adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.461.1917M (acknowledgments)
We are thankful to the anonymous referee whose detail in-depth comments helped us in improving the paper. This research has made use of data and/or software provided by the High Energy Astrophysics Science Archive Research Centre (HEASARC). ASM would like to thank Inter-University Centre for Astronomy and Astrophysics (IUCAA) for hosting him during subsequent visits. BR likes to thank IUCAA for hospitality and other facilities extended to him during his visit under their visiting Associateship programme.
Paper: 2018A&A...618L...2J (acknowledgments)
We thank the referee, G. Wade, for useful comments. Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 191.D-0255. This work has made use of the VALD database, operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna.
Paper: 2017MNRAS.471..463S (fulltext)
can be estimated combining the observed secular frequency shift (almost 0.8Hz over 17yr of observations) with that caused by the material torque (ranging between 0.35 and 0.56Hz, considering the NS radius and the corotation radius as possible truncation radii of the disc). The spin-down derivative required to compensate the estimated spin-up material torque and to produce the observed spin-down effect should range between 2.210 15 and 2.710 15 Hzs 1 , a factor of almost 1.5 and 1.8 higher, respectively, than the spin-down frequency derivative estimated ignoring the material torque. Using equation ( 5 ) and adopting the FPS equation of state previously described, we can estimate the NS dipole magnetic moment, hence the magnetic field at the magnetic caps in the range 2.210 8 B PC 310 8 G, very similar to the value suggested by Burderi etal. ( 2006 ). However, we note that other mechanisms such as magnetic propeller torque and gravitational radiation torque (see e.g. Hartman etal. 2008 , and references therein for more details) could contribute to the spin-down torque required to describe the observed secular spin evolution of SAXJ1808.43658. 4.2 Pulse energy dependence SAXJ1808.43658 shows an interesting behaviour of its pulse profile as a function of energy. As shown in Fig. 5 , the pulse fractional amplitude estimated from the fundamental component varies significantly between the XMMNewton and the NuSTAR observations of the source. We can exclude instrumental effects between the two satellites to explain such a discrepancy. Indeed, phase-coherence analyses on almost simultaneous XMMNewton and NuSTAR have been carried out on other sources (see e.g. Sanna etal. 2017 , for the AMXP IGRJ00291+5934) and they did not show any peculiar difference between the two detectors. Therefore, a possible explanation may be a significant source state variation occurred in the 3d gap between the two observations. A detailed study of this phenomenon is beyond the scope of this work, however, further investigations on the subject will be reported in a follow-up paper focusing on the spectral properties of SAXJ1808.43658 during its 2015 outburst (Di Salvo etal., in preparation). The peculiar trend showed by the fundamental component is consistent with that observed by the Rossi X-ray Timing Explorer during the 1998 outburst while it significantly differs from that of the 2002 (see e.g. Cui, Morgan Titarchuk 1998 ; Falanga Titarchuk 2007 ; Hartman, Watts Chakrabarty 2009a ). A similar behaviour has been observed for the other AMXPs such as IGRJ00291+5934 (Falanga etal. 2005 ; Sanna etal. 2017 ), IGRJ175113057 (Papitto etal. 2010 ) and XTEJ1751305 (Falanga Titarchuk 2007 ), although for the latter a good coverage of the trend below 4keV is lacking. On the other hand, the constant energy dependence of the fractional amplitude inferred from the second harmonic component seems to remain unvaried over the outbursts (see e.g. Cui etal. 1998 ; Hartman etal. 2009a ). The
Paper: 2015MNRAS.451.4021S (acknowledgments)
We thank the staff of the Karoo Observatory for their invaluable assistance in the commissioning and operation of the KAT-7 telescope. The KAT-7 is supported by SKA South Africa and the National Science Foundation of South Africa. AMS gratefully acknowledges support from the European Research Council under grant ERC-2012-StG-307215 LODESTONE. We also thank the anonymous referee for their careful reading of this manuscript and useful comments.
Paper: 2018MNRAS.479.5184A (fulltext)
survey volume between the models, the luminosity functions agree well for magnitudes brighter than M UV = −16 mag. This is essentially due to the fact that for relatively small amplification factors, the variations of the amplification and the effective volume are anticorrelated and tend to maintain the same faint-end slope. Furthermore, relative errors on the effective volume are smaller than what is observed at M UV > −16 mag. Figure 10. Impact of cluster mass models on the UV luminosity function in MACS1149: Left: the effective survey volume (and associated 1σ uncertainties) as a function of absolute intrinsic magnitude for each lensing model. The effective volume depends mainly on the geometrical configuration, the magnification map, and the surface area (cf. Fig 9 ) of each model. Right: Derived UV luminosity function using each cluster model and associated uncertainties. All models support a turnover in the faint-end of the LF but at different magnitudes and with different slopes. Beyond an absolute magnitude of M UV = −15 mag, the differences are too large to robustly constrain the LF shape. In the next section, we use the same procedure in each cluster to assess the lensing uncertainties in the final LF that combines the six clusters. 7.3 Final constraints on the UV LF We now compute the final UV luminosity function at ∼6 resulting from the combination of all the individual clusters. In order to account for all the uncertainties described in the previous sections, we use MCMC simulations to explore the full error space of each galaxy and each effective volume in a given magnitude bin. At the level of an individual galaxy, the MCMC simulations take into account the magnification factor uncertainties by using multiple lensing models. Similarly to the previous section, we randomly sample the completeness functions and lensing models shown in the left-hand side of Fig. 10 for the corresponding cluster. For each galaxy magnitude, the MCMC procedure randomly samples the four effective volumes, which are based on four different lensing models, and their associated uncertainties. For each iteration, the total sample is distributed in magnitude bins M i , with a size of 0.5 mag, for which φ( M i ) is computed with the associated cosmic variance errors estimated from the = 7 lensing curve in fig. 2 of Robertson et al. ( 2013 ). These simulations have the advantage of accounting for uncertainties in M i when estimating uncertainties on φ( M i ) because individual galaxies can change bins at each iteration, and at the same time, rectifying the limitations of the binning procedure. Simultaneously, we also fit the resulting LF results at each iteration to determine the likelihood of the modified Schechter parameters. We here combine our results with the bright-end constraints derived by Bouwens et al. ( 2015 ) from wide-area blank field surveys. To account for the difference in the redshift selection function between their = 7 sample and our combined
Paper: 2019MNRAS.490.3654C (fulltext)
medium these interactions can alter the structure of a galaxy, but the degree to which it is affected also depends on whether it was previously pre-processed. For example, dark matter haloes that were previously in a group environment tend to lose mass more slowly in the cluster environment as the more weakly bound dark matter in the outer halo was already removed in the group (Joshi, Wadsley Parker 2017 ; Han et al. 2018 ). Also, in some Virgo cluster dwarf ellipticals, counter-rotating cores have been observed (Toloba et al. 2014 ). Such features are very difficult to reproduce through the action of the cluster tides or harassment alone (González-García, Aguerri Balcells 2005 ). Thus, mergers in a previous group environment are likely required to explain the presence of such features in galaxies that are now cluster members. In addition to pre-processing, it is important to take into account that galaxies entering the cluster in a group face the combined environmental influence of both group and cluster, and it may be several gigayears until groups can be completely dissociated and virialized within the cluster (Cohn 2012 ). This process, arising in group-cluster mergers, is often referred to as post-processing . In Vijayaraghavan Ricker ( 2013 ), this process was studied using N -body cosmological simulations combined with idealized N -body/hydrodynamical simulations. They study the effects that the merger has on both group and cluster: there is an enhancement of the galaxy–galaxy merger rate during the pericentric passage of the group in the cluster; the merger shock leads to an increase in the ram pressure acting on both the group and the cluster galaxies; and the group galaxies may become tidally truncated. In this context it is clear that to fully understand the processes that are triggering changes in the cluster galaxy population it is crucial to understand their environmental history. While the quantity of pre-processed galaxies in clusters has been shown to be quite significant, as has the effect of the group environment upon them, our understanding of what happens to these galaxies once their hosts enter the cluster, the post-processing stage, has not been explored in so much detail. Thus, in the study presented here, we focus on the consequences of the group falling into the cluster environment. Using a set of high-resolution N -body simulations of galaxy clusters, we identify host and satellite haloes at different redshifts, and then track their evolution while they are under the influence of the cluster environment. We specifically include satellites that are stripped from their host halo by the cluster tides in order to better understand the role of the cluster in the dissociation of substructure. Understanding how groups breakup in clusters could potentially help us identify these pre-processed galaxies, once they have left their groups and mixed with the cluster population. However, as we will show later in this
Paper: 2021ApJ...910..155P (fulltext)
for the reaction of C 2+ with normal H 2 . The circles indicate the binned values of the overall reaction rate coefficient k 3 ( T ) obtained in the present study from the measured decay of numbers of detected C 2+ due to the reaction with normal H 2 in the ion trap. The asterisks indicate the binned values of obtained from the measured time evolutions of the number of detected C + product ions. Note that are lower limits for k 3b ( T ). Vertical error bars indicate a statistical error with included uncertainty of the temperature ±5 K, which affects the reactant’s number density. The error band includes a 20% systematic uncertainty of the H 2 reactant pressure measurement as an independent influence. Horizontal error bars include in addition temperature uncertainty ±5 K and the binning of data. By measuring the time evolutions of the number of detected C + ions at different temperatures, we obtained the temperature dependence of the rate coefficient . We use an additional index C + to stress the fact that the value was obtained in the experiment by measuring the production of C + ions. We have in mind that we cannot prove that all the produced C + ions are confined in the ion trap, and a part of them can escape. That is why the measured value is a lower limit for k 3b ( T ). The temperature dependence of the measured reaction rate coefficient for production of C + ions in reaction of C 2+ dications with normal H 2 is also given in Figure 3 . Only a very small fraction of ions corresponding to the production of CH + (see the text above and example in Figure 2 ) was observed, that is why these data are not included in Figure 3 . In Figure 4 , the present k 3 ( T ) is compared with the results of previous experimental studies. Plotted are the present data (binned values as in Figure 3 ) and, for comparison, the values of k 3 ( T ) obtained at 300 K with the SIFT technique by Smith Adams ( 1981 ) and by Viggiano et al. ( 1979 ). We also include the value obtained by Gao Kwong ( 2003 ) at higher collisional temperatures ( T ≈ 2 630 K) using a cylindrical RF ion trap. The value of the Langevin collisional rate coefficient k L for collisions of C 2+ carbon dications with H 2 k L (C 2+ + H 2 ) = 3.2 × 10 −9 cm 3 s −1 is also plotted in Figure 4 . Note that the measured values of k 3 ( T ) are just small fractions of k L , i.e., 4%–5%. Figure 4. Temperature dependence of the reaction rate coefficient k 3 ( T ) of the reaction of C 2+ carbon dications with H 2 , comparison with previous results. The horizontal dashed line indicates the value of the Langevin collisional rate coefficient ( k L = 3.2 × 10 −9 cm 3 s −1 ). The circles indicate the values of k 3 ( T ) obtained in the present study for the reaction of C 2+ carbon dications with normal H 2 . The asterisks indicate values of the reaction rate coefficient , obtained from measured time evolutions of the number of detected C + ions. The values of are lower limits for the actual values of
Paper: 2020MNRAS.493..847F (fulltext)
[Fe/H] ∼ −1.6 and an outer halo of [Fe/H] ∼ −2.2 (Carollo et al. 2007 ). Although both components are expected to have contributions from accreted stars, it is the outer halo that is dominated by accreted stars (Tissera, White Scannapieco 2012 ). In the suite of eight MW-like model galaxies in the simulation by Tissera et al. ( 2012 ) the stars accreted from satellites have ages between 9.8 and 12.6 Gyr and metallicities of −1.45 [Fe/H] −0.86. Such ages and metallicities are consistent with the initial epoch of star formation and metallicity upper limits for the five disrupted satellites in our work (see Table 2 ). Using MW-like model galaxies based on the Evolution and Assembly of GaLaxies and their Environments (EAGLE) simulations by Mackereth et al. ( 2019 ) concluded that the MW’s assembly history was dominated by the accretion of a small number of satellites of stellar mass 10 8.5−9 M ⊙ , in reasonable agreement with our findings (see Table 2 ). 7.1 The Kraken/Koala satellite K19 describe their simulation of 25 galaxies with MW-like masses within the EAGLE galaxy formation model (Crain et al. 2015 ). Their suite of MW-like model galaxies undergoes an average of 4.4 ± 2.3 minor mergers. with mass ratios of 1:100 up to 1:4. This corresponds to satellite masses of 10 8−9 M ⊙ at the time of infall. They suggest that the vast bulk of the accreted GCs come from two nearly equal mass (∼10 9 M ⊙ ) satellites that merged in the last 10 Gyr. A third lower mass (∼10 8 M ⊙ ) satellite brings in a handful of GCs. Additional accreted satellites are of very low mass, containing few, if any, GCs. They associated the low-mass satellite with the Sgr dwarf galaxy and one of the massive satellites with the Canis Major dwarf galaxy (here called Gaia -Enceladus). The remaining, and perhaps most massive satellite, was not associated with any known feature of the MW by K19 but instead was given the name Kraken . They suggested the Kraken had a stellar mass of 2 × 10 9 M ⊙ and was accreted 6–9 Gyr ago. Although K19 do not associate any particular MW GC with the Kraken, they do list 18 GCs with the potential to be from the disrupted Kraken satellite. Here we have assigned those 18 GCs across the Gaia -Enceladus, Sequoia, Koala, Helmi satellites, and the high-energy group. So, while we do suggest that the MW accreted an additional satellite of mass M * ∼ 10 9 M ⊙ (i.e. Koala), we make different GC assignments to K19 . In their terminology, satellite 3 is the Sgr dwarf, satellite 2 is Gaia -Enceladus/Canis Major, and satellite 1 could be the Koala galaxy with ω Cen as its former nucleus. Our analysis indicates that the MW has accreted five satellites of mass 10 8−9 M ⊙ consistent with K19 . Overall, K19 concluded that the MW formed quite rapidly with no major mergers since z = 4 (i.e. the last 12.3 Gyr). This makes it somewhat atypical compared to their suite of 25 MW-like models. 8 CONCLUSIONS Using the IOM, the AMRs, and alpha-element ratios of MW GCs we
Paper: 2016A&A...594A..20P (acknowledgments)
The Planck Collaboration acknowledges the support of: ESA; CNES and CNRS/INSU-IN2P3-INP (France); ASI, CNR, and INAF (Italy); NASA and DoE (USA); STFC and UKSA (UK); CSIC, MINECO, JA, and RES (Spain); Tekes, AoF, and CSC (Finland); DLR and MPG (Germany); CSA (Canada); DTU Space (Denmark); SER/SSO (Switzerland); RCN (Norway); SFI (Ireland); FCT/MCTES (Portugal); ERC and PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www.cosmos.esa.int/web/planck/planck-collaboration . This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231. Part of this work was undertaken at the STFC DiRAC HPC Facilities at the University of Cambridge, funded by UK BIS National E-infrastructure capital grants. We gratefully acknowledge the IN2P3 Computer Center ( http://cc.in2p3.fr ) for providing a significant amount of the computing resources and services needed for this work.
Paper: 2021MNRAS.503.4787W (fulltext)
models and derived systematics models and residuals for both transits in Fig. 2 . 3.2 Spectroscopic light-curve analysis To construct our spectroscopic light curves, we began by binning over the narrow wavelength regions shown in blue in Fig. 1 . For each transit, we extracted 53 individual light curves using a uniform bin width of 75 Å whilst avoiding the GMOS detector gaps. We show the resulting raw light curves in the left-hand panels of Figs 3 and 4 . The raw light curves for both transits show significant systematics which are largely independent of wavelength. This is a similar situation to that encountered for the FORS2 data set in Wilson et al. ( 2020 ) and we proceeded in much the same way by dividing each of the spectroscopic light curves by the common-mode corrections derived from the white light curves. Additionally, to remove any remaining high-frequency systematics, we also subtract the residuals from the white light curves fits. The net result of this procedure is to provide significant improvements to the precision of our transmission spectrum whilst preserving the relative values of the planet-to-star radius ratios. After applying the common-mode correction described above we find the best-fitting model for each spectroscopic light curve using the same process and the same systematics model as described in Section 3.1 . The main difference is that we fix the mid-transit time to the best-fitting value from the white light-curve analysis, whilst allowing the planet-to-star radius ratio, limb darkening parameters, linear baseline parameters and kernel hyperparameters to vary for each fit. As for the white light curves, we perform a joint analysis allowing for only a single planet-to-star radius ratio and limb darkening coefficient pair for each individual wavelength channel, but allow the hyperparameters to vary separately for each transit. To add another layer of flexibility to our model, we use wide Gaussian priors with a standard deviation of 0.1 for the limb darkening coefficients, with a mean given by the best-fitting values determined using pyldtk . We again used the same differential evolution algorithm to optimize the log-likelihood as for the white light curves, and fine-tuned using a Nelder–Mead simplex algorithm. Outliers deviating more than 4 σ from the predictive distribution were removed for each fit (this procedure typically clipped 1–2 points per light curve) and we used the same MCMC method described above to marginalize our posterior distributions. The best-fitting common-mode corrected GP models for the spectroscopic light curves are shown in the middle panels of Figs 3 and 4 and we list the measured planet-to-star radius ratios and uncertainties in Table 2 . Our GMOS transmission spectrum for WASP-121b is shown in Fig. 6 and the results are discussed further in Section 5 . Table 2. Transmission spectrum for WASP-121b recovered from the GMOS low-resolution spectroscopic light curves. 3.3 Modelling
Paper: 2015A&A...578A..93B (acknowledgments)
This work is based on observations with the VLA, operated by the NRAO, and the 100-m telescope of the MPIfR (Max-Planck-Institut für Radioastronomie) at Effelsberg. This work is based in part on archival data obtained with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. This publication makes use of data products from the Wide-field Infrared Survey Explorer (WISE), which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. The author thanks the operators at the Effelsberg telescope for their support, the VLA team for performing the observations and help during data reduction, Bill Sherwood for processing the Effelsberg data at and Uli Klein for help with generating Fig. 27 . Aritra Basu, Michal Hanasz, Marita Krause, Woong-Tae Kim, and Fatemeh Tabatabaei are acknowledged for useful discussions and comments. Special thanks go to Marita Krause and Elly M. Berkhuijsen for careful reading of the manuscript, and to the anonymous referee for suggestions that helped to improve the paper. Financial support from DFG Research Unit FOR1254 is acknowledged.