adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.468.1349P (fulltext)
velocity distribution of the gas within the velocity range from 495 to 495kms 1 (1kms 1 steps). The emission for one square degree centred on the SMCNOD exhibits two main peaks: 94 and 186kms 1 (which are shown in Figs 8 and 9 , respectively). These velocities agree with the velocity field related to the MS at the SMCNOD position, at an MS longitude L MS 25. See for example fig. 8 in Nidever etal. ( 2010 ). We discuss details about H i gas distribution in Section 4 . Figure 7. H i gas column density map from Kalberla Haud ( 2015 ), in zenith equal area projection, close to the south celestial pole. A grid of equatorial coordinates ( and ) is indicated. The SMC is close to the centre of the figure and the LMC is located near the bright spot in the lower-left corner. No significant excess of H i gas is observed at the position of the SMCNOD (dotted ellipse). Figure 8. H i gas emission map from Kalberla Haud ( 2015 ), in equatorial coordinates for the velocity channel 94kms 1 v LSR 95kms 1 . The SMCNOD position is highlighted by an empty ellipse and the SMC centre by a plus symbol. Figure 9. Same as Fig. 8 but for velocity channel equal to 186kms 1 (186kms 1 v LSR 187kms 1 ). 4 DISCUSSION In this section, we discuss the characteristics of the SMCNOD: we compare its stellar populations to those of the SMC (Section 4.1 ), its gas content (Section 4.2 ), possible scenarios for its origin (Section 4.3 ) and in the last subsection we provide a brief summary of the discovery, discussing the SMCNOD fate and some prospects for future analyses (Section 4.4 ). 4.1 The SMCNOD and SMC stellar populations The ( g r ) colour distributions of SMCNOD and SMC stars for 3 mag ranges in the Hess diagram from Fig. 2 are shown in the left-hand panel of Fig. 10 , whereas their number counts in bins of g magnitude filtered by the CMD mask and normalized (to the areas) are presented in the right-hand panel of the same figure. The colour distributions look very similar. For RC stars (solid lines), there may be a slight preference for the SMCNOD being a little bluer than the SMC. However, a KolmogorovSmirnov (KS) test indicates that the two RC populations come from the same parent distribution ( p 0.42). As for the LF comparison, the two distributions look similar as well. Figure 10. Histograms in bins of colour (left) and in bins of g magnitude (right) filtered by the CMD mask for the SMCNOD (third panel in Fig. 2 ) and the SMC (fifth panel in Fig. 2 ). In the right-hand panel, the Poissonian uncertainties are shown as shaded areas. The stellar colour distribution is very similar between the SMC and SMCNOD in the left-hand panel (comparing dotted red and blue lines and dashed red and blue lines), as well as the histogram in bins of magnitude for the stars filtered with the CMD mask in the last two panels in Fig. 2 (right-hand panel). 4.2 The SMCNOD and H<sc>i</sc> gas The results of considering the H i gas velocity channels are inconclusive. The v LSR =186kms 1 channel
Paper: 2018MNRAS.478.2315E (acknowledgments)
ACKNOWLEDGEMENTS We are very grateful to the constructive and detailed review carried out by our anonymous referee. His/her numerous and wise comments have helped to significantly improve this paper. We thank Manuel Peimbert, Jairo Méndez-Abreu, Leticia Carigi, and Rolf Kudritzki for their useful comments. This paper is based on observations made with the Gran Telescopio Canarias (GTC), installed in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias, in the island of La Palma, Spain. This research has made use of Aladin Sky Atlas developed at CDS, Strasbourg, France. This work has been funded by the Spanish Ministerio de Economía y Competividad (MINECO) under project AYA2015-65205-P. JGR acknowledges support from an Advanced Fellowship from the Severo Ochoa excellence program (SEV-2015-0548).
Paper: 2019A&A...623A..18G (fulltext)
the stepwise trajectories of incident CH ions within Titan’s upper atmosphere, all starting at an energy of 50 keV under normal incidence. Three different cases are indicated in the upper panel. For each incident ion, the portion of its trajectory between two consecutive collisions is displayed as the projection to the local plane formed by the vertical direction and the direction of ion velocity, with the x -axis aligned with the projected ion trajectory on the horizontal plane. Our model calculations indicate that some of the incident ions are backscattered and escape at the top of the atmosphere. For the condition of incidence shown in Fig. 2 , the backscattered CH ions typically undergo 20 collisions before they leave the top of the simulation box. Throughout our calculations, the effect of the background magnetic field, typically at the level of several nT (e.g., Backes et al. 2005 ; Bertucci et al. 2008 , 2009 ), is ignored for simplicity since the gyroradii of the incident ions could be estimated to exceed substantially their mean free paths due to elastic scattering by ambient neutrals. Taking incident CH as an example, the mean free path ranges from 110 to 6500 km over the full incidence energy range of 10 to 100 keV, where we use for illustrative purposes a background N 2 density of 1.7 × 10 7 cm −3 at 1500 km ( Strobel 2012 ). For comparison, the CH gyroradius is typically a factor of 5 larger, suggesting that the effect of ion gyromotion is of minor importance here. In addition, we note that the effect of the background magnetic field has not been included in previous modelings of Titan’s atmospheric sputtering (e.g., Shematovich et al. 2001 , 2003 ; Michael et al. 2005 ), and a similar approximation adopted here facilitates a comparison of the derived sputtering yields between different studies in terms of the differentchoices of collision parameterization and of background atmosphere (see Sect. 5 for details). 3 Sputtering yields We show in Fig. 3 the N 2 and CH 4 sputtering yields as a function of the incidence energy of the three categories of incident ions, all assuming normal incidence. The two different collision models, ZBL and ZBL+Firsov, are compared. Several interesting features are immediately seen in the figure. First, both the N 2 and CH 4 sputtering yields increase steadily with the increasing energy of incident ions, which is a natural result of the fact that more energetic ions tend to produce more atmospheric recoils along their energy degradation processes. Second, the sputtering yields increase with the ion mass, maximized for N and minimized for H + , and this is explained by more efficient momentum transfer to atmospheric recoils for heavier incident ions. Third, the sputtering yields for ZBL are enhanced over ZBL+Firsov due to the extra energy spent in electronic excitation, but such an enhancement is suppressed for low energy incident ions because they are not able to excite atmospheric neutrals
Paper: 2015ApJ...808...64M (acknowledgments)
The authors thank the referee for useful feedback which helped improve the final manuscript. This research has made use of data obtained with the NuSTAR mission, a project led by the California Institute of Technology (Caltech), managed by the Jet Propulsion Laboratory (JPL) and funded by NASA, and with XMM-Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA. We thank the NuSTAR Operations, Software, and Calibration teams for support with the execution and analysis of these observations. This research has made use of the NuSTAR Data Analysis Software ( NUSTARDAS), jointly developed by the ASI Science Data Center (ASDC, Italy) and Caltech (USA).
Paper: 2016A&A...585A.118D (acknowledgments)
The present work was funded by STFC (UK) through the University of Cambridge DAMTP astrophysics grant and the University of Strathclyde UK APAP network grant ST/J000892/1.
Paper: 2015ApJ...802...16Y (acknowledgments)
This work was supported by the Basic Science Research (2013R1A1A2061842) program through the National Research Foundation of Korea (NRF).
Paper: 2020ApJ...900L..13A (fulltext)
to a redshift. The statistical uncertainty associated with estimation of the source-frame masses is increased relative to that of the detector-frame masses owing to these dependencies. From the inferred posterior distribution of D L we compute redshift assuming a Planck 2015 ΛCDM cosmology with Hubble parameter H 0 = 67.9 km s −1 Mpc −1 (Ade et al. 2016 ) (and we address the effect of taking a larger value of the Hubble parameter in Section 2.2 ). Unless stated otherwise, mass measurements are quoted in the source frame of the binary. 2.1. Method and Signal Models To infer the source properties of GW190521, we analyzed 8 s of data in the LIGO and Virgo detectors around the time of the detection. The data are downsampled from 16,384 to 1024 Hz, as we expect no signal power above several hundred Hz owing to the total mass of GW190521. The parameter estimation analysis is done with two independently developed coherent Bayesian inference pipelines, LALInference (Veitch et al. 2015 ) and RIFT (Lange et al. 2018 ; Wysocki et al. 2019 ), which produce consistent results for the inferred source parameters. Both parameter estimation algorithms assume stationary Gaussian noise characterized by the power spectral density (PSD), which is inferred from the data by the BayesLine algorithm (Littenberg Cornish 2015 ). We compute the event likelihood in the frequency domain, integrating over the frequency band 11–512 Hz. We used three distinct GW signal models of BBH coalescence in our analysis: NRSur7dq4 (NRSur PHM), a surrogate waveform model built by directly interpolating NR solutions (Varma et al. 2019 ); IMRPhenomPv3HM (Phenom PHM), an inspiral-merger-ringdown waveform model that uses phenomenological frequency domain fits combining post-Newtonian calculations of the GW phase and amplitude (Blanchet et al. 1995 , 2005 ; Damour et al. 2001 ; Arun et al. 2009 ; Blanchet 2014 ) with tuning to NR solutions (Khan et al. 2020 ); and SEOBNRv4PHM (SEOBNR PHM), an inspiral-merger-ringdown waveform model that is based on the effective-one-body formalism (Buonanno Damour 1999 , 2000 ) and calibrated to NR (Ossokine et al. 2020 ). These three waveform models employ different approaches to reproduce the predictions from analytical relativity and numerical relativity; we expect to see differences in the parameter estimation from these three models as a result of those different approaches, and we can interpret those differences as a form of systematic error associated with the modeling. Note that the effects of the astrophysical environment, such as the presence of gas, on the GW waveform are expected to be negligible (Fedrow et al. 2017 ) in the late stages of inspiral, merger, and ringdown that we observe. NRSur PHM is constructed based on NR simulations with component spins that are not constrained to be aligned with the orbital axis, thus including the effects of spin–orbit precession. The model covers dimensionless spin magnitudes χ i ≤ 0.8 and mass
Paper: 2019MNRAS.488.1035K (fulltext)
But note that, as discussed above in Section 4.1 , a direct comparison of our results with the models of Madau Haardt ( 2015 ), Khaire Srianand ( 2015 ), and Puchwein et al. ( 2019 ) is difficult because of the inhomogenous redshift-dependent integration limits adopted by these authors. Before considering the deficit in the photoionization rate obtained from AGN at low redshifts relative to measurements from the Ly α forest as a photon underproduction crisis, it is worthwhile to recall various assumptions entering our derivation. With a spectral index of −1.7, our ionization rate estimate falls short of the Ly α forest measurements by a factor of ∼2 with an integration limit of M 1450 = −18 on the luminosity function. Madau Haardt ( 2015 ) implicitly assume an integration limit of M 1450 = −14 at ∼ 0 (see also Khaire Srianand 2015 ; Puchwein et al. 2019 ) as this limit corresponds to M * + 5 for the Schulze et al. ( 2009 ) luminosity function measurement. It is unclear if the assumption of unit LyC escape fraction is valid for such faint AGN, but we find that even with this extremely faint integration limit, the photoionization rate deficit is reduced by only about 25 per cent for a spectral index of −1.7. While this result does not change significantly if we assume a spectral index of −1.4 as suggested by Stevans et al. ( 2014 ), assuming a much harder spectrum with a spectral index of −0.56 (Scott et al. 2004 ) for faint ( M 1450 > −23) AGN completely alleviates the deficit for an integration limit of M 1450 = −18. One should also note that our ionization rate model ignores the large uncertainties in the H i column density distribution at the Lyman limit (Ribaudo, Lehner Howk 2011 ; Gaikwad et al. 2017a ; Shull et al. 2017 ). Alternatively, it may be possible to balance the ionization rate deficit by the contribution from low-redshift LyC-leaking galaxies (Izotov et al. 2016 , 2018a , b ). 4.5 Helium reionization We now consider the implications of our AGN luminosity function models for He ii reionization. The time evolution of the volume-averaged He iii fraction is given by (e.g. Haardt Madau 2012 ) where is the emission rate of ≥4 Ry photons per unit proper volume, 〈 n He 〉 is the average proper helium number density, and 〈 t rec,He 〉 is the average recombination time-scale for He iii . With our quasar emissivity model from Section 4.1 , the emission rate can be written as with the quasar spectral index α ν = −1.7 and the comoving 912 Å emissivity ε 912 () is obtained from the fits shown in Fig. 8 . The recombination time-scale in equation ( 29 ) is given by where 〈 n H 〉 = 1.881 × 10 −7 (1 + ) 3 cm −3 is the average proper hydrogen number density, α B is the Case B He iii recombination coefficient (Hui Gnedin 1997 ), and χ = 0.079 is the cosmic number fraction of helium for a cosmic helium mass fraction of Y He = 0.24. We assume that the clumping factor C for helium is the same as for hydrogen, for which Shull et al. ( 2012a ) obtained
Paper: 2018ApJ...864...37D (fulltext)
A typical peculiar velocity of 300 km s −1 equates to a proper motion of 6.3 μ as yr −1 at 10 Mpc. A sample of objects with proper motion uncertainty σ μ = 100 μ as yr −1 would require roughly 250 independent measurements to reach this proper motion value and roughly 2300 measurements to achieve 3 σ significance. Such averaging to reduce statistical errors would only be possible if one knew a priori the orientation of each proper motion vector in the sample. Since this is not known, it is appropriate to examine the correlation of extragalactic pairs. Since the number of galaxies in a volume scales as N ∝ D 3 , and the number of possible pairs scales as , then the number of pairs scales as N p ∝ D 6 . The uncertainty in the transverse velocity correlation scales as . Using Equation ( 60 ), and assuming that the fractional proper motion uncertainty is much larger than the distance uncertainty, . The uncertainty in the correlation therefore scales roughly as . This relationship assumes that x / D ≲ 1, that pairs represent independent measurements of the peculiar velocity field (not strictly true), that the proper motion measurements in value and uncertainty are uniform among the sample, and it neglects the random orientations of pairs. Including these effects will impact the constant of proportionality and may diminish the favorable impact of distance on the uncertainty in the measured correlation statistic. The optimal strategy for measuring the transverse peculiar velocity created by large-scale structure will depend on the data in hand: the redshift distribution, pair separations, sample size, and proper motion precision. VLBI proper motions will likely have redshifts of z ∼ 1, proper motion uncertainties per object of σ μ ∼ 10 μ as yr −1 , and sample sizes of 500–1000 objects (e.g., Darling 2013 ; Truebenbach Darling 2017 ). Gaia AGN proper motions will have lower redshifts, smaller pair separations, and sample size ∼10 5 , but larger per-object proper motion uncertainties of ∼200 μ as yr −1 (e.g., Paine et al. 2018 ; Truebenbach Darling 2018 ). 6. Conclusions and Future Work We have developed two-point transverse peculiar velocity correlation statistics that connect the matter power spectrum to extragalactic proper motions. We have explored the impact of pair separation, distance, and angular separation on these correlations and suggested some strategies for future extragalactic proper motion surveys. Except for very nearby pairs of objects that subtend large angles, close pairs are more sensitive to large-scale structure than widely separated pairs, as one might expect. We develop correlation statistics for randomly oriented pairs of objects with various separation-to-distance ratios. Finally, we suggest that the sensitivity to the transverse velocity correlation improves with distance in volume-limited surveys, but the optimal detection strategy will depend on the nature of the proper motion data in hand. The work presented here has
Paper: 2016MNRAS.455.2627A (acknowledgments)
We thank the anonymous referee for useful comments that helped us to improve the content of the paper. Based on observations made with the GTC, installed in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias, in the island of La Palma. We acknowledge support from Mexican CONACYT grants CB-2010-01-155142-G3 (PI: YDM), CB-2011-01-167281-F3 (PI: DRG) and CB-2014-240562 (PI: MR). K.Z.A.-C. acknowledges support from CONACYT grant 351585.
Paper: 2016ApJ...824..113X (acknowledgments)
The authors thank the anonymous referee for the valuable comments. This work is partially supported by the National Basic Research Program (973 Program) of China under grant No. 2014CB845800.
Paper: 2021MNRAS.504.4940P (fulltext)
sensitivity of COSIMA to D and H, as described in the next section. 2.2 Instrument mass fractionation The effect of instrument mass fractionation (hereafter IMF) varies from element to element. For sulphur isotopes, the effect was too small to be measured with COSIMA. For oxygen isotopes, the effect was a few per cent. Because D and H have the largest relative mass difference of any isotopes of any element, the effect of IMF is expected to be quite strong. This effect must be measured, using a suitable analogue for the cometary dust. The measurement was undertaken with the COSIMA reference model (RM) using samples of insoluble organic matter (IOM) from carbonaceous chondrites. Because no evidence for hydrated minerals has been seen in the cometary dust particles analysed by COSIMA (Bardyn et al. 2017 ) and because water is unlikely to remain in dust particles collected by COSIMA grains (as discussed above), it is expected that the H measured with COSIMA will be coming primarily from the high molecular weight organic matter of the sort discussed in Fray et al. ( 2016 ). Meteorite IOM is also macromolecular, with a complex structure. There is a similarity in SIMS response between the two substances, although the H/C ratio in the cometary matter tends to be higher than that in IOM (Fray et al. 2016 ; Isnard et al. 2019 ). Such organic matter has been estimated to constitute 45 per cent of the cometary dust particles by weight (Bardyn et al. 2017 ). Because of this, IOM seems to be the best available choice as an analogue material to measure IMF. IOM samples provided by the Carnegie Institution of Washington were subjected to TOF-SIMS analyses in the COSIMA RM. The D − /H − ratios measured for the various samples in COSIMA are plotted versus the D/H isotopic ratio measured at Carnegie by elemental analysis (Alexander et al. 2010 ) in Fig. 1(a) , while Fig. 1(b) shows the same data with a line that is forced to pass through the origin fitted to it. The inverse of the slope of that line gives the IMF for D/H in COSIMA – a factor of 3.19 ± 0.86. The uncertainty in this result – which is the dominant source of systematic error in the D/H isotopic ratio – is considered in the next section. Figure 1. Panel (a) shows the D − /H − ionic ratio measured in the COSIMA reference model for samples of insoluble organic matter (IOM) from a selection of chondrites provided by the Carnegie Institution plotted versus the D/H isotopic ratio measured in the lab at Carnegie. The difference between the two measurements is due to the COSIMA instrument mass fractionation (IMF). Panel (b) shows the same data, fit with a line forced to pass through the origin. The inverse of the slope of the line gives the IMF for the D/H ratio in COSIMA: a factor of 3.19. 2.3 The particle Jessica Lummene.2 The particle Jessica is different from the other particles in that a very long measurement was done upon it – over 12 h at each location for four locations, when for other particles
Paper: 2018ApJ...859...63M (acknowledgments)
The authors thank F. Halzen and A. Kheirandish for inspiring discussions that led to the section on neutrinos and G. Sigl for comments and feedback on the anisotropy of the arrival direction. Furthermore, the authors would like to thank the referee for their comments, which helped to clarify certain parts of this work. In addition, E.G.Z. thanks C. Pfrommer for interesting discussions, and L.M. thanks B. Eichmann for reading early parts of this work. The authors also thank C. Kopper for information on the IceCube data. L.M. and J.B.T. acknowledge support from the RAPP Center (Ruhr Astroparticle and Plasmaphysics Center sponsored by the MERCUR project St-2014-0040) and from the Research Department of Plasmas with Complex Interactions (Bochum). C.B. is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1256259. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Support was also provided by the Graduate School and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin–Madison with funding from the Wisconsin Alumni Research Foundation. C.B. and E.G.Z. also acknowledge support from the University of Wisconsin–Madison and NSF Grant No. AST-1616037. L.M. thanks the Astronomy Department of the University of Wisconsin, where part of this work was carried out, and E.G.Z. thanks the Department of Astronomy and Astrophysics at the University of Chicago, where it was completed. Software: matplotlib (Hunter 2007 ), pandas (McKinney 2012 ), numpy , scipy (van der Walt et al. 2011 ), healpy (Górski et al. 2005 ), ipython (Pèrez E 2007 )