsTsB. The black arrows on the bottom mid-panel indicate the locations of strong magnetic “necklaces.” In this merging case, the emerging radial field mostly consists of one large bipolar structure. Figure 17. Same as Figure 15 , but for case CsTsBw. In this weaker field case, the magnetic structure has been more affected by convective motions, and the
Paper: 2018MNRAS.481.1115C (fulltext)
Liao et al. 2015 ; Bonvin et al. 2017 ). Similarly, the ongoing public time-delay lens-modelling challenge (TDLMC; Ding et al. 2018 ) aims to test the accuracy of lens imaging modelling algorithms based on different source reconstruction techniques (e.g. Warren Dye 2003 ; Koopmans 2005 ; Vegetti Koopmans 2009 ; Oguri 2010 ; Birrer, Amara Refregier 2015 ; Nightingale Dye 2015 ). Additionally, the TDLMC may shed light on how critical the mass-sheet transformation (MST), a special case of source-position transformation, is (Falco, Gorenstein Shapiro 1985 ; Schneider Sluse 2013 , 2014 ; Xu et al. 2016 ; Birrer et al. 2017 ). All in all, the goal of the TDLMC is to understand how well we can control the systematic effects on σ κ . In addition, Birrer et al. ( 2015 ) used mock data to study whether we can use lens imaging to detect small perturbations on σ κ , while Chen et al. ( 2016 ) used mock data to study the impact of the unstable point spread function on σ κ when using adaptive optics imaging to study H 0 . However, it is difficult for mock data set challenges to reveal the systematics caused by unknown physical phenomena because the mock data only include known processes. The second method to assess systematic effects is to study physical processes that have not been previously considered. For example, Tie Kochanek ( 2018 ; hereafter TK18 ) have questioned the use of measured time delays in cosmography, by showing that, under the assumption of the ‘lamp-post’ model for accretion discs and differential magnification of the disc stars in the lensing galaxy (i.e. microlensing; Wambsganss 2006 ), the measured time delays may introduce a bias in the inferred value of H 0 . Under this assumed disc model, regions of the accretion disc that are separated by distances of the order of of light days vary in a coherent manner in response to activity in the centre of the disc. Differential magnification of portions of such a disc can introduce a phase delay due to the distance from the centre of the disc, and can shift the time-delay light curves by up to days depending on the accretion disk configuration and the microlensing pattern (see more description in Section 2 ). Since each lensed image has a different microlensing pattern, the sum of this proposed microlensing time-delay effect for any pair of lensed images can be non-zero. Therefore, the time delays we measure are not only the cosmological time delays but a combination of cosmological time delays and microlensing time delays, This effect, under certain assumptions, can significantly broaden the uncertainty on time-delay measurements, since it is embedded in the time-delay light curves (see fig. 10 in TK18 ). Although long-term monitoring can partially average out and mitigate this microlensing effect on time delays, the non-zero mean cannot be removed (see table 2 in TK18 ). Thus, TK18 have claimed that the current uncertainty of H 0 measurements from TDSL could potentially be underestimated
Paper: 2016ApJ...819...42T (fulltext)
et al. ( 2012 ) applied the one-dimensional scan observation at two different frequency channels, and estimated that the temporal change of plasma density contributed 4 GHz and 6 GHz to the frequency drifts in two microwave type III bursts, respectively. However, in our work we found that most microwave type III pairs have frequency drifts 4 GHz . Only 3 among the 11 events have absolute frequency drifts >4 GHz ; see in Table 1 . Actually, in the framework of the PE mechanism the separate frequency ( f x ) reflects the plasma density in the flare energy release site, and the temporal variation of f x may reveal the plasma density change during the microwave type III burst pair train. Table 1 lists the temporal change of separate frequencies, and most of them are very slow, even close to zero. Among the 11 type III pair events, the maximum occurred in the event on 2006 December 13, which is about 10 MHz s −1 , and s −1 . Then s −1 . The observation shows that the relative frequency drift () is from 0.3 s −1 to 5.3 s −1 , which is three orders bigger than that caused by the plasma density temporal variation. It seems that the plasma density temporal variation does not contribute to the formation of frequency drift in type III pairs as much as in microwave type III bursts reported by Altyntsev et al. ( 2007 ) and Meshalkina et al. ( 2012 ). The plasma density gradient dominates the frequency drifts of type III pairs. Therefore, we have the relations and . With these approximations, the velocity can be expressed as The frequency gap () between the normal and RS branches is seldom mentioned in the previous literature. This work shows that the observed results of are ranging from 10 MHz to 1700 MHz, and are different from event to event. Then, what factors do dominate the frequency gap? Li et al. ( 2011 ) simulated the generation and propagation of type III bursts based on the assumption of a power-law model (PL; Allen 1947 ) and offset power-law model (OPL; Aschwanden Benz 1995 ), respectively. They find that frequency gaps may be very large ( MHz) when electron beams propagate in offset power law plasma, and relatively small ( MHz) in power law plasma. It seems that the frequency gap may be related to the distribution of plasma density. According to the PE mechanism, the frequency gap means a density difference between the start sites of normal and RS type III bursts. With a density scale length H n , a space length can be derived: L c can be regarded as an estimation of the length of acceleration regions. The electrons are accelerated in this region and get a relatively high energy, then trigger microwave type III bursts outside this region. It is possible that L c is only an upper limit of the acceleration region. Panel (4) of Figure 5 show that the frequency gap has no dependence on the separate frequency. This fact implies that the length of the acceleration region is possibly irrelevant to the plasma density. It is possible that the
Paper: 2020A&A...636A..56P (fulltext)
Cyan and brown lines represent MI and MG, respectively, with v ∞ ≃ v flat = v tot ( r cut ), where r cut denotes the radii at which the integrals are cut. The inset is a magnification of N range = [0, 10]. Right panel : Δ Q MI − Δ Q MG as a function of radial range. The error bars are smaller than the points. From the left panel of Fig. 3 it is clear that MI and MG drift similarly as the range is decreased. As long as the drifts of MI and MG are approximately equal, the measured Q can be corrected similarly to the case of the acceleration scale in the previous section. The right panel of Fig. 3 shows Δ Q MI − Δ Q MG in the context of varying range. |Δ Q MI − Δ Q MG |≲0.01 ≃ const for N range ≳ 3, meaning that the measured Q can be corrected with Δ Q (range) = Δ Q MI ≃ Δ Q MG for galaxies which fulfill max[ r ] ≳ 3 r d . Table 2 lists Δ Q (range) for different ranges. The approach to the theoretical limit for increasing range is slow for the Kuzmin disk. For this reason Δ Q (range) = 0 is only reached for N range > 55 in this case. The corrections in Table 2 isolate the range effect from the acceleration effect. To mimick the observational situation, however, we have repeated the same exercise considering galaxies with max( g obs ) 0.15 a 0 , applying the corrections in Table 1 . We find that the corresponding corrections due to N range are very similar to those given in Table 2 . Table 2. Range corrections. Notes. The uncertainty on Δ Q (range) is negligible. 2.3. Sampling resolution In order to gauge the effect of the spatial resolution of the observations, Q is calculated as a function of the spacing between sampled points in units of r d . The sampling for each galaxy is performed in the range r j ∈ [0.01, 200] r d . In order to compute Q from a discrete set of points, the integral of Eq. ( 2 ) is discretized viz with where v f ≃ v ∞ being the asymptotically flat velocity which is determined as the arithmetic mean between the chain of points that are within 5% of the arithmetic mean of the two outermost points (similarly to Lelli et al. 2016 ). If the third outermost point is not within 5% of the arithmetic mean of the two outermost points, the galaxy is not assigned an asymptotically flat velocity. The left panel of Fig. 4 shows the arithmetic mean of Q calculated via Eq. ( 7 ) – as predicted by MI (black) and MG (blue) – of the mock data for which max( g obs )≤0.01 a 0 as a function of the spacing between the sampled points in units of r d . As expected, Q approaches the theoretical value for extremely small spacings ( r 0.1 r d ) and progressively increases for larger spacing between the sampled points. Fig. 4. Left panel : arithmetic mean of Q for all mock galaxies with max[ g obs ]≤0.01 a 0 as a function of spacing between sampled points. Black and blue lines represent MI and MG, respectively. Right panel : Δ Q MI − Δ Q MG as a function of spacing between sampled points. From the right panel of Fig. 4 it is clear that the increase for
Paper: 2021A&A...651A..73P (fulltext)
Collaboration XXVII 2016 , ESZ, PSZ1, PSZ2, respectively) and ACT ( Hasselfield et al. 2013 ; Hilton et al. 2018 ) surveys. A total of 119 clusters are detected within the ACT footprint by either instrument. Here we focus on the 34 joint detections. We exclude three sources partially covered at the edges of ACT footprint or falling into the mask of Planck point sources (used to construct the PACT y -map). Our final sample is thus comprised of 31 clusters of galaxies, hereafter referred as PACT31, with 18 sources distributed in the equatorial strip and 13 in the southern one. By construction our sample is neither representative nor complete. The clusters range between 0.16 and 0.70 in redshift and from 3.7 × 10 14 to 1.3 × 10 15 M ⊙ in M 500 , where M 500 is the total mass contained within R 500 . The mean signal-to-noise ratio (S/N) of the sample is 6.0 in the Planck catalogues and 6.3 in ACT’s. The range of angular size over the sky is 2.5 θ 500 7.9 arcmin, with a mean value of 4.2 ± 1.1 arcmin. We also consider the sample of 62 massive local clusters used in Planck Collaboration Int. V (2013 , P13 hereafter) to derive the pressure profile of the hot intra-cluster gas from the first Planck all-sky survey. Here the Planck maps used to derive the y -map (see above) are those from the all-sky survey (i.e., 2015 data release Planck Collaboration VIII 2016 ). We therefore use this sample, hereafter PLCK62, in order to compare to the profiles derived from our PACT map (see Sect. 4 ). From the second Planck catalogue, PSZ2, the redshift of the PLCK62 sample ranges between 0.04 to 0.44 and the S/N from 7 to 49. The covered mass interval is 2.4 × 10 14 M 500 2.0 × 10 15 M ⊙ , for an angular size one of 3.7 θ 500 22.8 arcmin with a mean value of 9.8 ± 5.4 arcmin. Masses for the PLCK62 sample are M 500 hydrostatic masses from P13, derived from the XMM-Newton observations. For the PACT31 sample, we make use of the M 500 values provided in the PSZ2 catalogue. The latest are derived from an Y 500 − M 500 relation calibrated with Planck SZ integrated fluxes and XMM-Newton hydrostatic masses ( Planck Collaboration XX 2014 ; Planck Collaboration XXIV 2016 ). We checked the consistency between the P13 hydrostatic mass and the PSZ2 masses for the PLCK62 sample, finding a mean ratio of 0.98 ± 0.11. We provide, in Fig. 1 , the distribution of the two samples in the M 500 − z , M 500 − θ 500 and θ 500 − z plans. Fig. 1. Distribution of the PACT31 (red dots) and PLCK62 (blue dots) in the M 500 − z(left) , M 500 − θ 500 (middle) and θ 500 − z(right) . Crosses picture the median value and associated maximum absolute deviation over each sample. Values for the redshift, masses and angular radii are taken from the PSZ2 catalogue ( Planck Collaboration XXVII 2016 ). 3. Computation of the y and pressure profile To recover the pressure profiles from the SZ y -map, we strictly followed the method described and used by P13. The SZ flux is the product of the SZ spectrum