adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...817..126L (fulltext)
estimate the average period of the rotational motion, which turns out to be 569 s (with an error of 91 s). Taking the average width 38 Mm of the jet into account, the average line speed of the rotational motion during the off-limb stage is then estimated to be about 209.8 ± 33.6 km s −1 . As the upward Lorentz force acting on the jet after reconnection resulted from the untwisting motion (Shibata Uchida 1985 ), the nearly invariable rotating period is consistent with the above result that the downward acceleration in the late ascending phase and in the descending phase were almost the same. 3.2. Kinetics of the Twin Jets A similar method reveals almost the same inclination of the twins’ axes as the preceding jet related to the plane of the sky. To probe the axial motions of the twin jets, we place two slices along their axes labeled as “C1” and “C2” in Figure 1 (e) for the left and right twin jet respectively. Their corresponding running-difference time–distance plots are shown in Figures 3 (C1) and (C2) with the y -axis representing the distance along the slices after correcting the projection effects. Figure 3. (C1) and (C2): running-difference time–distance plots for slices “C1” and “C2” in Figure 1 (e) along the axis of the left and right twin jet respectively. The blue stars represent trajectories of materials within the twins, with the blue curves representing their parabolic fitting results. (D1)–(D4): running-difference time–distance plots for slices “D1” to “D4” perpendicular to the twins’ axes as shown in Figure 1 (e). Only very weak rotational motion of the twins could be found at their early off-limb stage from these four time–distance plots. As in Figure 3 (C1), the left twin jet reached a maximum distance along the slice of about 330 Mm at around 01:09 UT. The downward acceleration during its ascending phase and descending phase turned out to be similar. Parabolic fitting (blue dashed curve in Figure 3 (C1)) on one of the trajectories (blue stars in Figure 3 (C1)) gives us an average deceleration of about −228.9 ± 8.1 m s −2 . It is hard to determine the maximum distance of the right twin jet (Figure 3 (C2))—it seemed to reach a distance that was beyond the FOV of SDO observations or faint enough to be invisible. However, the dynamics seem to be similar to the left one with the average downward acceleration of about −234.5 ± 10.7 m s −2 (one of the trajectories is shown as the blue stars with the parabolic fitting result shown by the blue dashed curve in Figure 3 (C2)). All of the above values resemble the average local gravity (−220 m s −2 ), which indicates that there was almost no extra driving force acting on the twins. Meanwhile, we place four parallel slices “D1” to “D4” at different heights perpendicular to the twins’ axes to investigate the rotational motion of the twins if there was any. Corresponding running-difference time–distance plots are shown in Figures 3 (D1) to (D4) with the y -axis representing the distance
Paper: 2021ApJ...910....1H (fulltext)
excited normal modes, since we only need the normal-mode amplitudes. We then obtainwhere S p ( ω ) is the spectral density of effective pressure fluctuations acting on the Moon’s surface isotropically owing to a steady flux of meteoroids, which also depends on the formation of ejecta, elasticity properties of the impact, and the transfer efficiency of impact energy to the seismic field (typically a small fraction of the kinetic energy of a meteoroid; Lognonné et al. 2009 ). The last equation has the on-resonance formThe term depends on the Moon’s internal structure and is of the order of 0.2/ M for lower-order quadrupole modes, where M = 7.3 × 10 22 kg is the mass of the Moon, so that we can approximate the last equation asThe spectral density of pressure fluctuations is related to pressure rms by below 1 Hz (Theodorsen et al. 2017 ), where τ is the average duration of meteoroid impacts henceforth assumed to be τ = 0.1 s neglecting complications like the formation of ejecta. The effective pressure 〈 p 〉 produced by meteoroids with flux F and momentum mv is given bywhere α is the efficiency of momentum transfer to the seismic field. For our order-of-magnitude estimate, we go with the approximation , where we use F 0 = 10 −5 m −2 s −1 for the flux and m 0 v 0 = 10 −3 kg m s −1 for the average meteoroid momentum (Grün et al. 2011 ), and α = 0.01, which seems to be a generous overestimation of the impact-energy-to-seismic conversion efficiency (Lognonné et al. 2009 ; Quillen et al. 2019 ). Note that this expression is independent of the dynamical timescale τ of the impact since we are interested in the average force or pressure, which means that the relevant timescale Δ t of momentum transfer Δ P is given by the average time between impacts, which is contained in the meteoroid flux. It is possible that the total (undirected) momentum transfer onto the Moon is dominated by rare impacts of larger meteoroids, where rare means less frequent than once per Q n / f n , which is about once per day for the lower-order quadrupole modes. With 4 π R 2 × 1 day = 3 × 10 18 m 2 s, we estimate that impacts of meteoroids with mass greater than 1 kg occur less than once per day (Grün et al. 2011 ). These events need to be treated individually and cannot be considered part of the stationary background noise anymore. As for any other larger seismic disturbance, one needs to exclude the stretch of data containing the large impact event from GW analyses, or attempt a subtraction of the seismic signal from the seismometer data. Inserting the expression of the surface pressure from meteoroid impacts into Equation ( A6 ), we obtainConfronting this equation with the on-resonance normal-mode response to GWs, we can say that the effective strain noise in mode n from meteoroid impacts iswhere the numerical value depends weakly on the order of normal modes below about 10 mHz according to our simplified response model. The value lies well below the LGWA strain-noise target of 10
Paper: 2020ApJ...888...86Z (fulltext)
axis due to the boundary conditions. The minimum value of v θ is also located around θ ∼ 43°. Moreover, the bottom right panel of Figure 1 presents the Mach numbers. We plot this figure to check the existence of the sonic point in the high-latitude region. As we can see, no sonic points exist in this region. The Mach number decreases from the equatorial plane to about 65°, where the radial velocity is zero. Then, it increases until θ = 30° and again decreases rapidly to the rotation axis. This behavior can be explained by the isothermal sound speed, c s = p / ρ . The profiles of the density and the total pressure in Figure 1 show that the density decreases rapidly from the equator to the pole while the pressure is almost constant in the range 0 θ 30°. Therefore, the Mach number declines in this range. Also, has a maximum value at θ = 43° and becomes null at both axes due to the boundary conditions there, i.e., v θ = 0. For both lines, the Mach numbers are less than unity, which clearly shows that there is no critical point at high latitudes. Figure 1. Angular profile of the physical variables at r = 10 r s . Top left panel: radial velocity in units of the light speed, c . The dashed line shows the location of v r = 0, which is about 65°. Top middle panel: latitudinal velocity in units of the Keplerian velocity, v 0 . Top right panel: angular velocity in units of the Keplerian angular velocity, Ω 0 . Bottom left panel: density in units of the density of the midplane at r 0 , i.e., ρ 0 . Bottom middle panel: radiation pressure in units of . Bottom right panel: Mach numbers. In the left panel of Figure 2 , we plot the two-dimensional distribution of the density overlaid with the streamlines of the flow. The results are shown for and the poloidal velocity is normalized by 0.1 c . It can be seen that the density tends to be larger around the midplane than around the polar axis. Moreover, the streamlines are directed toward the black hole at low latitudes with small magnitudes. At high latitudes, they are pointed outward and become outflow. For a high accretion rate, as in our present study, the outflow can be driven by the radiation pressure produced at inner radii. This strong radiation interacts with the gas particles and can push them away as a high-velocity outflow. This figure clearly shows the region of a high velocity field at high latitudes at r = 10 r s . As shown in the top left and top middle panels of Figure 1 , the poloidal velocity of the outflow reaches about ∼0.25 c . These results are fully consistent with the results obtained in Yang et al. ( 2014 , see their Figure 1) and Ohsuga et al. ( 2005 ), where the inflow is presented at low latitudes around the equatorial plane of the disk while the outflow is presented in the high-latitude regions. Figure 2. Two-dimensional distribution of the density (left) and the temperature (right) based on the self-similar solutions. Both panels are overlaid with the poloidal velocity. In the left
Paper: 2016MNRAS.462L...1A (fulltext)
mode i is given by where P i is the discrete version of the power spectrum P ( k ). In the fixed-power approach, the PDF for mode i is instead given by where δ D indicates the Dirac delta-function. One can sample from Pr f straightforwardly by setting with θ i drawn with uniform probability between 0 and 2π, and θ − i = −θ i . The second of the pair of simulations is then generated by transforming θ i → π + θ i (Pontzen et al. 2016 ). Sampling from Pr f results in an ensemble that is not equivalent to sampling from Pr g . However, Pr f can stand in place of Pr g for many practical calculations (the analytic justification is discussed in Section 3 ). We verified that, despite the fixed amplitudes, the one-point input overdensity PDF in real space, , is still a Gaussian deviate owing to the central limit theorem. Furthermore, Fig. 1 shows the distribution of overdensities in the initial conditions at z = 9, δ IC , averaged over spheres of 8 h −1 Mpc radius for a subset of traditional simulations (grey lines) and the two paired-and-fixed (orange lines; these overlap almost perfectly). The corresponding PDF for the combined volume of the two paired-and-fixed simulations is shown by the red dots. There is excellent agreement between this characterization of the density fields of traditional and fixed simulations, with both following a near-Gaussian distribution. The mild skewness (which also agrees between the cases) arises from the 2LPT particle displacements. Figure 1. The probability density function of non-linear overdensities in spheres of radius 8 h −1 Mpc, as measured in the fixed simulations (orange lines) and in an ensemble of Gaussian simulations (grey lines), at the starting redshift, z = 9. The blue line and red circles show the mean of the respective cases, as indicated by the legend. 2.2 Results Fig. 2 shows the dark matter power spectrum measured from the z = 1 outputs. In the top panel, the results of the fixed pair (red circles) are indistinguishable from the traditional ensemble mean (blue line) over all the scales plotted, confirming that the approach correctly predicts the ensemble average power spectrum in linear and in non-linear regimes. Figure 2. The power spectrum of the dark matter at z = 1. In the top panel, measurements from the ensemble of 300 traditional simulations are shown as grey lines, with the mean shown by a blue line. The solid red circles show the average of the two simulations in the paired-and-fixed set. Finally, the horizontal dotted line marks the shot noise limit. In the bottom panel, we show the differences with respect to the average ensemble measurement, in units of the standard deviation in the ensemble. As in the top panel, red symbols show the final estimate from the pair of fixed simulations. We additionally show residuals in each of the two individual fixed simulations by the orange lines. The envelopes bounded by dashed lines mark a 1 per cent (left) and 0.1 per cent (right) uncertainty in
Paper: 2020ApJ...899..154S (fulltext)
a fair fraction of these lower luminosity candidates are attributed to gas motions in the narrow-line region due to other effects such as outflows (e.g., Rosario et al. 2011 ; Comerford et al. 2012 ; Fu et al. 2012 ; Müller-Sánchez et al. 2015 ). Only a modest number of more luminous dual quasars have been identified at close separation, i.e., during the final stages in the merger at ≲20 kpc (Komossa et al. 2003 ; Hennawi et al. 2006 ; Inada et al. 2012 ; Shields et al. 2012 ; More et al. 2016 ; Eftekharzadeh et al. 2017 ; Goulding et al. 2019 ), due to limitations with instrumental spatial resolution. Current and future wide-field deep imaging surveys have the potential to significantly improve upon the number of confirmed dual quasars, particularly at higher luminosity and closer separations. For instance, the wide-area coverage, depth, and superb seeing of the Subaru Strategic Program (SSP; Aihara et al. 2018 , 2019 ) with Hyper Suprime-Cam (HSC; Miyazaki et al. 2018 ) can be used to search for rare and key phases of black-hole growth, including dual quasars as signposts of major galaxy mergers. Simulations show that quasar accretion is episodic (e.g., Capelo et al. 2017 ), thus the SMBHs in a major merger will both shine as a quasar only a small fraction of the merger time. The areal coverage of HSC is now sufficient to detect these rare events in significant numbers. Just as importantly, the median seeing of the HSC imaging is in the i -band, meaning that dual quasars can be identified with projected separations as small as ∼3 kpc at z ∼ 0.3. For those at z 1, the underlying host galaxies can be robustly detected with HSC including those with faint structures such as tidal debris from an ongoing merger (Goulding et al. 2018 ). To exploit the capabilities of HSC on this topic, we are carrying out a program to search for Sloan Digital Sky Survey (SDSS) quasars, falling within the HSC survey footprint, that are dual quasar candidates based on an automated 2D image detection algorithm to identify multiple compact emission on the scale of the HSC point-spread function (PSF). These objects were initially identified as single SDSS quasars based on optical photometry and spectroscopy, given the lower spatial resolution of the SDSS data. Dual quasars with separations >2′′ are also missed by SDSS, as the survey could not obtain spectra of close pairs of objects due to a limitation on the minimum distance between two optical fibers on a single spectroscopic plate. Even so, subsequent dedicated spectroscopic follow-up programs of dual quasar candidates have confirmed 47 cases with most at wider separations ( Eftekharzadeh et al. 2017 ) with essentially no overlap with the HSC SSP program. Using the Subaru HSC SSP imaging, we report here on the identification of dual quasar candidates having projected separations between 3 and 30 kpc. An optical spectroscopic campaign is underway using Keck I/Low Resolution Imaging Spectrometer (LRIS), Subaru/FOCAS
Paper: 2019MNRAS.487.2038C (fulltext)
method applied to the observed data as described in Section 3 . For each template the process was repeated 100 times and the distribution of recovered metallicities is shown in Fig. B1 . It can be seen that although there is a systematic offset to lower metallicities for all but the lowest metallicity template (for which it is impossible to measure a lower metallicity), the effect is relatively small (≤10 per cent). We therefore conclude that the low metallicities derived for our VANDELS sample are robust against the effect of redshift uncertainties. Figure B1. The distribution of recovered metallicity for each of the five default Starburst99 WM-Basic template spectra ( Z ∗ = 0.001, 0.002, 0.008, 0.014, and 0.040) after mimicking the effect of redshift uncertainties when creating composite spectra (see text for details). Each panel shows one of the five default templates with the true metallicity indicated by the dashed vertical red line. The distribution of recovered metallicities is shown in black. The offset between the median recovered metallicity and the true metallicity is quoted in the top left-hand corner of each panel. APPENDIX C: BPASSv2.1 STELLAR POPULATION SYNTHESIS MODELS In our main analysis, we focused on using the SB99 WM-Basic stellar population synthesis models to fit the VANDELS spectra for the reasons outlined in Section 3 . However, we have also performed the same analysis using the BPASSv2.1 stellar population synthesis models described in Eldridge et al. ( 2017 ). It is well known that using different models will cause systematic offsets in derived parameters (e.g. Conroy 2013 ; Cid Fernandes et al. 2014 ) and it is useful to have an estimate of how significant this offset might be in the case of FUV-derived metallicities. The unique feature of the BPASSv2.1 models is the inclusion of massive binary star evolution, which can have a strong effect on the predicted UV spectrum, particularly at low metallicities. For example, at Z ∗ /Z ⊙ 0.35, a phenomenon known as ‘quasi-homogeneous evolution’ can occur (QHE; e.g. Eldridge Stanway 2012 ), which results in stars living longer on the main sequence and becoming hotter than single stars at similar mass and metallicity. QHE can have a profound effect on the estimated ages and ionizing photon output of stellar populations. Stellar metallicities should be less affected, particularly since the O-star models in BPASSv2.1 are also generated with WM-Basic code, albeit with a different parameter set to SB99. Nevertheless, it worth testing this assumption. We considered a 100 Myr constant star formation BPASSv2.1 model that included binary star evolution and had an IMF cut-off of 100 M ⊙ , with IMF index of −1.3 between 0.1–0.5 M ⊙ and −2.35 above 0.5 M ⊙ . The model is referred to as BPASSv2.1-100bin. We considered the following set of metallicity values Z ∗ = (0.001, 0.002, 0.003, 0.004, 0.006, 0.008, 0.010, 0.014, 0.020, 0.030, 0.040), interpolating between the models in the same
Paper: 2019MNRAS.483.3007P (fulltext)
both approaches, was carried out by restricting the EGMRT antenna locations to lie within the central square. Further, we included only the 14 GMRT central square antennas as ‘fixed’ antennas in the optimization. In the tomographic projection method, beginning with the above fixed GMRT antennas, we add N new antennas to the array, starting with N = 1, and increasing the number of new antennas by one at each iteration. For each value of N , we carried out the tomographic projection optimization for 100 random initial conditions for the new antenna locations, and evaluated the final Residual RMS for each of the 100 realizations. We then chose the realization with the lowest Residual RMS as the optimal configuration for the N new antennas. This minimum Residual RMS was saved, along with the locations of the new antennas, and the process repeated for ( N + 1) new antennas. The Residual RMS initially declines steeply with each new added antenna, as the UV coverage approaches a 2D Gaussian UV distribution. However, beyond some number of new antennas, there is no significant decrease in the Residual RMS, i.e. improvement in the UV coverage, on adding more antennas. We fixed the number of new antennas to the number above which the Residual RMS does not change by more than ≈10 per cent with the addition of further new antennas. In the random-sampling method, we again add N new antennas to the array, starting with N = 1, and increasing the number of new antennas by one at each iteration. However, here we randomly assign the new antennas to available cells in the GMRT central square, keeping the locations of the existing GMRT antennas fixed. We then evaluate the Residual RMS for the configuration, comparing the UV coverage of the configuration with the ‘ideal’ 2D Gaussian of FWHM = 0.5 km. We carry out this random assignment process 10 4 times for each value of N , and determine the minimum Residual RMS for the 10 4 evaluated configurations. This minimum Residual RMS is then stored, again along with the locations of the new antennas, and the process is repeated, by adding ( N + 1) antennas. We find a pattern similar to that seen in the tomographic projection approach, i.e. that the Residual RMS initially declines steeply with each added antenna, but, beyond some number of new antennas, does not decrease significantly on adding more antennas. Again, the number of new antennas is then fixed to the number above which the Residual RMS does not change by more than ≈10 per cent with the addition of further new antennas. As noted earlier, the above process was carried out independently for four declinations, δ = −30°, +30°, and +60°. Fig. 3(A) shows the Residual RMS plotted against the number of antennas for the four different declinations, with the results obtained via the tomographic projection method. The dashed vertical line indicates eight added antennas, beyond which the Residual RMS improves only slowly with additional antennas (for all four
Paper: 2020ApJS..247...61F (fulltext)
and rest-frame UV spectra for each of the ALPINE galaxies are shown in Appendix B . Throughout the paper, we assume the ΛCDM cosmology with , Ω Λ = 0.70, and Ω m = 0.30. All magnitudes are given in the AB system (Oke 1974 ) and stellar masses and SFRs are normalized to a Chabrier ( 2003 ) initial mass function (IMF). 2. Spectroscopic Data and Selection 2.1. Spectroscopic Selection of ALPINE Galaxies The ALPINE survey is only possible due to a spectroscopic pre-selection of galaxies from large spectroscopic surveys on COSMOS and ECDFS. This is because the ALMA frequency bands are narrow (), and in order to observe emission, the redshift has to be known within a precision of . The galaxy selection is refined to optimize the efficiency of the ALMA observations by creating groups of galaxies in spectral dimensions. Our sample also includes seven galaxies that were previously observed with ALMA by Riechers et al. ( 2014 ) and Capak et al. ( 2015 ). These are HZ1 , HZ2 , HZ3 , HZ4 , HZ5 , HZ6 / LBG-1 , and HZ8 , which correspond to the ALPINE galaxies DC _ 536534 , DC _ 417567 , DC _ 683613 , DC _ 494057 , DC _ 845652 , DC _ 848185 , and DC _ 873321 , respectively. Furthermore, four galaxies from the VUDS survey ( vc _ 5101288969 , vc _ 5100822662 , and vc _ 510786441 in COSMOS and ve _ 530029038 in ECDFS) are observed twice (resulting in a total number of 122 observations). The duplicate observations are used for quality assessment. Bethermin et al. ( 2020 ) describes the combination of these observations. The rest-frame UV spectroscopic data from which the ALPINE sample is selected combine various large surveys on the COSMOS and ECDFS fields. Out of the 105 ALPINE galaxies on the COSMOS field, 84 are obtained by the large DEIMOS spectroscopic survey (Capak et al. 2004 ; Mallery et al. 2012 ; Hasinger et al. 2018 ) at the Keck telescope in Hawaii. The remaining spectra on the COSMOS field are obtained from the VIMOS Ultra Deep Survey (VUDS; Le Fèvre et al. 2015 ; Tasca et al. 2017 ) at the VLT in Chile. In total, six of the VUDS spectra are independently also observed as part of the Keck/DEIMOS survey ( vc _ 5100559223 , vc _ 5100822662 , vc _ 5101218326 , vc _ 5101244930 , vc _ 5101288969 , vc _ 510786441 ). The redshifts are consistent within , and we do not find any systematic offsets between the two observations (see also Section 2.4.1 ). Out of the 13 galaxies in the ECDFS field, 11 are obtained from spectroscopic observations with VIMOS (9) and FORS2 (2 29 29 One of these galaxies, ve _ 530029038 , has also been observed by the VUDS survey. ) at the VLT (Vanzella et al. 2007 , 2008 ; Balestra et al. 2010 ), and two come from the HST grism survey GRAPES (Malhotra et al. 2005 ; Rhoads et al. 2009 ). The spectral resolution of the different data set varies between R ∼ 100 (ECDFS/ GRAPES grism), R ∼ 180 (ECDFS/VIMOS), R ∼ 230 (COSMOS/VUDS), R ∼ 660 (ECDFS/FORS2), and R ∼ 2500 (COSMOS/DEIMOS). Biases toward dust-poor star-forming galaxies