adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...827L..15T (fulltext)
In the case of ErisMod Low simulation pair the first replacement was made at redshift 1.1 and the last at redshift 0.1. In total, there were four replacement epochs. The final count of high-resolution objects in each of the ErisMod Low runs was 17. The progenitors as well as other halos found in the proximity of the main halo at an epoch of replacement went through a process of selection. If the respective dark matter cloud was found to be monolithic, not elongated, not part of a group of overlapping halos, and with no identifiable satellites, then a high-resolution model will be generated and used as a replacement. This procedure leads to the exclusions of between 40% and 60% of all considered halos at a certain epoch of replacement. Thus, the objects that are replaced are in general more resilient to the tides due to their compactness. The two simulation pairs ErisMod High and ErisMod Low should be seen as complementary since they are both derived from the same simulation ErisDARK and the first epoch of replacement for ErisMod Low starts after the last epoch of replacement in the ErisMod High simulation. Information regarding the orbits, the original virial mass, as well as the redshift of the first pericenter passage for all replaced objects is available in Figure 1 . Figure 1. Dwarf galaxy distances from host ranges for the four ErisMod simulations are plotted as bars. The dwarf galaxy number is sorted by halo mass at infall. At the top of each bar, the marked number (blue color) denotes the pre-infall virial mass of the objects in units of 10 9 M ⊙ . Near the bottom of each bar, the neighboring number (red color) indicates the count of the pericenter passages done by the galaxy until redshift zero. 3. RESULTS For the following analysis we consider subsets of the satellite populations presented in Figure 1 . We are interested in objects that at z = 0 are within 247 kpc of the center of the main halo, namely, inside the virial volume of the host halo. This was done in order to achieve consistency with previous analysis of the TBTF problem from the literature. Moreover, we exclude from analysis all the objects that have lost more than 99% of their mass before redshift zero, as the low number of particles will make the results of the analysis not trustworthy. This class of objects includes galaxies that have lost such a large fraction of their mass that they become irrelevant for the TBTF problem. The circular velocity of the satellites is defined as the function of distance from center: The results are shown in Figures 1 and 2 . In Figure 1 , the profiles of the halos at the epoch of replacement are plotted for comparison. Moreover, in Figure 2 , we have added the estimated circular velocities at the half-light radius available for the MW dSph satellites based on the data compiled by McConnachie ( 2012 ). They are derived from the line of sight velocity dispersion data using the equation:originally proposed by Klypin et al. ( 1999 ) and later
Paper: 2019ApJ...887...33L (acknowledgments)
The work is jointly supported by the National Natural Science Foundation of China (grant Nos. 41531073, 41731067, 41874202, 41861164026, 41574171, 41774184, and 41874205) and the Specialized Research Fund for State Key Laboratories. This work utilizes data obtained by the Global Oscillation Network Group (GONG) program, managed by the National Solar Observatory, which is operated by AURA, Inc., under a cooperative agreement with the National Science Foundation. The data were acquired by instruments operated by the Big Bear Solar Observatory, High Altitude Observatory, Learmonth Solar Observatory, Udaipur Solar Observatory, Instituto de Astrofísica de Canarias, and Cerro Tololo Inter-American Observatory. The work was carried out at the National Supercomputer Center in Tianjin, China, and the calculations were performed on TianHe-1 (A). The authors thank the reviewer for constructive suggestions for the improvement of the manuscript.
Paper: 2016MNRAS.457.3743D (fulltext)
the significant (and measurable) differences between the two models at the resolution of our spectrophotometric data, especially for the Mg UV and D4000 indices (marked in both plots as coloured areas). In Fig. 10 , we present the stacks built for the mature and senior galaxy populations together with the models characterized by their average properties. We also show the indices measured in the stacks of the mature, intermediate, and senior galaxy populations, and the tracks predicted by the model characterized by the average time-scales and dust attenuations quoted in Table 3 for each sub-population. The indices measured directly from the stacks are consistent with those predicted by the tracks of the models with the average properties of each sub-population. We demonstrate that the spectrophotometric data directly show the differences in the stellar population properties for the three sub-samples. Note that the origin of the average stellar population properties is the full SED spectral fits, and here we are comparing those models with finer resolution data in a limited wavelength range. The two analyses are not completely independent, but the information encoded in the data at the resolution which makes index measurements possible could easily be erased or degraded by fitting the whole SED from the UV to the mid-IR. Our test demonstrates that we are seeing differences in the galaxy populations in both the global SED and the spectral indices. We, thus, conclude that our results about the ages of MQGs at z = 1.0–1.5 are robust and not an artefact linked to the SED-fitting degeneracies. Figure 10. Left-hand panel: stacks for the senior (thick red line) and mature (thick blue line) populations normalized in the Mg UV (upper panel) and D4000 regions (lower panel). The average error of the stacks is ∼10 per cent. We used the dispersion as uncertainties (actual errors calculated by propagating the observed flux uncertainties were negligible). We also plot the normalized models with the average properties of each sub-population (thin red and blue lines). Right-hand panel: Mg UV versus D4000 plane, with the indices measured in the stacks for the mature, intermediate, and senior galaxies (blue, green, and red diamonds with error bars). For comparison, we also show the evolutionary tracks for two models with different star formation time-scales (an SSP and a delayed exponential model with τ = 1000 Myr) and two different dust attenuations ( A v = 0.0 mag and A v = 1.0 mag). The open circles represent the expected index values for each track at different ages (given in Gyr in the plot). The three coloured lines show the tracks predicted by the models with the average properties of each galaxy population (given in the legend). The filled coloured circles mark the indices values at different ages (0.5, 1.0, 2.0, 3.0, 5.0 Gyr, from smaller to larger symbols). The indices measured in the stacks are compatible with the average properties derived from the
Paper: 2015ApJ...801....6B (acknowledgments)
The authors thank the anonymous reviewer for a thorough and careful reading and many useful and critical comments that resulted in significant improvements to the paper. The authors thank Professor J. Krelowski (Centre for Astronomy, Nicholas Copernicus University) for his comments on our analysis. The HARPS CH spectrum was observed in 2007 March under program 078.C-0403(A). L.B. and F.C. appreciate sponsorship through a Spectral Sciences, Inc. internal research and development project. G.A.G. acknowledges the support of Chilean fund FONDECYT-regular (project 1120190).
Paper: 2019MNRAS.485.2026B (acknowledgments)
ACKNOWLEDGEMENTS AKB, TD, SE and ADM were supported by the National Science Foundation through grant number 1616168. ADM also acknowledges support through the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under Award Number DE-SC0019022. AKB is thankful to Duncan Campbell and Francois Lanusse for useful discussions.
Paper: 2019MNRAS.482..626K (acknowledgments)
ACKNOWLEDGEMENTS This work was financially supported by the Grant-in-Aid for JSPS Fellows for young researchers (MK). CD acknowledges the Science and Technology Facilities Council through grant ST/P000541/1 for support.
Paper: 2017MNRAS.472.2906W (fulltext)
Collaboration XIV 2016b ); see Wen Wang ( 2017 ) for more details on the calculations of CMB (represented by the green contours in Figs 3 and 4 ). 2 We can see that when the observations of lensed GWEM systems are verified in the future, CMB constraints would benefit from having the constraints of lensed GWEM systems overlaid. 4 SUMMARY AND DISCUSSION Although the constraints on cosmological parameters have reached a high precision, all of the constraints so far have relied on EM observations alone. New multimessenger signals exploiting different emission channels are worth exploring in cosmology. Recently, Liao etal. ( 2017 ) proposed that future time-delay measurements of strongly lensed GW signals and their EM counterparts have great potential to infer cosmological parameters. Compared with the traditional approach of using strongly lensed quasar systems observed in the EM domain, the approach with lensed systems observed in both GW and EM windows has two advantages in constraining cosmological parameters. First, the time delays () between lensed images inferred from the GW signals would reach an extremely high accuracy (0.1s) from the detection pipeline, and such accurate measurements of would play an important role in boosting the development of precision cosmology. Secondly, with gravitationally lensed GWs and EM signals, the accuracy of lens modelling could be improved to some extent, leading to better constraints on cosmological parameters. In the EM window, Paraficz Hjorth ( 2009 ) suggested that the joint observations of the time delay () between lensed quasar images and the velocity dispersion () of the lensing galaxy are more effective to constrain cosmological parameters than or 2 separately. In this work, we apply the / 2 method, for the first time, to the strongly lensed systems observed in both GW and EM windows. We prove that both and / 2 from strongly lensed GWEM systems can serve as powerful cosmic rulers. From the comparison of the two different methods, we confirm that the / 2 method can provide tighter constraints on m than the method (i.e. using / 2 can make it easier to differentiate different cosmological models). Furthermore, we show that with a moderate sample size of 50, a constraint on the dark-energy equation of state w can be reached that is comparable to the 580 Union2.1 SNe Ia sample. Combining the and / 2 methods, it is possible to achieve higher accuracy in constraining w . The recent Advanced LIGO observations of binary black hole mergers GW150914 (Abbott etal. 2016a ), GW151226 (Abbott etal. 2016b ) and GW170104 (Abbott etal. 2017 ) have initiated the era of GW astronomy. Because of the high sensitivity, the planned third-generation GW detectors, such as the ET, could observe strongly lensed GWs. Recent works (Pirkowska etal. 2013 ; Biesiada etal. 2014 ; Ding etal. 2015 ) have carefully studied the prospects of observing strongly lensed GWs from merging double compact objects, which predicted that the ET
Paper: 2020MNRAS.497.2586H (fulltext)
(McCourt et al. 2018 ; Mandelker et al. 2019 ) and by estimates of cool-phase cloud sizes inferred from measured column densities and derived number densities (e.g. Pieri et al. 2014 ; Crighton et al. 2015 ; Stern et al. 2016 ). Furthermore, with current computational capabilities it is infeasible to maintain even kpc-scale resolution throughout volumes that are tens of Mpc on a side, and standard Lagrangian or mesh refinement schemes will not automatically resolve the regions of the CGM where gas phases interact. Physical processes in addition to radiative hydrodynamics, such as thermal conduction, viscosity, and magnetic fields, may also have significant effects on cloud evolution (Marcolini et al. 2005 ; Orlando et al. 2005 ; Vieser Hensler 2007 ; McCourt et al. 2015 ; Armillotta, Fraternali Marinacci 2016 ; Brüggen Scannapieco 2016 ; Armillotta et al. 2017 ; Li et al. 2020 ), but they are rarely incorporated self-consistently in cosmological simulations. In the approach proposed here, we eject wind particles as in previous simulations but follow their evolution and interaction with the ambient CGM using an analytic subgrid model. We model the gas in each wind particle as a collection of cold clouds, and we calculate the exchange of mass, momentum, energy, and metals between these clouds and the surrounding CGM gas. A wind particle loses mass as it evolves, and it is dissolved when its mass falls below some threshold, or when its velocity and physical properties sufficiently resemble the surrounding gas, or when it rejoins a galaxy and contributes its remaining mass and metals to the ISM. This general method can be implemented in cosmological simulations that use smoothed particle hydrodynamics (SPH) or Eulerian or Lagrangian mesh codes. In future work, we will present results from implementing this Physically Evolved Wind (PhEW) model in cosmological simulations with the gizmo hydrodynamics code (Hopkins Raives 2016 ), employing the star formation and feedback recipes described by Davé et al. ( 2019 ) and the wind launch prescriptions described by Huang et al. ( 2020 ), which are themselves tuned to reproduce outflows in the FIRE simulations (Muratov et al. 2015 ). In this paper, we present the wind model itself. This model is based on results from very high resolution simulations of the ‘cloud-crushing problem’, which examine the evolution of an individual cold clouds moving supersonically relative to an ambient, hotter flow (see Banda-Barragán et al. 2019 for a recent compilation of such simulations). We concentrate in particular on the cloud-crushing simulations of Scannapieco Brüggen ( 2015 , hereafter SB15 ), which do not include thermal conduction, and the simulations of Brüggen Scannapieco ( 2016 , hereafter BS16 ), which do. These simulations model idealized situations with mass and spatial resolutions far higher than that achieved in any galaxy formation or cosmological hydrodynamic simulations. PhEW provides a method to
Paper: 2019MNRAS.483L..52B (acknowledgments)
ACKNOWLEDGEMENTS We thank the anonymous referee. EB, JRM, and CT acknowledge STFC grant R/151397-11-1. EB thanks C.M. Harrison and J. Scholtz for useful discussions that helped improving the clarity of the results and discussion.
Paper: 2018ApJ...859...28Q (fulltext)
component is induced. As a result, a weaker toroidal field pressure is available for the disk wind ejection and acceleration. In this sense, the increasing magnetic diffusivity also suppresses the ejection process in the system. 7.2. Efficiency of Outflow Launching We have discussed above the influence of magnetic diffusivity on the ejection process. In general, magnetic diffusivity lowers the ejection rate. However, we have noticed that the magnetic diffusivity may lead to a higher outflow efficiency . Here we define the outflow efficiency aswhere and are the total ejection rate and the inner accretion rate, respectively, defined in Section 3.5 . We note that outflow efficiencies ξ > 1 are possible. In Table 2 we show the outflow efficiency ξ for the different simulations averaged from t = 2000 to t = 3000. In general, the outflow efficiency of disks with η 0 > 10 −4 is much larger than that of weakly diffusive disks with η 0 10 −4 . At this stage we may only speculate about the reason for this trend. One possibility may be the field structure just above the disk. The region above the accretion disk evolves as in ideal MHD. Material is frozen to the field lines and either flows along the field or needs to “push” the magnetic field component that is perpendicular to the motion. We find that the simulations with lower disk magnetic diffusivity result in a more tangled field structure (as discussed above, most probably due to the not fully stable initial disk hydrodynamics). One may argue that the tangled field is advected upward along with the outflow and then lowers the efficiency of acceleration. Also, mass loading is known to be governed by the physical conditions at the disk surface or the sonic surface. In our case of a super-Alfvénic injection, Lorentz forces will play a major role. We conjecture that for a tangled magnetic field the Lorentz forces are also tangled and thus less efficient in launching. In Figure 16 we show the field-line structures for simulations D 1 (left panel) and D 10 (right panel) at t = 2000. In the nondiffusive simulation D 1 (outflow efficiency ξ = 0.5), the matter flow inside the disk becomes turbulent, also disturbing the smooth structure of field lines. The disturbance propagates outward, leading to a field structure that is suppressing efficient acceleration. On the other hand, the field lines in simulation D 10 (outflow efficiency ξ = 2.9) still keep their original smooth structure at t = 2000. Figure 16. Density and magnetic field line distribution (zoomed in) for simulations D 1 (left) and D 10 (right) at t = 2000. In simulation D 1 the field lines inside the disk have a more turbulent distribution (especially within r 20), most probably due to the MRI. Another possibility for the observed interrelation between diffusivity and mass loading can be ohmic heating. This will result in a somewhat higher sound speed at the disk surface for the more diffusive disk. From steady-state MHD wind theory and
Paper: 2019MNRAS.490L..17S (acknowledgments)
ACKNOWLEDGEMENTS I thank the Leverhulme Trust for award of a Leverhulme Emeritus Fellowship.
Paper: 2021MNRAS.504.4751J (fulltext)
differences between spots and the photosphere Δ T spot (= T phot − T spot ) are listed in Table 2 . We follow Unruh et al. ( 2008 ) and use a 1D radiative equilibrium stellar atmosphere model with a cooler effective temperature than the quiet photosphere to represent spots. Mean MURaM quiet star spectra and 1D model spectra with equivalent effective temperature agree very well, although small differences are present for all spectral types. To minimize the effect of model differences on the resulting spot intensities, ATLAS9 spectra were calculated from 1D model stellar atmospheres at effective temperatures T phot and T spot for the quiet star and spots, respectively. The ratio of intensities was multiplied with the quiet star intensities from MURaM to provide spot intensities for our models. We show Kepler band spot intensity contrasts, I ( T spot )/ I ( T phot ) − 1 in Fig. 3 (bottom panel) for all spectral types. In the Kepler band, spots always have negative contrasts several times larger than that of facular regions, regardless of limb distance. Spot contrasts become more negative with increasing stellar effective temperature (a direct consequence of the spot temperature differences chosen). 3 MODEL SETUP We performed a number of investigations using the light-curvemodelling approach described in Section 2 : obtaining typical variability levels assuming a Sun-like feature distribution and evaluating the impact of spot temperature, active region latitude, and facular-to-spot coverage fraction on variability. Here, we describe how Sun-like feature distributions are generated and the assumptions made. 3.1 Latitude and longitude distributions We aim to model brightness variability on rotational time-scales. Focusing on the Sun, spots, and facular regions are observed to occur in latitudinal bands in both the northern and southern hemispheres. The central latitudes of these ‘active bands’ are known to change throughout the solar activity cycle, beginning at higher latitudes at the start of each 11-yr cycle and moving towards the equator as the cycle progresses (Hale et al. 1919 ). To model this, we use typical solar cycle averaged latitudinal distributions. These distributions are based on Borgniet et al. ( 2015 ), in which average spot and facular latitude distributions are presented for solar cycle 23 (1996 May–2007 October), derived from USAF/NOAA sunspot group data and MDI/SOHO magnetograms (Scherrer et al. 1995 ), respectively. Transit mapping observations of the K4V star HAT-P-11 show a very similar spot distribution to the Sun (Morris et al. 2017 ). This result indicates that the solar cycle averaged distributions in our simulations can also apply to some K-type stars. We fitted the solar spot and facular region latitude data with equatorially symmetric Gaussian distributions (for spots, 〈φ〉 = ±16°, σ φ = 8°; for faculae, 〈φ〉 = ±16°, σ φ = 12°) shown in Fig. 4 (top panel). Feature longitudes are randomized. As the number of
Paper: 2019MNRAS.490.3806N (fulltext)
as done for the RMS . For the following analysis, we used these mean P2P RMS trends to identify, for each star, the best photometry: for each star in the input catalogue with a given T magnitude, we selected the light curve obtained with the photometric method that at the given T returns the lower mean P2P RMS . In this phase we also excluded all the light curves that have 300 photometric points and whose mean magnitude differs from that expected from the input catalogue >2 magnitudes. Panel (c) of Fig. 6 shows the 6.5-h RMS distribution (gray points) for the previously selected light curves. This transit noise 6.5-h RMS is obtained as described by Gilliland et al. ( 2011 ), Gilliland et al. ( 2015 ), and Van Cleve et al. ( 2016 ): we flattened each light curve interpolating to it a 5th-order spline with 65 break points and removing out the outliers. We computed the standard deviation of a running mean with a window length equal to 6.5 h (13 points). The azure line in panel (c) is the mean trend of the 6.5-h RMS distribution, calculated as previously described: the trend reaches a minimum at ∼34 ppm. For T 10, the distribution is bi-modal: stars that are not variable or whose period variability is >6.5 h have 6.5-h RMS ∼ 20 ppm. The stars with 6.5-h RMS ≳ 30 are highly variable on short time-scales and the flattening is not perfect. For stars with 10 ≲ T ≲ 14, we are able to detect exoplanets transits with depth 1 mmag, while for T ≳ 14 it is possible to find transits with depth 0.01 mag. 3 CANDIDATE EXOPLANET TRANSIT FINDING We searched for transiting signals among the light curves selected in the previous section. As described in Section 2.5 , we flattened all the light curves using 5th order splines defined on 65 knots. For each light curve, we removed out the outliers and the photometric points with 1347.4 t BTJD 1349.4, whose photometry is affected by pointing problems and that, despite the cotrending, still have some residual systematic artifacts. For each light curve, we extracted both the Box-fitting Least-Squares (BLS, Kovács, Zucker Mazeh 2002 ) and Transit-fitting Least Squares (TLS, Hippke Heller 2019 ) periodograms. For both techniques we searched for transiting objects having period 0.6 ≤ P ≤ 14 d, and for each star we extracted the parameters useful to discriminate between light curves with and without candidate transits, such as the signal detection efficiency (SDE), the signal-to-noise ratio (SNR), the depth of the transit. To detect candidate transits we performed two independent analysis on BLS and TLS outputs and at the end we joined the results. In Fig. 7 , we summarize the procedure adopted for the selection of candidate transiting exoplanets both for TLS (left-hand panels) and BLS (right-hand panels) technique. As described in Nardiello et al. ( 2015 ), we excluded spurious periods due to systematic errors by constructing the histogram of the period and removing the stars that form the spikes in the histogram: the red