adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.486.5934J (fulltext)
limit. The simple method above ignores that limits will be more stringent when considering larger volumes over which the detection probability of individual repeating FRBs is smaller. Furthermore, it cannot account for changing population densities with redshift, i.e. Φ → Φ( z ). The probability p ≥2 ( F th , z ) of detecting two or more bursts from a given FRB depends on both the distance z and the beam sensitivity B , which governs the detection threshold F th = F 0 / B as discussed in Section 2.3 . The expected number of observed repeating FRBs, Λ rep , in a given survey is then given by where ε( z ) weights the sensitive solid angle over beam sensitivity according to the detection probability. Unlike equation ( 14 ), the integral in equation ( 21 ) extends over all z , allowing for many repeating FRBs at large distances to contribute to the expected number of events. The factor p ≥2 ( B, z ) in the integrand gives different weights per unit volume – generally, this will be close to unity for z ≲ z lim , and fall more rapidly than increasing volume beyond this. To calculate p ≥2 ( B, z ), it is most useful to first calculate the expected number of bursts, λ( B, z ), so that p ≥2 ( B, z ) ≡ p ≥2 (λ( B, z )). λ( B, z ) can be found for the burst energy distribution of equation ( 1 ) by inverting equation ( 9 ), with z lim → z and λ lim → λ, i.e. In the case of a Poissonian distribution, p ≥2 (λ) is given simply by although the result will be different for different distributions (see e.g. Section 4 ). Evaluating p ≥2 allows equation ( 21 ) to be evaluated for any hypothesized Φ. It is useful to write Φ as the product of a z -dependent factor φ( z ) and its value in the current epoch, Φ 0 , i.e.: This allows limits on Φ 0 to be set for any given observed number of repeating FRBs by choosing appropriate Λ rep = Λ lim . The limit Φ lim on Φ 0 can be calculated as The implicit assumption here is that Φ lim will be an upper limit on the FRB density. However, this need not be the case, and both upper and lower limits could be calculated by choosing appropriate Λ lim should one or more repeating FRBs be detected, as is the case for the recent results from the Canadian Hydrogen Intensity Mapping Experiment (CHIME; CHIME/FRB Collaboration et al. 2019b ). 6.1 Volumetric limits from the ASKAP/CRAFT lat50 survey In order to limit Φ 0 , the z -dependence of the FRB population density φ( z ) is chosen to be an integer power n of the star formation rate (SFR). As per Macquart Ekers ( 2018b ), values of n = 0 (no dependence), n = 1 (stellar origin), and n = 2 (approximately tracing AGN activity) are considered. Evidently, this does not encompass models with significant delay-time, e.g. long-duration mergers, as tested by Cao, Yu Zhou ( 2018 ). The normalized SFR is taken from Madau Dickinson ( 2014 ), giving For the standard parameter set, n = 0, 1, 2, and using Φ 0 = 1 Mpc −3 , the integrand in equation ( 22 ) is shown in Fig. 11 . This is proportional to
Paper: 2019ApJ...870..130N (acknowledgments)
The authors thank the anonymous referee, whose comments improved this manuscript. We are grateful to Melanie Krips, Karl Schuster, and all the staff of the IRAM facilities for their excellent support.
Paper: 2016MNRAS.461.1288S (acknowledgments)
We sincerely appreciate the comments of our reviewer, Alexander James Mustill, and his work to improve this paper. Our gratitude to Dimitri Veras and Renu Malhotra for their insightful and encouraging comments on an early draft. RAS is supported by the National Science Foundation under Grant no. AST-1410174 and Grant no. DGE-1143953. KMK is supported by the National Science Foundation under Grant no. AST-1410174. AS is supported by the European Union through ERC Grant no. 279973. The numerical simulations presented herein were run on the El Gato supercomputer, which is supported by the National Science Foundation under Grant no. 1228509.
Paper: 2017ApJ...836...92D (fulltext)
UKIRT Infrared Deep Sky Survey (UKIDSS), Bochanski et al. ( 2014 ) were able to identify two stars with distances above 200 kpc, making them the most distant known MW stars. Figure 10 shows that these most distant stars appear consistent with the remote northern stream branch predicted in our simulation. Bochanski et al. ( 2014 ) already associated the M-giants in a distance range of 20–90 kpc in the UKIDSS data with Sgr. However, the more distant detections in their sample were not connected with the stream because the models of Law Majewski ( 2010 ) do not feature stars beyond ∼75 kpc. Our study shows that some of the most distant known MW stars could have originated in Sgr. 4. Discussion and Conclusions We have taken a two-pronged approach to building a new model of the Sgr orbit through the MW halo. Rather than integrate the present-day phase-space coordinates backward in time, we perform an exploration of parameter space with a semianalytic integration of the Sgr trajectory starting 7–8 Gyr ago. This method is chosen because it allows for the inclusion of non-time-reversible effects. In particular, dynamical friction and tidal stripping are expected to be important in the regime where the mass of the Sgr progenitor is ≳10 10 M ⊙ . We then build on these results to simulate the trajectory of Sgr with GADGET and compare the resulting tidal stream to observational data. Our main conclusions are as follows. 1. Comparing the simulated position and velocity of Sgr today to the measured quantities, we find that our analytic model favors a narrow range of the initial orbital angular momentum of Sgr, with large incidence angles ( θ init ≳ 60°) and initial velocities in the range 80 km s −1 ≲ v init ≲ 100 km s −1 . 2. The mock Sgr stream resulting from the GADGET simulation reproduces most of the sky positions, heliocentric distances, and line-of-sight velocities found by 2MASS (Majewski et al. 2004 ). Similarly to previous studies, the line-of-sight velocities of the leading arm are not replicated by the model, suggesting that further work on the triaxiality of the MW halo or the inclusion of the LMC influence is necessary. 3. The simulated debris stream projected on the plane of the Sgr orbit is in unprecedentedly good agreement with the SDSS stellar tracers from Belokurov et al. ( 2014 ). The apocentric distances and position angles of the stream are reproduced to within 20% of the measured values. In particular, the model of the Sgr orbit presented here is the first to naturally reproduce the recently detected distant trailing arm at ∼100 kpc, optimizing only for the remnant coordinates rather than fitting for the stream properties. We believe that this feature arises because of the larger initial separation (125 kpc) used in our simulation compared to other works in the literature (e.g., Law Majewski 2010 ; Purcell et al. 2011 ; Gómez et al. 2015 ). 4. Above all, this work predicts the existence of two novel and distant arms of the Sgr stream.
Paper: 2019ApJ...884...99F (acknowledgments)
It is a pleasure to thank Maosheng Xiang and Ted Mackereth for providing us insights on their results for our safety checks, and Gregory Green for practical help on using the Bayestar17 extinction map. We thank Diane Feuillet and Andy Gould for useful discussions on APOGEE selection function, and Alina Boecker and Ryan Leaman for advice on stellar populations. We are grateful to Sofia Feltzing and Paul McMillan for helping to improve the clarity of the writing. This project was developed in part at the 2018 NYC Gaia Sprint, hosted by the Center for Computational Astrophysics of the Flatiron Institute in New York City and at the 2018 Gaia –LAMOST hack-a-thon, held in Shanghai. N.F. acknowledges support from the International Max Planck Research School for Astronomy and Cosmic Physics at the University of Heidelberg (IMPRS-HD). H.-W.R. received support from the European Research Council under the European Union’s Seventh Framework Programme (FP 7) ERC Grant Agreement n. [321035]. Y.S.T. is grateful to be supported by the NASA Hubble Fellowship grant HST-HF2-51425 awarded by the Space Telescope Science Institute. The following software was used during this research: Astropy (Astropy Collaboration et al. 2013 ), Galpy (Bovy 2015 ) Matplotlib (Hunter 2007 ).
Paper: 2017MNRAS.468L..55M (fulltext)
20092010 with Fibre-fed, Extended Range, chelle Spectrograph (FEROS), ( R 48000) installed on the La Silla 3.6-m telescope and are in the form of flux and wavelength calibrated spectra, all pre-processed using the FEROS standard data reduction pipeline. In addition, we present two spectra ( R 60000) recorded during 2013 August with the sandiford echelle spectrograph, which is mounted on the 2.1-m telescope at the McDonald Observatory, Texas. The raw data were extracted and reduced using the standard software packages of Horne Horne ( 1986 ). All absorption-line velocities quoted in this Letter are in the heliocentric frame of reference. Every HD 183324 spectra had narrow absorption lines positioned at the bottom of a rotationally broadened, but deep, stellar Ca ii -K absorption line at 3933.7. The stellar continua were each fit with a ninth-order polynomial over the same velocity ranges in order to determine a residual intensity absorption profile for the far weaker circumstellar/interstellar Ca ii K-lines. This process has been described in detail elsewhere (Welsh Montgomery 2015 ), and the resultant eight residual intensity profile plots are shown in Fig. 1 . All eight profiles consist of two complexes of absorption lines, with one set occurring at negative velocities spanning the 40 to 10kms 1 range, and the other set spanning mostly positive velocities in the 10 to 65kms 1 range. The EW of both absorption complexes were measured over these respective velocity ranges and are listed in Table 1 . The negative velocity absorption profiles are generally characterized by a two-component structure with velocities at V 22 and 33kms 1 , although an additional weak component at V 41kms 1 is detected in the 2010 August 31 data. The values of these two main absorption velocities are close to those of the projected velocities of the Mic and Eri local interstellar gas clouds (Redfield Linsky 2008 ). This two-component absorption component has a highly variable EW that varies from 4.0 to 10.0m. Previous observations of HD 183324 in 2011 also show a similar absorption variability of this two-component structure (Welsh Montgomery 2013 ). It is unlikely that this absorption variation is due to the passage of an interstellar cloud residing between us and HD 183324, and thus it is most probably due to the addition of circumstellar gas from evaporating exocomets. Figure 1. Observations of the Ca ii 3933.7 line showing variable circumstellar absorption over the 10 to 65kms 1 velocity range. The radial velocity of the star is at 12kms 1 . Table 1. HD 183324 Ca ii absorption data. The positive velocity profiles shown in Fig. 1 are characterized by absorption components whose strength and velocity change appreciably over time. The combined EW value of this absorption component varies from 1.8 to 9.5m and has a velocity structure with variable central velocities spanning 10 to 60kms 1 . Variability of this component was also observed at a lower level (EW = 0.8 to
Paper: 2016A&A...585A..58V (fulltext)
maps (intensity maps) of both CO and CO. For each target we show the direct comparison of the images of the intensity map of the best-fit model and the constraint on both and through spectra and intensity cuts through the major and minor axis of the zero-moment map. In three of the four targets an inner gas cavity (smaller than the dust cavity) was required to fit the data. In SR21 the 7 AU cavity previously derived from rovibrational emission was adopted, but no gas cavity is resolved at the spatial resolution of (24 AU) in our observations. In all disks, the depth is constrained to within an order of magnitude and the cavity size to within 5 AU, mainly through the comparison of the spatially resolved emission, but further confirmed by the line wings in the spectra. In the intensity cuts in Fig. 5 the parameter is varied. The comparison shows that the uncertainty on the density drop is lower than an order of magnitude. In Appendix C we show the intensity cuts for larger and smaller gas cavity radii, respectively. These plots show that the uncertainty on the gas cavity radius is typically 5 AU and the uncertainty on the depth is lower than one order of magnitude. Furthermore, in both SR21 and DoAr44 an additional drop between and is required to fit the data. The models of the CO fits of van der Marel et al. (2015b) match the isotopolog data in the outer disk remarkably well. Only minor corrections in surface density and gas-to-dust ratio were required to fit the new data. However, inside the cavity the previously derived results for the gas surface density were found to be inconsistent. A modest drop in the gas density inside the dust cavity radius was sufficient to explain the CO data, but no gas cavity was seen. Since the gas cavity becomes visible in the isotopolog data and turns out to be smaller than the dust cavity radius, the gas cavity radius could be fit independently of the depth. 4.1. Results for individual targets HD 135344B HD 135344B shows a clear gas cavity, both in CO and CO images, which is significantly smaller than the dust cavity (see top two panels of Fig. 1 ). The modeling constrains the gas cavity radius to 30 AU (dust cavity radius is 40 AU), and the drop is (top right panels in Fig. 5 ). Previously, the model based on the CO data required of for a cavity radius of 40 AU. A 30 AU gas cavity was also derived in van der Marel et al. (2015b) considering the inner radius of the small dust grains derived from the scattered-light emission ( Garufi et al. 2013 ), but a value as low as underproduces the CO emission inside the cavity. As the CO is optically thick and mainly traces the temperature at the surface, this new result suggests that the temperature structure is somewhat higher than in our model, perhaps because of some residual dust in the cavity (increasing the CO abundance by its shielding and changing the heating-cooling balance) or the vertical structure (shadowing on the outer disk; see Bruderer 2013 , for a
Paper: 2016ApJ...827...62M (fulltext)
considered (see Table 1 ). Most of the population of H 2 will be in these ( v , j ) states under the conditions of ISM where radiative cooling of H 2 can dominate. When a bulk system of hydrogen molecules is thermally equilibrated, over 99% of the population is in these states at 1000 K and over 50% of the population is in these states at 5000 K. Table 1 Energy Levels of Selected ( v , j ) States in eV Note. a Energy relative to potential energy minimum of H 2 . For comparison, the energy at the dissociation limit is 4.74825 eV. The collision systems He + H 2 , H + H 2 , and H 2 + H 2 are among the most widely studied systems in reaction dynamics. For the electronic ground states of these systems, the interaction potentials are known to chemical accuracy (within 0.04 eV) and, in some cases, to spectroscopic accuracy (within 0.001 eV). This means that it is possible to simulate, to a high degree of precision, collisions within each of these collision systems. Exact quantum calculations are restricted to reduced dimensionality or to the lower energy states. For full-dimensional calculations involving dissociation, quantum calculations are not always feasible and quasiclassical trajectory (QCT) methods may be used. The development of ab initio potentials for the computational study of H 2 + H 2 paralleled the availability of computational resources. Schwenke ( 1988 ) developed a H 2 + H 2 potential to study the recombination of H atoms in the presence of H 2 using QCTs. The global H 2 + H 2 potential of Boothroyd et al. ( 1991 ) was developed with the intention of permitting full-dimensional calculations of energy transfer and dissociation to be carried out. The functional forms have derivatives that are evaluated analytically. The van der Waals well was fitted empirically. This is in contrast to earlier calculations which had been focused on those parts of the potential relevant to van der Waals interactions of H 2 molecules with low internal energy. Aguado et al. ( 1994 ) fitted the points of Boothroyd et al. ( 1991 ) to a functional form that was particularly convenient for quantum calculations. Although not spectroscopically accurate, these global potentials were deemed to be close to chemically accurate (Aguado et al. 1994 ). Diep Johnson ( 2000a , 2000b ) developed a potential for H 2 + H 2 which was focused on the (H 2 ) 2 dimer where each H 2 was treated as a rigid rotor in the ground vibrational state. Boothroyd et al. ( 2002a ) refined their earlier H 2 + H 2 potential with over 40,000 additional ab initio points. Like the earlier potential from this group, this BMKP2 potential also had analytical derivatives and an empirically fitted van der Waals region. The potential of Hinde ( 2008 ) was determined to spectroscopic accuracy for the (H 2 ) 2 dimer. Particular attention was paid to the van der Waals well for atomic separations within H 2 corresponding to low ( v , j ) states. The availability of these potentials permitted detailed
Paper: 2018A&A...615A..68R (fulltext)
the limit in local color was slightly larger than the median value at U − V ~ 0.65 with a magnitude step significant at 7.4 σ . On the other hand, maximizing the mass step significance led to a mass limit of about , corresponding to a magnitude step of 7.2 σ significance.In this case, the separation does not lie close to the median value, since 31% of our sample is found below the new mass limit, and 69% above. This new limit is closer to the mass bin separation of , chosen in allprevious studies on host galaxies. In particular, Childress et al. (2013) found an apparent Hubble residual transition between 10 9.8 and . Choosing this mass separation yields comparably significant mass and color steps. The top and bottom panels of Fig. 19 show the evolution of step significance as a function of the cut position when local U − V colors and host stellar masses are used as a third standardization parameter. The sample median lies close to the maximum local color step significance, whereas the most significant mass step implies a bin separation at . Table 10 Summary of all tests performed throughout the paper with the corresponding number of SNIa in the sample, the local U − V color step, the host stellar mass step, and step values when corrected for one or the other effect. However, the correlations between the Hubble diagram residuals derived from a three-parameter standardization and our host variables yield additional information. When residuals are corrected for local U − V color, the remaining maximum mass step significance is of 3.8 σ (i.e., the maximum correlation between stellar mass and corrected Hubble diagram residuals using the most significant local step). Conversely, the remaining maximum local color step after mass correction as a third variable is of 5 σ (i.e., the maximum correlation between local U − V color and corrected Hubble diagram residuals using the most significant mass step). Hence, maximizing the significance still shows that scatter in the Hubble diagram is better accounted for when the supernova brightness is corrected for local environment. It is also worth noting that the local U − V color does not fully account for the remaining scatter either. A better proxy, or a combination of proxies, linked to properties of the local environment of SNIa, is yet to be found. 7 Summary and perspectives As elaborated in Sect. 1 , many studies in the literature have found compelling proof of a dependence of Type Ia supernovæ properties on their close or distant environment, concerning a few hundred events at most that exploded in the nearby Universe. In this work, we measured the photometry of the host galaxy and local fluxes in regions with a radius a 3 kpc for 882 supernovæ that are distributed in a wide redshift range, from z = 0.01 to z = 1.1. We consistently measured photometry from SNLS and SDSS images, and gathered additional photometry from the 2MASS catalog when needed, in order to complete our sample of host galaxy
Paper: 2019MNRAS.486.2137M (fulltext)
spectrum, using tbnew with the spectral fit parameters. Plainly this severely limits the sensitivity of these data, but we note that lower absorption columns for other objects may make this more visible [e.g. MAXI J1820 + 070 in Kara et al. ( 2019 ), where the column is only 1.5 × 10 21 cm −2 ]. Figure 11. The effect upon the overall thermal spectrum of considering the additional emission produced by the turbulent disc region. The red solid line denotes the xspec diskbb spectrum using parameters of Table 1 . The blue solid line denotes a bbodyrad spectrum with the same temperature as the diskbb component, but with of the power, inferred from the fit parameter of . The black solid line denotes the sum of these two components. The black dashed line denotes the absorbed total spectrum. We see that, when absorption is accounted for, the difference in shape arising from the bbodyrad component is inconsequential for the observable energy range. Placing our resultant truncation radius in the context of other studies, the same NuSTAR data set is fit by Wang-Ji et al. ( 2018 ) as ‘Obs 1 2015’, though they use the simultaneous Swift XRT data to extend this down to lower energies rather than the higher signal-to-noise ratio (but possibly worse cross-calibrated) XMM–Newton data. They fit the time-averaged spectrum with a simpler continuum model, with only a single-Compton component (rather than two as used here). Their derived relativistic smearing requires a disc with extreme iron overabundance that extends down to around 2.5 R g , an order of magnitude smaller than the radius derived here. If we remove one of our Compton continua, we find a similar fit from our XMM–Newton data, which also requires an extreme iron overabundance of A Fe = 8.67, and a small innermost disc radius of 2.69 R g (for details see Appendix B ). This fit is considerably worse (combined Δχ 2 > 300) than the two-Compton continuum model used in our spectral fits, where the reflection spectrum is solar abundance, and arises from a disc with an inner radius of 30 R g . This shows that the inferred relativistic smearing is highly sensitive to the assumed continuum shape. Here, we model only the first observation of this observing run (O1), and we set the spectral components in the timing section from fitting to the time-averaged spectral data alone. In future work, we will extend the modelling framework to fit to the later observations in this run (O2, O3, O4 of DM17 , with ObsIDs 0760646301, 0760646401, 0760646501, respectively), where the source has declined further into the hard state. To this end, we aim to develop a fully integrated fitting procedure, in which the spectral data are fit iteratively with the cross-spectrum, in order to close the loop when determining the shape of the underlying spectral components of the timing model. 11 CONCLUSIONS In MD18a / b , we developed a model for the spectral-timing properties of accreting hard-state black hole binaries, whereby