adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.464.3256L (fulltext)
of their stellar mass at the present was already in place by z ∼ 2 via star formation (see Fig. 8 ). Figure 9. The average stellar mass assembly history of central galaxies as a function of redshift for haloes of different present-day masses, predicted by various empirical models as indicated. For Milky Way sized haloes, however, the differences between the model predictions are milder. All the models predict that about half of stellar mass was in place by z ∼ 1. There is a significant difference between L15 -U and other models at high z . For example, L15 -U predicts that about 15 per cent of the final stellar mass was assembled by z ∼ 2, while less than 10 per cent was predicted by the other models. For central galaxies in present-day massive haloes with M h (0) > 10 14 M ⊙ , the predictions of different models again become very different. M13 predicts a much later assembly for these galaxies than any other models. The predictions of B13 look similar to L15 -U, but the increase in stellar mass with time is due to different reasons. While L15 -U predicts that the increase at z 2 is dominated by accretion of stars from satellites, B13 predicts that a significant fraction of the increase at z 2 is actually due to in situ star formation (see Fig. 8 ). This difference is again due to the boost of star formation in low-mass haloes at high z in the L15 -U model. The increased amount of stars formed in progenitors at high z makes the accretion of stars more important in the growth of stellar mass in a massive galaxy, and the fraction of stars formed in situ has to be decreased proportionally in order to match the final stellar mass of the galaxy. The results demonstrate the importance of properly modelling the star formation in low-mass progenitors at high z in order to understand the star formation and stellar mass assembly histories of massive galaxies at the present day. 4.4 Stellar mass functions of high-redshift galaxies Fig. 10 shows the predictions of the empirical models for the SMFs of galaxies at a number of redshifts. The predictions of B13 and M13 are similar in both slopes and amplitudes at the low-mass ends, but B13 predicts many more massive galaxies than M13 , particularly at high redshifts. Y12 predicts significantly flatter slopes at the low-mass ends, and more galaxies in the intermediate mass range, than the other three models. The SMFs predicted by the L15 -U model match the predictions of B13 at M * > 10 10 M ⊙ , but are significantly steeper at the low-mass ends. Figure 10. The model predictions for the field SMFs at high redshifts (solid) in comparison with observations. We select some observational SMFs of high-redshift galaxies from the literature to compare with the model predictions. Specifically, we use the SMFs at 1.3 z 3.5 given by Pérez-González et al. ( 2008 ) and Marchesini et al. ( 2009 ). Pérez-González et al. used a sample combining data in three different fields with a total area of 664 arcmin 2 that have a total
Paper: 2016A&A...586A.125D (fulltext)
by Spite (1967) and regularly improved since then. The package allows us to compute synthetic spectra by interpolating over a large grid of hydrostatic, LTE, and plane-parallel or spherical stellar atmospheres models (MARCS, Gustafsson et al. 2008 ). For all the elements studied here, we assumed the standard solar abundances provided by Grevesse et al. (1996) , namely (Fe), (Y), (La), (Ce), (Nd), and (Eu). Recent spectroscopic estimates of solar abundances by Scott et al. (2015) and by Grevesse et al. (2015) indicate very similar abundances. Indeed, the difference in dex ranges from +0.01 for Eu to 0.03 for Fe and Y, to 0.06 for La, and to 0.08 for Nd, while the new Ce abundance is identical to the old one. 2.2.1. Hyperfine structure and isotopic splitting Several lines used in our abundance analysis are affected by hyperfine structure (HFS) and/or isotopic splitting in the line profile. We searched in the literature for atomic data required to compute the fine-structure components that form these lines. We found that several of them are already available and for those that are not available we adopted the same approach discussed in M13 to compute the HFS. The atomic data required to compute the HFS of La and Eu were taken from Lawler et al. (2001a , b) , respectively. For the La line at 6774.27 Å we adopted the HFS already computed by M13. The same outcome applies to Y: we adopted the HFS data given by M13, but they are only available for three (out of six) lines of this element ( = 5119.12, 5289.81, 5728.89 Å). No atomic data have been found in the literature for our lines of Ce and Nd. Only the odd isotopes Nd and Nd have HFS, but their effects can be safely ignored – in the solar system these isotopes constitute only 20.5% of the total Nd abundance, their lines are very narrow, and recent laboratory transition probabilities by Den Hartog et al. (2003) indicate no evident HFS structure for more than 700 lines of Nd II. 2.2.2. Abundances corrected from HFS We derived the abundances of the current sample of 73 Cepheids by accounting for the HFS of the elements and lines mentioned in the previous section. In order to quantify the effects of the HFS on the derived abundances and their dependence on other parameters, we performed a comparison between our abundance results before and after performing the HFS analysis. The mean differences in abundance are summarized in Table 1 . The differences are much larger for some of the La lines, but they are very close to zero for both Y and Eu lines. For the possible dependences on other parameters, we found that these differences and their dispersions become smaller with increasing (specially for > 5500 K), with increasing surface gravity (specially for > 0.5), and with decreasing pulsation period ( 1.0). No clear correlation with metallicity is observed. Finally, we note that the lines selected to measure n-capture elements are typically weak (smaller than 180 mÅ) and unsaturated. This means that
Paper: 2019ApJ...873...20R (fulltext)
model when adding the centimeter data emission with = 2.60 and with parameters of m ⋆ = 16 M ⊙ , a core of mass M c = 480 M ⊙ , and a Σ cl = 0.1 g cm −2 clump. The source detected at the Inner scale (or source 8a in Beltrán et al. 2016 ) is slightly extended at 6 cm, and it is blended with other components; however, it is a point source at 1.3 cm and 0.7 cm. Thus, it is possible that we are missing part of the flux at 6 cm, and this may explain why the models seem to overestimate the expected free–free emission at 6 cm. Our resulting protostellar mass of 16 M ⊙ is consistent with the value of of 18 M ⊙ estimated by Sánchez-Monge et al. ( 2013 ) when fitting the velocity field of the core with a rotating Keplerian disk, although they argue that this value corresponds to the total mass of a binary system in core B. Our results disfavor the models with protostellar mass and with relatively low core masses and high Σ cl clumps, because the expected free–free emission for these models is many orders of magnitude lower than the observed one. G45.47+00.05: The observed centimeter continuum emission from G45.47+00.05S (which is the dominant source in the region) is significantly higher (by a few orders of magnitude) than the predicted free–free emission from any of our IR-derived ZT models. This is despite the fact that the best two models from the ZT results already predict a rather high-mass protostar in the range of 30–50 M ⊙ embedded in a massive core, a relatively high Σ clump, and low accretion rates in the range of ∼10 −5 M ⊙ yr −1 . Among the eight sources presented in this study, this is the only case where none of the free–free models can describe the centimeter emission observed in the source. This region is also the most luminous and likely the most evolved one in this sample, which leads us to think that the radio emission from this source may be boosted by the process of photoevaporation. Photoevaporation is not yet accounted for in the TTZ model, which only considered photoionization of the magnetocentrifugally driven wind. However, as the protostar increases its mass above ∼20 M ⊙ , the ionizing radiation becomes dramatically stronger, creating a photoevaporation flow from the disk and infall envelope that is exposed by the outflow cavity wall, i.e., a wind driven by ionized gas-pressure. By enhancing the mass of ionized gas, the centimeter continuum emission is expected to be much higher than that predicted by the TTZ model without such a photoevaporation flow. IRAS 20126+4104: Including the centimeter continuum fluxes to better sample the SED, we can see in Figure 3 that the best five models seem to be overestimating the expected free–free emission. Therefore, after exploring other resulting models from the DLT17 study and the ZT models, we found a matching model with = 3.38 and = 5.42, which yields protostellar parameters of , core mass M c = 240 M ⊙ , and Σ cl = 0.1 g cm −2 . This protostellar mass is consistent with the value of
Paper: 2018MNRAS.474..219S (fulltext)
HEMT receiver followed by an autocorrelation spectrometer configured to record 4096 channels over 4 MHz for each polarization, yielding a spectral resolution of 0.09 km s −1 after Hanning smoothing. The system temperature varied between approximately 30 and 50 K during the observations. The telescope has a half power beam-width of 5.8 arcmin and rms pointing errors of 25arcsec. The spectra were obtained in a frequency-switching mode as a series of 30 s integrations. Most sources had an on-source integration of approximately 30 min. The system was regularly calibrated by observing the continuum source 3C123 (Ott et al. 1994 ) and daily checked using the source G32.744−0.076. This methanol maser source was reported as non-variable within ∼5 per cent (Caswell et al. 1995b ). Indeed, high-cadence observations reveal that some spectral features of the source have not shown variability higher than 8 per cent on time-scales from weeks to a few years. Detailed analysis of the light curves of five main spectral features of the source was reported in Szymczak et al. ( 2014 ). The features at 30.3, 38.5 and 39.1 km s −1 were non-variable during the monitoring period and the standard deviation of the flux density of these features indicates that the resulting accuracy of the absolute flux density is better than 10 per cent (Szymczak et al. 2014 ). The system temperature for the observations was measured by a continuously switched noise diode. Standard procedures were used to reduce the spectra. The typical rms noise level in the final spectra was 0.20-0.35 Jy at 0.09 km s −1 velocity resolution after averaging over polarization and time. There was no detectable interference on the spectra of our targets with the exception of G27.286+0.151. 2.3 Effect of offset observation and confusion For several sites in the sample (Table 1 ), accurate positions have recently been published (e.g. Bartkiewicz et al. 2014 , 2016 ; Breen et al. 2015 ), which show that for a majority of the targets the observed positions deviate from the interferometric positions by less than 0.4 arcmin. These differences (the fourth column of Table 1 ) are much less or comparable to the telescope pointing errors and have no significant effect on the flux density measurements. The measured profile of the telescope beam (Fig. 2 ) implies that our observations at an offset higher than 1.3 arcmin systematically underestimate the flux density by 9 per cent and its uncertainty increases by more than 10 per cent due to the pointing errors. There are 10 sites (11 distinct sources) in the sample for which the flux density measurements are significantly influenced by these effects and all of them are discarded in the following statistical analysis. Furthermore, the spectra of 18 sources (12 star-forming sites) are contaminated by confusion introduced by other sources in the telescope beam or side-lobe response from nearby bright sources and these sources are also excluded from the statistical
Paper: 2015ApJ...808..112H (acknowledgments)
This work was supported by grant HST-AR-12123.001-A from the Space Telescope Science Institute. W.A.F. participated in the Research Experiences for Undergraduates (REU) Program of the National Science Foundation under award number 1262810. A. M. R. acknowledges support from the Kenilworth Fund of the New York Community Trust. The HST observations presented here were obtained from the Mikulski Archive for Space Telescopes (MAST). STSci is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555.
Paper: 2016ApJ...823..106P (fulltext)
i and inclination do not affect the derived ephemeris in this paper. 4. PROSPECTS FOR GWS DETECTABILITY 4.1. Precision Estimates Throughout Advanced LIGO (aLIGO)/VIRGO (AdV) Observations Observing runs for GW searches are planned to commence in 2015, and will run through to 2022+. Estimated run duration for 2015, 2016–17 and 2017–18, are 3, 6, and 9 months, respectively. Starting from 2019, more extended runs are expected take place with aLIGO and AdV per every year. In addition, LIGO India is also expected be included in the observing schedule from 2022 onwards (LIGO Scientific Collaboration et al. 2013 ). The inferior conjunction value for the future GW searches ( T n ) will depend on the uncertainties on both T 0 and P orb when aLIGO/VIRGO observations commence. We consider here how the error on T 0 may be projected in time to any given epoch. Given the epoch for inferior conjunction, T n = nP orb + T 0 , following Galloway et al. ( 2014 ), we derive the projected uncertainty for T n aswhere and are the uncertainties in the P orb and T 0 , respectively, and is the cross-term of the covariance matrix. Substituting the values from Table 2 , we express the error on T n as follows: Here, is the uncertainty in the epoch of inferior conjunction at the time of t in days since T 0 . The evolution of is shown as a function of time in Figure 3 . In the absence of additional epochs of observations, term will grow linearly due to the factor, 4.1× day. The effective uncertainty will grow approximately as a function of time, 2× in 2018 and 3× or greater in/after 2022 compared the current error level. Figure 3. Projected uncertainty throughout the aLIGO observing period (shaded regions) is plotted as a function of time. The solid line denotes the approximately linear growth of the effective error throughout the aLIGO observing period (from 2015 onwards) in the absence of the additional epochs of optical data. The open squares describe the approximate effect on the additional observing epochs and the T 0 uncertainty is represented by the dotted curves. The estimated orbital period uncertainty ( σP orb ) is denoted by a dashed line. The above estimated values show that additional epochs of RV measurements will be needed to further refine the system parameters in order to improve the future GW searches. Based on the following assumptions, we also carried out simulations to estimate the effect of additional epochs of RV measurements. First, that the uncertainty on T 0 decreases as the total number of observations increases. Second, that the uncertainty on P orb decreases as the total span of the observations increases. We generated 6 epochs of 100 simulated radial velocity measurements in total. We considered each observing epoch to consist of 15 random observations of LT between 2015 and 2025. We have estimated the parameter uncertainties by combining all existing data with the simulated 100 RV measurements. Simulations allow us to improve the uncertainty
Paper: 2016MNRAS.461.2192C (fulltext)
dispersion by scaling the dynamical mass to the Einstein radius: A key ingredient in all measurements concerning strong lensing systems is the observed velocity dispersion, which is a projected, luminosity-weighted average of the radially dependent velocity dispersion profile of the lensing galaxy. In order to predict this value based on a set of galaxy parameters, we start with equation ( 6 ). Note that this equation is valid when the relationship between stellar number density and stellar luminosity density is spatially constant, an assumption unlikely to be violated appreciably within the effective radius of the early-type lens galaxies under consideration. Furthermore, the actual observed velocity dispersion is measured over the spectrometer aperture θ ap blurred with atmospheric seeing. In our analysis, we will apply the aperture weighting function provided by Schwab, Bolton Rappaport ( 2010 ) where χ = θ ap /σ atm and σ atm is the seeing recorded by the spectroscopic guide cameras during survey observations. Considering the effects of aperture with atmospheric blurring and luminosity-weighted averaging (see Schwab et al. 2010 for details), the observed velocity dispersion can be expressed as The observed stellar velocity dispersion is a luminosity-weighted average dispersion inside the fibre aperture. Spectroscopic data obtained in different surveys comprise luminosity-averaged line-of-sight velocity dispersions σ ap measured inside different apertures. According to Cao et al. ( 2015 ), whose compilation of lenses is used in this paper, SLACS and BELLS source papers reported effective circular apertures, while for the SL2S and LSD surveys they have been assessed from the sizes of the slit reported in source papers. In our analysis, we take for σ atm the median value recorded by the spectroscopic guide cameras during survey observations. More specifically, we have added 1.4 arcsec seeing for the SLACS spectroscopic observations according to Bolton et al. ( 2008 ) and 1.8 arcsec seeing for the Baryon Oscillation Spectroscopic Survey spectroscopic observations according to Bolton et al. ( 2012 ) and Shu et al. ( 2015 ). For the SL2S sample, the seeing for each individual lens was taken after Sonnenfeld et al. ( 2013b ). In the case of LSD systems: CFRS03-1077, HST14176 and HST15433, the seeing data were taken from Treu Koopmans ( 2004 ). Since they reported two seeing values per lens, we have taken the median. For Q0047−281, we took the seeing value after Koopmans Treu ( 2003 ), but for MG2016+112 we assumed 0.8 arcsec seeing because there was no seeing reported in the source paper. From the above equation, one can immediately observe that there are degeneracies among α, β and δ. More importantly, if we accurately knew the distance ratio D s / D ls , we could get more stringent constraints on the parameters α and δ describing the mass distribution of lensing galaxies. In our analysis, the angular diameter distance D 12 ( z ) between
Paper: 2018A&A...609A.106T (fulltext)
Bildsten 2010 ), we obtain ejecta mass , kinetic energy erg, magnetic flux density , rotation period ms. Here we have assumed an expansion velocity at peak of 5600 km s from the Fe ii velocity, and opacity . This simple model cannot reproduce the early peak, and therefore we fit only the later epochs; however, including all the epochs provides almost identical parameters. To reproduce the early peak in the magnetar scenario as well, we consider the early light curve as possibly produced by a magnetar-induced shock breakout (SBO), as described by Kasen et al. (2016) . Using the above derived parameters from the fit of the main peak with the magnetar model, we can derive the time of the SBO peak and its luminosity ( Kasen et al. 2016 , their Eqs. (26) and (27)). Both these values are off from what we actually observe in PTF11mnb; we obtained a peak epoch for the SBO of 83 days, with luminosity erg. Therefore, we do not favor a magnetar as the energy source for the early peak. Since a magnetar-induced SBO model does not fit the early light curve of PTF11mnb, whereas the early peak was nicely fit by a Ni decay model (see Sect. 6.2 ), here we also consider a hybrid model in which the first peak is due to a regular SN explosion powered by radioactivity, while the main peak is powered by a magnetar. It is indeed possible to fit the bolometric light curve with a simple Arnett model ( Arnett 1982 ) representing the radioactive component, plus a magnetar model ( Kasen Bildsten 2010 ) that injects energy in the ejecta starting a few days after explosion. This is illustrated in Fig. 11 , where the Arnett model is plotted as a dotted blue line and the magnetar model by a dashed black line. The sum of the two models, shown as a dashed red line, fit the bolometric light curve well. The magnetar injection of energy would have to start after 18 days from the SN explosion, otherwise it is not possible to reproduce both peaks simultaneously. In such a scenario, the SN explosion is characterized by , erg, , and the magnetar by and ms. The magnetar parameters in the hybrid model are different from those of the magnetar-only model previously described. Despite the nice fit of this hybrid model to the bolometric light curve, we still favor a progenitor scenario with the explosion of a massive star and a double-peaked Ni distribution because such a scenario requires a single source of energy and fewer parameters and, in particular, it naturally fits the post main-peak light curve. 6.4. Early light-curve modeling The early -band light curve of PTF11mnb appears rather flat in the first three epochs, which occur merely 4 d after the last non-detection. To reproduce this early emission, we adopted the model by Piro Nakar (2013) for the shock breakout cooling tail, assuming and kinetic energy and ejecta mass from the hydrodynamical fit to the bolometric light curve. We solved for the explosion epoch and for the progenitor radius, obtaining a radius of 90–105 and an