adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.499.1450P (fulltext)
evolution. The transition occurs at the same time as light curve evolves on to the linear tail. Similar evolution, across a similar time-scale, is seen in Type IIb SN 2008bo (Bianco et al. 2014 ), with the sole difference over the first 120 d being that SN 2008bo has an initial blueward evolution until ∼12 d after explosion that is not seen in SN 2018gjx. This blue/red/blue evolution is commonly seen in SE-SNe (Prentice et al. 2016 ). However, SNe with strong shock-cooling tails in their light curves (e.g. SNe 1993J, 2013df, 2016gkg; Richmond et al. 1994 ; Morales-Garoffolo et al. 2014 ; Tartaglia et al. 2017 ) evolve straight from blue to red at these early times, and it is to these that SN 2018gjx is most similar. Comparatively, SNe Ibn show great diversity in their colour evolution. SN 2015G is most like SN 2018gjx and is plotted using the estimated explosion date of 3 d prior to the discovery date (2015-03-23.778, but see Shivvers et al. 2017b , for a discussion on t exp ). SN 2006jc displays an inverted evolution to that of SN 2018gjx, and two other SNe Ibn LSQ13ddu (Clark et al. 2020 ) and SN 2014av (Pastorello et al. 2016 ), appear to follow the trend of blue to red, albeit from a bluer starting point. 3.2 Pseudo-bolometric and absolute <italic>r</italic>-band light curves An optical pseudo-bolometic light curve was constructed by converting the dereddened multicolour photometry to flux and building spectral energy distributions (SEDs) for each date. These SEDs were then integrated over 4000−10 000 Å, covering the effective range of the griz bandpasses and converted to pseudo-bolometric luminosity using a fixed luminosity distance of 35 Mpc. This method is described in more detail in Prentice et al. ( 2016 ). The pseudo-bolometric light curve of SN 2018gjx is shown in Fig. 4 in comparison with SNe IIb and SNe Ibn. It is a requirement of the bolometric light-curve methods that the transients are observed in BVRI, gri , or equivalent and while some interpolation and extrapolation can be done to account for missing data, we find that many SNe Ibn are only well-sampled in one or two bands, which limits our comparison sample. The peak luminosity of ∼1.4 × 10 42 erg s −1 places the first peak in a similar range to that of the SNe IIb that show shock-cooling tails. After this, the light-curve evolution diverges and fails to rise a second peak. It instead tracks the evolution of SN 2008bo, although with a steeper linear decline of ∼0.026 mag d −1 . The decline is steeper than for most SNe IIb (Prentice et al. 2019 ). Figure 4. Upper panel: The pseudo-bolometric light curve of SN 2018gjx compared with SNe IIb. The unusual SN IIb 2008bo has a remarkably similar light curve after 10 d. Lower panel: Absolute R / r LCs of SNe Ibn (squares) and SNe IIb (diamonds) in comparison with SN 2018gjx (black stars). Type Ibn SN 2015G (orange), Type IIb SN 2008bo (red), and Type IIb SN 2018ivc (gold) show similar evolution. The dramatic decline of SN 2006jc is
Paper: 2015A&A...581A..84T (acknowledgments)
A.T. is supported by the DFG Emmy Noether grant Hi 1495/2-1 and the Bonn-Cologne Graduate School of Physics and Astronomy. I.G. would like to thank the science department and “International Research Fellow” programme of ESA-ESTEC in Noordwijk for the partial support during the completion of this paper. A.C.S. acknowledges the receipt of CNPq/BJT-A fellowship 400857/2014-6. The authors would like to thank Sophia Lianou for kindly providing us with their photometic tables and measurements of the IKN field RGB stars metallicities. We are also grateful for the helpful discussions with Søren Larsen and the anonymous referee. This research made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. We acknowledge the use of the HyperLeda database ( http://leda.univ-lyon1.fr ).
Paper: 2017ApJ...847...33C (acknowledgments)
We thank the anonymous referee for helpful comments. We thank Katherine Alatalo and David Meier for helpful discussions. Y.C. and T.W. acknowledge support from NSF through grants AST-1139950 and AST-1616199. E.R. is supported by a Discovery Grant from NSERC of Canada. Support for CARMA construction was derived from the Gordon and Betty Moore Foundation, the Eileen and Kenneth Norris Foundation, the Caltech Associates, the states of California, Illinois, and Maryland, and the NSF. CARMA development and operations were supported by funding from NSF and the CARMA partner universities.
Paper: 2018MNRAS.475.4978M (fulltext)
At every rotational phase, a cross-section – or ‘great circle’ – is defined by a west longitude and an east longitude 180° to the east. The first step in calculating the cross-sectional area is finding the distance from the centre of the planet to every point on the projected silhouette. This distance might not necessarily be the elevation at the point on the great circle because the silhouette could be dominated by a taller point that sits on a longitude that isn’t used to define the great circle. In other words, a taller point might be behind or in front of the point on the great circle from our point of view. To account for this projected geometry issue, we first define an ‘α angle’, which is the number of degrees a planet would have to rotate in order for its tallest feature to disappear below the horizon after it appears on the great circle. For example, Everest is the tallest mountain on Earth, and Earth would have to rotate 32 from the time when Everest is on the great circle before the tip of Everest would no longer appear in the planet’s silhouette. Here, l is the latitude of the highest point on the planet and h is the elevation of the highest point. For both the west and east longitude lines used to define the great circle, we step through in latitude, and at every latitude, we find the maximum projected height. The projected height of point A is where d is the actual elevation of point A and β is the angle between A and the point along the great circle. β ranges from −α to +α. The projected heights give us the distances from the centre of the planet to every point along the silhouette. The area of the silhouette can then be found using trapezoidal integration. Once the planet’s cross-sectional area is found, the transit depth can be calculated using equation ( 1 ). 3.5 Defining scatter The projected geometry method described above provides a list of transit depths for every rotational phase, so σ B is easily defined as the std of all of the depths. We use these σ B values to derive the general relationship between B an σ B in Section 3.6 . In the Earth’s case, a transit would last for up to approximately 13 h – more than half a rotational period – and thus we would observe the full range of phases necessary to calculate σ B . However, other transiting planets may not last long enough for us to observe a sufficient fraction of a planet’s rotation. Instead, individual transits will show various ranges of rotational phases, which may or may not overlap depending on the planet’s rotational period. Unfortunately, this means that we might not observe every rotational phase. When this is the case, the σ B we find in our observations may be less accurate than the ‘true value. In this section, we merely describe the two ways to evaluate σ B . The first way is to find the average depth of each individual transit (see Fig. 4 ), and take the std of those averaged depths. The second way is to fold the light curve and take the std of the
Paper: 2015ApJ...800...96L (acknowledgments)
S.H.L. acknowledges support from NASA grant NNX11AK61G. C.J.N. acknowledges support provided by NASA through the Einstein Fellowship Program, grant PF2-130098. R.G.M.'s support was provided under contract with the California Institute of Technology (Caltech) funded by NASA through the Sagan Fellowship Program. We thank the referee for suggesting improvements in the description of the results.
Paper: 2020MNRAS.494.1143B (fulltext)
have been active for . The projection has a depth of and is centred on the BH. This disc galaxy provides the initial conditions for simulations with a disc galaxy in an NFW halo. In the left-hand panel of Fig. 12 , we plot the mass of the baryonic components, i.e. gas, stars, metals, and dust, of the halo with r 200 as a function of time. After , a stellar disc with a mass has formed in the xy plane of the halo with a radial extent of . Over the first , a dust mass rapidly forms, after which the dust mass grows much more slowly and the final halo has a dust mass . The build-up of gaseous metals initially occurs at a slower rate, but after the dust and metal content of the halo is roughly similar, with a cumulative dust-to-metal ratio of 1.15 within r 200 . Figure 12. Properties of the initial disc galaxy. Left : Gas (red solid), stellar (blue dash–dotted–dotted), metal (purple dash–dotted), and dust (pink dashed) mass components with r 200, crit . The global halo properties are reasonable for a disc galaxy. Centre : Radial dust mass surface density profile for the relaxed NFW halo with D = 0.01 (blue dash–dotted–dotted), the NFW halo with a disc galaxy and D = 0.01 (light purple dash–dashed), and the NFW halo with a disc galaxy and self-consistently modelled dust (red solid). Right : Dust-to-gas ratio radial profile as a function of time for the NFW halo with a central disc galaxy and self-consistently modelled dust. We plot t = 0.5 (blue dash–dotted–dotted), 1.0 (purple dash–dotted), 1.5 (light purple dash–dashed), 2.0 (pink dashed), and (red solid). Dust is confined to the disc due to the lack of feedback and satellite mergers during its formation. The formation of the disc galaxy leads to a contraction of the halo. The central panel of Fig. 12 shows the radial dust surface density profile, where the halo has been projected along the z -axis of the simulation (i.e. the galaxy is face on). For the initial NFW halo with a constant dust-to-gas ratio D = 0.01, the surface density of the dust decreases strongly with radius from at to 5 × 10 −4 at r 200 . After the formation of the disc, still assuming D = 0.01, central dust surface density of dust at increases to . However, due to material condensing to form the disc galaxy the dust surface density is lower than the initial NFW halo beyond . Modelling the formation of dust we find that its central surface density increases still further, reaching a dust surface density of at . The lack of feedback during the formation of the disc galaxy means that we achieve higher than typically observed central dust densities. In addition, the idealized nature of the simulation results in the circumgalactic medium (CGM) of the halo being free from dust because dust is not produced in the CGM but instead is acquired through in-falling satellites. The evolution of the dust-to-gas ratio radial profile during the formation of the disc galaxy is shown in the right-hand panel of Fig. 12 . The dust initial forms in a
Paper: 2016ApJ...824...39V (fulltext)
in the literature. However, as time progresses the action is conserved with less accuracy, as indicated by the increase of the standard deviation up to the value of 50% after 6 Gyr of evolution. This also holds for a subset of stars that radially migrates by resonant scattering at corotation. These results have implications for the vertical structure of the disk when stars radially migrate to the outer part of the disk. In this model stars migrating outwards are a heavily biased subset of stars with preferentially low vertical velocity dispersion. Thus, extreme migrators outwards are characterized by having a have small amplitude of their vertical excursion, independent of the conservation of the vertical action. In the MW-like galaxy model, disk 2 , the bar formation causes large angular momentum changes, with significant in-plane and vertical heating. Thus, in the presence of the formation of the bar and combined with significant disk heating, our results show that the vertical action cannot be assumed a constant of motion. Indeed, in this case the fractional changes in the estimates of the action for a set of stars progressively increase by time, with a standard deviation that approaches 80% after 6 Gyr of disk evolution. If confirmed, this result casts doubts on the possibility of predicting the vertical structure of the disk for a set of extreme migrators outwards, by assuming that their vertical action is conserved. APPENDIX MEASURING THE VERTICAL ACTION The vertical action j z is defined as:where z and v z describe the position and velocity along the orbit, respectively. If the potential is nearly axisymmetric this quadrature can be calculated using the algorithm described in (Solway et al. 2012 ). We adopted here a similar prescription as explained below: 1. At a given time t the gravitational potential Φ, its radial gradient , and vertical gradient are computed on a polar grid centered at the minimum of the (fixed) halo potential. The grid dimension is , with the cylindrical coordinates ranging as follows: (logarithmically spaced), , and , where is the softening length. Note that these ranges span six times the scale-length and twenty times the scale height. The potential is numerically calculated at each node of the grid using a tree algorithm that includes up to the quadrupole contribution. We do not impose a symmetry about the galactic mid-plane, in fact, modest asymmetries that can develop over time are naturally captured by the grid. 2. At each point ( R , z ) the potential is calculated by averaging over the azimuthal points:Equivalent expressions are derived for and . This approximation relies on the fact that the potential is nearly axisymmetric, this however may not be the case after if a bar grows at the center of the galaxy. Once the potential is computed at t time, for each star particle the following quantities are evaluated: the vertical angular momentum L z , the guiding radius as the solution to the equation:and the
Paper: 2021MNRAS.505.5686C (fulltext)
TESTING <sc>binulator</sc>+<sc>gravsphere</sc> ON MOCK DATA In Section 4.1 , we introduced an updated version of the gravsphere code (Read Steger 2017 ; Read et al. 2018 ) with a new reworked binning method, the binulator . In this Appendix, we present tests of the new code on mock data drawn from the Gaia Challenge 2 spherical and triaxial suite (Read et al. 2021 ). We focus on two mocks that were particularly challenging for the previous version of gravsphere : PlumCuspOm and PlumCoreOm. The former is a Plummer sphere embedded in a spherical, cuspy dark matter halo; the latter is the same embedded in a dark matter halo with a central constant density core. In both cases, the velocity dispersion profile is isotropic at the centre and maximally radially anisotropic at large radii. Both mocks are described in detail in Read et al. ( 2021 ). Here, we model both assuming Gaussian velocity errors on each star of 2 km s −1 for 100, 1000, and 10 000 randomly sampled kinematic tracers. We assume that the photometric light profile is in all cases well sampled with 10 000 stars, similarly to the situation with real data for nearby dsPh galaxies (e.g. Read et al. 2019 ). Previously, gravsphere struggled on these mocks. For both PlumCoreOm and PlumCuspOm with 1000 tracers, it returned a dark matter density that was systematically low for , shifting, however, to for 10 000 tracers. The velocity anisotropy was well recovered in all cases, except PlumCoreOm for which 1000 tracers were insufficient to detect the radial anisotropy at large radii (Read et al. 2021 ). In Gregory et al. ( 2019 ), gravsphere was tested on mock data for fewer tracers (20, 100, and 500 tracer stars). There, a similar bias towards low densities at large radii was reported. We show results applying the updated gravsphere with its new binulator binning module to the PlumCoreOm and PlumCuspOm mocks in Fig. A1 . In all cases, we obtain an unbiased recovery of both the dark matter density profile, , and the symmetrized stellar velocity anisotropy, . The code is no longer biased to low density at large radii. This owes to a combination of the improved data binning, the proper inclusion of the full error PDF for the virial shape parameters, and to our switch to using the coreNFW profile (recall that the non-parametric power-law-in-bins profile used previously by default in gravsphere biases models towards falling very steeply at large radii in the absence of data). Notice that now, even with just 100 tracers, we are able to detect that the PlumCuspOm is denser and falls more steeply than PlumCoreOm. For PlumCuspOm, we are also able to weakly detect that the velocity distribution is radially anisotropic at large radii. As we move to 1000 and 10 000 tracers, we obtain an increasingly high-fidelity recovery of both the density profile and the velocity anisotropy. Figure A1. Testing the updated gravsphere code and its new binulator module on spherical mock data from the Gaia Challenge suite.
Paper: 2017ApJ...848L...4K (fulltext)
of higher-Γ EEs. We also estimate P k using declination-averaged effective area for IceCube, , shown as IC () in Table 2 , which shows slightly higher for EE-opt-dist. Although the declination dependence of does not change our conclusion much, the declination-dependent analysis is important for more quantitative evaluations. Table 2 The Detection Probabilities, , for d L = 300 Mpc Note. IC: IceCube, Gen2: IceCube-Gen2, up+hor: upgoing+horizontal events, down: downgoing events, all: covering-factor-weighted average over the up+hor and down, : using the declination-averaged effective area. Using the relation , we estimate as a function of d L , which is shown in Figure 2 . Here, we ignore the effects of cosmological redshift, since we focus on the local universe at Gpc. The vertical dotted lines show d L = 300 Mpc and d L = 600 Mpc, which corresponds to the sensitivity ranges of face-on NS–NS and NS–BH mergers by aLIGO, respectively. For NS–BH mergers, since the distance is longer, is lower than those for NS–NS mergers. The detection probability of nearby events is affected by , while that of distant events is not. Figure 2. Detection probability as a function of luminosity distance d L . The upper and lower panels are with IceCube and IceCube-Gen2, respectively. The vertical thin-dotted lines show d L = 300 Mpc and d L = 600 Mpc. We estimate the detection probability within a given time interval, , which is estimated to be , where N is the number of EEs for the time interval within the covering area of neutrino detectors. The local SGRB rate is (e.g., Nakar et al. 2006 ; Wanderman Piran 2015 ), so the event rate within the sensitivity range of aLIGO (300 Mpc) is . According to the Swift results, ∼25% of SGRBs are accompanied by EEs (Sakamoto et al. 2011 ), noting that softer instruments could detect more EEs (Nakamura et al. 2014 ). Here, we simply assume that half of SGRBs have EEs, leading to N ∼ 2–5 for years. Within the sensitivity range of NS–BH mergers by aLIGO (600 Mpc), the SGRB rate is , leading to ∼9–22 EEs for a 5 year operation. The estimated values of are tabulated in Table 3 . We find that the simultaneous detection of gamma-rays, neutrinos, and GWs is possible in the era of IceCube-Gen2 and aLIGO/aVirgo/KAGRA, assuming a cosmic-ray loading factor, . This will allow us to probe the physical conditions during EEs, including the cosmic-ray loading factor and the Lorentz factor (see Section 4 ). Table 3 The Detection Probabilities within a Given Time Interval, Note. The SGRB rate is assumed to be . In the near future, KM3NeT will be in operation. While IceCube is more suitable to observe the northern sky, KM3NeT will achieve a better sensitivity for the southern sky, helping us improve the possibility of detections. In reality, not only Γ but also the other parameters for EEs (, , , α , β , , , d L ) should be distributed in certain ranges. However, their distribution functions are quite uncertain, and detailed discussion of
Paper: 2019MNRAS.484.2575T (acknowledgments)
ACKNOWLEDGEMENTS We thank the Gemini staffs for preparing and carrying out the observations. We thank the anonymous referees for many insightful comments. We acknowledge K. Matsuoka, Y. Matsuoka, R. Momose, T. Nagao, M. Nakamura, A. Schulze, V. Wong, X. B. Wu for useful discussions. TG acknowledges the support by the Ministry of Science and Technology of Taiwan (MoST) through grant 105-2112-M-007-003-MY3, and YO acknowledges the support by the MoST of Taiwan through grant 106-2112-M-001-008- and 107-2119-M-001-026-. CJ is supported by the Hundred Talents Program of the Chinese Academy of Sciences. CJ acknowledges the National Natural Science Foundation of China through grant 11873054. CD acknowledges the Science and Technology Facilities Council (STFC) through grant ST/P000541/1 for support. EKE acknowledges a post-doctoral fellowship from TUBITAK-BIDEB through 2218 program.
Paper: 2017ApJ...846..149C (acknowledgments)
We thank the anonymous referee for helpful suggestions. The National Center for Atmospheric Research (NCAR) is sponsored by the National Science Foundation. We thank Dr. Mark Miesch for comments that improve this manuscript. F.C. is supported by the Advanced Study Program postdoctoral fellowship at NCAR. This work is also supported in part by the NASA LWSCSW grant NNX13AJ04A and NASA grant NNX14AI14G. Computing resources were provided by the NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation, on Yellowstone ( http://n2t.net/ark:/85065/d7wd3xhc ).