adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.499.2076S (fulltext)
and remain the same also in the saturated stage. The PDFs in the saturated stage are more intermittent than in the kinematic stage, but the PDFs both in the kinematic and saturated stages are more intermittent for the case than for any of the cases (compare the x -axis values and the heavy tails at higher values in Figs 5a and 8c ). However, the growth rate and the structure of the magnetic fields in the kinematic and saturated stages, even for the case, are unaffected by the configuration of the seed field. The overall conclusions, even with changes in the plasma parameters, remain the same as before: the structure and strength of the seed magnetic fields do not affect the growth rate, final saturation level, shape of the magnetic power spectrum, and the statistical structure of the small-scale dynamo generated magnetic fields. Thus, the detailed properties of the seed field or even their various generation mechanisms are relatively unimportant when studying evolved magnetic fields that were amplified by the turbulent small-scale dynamo. 4 DISCUSSION From our results, we show that the seed magnetic field information is lost (within a few eddy turnover times, as seen in Figs 2a , 6a , 7a , and 8a ). Then, the dynamo-generated magnetic field reaches a statistically similar state for all three seed fields, which is determined by the properties of the turbulence (such as Re and ) and magnetic resistivity (or equivalently Rm). This also shows that the small-scale dynamo is very efficient in amplifying (given that Rm is greater than its critical value, Rm (crit) ) any non-zero seed field. 4.1 Small-scale magnetic fields generated by the tangling of the large-scale field If Rm Rm (crit) , the small-scale dynamo is inactive. However, for the case with non-zero mean seed field (UniRm100), small-scale magnetic fields can still be generated by the tangling of the large-scale (or mean) uniform seed field (see appendix A in Seta et al. 2018 , for discussion on various mechanisms by which small-scale magnetic fields can be generated). Such a mechanism is only applicable in systems, such as spiral galaxies, where the large-scale field is maintained by other processes, i.e. α − Ω large-scale dynamo (Krause Rädler 1980 ; Shukurov Sokoloff 2008 ; Chamandy et al. 2014 ) or cosmic ray driven dynamo (Parker 1992 ; Hanasz et al. 2004 ). In our simulations, the large-scale uniform field is maintained at all times because the MHD equations (equations 1 – 4 ) preserve the net seed magnetic flux in a triply periodic numerical domain. We demonstrate the growth of small-scale magnetic fields by the tangling of the uniform seed field in Fig. 9 by choosing Rm = 100 Rm (crit) (≈165). In Fig. 9(a) , we show the evolution of magnetic and turbulent kinetic energies for models with all three seed field cases (UniRm100, PLRm100, and ParRm100). The magnetic energy only grows (linearly) for the case UniRm100 and decays for the other two cases (PLRm100 and ParRm100). Figure 9. Same
Paper: 2019ApJ...877...85E (fulltext)
11 . For the most massive bin, the HSE mass is biased low at a constant level of about 24%. This bias drops with decreasing halo mass, with the mean bias in the radial range being , and 10% for the three lower-mass bins. These findings are in good agreement with the HSE mass biases for halos found in previous NR simulations (Kay et al. 2004 ; Nelson et al. 2012 ). Here we have assumed full information on both the density and temperature profiles in Equation ( 9 ). Incomplete knowledge on either of these quantities has the potential to further bias the HSE estimate. Figure 11. Bias between the HSE mass estimate and simulation mass at z = 0. Each line traces the median relation, with the red (black) error bars showing the 25th and 75th percentiles of the highest-mass (lowest-mass) bin. We show the result down to only , as the relation becomes dominated by noise on smaller scales. We can obtain a better understanding of the HSE mass bias by measuring the nonthermal pressure for each individual halo. Following Battaglia et al. ( 2012a ), we focus here on bulk flows, which should capture the major contribution to nonthermal support. We compute the corresponding kinetic pressure based on mass-averaged velocity fluctuations:The velocity fluctuations are made with respect to the baryon center of mass, , and mass-averaged velocity, , within R 200 :The resulting kinetic pressure profiles are shown in Figure 12 as the fraction of the total pressure. These are compared to the fitting function from Battaglia et al. ( 2012a ), which is based on the Shaw et al. ( 2010 ) power-law fitting function with an additional mass dependency that scales as . Figure 12. Fraction of the kinetic pressure with respect to the total (kinetic plus thermal) pressure for each of the four mass bins in the NR run at z = 0. The light-shaded solid lines trace the fitting function from Battaglia et al. ( 2012a ), which is based on power-law slopes of 4/5 in radius and 1/5 in mass. The fitting function is computed using the median halo mass in each bin. We show the 25th and 75th percentiles of the highest-mass (lowest-mass) bin as the red (black) error bars. The fitting function does a good job at matching our simulation results for but dramatically underestimates the kinetic pressure on smaller scales. In this case, a broken power law with a shallower slope on small scales would be more appropriate. We expect the NR results to be accurate for since bulk flows on these scales will be gravitationally sourced and mostly unaffected by cooling and feedback processes occurring on smaller scales (Battaglia et al. 2012a ; Nelson et al. 2014 ). Indeed, the fitting function in Figure 12 was calibrated against simulations with cooling and feedback. Earlier works have shown that kinetic pressure is greater in less relaxed systems that have undergone recent major mergers (Jeltema et al. 2008 ; Piffaretti Valdarnini 2008 ; Lau et al. 2009 ; Nelson et al. 2014 ). The rise of the kinetic
Paper: 2019ApJ...872...33R (fulltext)
the same procedure as used for generating the LAXPC PDS. NuSTAR observations of 2015 October 22 show a single broad peak at ∼1 mHz with 1024 bins per interval, whereas two clearly resolved peaks at ∼1 and 2 mHz are visible in the PDS with 512 bins per time interval. In the NuSTAR PDS of 2015 October 30, QPO peaks at ∼1 and 2 mHz are detected using 1024 bins as well as 512 bins per interval. We have, therefore, used 512 bins per interval for both of the NuSTAR observations to generate PDSs, as shown in Figure 5 . The NuSTAR PDS of 2015 October 30 also shows the presence of 12 and 27 mHz QPOs with a coherence factor and rms (%) of 8 and 3% and 4 and 3%, respectively. A summary of the characteristics of the ∼1 mHz and ∼2 mHz QPOs from LAXPC and NuSTAR are presented in Table 3 . Figure 5. PDSs (in the units of (rms mean −1 ) 2 Hz −1 ) of 4U 0115+63 obtained for the outburst from NuSTAR in the 3–79 keV energy band ((a), (e)). PDSs in the 3–80 keV energy band of LAXPC 10 (b), 20 (c), and 30 (d). Arrows indicate ∼1 mHz and ∼2 mHz QPOs in the PDSs of NuSTAR and LAXPC detectors. Table 3 Summary of the Characteristics of the ∼1 mHz and ∼2 mHz QPOs Observed from LAXPC and NuSTAR To investigate the energy dependence of the QPO characteristics, PDSs were extracted in the 3–15, 15–30, 30–50, and 50–80 keV bands for each of the LAXPC units. This was achieved by extracting data from Layer 1 for 3–15 keV, Layers 1 and 2 for 15–30 keV, and Layers 1–5 for the 30–50 and 50–80 keV energy bands. PDSs in the units of (rms mean −1 ) 2 Hz −1 are generated with 10 s binning and by subtracting Poissonian noise. Prominent QPOs are observed at ∼1 mHz and ∼2 mHz in the energy bands up to 50 keV. Above 50 keV the 1 mHz and 2 mHz QPOs are insignificant. Details of QPO characteristics like frequency, rms amplitude, and quality factor are summarized in Table 3 . The coherence factor ( ν / δν ) lies in the range 7.6–11.1 for the ∼1 mHz QPO and 2.8–6.0 for the ∼2 mHz QPO, respectively. It can be observed from Table 3 that the coherence factor of the ∼1 mHz QPO decreases from 3–15 keV to 15–30 keV and then increases at higher energies. rms amplitude (%) of the ∼1 mHz QPO increases from 3–15 keV to 15–30 keV and then decreases at higher energies. It may be noticed that the coherence factor increases with energy for the ∼2 mHz QPO and rms amplitude (%) of the ∼2 mHz QPO increases until 30 keV and then decreases at higher energy. 3.1.1. ∼1 mHz and ∼2 mHz QPOs from <italic>RXTE</italic>/PCA Observations PCA/ RXTE Standard 1 mode light curves (10 s binned) in 2–60 keV, generated using the FTOOLS task “lcurve” in HEASOFT for of all of the observations showing ∼mHz QPOs, are presented in Figures 6 (a), (b), and (c). It may be noted that during the 2004 observation, three PCUs were on, while in 2008 observations two PCUs were on, and the count rates in the figures reflect this. In the 2011 light curve in Figure 6 (c), the jump in the count rate in the middle of the light curve is not
Paper: 2019ApJ...879..118H (fulltext)
data. Section 3 shows an example of SIR and presents the identification criteria for SIRs and shocks. Section 4 presents the statistical distributions of the properties of SIRs and their associated shocks, and compares the results with those at 1 au. The last section concludes with a summary of the contributions. 2. <italic>MAVEN</italic> Data The MAVEN spacecraft carries nine sensors that measure the solar wind and the plasma and neutral environment at Mars. In this work, we use data from the following three instruments: Solar Wind Ion Analyzer (SWIA; Halekas et al. 2015 ), Magnetometer (MAG; Connerney et al. 2015 ), and Solar Wind Electron Analyzer (SWEA; Mitchell et al. 2016 ). SWIA is an electrostatic analyzer that measures 3D distributions of ions from 25 eV to 25 keV. When MAVEN is in the solar wind, SWIA provides the solar wind parameters with a cadence of 4 s. MAG measures the three components of magnetic field with 32 Hz cadence. We use 4 s resolution MAG data that are extracted from the full resolution measurements and are in one-to-one correspondence with SWIA data. SWEA provides the energy and angular distributions of 3–4600 eV electrons with 2 s time resolution. We utilize the MAVEN data in Mars Solar Orbital (MSO) coordinates, in which X points from Mars toward the Sun, Y points in the opposite direction of the Mars orbital velocity component perpendicular to X, and Z completes the right-handed system. 3. Observations of an SIR during 2016 August 2–3 Figure 1 shows solar wind plasma and magnetic field data from MAVEN during 2016 August 2–3 encompassing a typical SIR event. The SIR is clearly identified by the signatures including a compression of IMF ∣ B ∣, an increase of proton speed V p , an increase of proton number density N p , an enhancement of proton temperature T p , and a significant enhancement of total perpendicular pressure P t (e.g., Guo et al. 2015 , 2016 ; Wang et al. 2018 ). P t is defined as the sum of the magnetic pressure and plasma thermal pressure perpendicular to the magnetic field (Gosling et al. 1987 ). Note that SWIA does not have the capability to discriminate ions based on mass, and thus the plasma parameters are calculated assuming that all ions are protons. SWEA does not provide direct measurements of solar wind electron temperature and density. In the calculation of P t , the electron contributions are not included and the proton temperature is assumed to be isotropic. An SI in the SIR, characterized by a decrease in proton density and a simultaneous increase in proton temperature, can be discerned at about 13:25 UT on August 2. A pair of forward–reverse shocks bounding the SIR are observed at about 03:44 UT on August 2 and at about 07:37 UT on August 3 respectively. Figure 1. MAVEN observations of an SIR at 1.52 au during 2016 August 2–3. From top to bottom: the MAVEN spacecraft altitude, pitch angle distributions of electron (∼282 eV), electron energy spectra, ion energy spectra, proton speed ( V p
Paper: 2021MNRAS.503.3000M (fulltext)
difference in realization may cause a bigger difference in the mass evolution (however, still very small) than using the same realization but with a different code. Figure 7. Comparison of models performed with different N -body codes. The blue and orange lines are the differences in the remaining bound masses between models 18 and 19 on one hand (performed with φ grape and ph4 , respectively) and 17 on the other (performed with nbody6++ ). The light green lines are similarly the differences between models 17A and 17B on one hand and 17 on the other that only differ in random seed. These models are in our weak tidal field eccentric orbit and have N = 10 5 stars. No gravitational softening was used. The small and seemingly random residuals indicate that there is no systematic difference in these models. Despite the differences between nbody6++ , φ grape , and ph4 , we find that the evolution of N = 10 5 clusters in weak tidal fields is nearly identical from one code to the next. 3.3 Softened models Gravitational softening is a technique to ease the computation of N -body simulation. In ‘Plummer’-type softening, the gravitational force between two bodies is modified from ∼ r −2 to ∼( r 2 + a 2 ) −1 , where a is called the softening length. For r ≫ a , the modified force approaches the true Newtonian force, but otherwise it approaches a constant. This hinders the formation of bound systems on scales ≤ a which otherwise have small time-steps and are difficult to integrate unless a regularization technique is used. Modifying the gravitational force in such a way has implications for hyperbolic encounters as well, the effect is decreasing the Coulomb logarithm and thus increasing of the two-body relaxation time. 3.3.1 Model with <italic>N</italic> = 10<sup>5</sup> Model 22 is the softened equivalent of model 17 presented in Section 3.1 . The two models evolve in a very similar way, and most quantities (total mass-loss, Lagrange radii) match when 17 is ‘slowed down’ by a factor of 1.18. Between 0.2 and 5 Gyr (roughly the time of the core collapse), model 17 experiences 461 kicks and 8593 sweeps, while model 22 experiences 129 kicks and 6620 sweeps. The softer potential causes a significant reduction in the number of kicks (as expected given the treatment of close encounters) and a moderate reduction in the number of sweeps. 3.3.2 Model with <italic>N</italic> = 10<sup>6</sup> Model 6 is the softened equivalent of model 1 presented in Section 3.1 . It behaves unexpectedly. While it is similar to model 1 , one would expect a somewhat lower mass-loss rate due to the gravitational softening, as seen when comparing models 22 and 17 . However, the result is the opposite. The difference in kick rate is especially stark: Between 0.2 and 5 Gyr, model 1 experiences 558 kicks and 14 530 sweeps, while model 6 experiences 5369 kicks and 16 607 sweeps. This ∼tenfold increase in the kick rate (as opposed to a decrease by a factor of 3.6 when softening is added in
Paper: 2018ApJ...867...18N (fulltext)
an sGRB that can be observed along its axis. This successful jet with its cocoon, which is sometimes called the “structured jet,” is scenario E in Figure 2 of Mooley et al. ( 2018b ), and it is similar to the one considered by Lazzati et al. ( 2018 ) and Margutti et al. ( 2018 ). In the second simulation the jet is launched for a short duration and is choked before breaking out of the ejecta. In both cases the cocoon does not affect the UV/optical emission on a timescale of half a day, but we show that the shock breakout of the successful jet can potentially generate the γ -ray signal (Figure 4 ). In both simulations there are afterglow parameters (ISM density and microphysics) for which the observed radio and X-ray emission is produced as shown in Figure 3 . Figure 4. Shock breakout γ -ray light curve (top panel) and spectrum (bottom panel) from the successful jet simulation. The blue thin line is the spectrum during the peak, while the red thick line is the spectrum during the tail. 4. Was There a Relativistic Jet in GW170817? A relativistic jet is necessary to produce an sGRB. Therefore, the question whether a relativistic jet can successfully break out of a neutron star merger ejecta and in particular whether a jet broke out in this event is interesting. In GW170817 we consider the option of a successful jet as possible but less likely. First, it does not explain the early optical light. Second, and more importantly, to explain the observed radio emission, either the jet should be much weaker than the cocoon, so that its contribution is unimportant, or it should be so powerful that its contribution begins only at very late time, at least months after the merger. Without fine-tuning, the jet is not expected to have much less energy than the cocoon (Nakar Piran 2017 ), while a very powerful jet produces a very luminous sGRB, and these are known to be extremely rare (Nakar 2007 ; Wanderman Piran 2015 ). However, this is only circumstantial evidence. Unfortunately, a weak successful jet does not contribute to the observed emission, and therefore we may never know whether it existed or not. A powerful jet, however, might be detectable. If it is powerful enough, it may be detectable while being still off-axis. In that case the radio light curve should rise sharply and then decline as a power law, after a short plateau. This unique prediction of the jet signature is the easiest to identify. A less powerful jet may contribute to the radio light curve, but its contribution may not be easily identified (see, e.g., Figure 3 and Lazzati et al. 2018 ; Margutti et al. 2018 ), since a continuous rise to a peak followed by a decay can arise either from a successful or from a choked jet (see Figure 3 and Nakar Piran 2018 ). Additional information, especially in the radio near the time that the afterglow flux peaks, may provide the key to resolve this question. First, the radio image of the source is expected to be resolved soon. Figure 5 shows the images
Paper: 2019MNRAS.486..966L (fulltext)
Apellániz 2013 ). In Section 2 , we present the characterization and definition of the GALANTE photometric system. In Section 3 , we analyse in detail the set of GALANTE standard stars. Transformation equations between the SDSS and the GALANTE photometric system are obtained in Section 4 , setting up the comparison between the synthetic magnitudes of 378 stars from the Next Generation Spectral Library (NGSL) plus 122 stars from a new catalogue from MAW in both photometric systems. Next, in Section 5 we estimate zero-points from the GALANTE photometry using the SDSS and RefCat2 . Finally, in Section 6 we summarize the results of this paper. 2 THE GALANTE PHOTOMETRIC SYSTEM 2.1 DESCRIPTION Deriving physical stellar (or galaxy) parameters is the main goal of every photometric system. In our case, we achieve this aim with a new filter set designed to plug some gaps found in previous surveys. The GALANTE photometric system has been defined using four J-PLUS filters (Cenarro et al. 2019 ) and three purpose-built filters. This composition of intermediate- and narrow-band filters covers the optical range from 3000 Å to 9000 Å. This filter set has been selected with a direct goal: deriving stellar effective temperatures for hot stars in an optimal way (Maíz Apellániz Sota 2008 ; Maíz Apellániz et al. 2014 ; Maíz Apellániz Barbá 2018 ). We will also be able to obtain the gravity and metallicity (the latter for stars cooler than 10 000 K), allowing us to discriminate between giants, supergiants, dwarfs, and between solar or SMC metallicities. In Figs 1 and 2 , we present the set-up of the filters. Figs 1(a) and (b) show the layout of the J-PLUS and GALANTE filters, respectively, leaning on the Vega spectrum. Note that in this case we only plot the transmission curves of the filters, allowing to the reader a visual location of each one. Focusing on GALANTE, Fig. 1(b) plots the configuration of the selected GALANTE filters, which enable us to measure the key zones in the spectra to derive physical parameters. In Fig. 2 , we plot the response functions of the J-PLUS and the GALANTE photometric system filters. In discontinuous grey, we represent the response curve of the CCD and mirror, while discontinuous green is the CCD, mirror and atmospheric transmission at 1.3 airmass curve. The SDSS response curves are represented in continuous black, and the other continuous curves are the GALANTE and J-PLUS filter response curves convolved with CCD, mirror and atmospheric transmission at 1.3 airmass. Using Fig. 3 , we can also explain the function of each GALANTE filter with greater accuracy. Filter F348M is a u -like filter dispose to measure the continuum from the left of the Balmer jump. We can combine this one with F420N and F450N to measure the Balmer jump on both sides and derive the T eff of stars. Both filters of our design, F420N and F450N, are the most original (compared to other large-scale recent photometric surveys) of the set-up. They have been
Paper: 2020ApJ...892...17D (fulltext)
(See Also Section 2.7 ) Note. The motivation behind each assumption is described in the corresponding sections. We briefly summarize (column 4) the effect produced by each assumption to help the reader distinguish the genuine trends from those induced by our prior hypotheses. The reference BHAR/SFR trends are taken from Mullaney et al. ( 2012 , M12), Rodighiero et al. ( 2015 , R15), and Aird et al. ( 2019 , A19). (∗∗) The relative normalization is chosen to fit the corresponding data points of Figure 4 . 2.3. Galaxy Stellar Mass Function The first step displayed in Figure 1 consists in setting the input galaxy M ⋆ function at different redshifts. The prescription is taken from Davidzon et al. ( 2017 ), who exploited the latest optical to infrared photometry collected in the COSMOS field over the UltraVISTA area (1.5 deg 2 , see Laigle et al. 2016 ). They provide the M ⋆ function separately between SF and passive galaxies, based on the [NUV − r ]/[ r − J ] colors (Ilbert et al. 2013 ). Throughout this work, we consider only the M ⋆ function relative to the SF galaxy population (i.e., MS and SB galaxies). Next, we split the M ⋆ function of SF galaxies among the MS and SB populations. Given that the relative fraction of the two populations has been shown not to vary with M ⋆ (e.g., Rodighiero et al. 2011 ; Sargent et al. 2012 ), we only consider how their relative fraction evolves with redshift by following the prescription of Béthermin et al. ( 2012 ). The fraction of SB galaxies ( f SB ) appears to evolve linearly, from f SB = 0.015 ( z = 0) to f SB = 0.03 ( z = 1), while it stays flat at higher redshifts. By simply scaling the SF galaxy M ⋆ function down by f SB (or 1 − f SB ), we end up with the M ⋆ function of MS and SB galaxies at each redshift (Figure 2 ). We note that the input M ⋆ function of Davidzon et al. ( 2017 ) is already corrected for the Eddington bias, which might have some impact at the high- M ⋆ end. After dissecting among MS and SB galaxies, we interpolate the corresponding M ⋆ function at redshift z = 0.5, z = 1, z = 2, and z = 3 across a M ⋆ range of 10 8 M ⋆ 10 12 M ⊙ . Figure 2. Galaxy M ⋆ function at various redshifts for SF galaxies, taken from Davidzon et al. ( 2017 ). Solid and dashed lines mark MS and SB galaxies, respectively. 2.4. The MS and the SB Loci We use the MS prescription presented in Schreiber et al. ( 2015 ), which incorporates a redshift evolution and a bending toward higher M ⋆ . Schreiber et al. ( 2015 ) studied a sample of Herschel -selected galaxies out to z ∼ 4, dissecting the observed distribution of MS offsets (=SFR/SFR MS , see their Equation (10) and Figure 19) among the MS and SB populations. By fitting that distribution via a double lognormal function, MS galaxies are centered at 0.87 × SFR MS , while SB galaxies are centered at 5.3 × SFR MS (Schreiber et al. 2015 ). Both relations were rescaled to a Chabrier ( 2003 ) IMF. The 1 σ dispersion for both the MS and SB loci is assumed to be 0.3