adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...850...84H (fulltext)
variations in the inferred and for highly reddened SNe if the dust distance is in the right range (Bulla et al. 2018 ). 2. For nonuniform ISM dust, the later phases will have values more consistent with each other while earlier phases will show larger and random (but correlated) variations. (For example, see Figure 8 for F13 : while the variations in F13 are over-stated, as discussed here, the trends are qualitatively correct). This effect is independent of the distance between the SN and foreground dust, and we find that typically it will be small even for highly reddened SNe. 4. Conclusions Even though there is clear evidence for ISM density fluctuations on scales of 10 au or even below, we show that due to the steep spectral index of the ISM column density power spectrum and because the SN photodisk is much smaller ( in diameter) than the typical ISM structure (), substantial variations on pc scales translate to much smaller fluctuations on the scale of SN photodisk. We provide a way to set the normalization for ISM density fluctuations on length scales relevant to SNe Ia based on MW dust emission observations (Miville-Deschênes et al. 2007 ). We have found that the expected time variations of EW(Na i ) and for SNe Ia due to nonuniform ISM would be typically much smaller than the predictions in Patat et al. ( 2010 ) and Förster et al. ( 2013 ), respectively. The observations of two SNe Ia highly reddened by at least mostly interstellar dust, SN 2012cu and SN 2014J, are consistent with the results of our analysis. The observed color variations for SN 2006X have been attributed to a light echo (Bulla et al. 2018 ) rather than nonuniform ISM, and so are also consistent with our analysis. We therefore conclude the following, if nonuniform ISM density in the SN host galaxy follows a scale-free power-law power spectrum. (1) The expected variation of extinction for SNe Ia with mag is negligible and is certainly smaller than what is currently scientifically relevant for SN cosmology. This, combined with mounting evidence that interstellar dust is likely the dominant source of reddening for SNe Ia, significantly reduces the concern about time variation of extinction for SNe Ia due to nonuniform dust density in cosmological studies. (2) For the study of extragalactic ISM density nonuniformity, it may be possible to detect small variations in extinction across phases for a highly reddened SN. The rms can be as large as ∼0.1 mag over a phase range of, say, from −10 to +50 days, with the shallowest spectral index (). A larger phase coverage would be better for this purpose (earlier observations would especially help, as they allow the observation to sample closer to the full extent of the photodisk at the last observed phase). Huang et al. ( 2017 ) demonstrated that in order for such a detection to be feasible, it is important to not only have an unreddened SN that is well-twinned with the dust-obscured SN, but the observed phases of these two SNe
Paper: 2016ApJ...821...67S (fulltext)
for our purpose. The electron screening effect is negligibly small in the region of ρ = 10 6 –10 7 g cm −3 and T > 2 × 10 9 K, where carbon detonation would occur, so we do not include this effect. Figure 7 shows the comparison between simulations with and without carbon burning for the case of . The red lines indicate the case with carbon burning, while the black ones indicate the case without. Although the orbital evolution (Figure 7 (a)) is almost the same, the maximum temperature (Figure 7 (c)) realized in the case with carbon burning is higher than the case without during the merger phase. This difference comes from the nuclear energy release (Figure 7 (b)). We confirmed that the inclusion of carbon burning increases the maximum temperature. Figure 8 summarizes the density and temperature of a particle which has the smallest τ CC / τ dyn ratio for all the cases we recalculated, including nuclear burning. The symbols surrounded by black frames are the results of the cases with carbon burning. Figure 7. Merger of WDs. The red lines indicate the case with carbon burning, while the black lines indicate the case without. (a) The orbital evolution, (b) the energy release rate of carbon burning, and (c) the time evolution of maximum temperature. Figure 8. Same as Figure 3 , but for the case with carbon burning. The symbols surrounded by black frames are the case with carbon burning while those without black franes are the case without. The black dashed line is the demarcation of the detonation condition by Seitenzahl et al. ( 2009 ). Since our simulations include only carbon burning, the effect of nuclear reactions would be underestimated. To check this effect, we perform post-processing calculations using an α -chain reaction network containing 13 species from 4 He to 56 Ni (Timmes 1999 ). Figure 9 shows the results for a particle which has the highest temperature in the case of . This calculation was done for a period in which carbon burning continued. The red lines indicate the case with the α -chain reaction network, while green lines indicate the case with only carbon burning. Because we find essentially no difference between them, our simulations including only carbon burning are appropriated to estimate the effect of nuclear burning, and being consistent with the previous studies using α -chain networks (Pakmor et al. 2010 , 2011 , 2012b ; Raskin et al. 2012 ), at least until the initiation of detonation. Figure 9. Post-processing analysis in the case of . We have calculated nuclear energy generation using the 13 species α -chain reaction network (Timmes 1999 ). Green lines denote the case of only carbon burning while the red lines indicate those of post-processing results. (a) Density, (b) temperature, (c) specific internal energy, and (d) pressure. Thus, the inclusion of carbon burning lowers q cr to realize violent merger-induced explosions if we use the same criterion as in Section 3.2 . In Figure 10 (a), the green triangles are the
Paper: 2017ApJ...843..129B (acknowledgments)
We acknowledge many useful conversations on the algorithms presented in this paper with Marijn Franx. We greatly appreciate Pratika Dayal, Kristian Finlator, Nicholas Gnedin, Chuanwu Liu, Brian O’Shea, Pierre Ocvirk, and Bin Yue for sending us the predictions they derive for the LF results at high redshift. We thank Kristian Finlator for discussing with us at length different turn-over mechanisms that would impact the UV LF at low luminosities. Austin Hoag kindly sent us the v2 Bradac models for HFF cluster MACS 0416. R.S.E. acknowledges financial support from the European Research Council under Advanced Grant FP7/669253. In addition, we acknowledge the support of NASA grant HST-AR-13252, NASA grant HST-GO-13872, NASA grant HST-GO-13792, NWO vrij competitie grant 600.065.140.11N211, and NWO TOP grant TOP1.16.057.
Paper: 2016MNRAS.458..816H (acknowledgments)
We thank Todd Thompson for helpful discussions, and the anonymous referee for helpful suggestions. Support for PFH was provided by the Gordon and Betty Moore Foundation through Grant #776 to the Caltech Moore Center for Theoretical Cosmology and Physics, an Alfred P. Sloan Research Fellowship, NASA ATP Grant NNX14AH35G, and NSF Collaborative Research Grant #1411920. Numerical calculations were run on the Caltech compute cluster ‘Zwicky’ (NSF MRI award #PHY-0960291) and allocation TG-AST130039 granted by the Extreme Science and Engineering Discovery Environment (XSEDE) supported by the NSF. EQ is supported in part by NASA ATP Grant 12-ATP12-0183, the David and Lucile Packard Foundation, and a Simons Investigator Award from the Simons Foundation. CAFG was supported by a fellowship from the Miller Institute for Basic Research in Science, by NASA through Einstein Postdoctoral Fellowship Award PF3-140106 and grant 10-ATP10-0187, by NSF through grant AST-1412836, and by Northwestern University funds.
Paper: 2019ApJ...884L..46C (fulltext)
Snowball bifurcation less likely (Joshi Haberle 2012 ; Shields et al. 2013 , 2014 ). Planetary heat transport, if efficient enough, could destabilize partially glaciated states on a tidally locked planet and reintroduce a Snowball bifurcation as was discussed by Checlair et al. ( 2017 ). However, the authors did not explicitly model ocean heat transport, motivating further investigation into its role in possibly retrieving the Snowball bifurcation. Considering dynamic oceanic processes could increase the overall planetary heat transport, potentially making a bifurcation possible. Hu Yang ( 2014 ) previously modeled a tidally locked planet orbiting an M-star using an atmospheric GCM (CAM3) coupled to a dynamic ocean. They did not directly look for bifurcations or hysteresis, but found a smooth transition from a partially glaciated to a fully ice-covered planet, suggesting the potential lack of a bifurcation. In this Letter, we investigate the effects of including a dynamic ocean on the Snowball bifurcation for tidally locked planets in a coupled ocean–atmosphere GCM (ROCKE-3D). 2. Methods 2.1. Model Description We performed our calculations using ROCKE-3D (Way et al. 2017 ), a fully coupled ocean–atmosphere general circulation model (GCM) that is derived from NASA Goddard Institute for Space Studies ModelE2. ROCKE-3D has previously been used to examine the climate states of slowly rotating and tidally locked exoplanets, including Warm and Snowball climates (Del Genio et al. 2017 ; Way et al. 2018 ; Jansen et al. 2019 ). The reader is referred to Way et al. ( 2017 ) and references therein for details regarding ROCKE-3D and its parent model, but we note that ROCKE-3D includes a thermodynamic–advective sea ice model. In contrast with previous studies of Snowball bifurcations on tidally locked planets (Checlair et al. 2017 ), ROCKE-3D considers ice advection, allows for partial ice coverage within cells, and includes dynamical ocean heat transport. Our present investigation using ROCKE-3D thus represents a significant advance over prior work. 2.2. Model Configurations Our experiments consider an Earth-like planet with regard to mass, radius, surface gravity, and atmospheric mass and composition (although we exclude O 3 ). Our simulated planet nonetheless differs from the Earth in several ways. We simulate a tidally locked planet in a circular orbit with a 50 day period around an M-star (Kepler 1649, M5V). We chose the stellar spectrum from ROCKE-3D’s default spectra. It is based on the BT-Settl spectrum with T eff = 3200 K, log g = 5, Fe/H = 0 (Allard et al. 2012 ). The surface of the planet is covered exclusively by ocean. The ocean in our model consists of five layers and is considerably shallower than Earth’s, reaching a maximum depth of 189 m. In this configuration, running the model on 44 CPU cores requires on average 180 hr of wall time. A deeper ocean would increase the computational cost considerably. Our model configuration is summarized in
Paper: 2018MNRAS.475L..52S (acknowledgments)
ACKNOWLEDGEMENTS The authors thanks an anonymous referee for comments that improved the contents of this Letter. TS thanks T. H. Saitoh and R. Maiolino for useful discussion. TS and the other authors thank the ALMA staff for their kind support. NL acknowledges support by National Key RD Program of China #2017YFA0402704 and NSFC #11673028. This Letter makes use of the following ALMA data: ADS/JAO.ALMA#2015.1.00003.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ.
Paper: 2016ApJ...817..135L (fulltext)
years. This is due in large part to deep, wide-field imaging surveys such as the Two Micron All Sky Survey (2MASS; Skrutskie et al. 2006 ) and the Sloan Digital Sky Survey (SDSS; York et al. 2000 ), which have enabled large-scale studies of the Milky Way to its outermost radii, and have resulted in the discovery of a variety of new and interesting substructures that lie within the Milky Way’s halo (Ivezić et al. 2012 ). Our understanding of the formation of the Galactic halo has therefore evolved from a simple Eggen, Lynden-Bell, Sandage (ELS; Eggen et al. 1962 ) monolithic collapse model to a much more complex and dynamic structure that is still being shaped by the merging of neighboring smaller galaxies (Searle Zinn 1978 ). This merger process is predicted by hierarchical ΛCDM models of galaxy formation (Steinmetz Navarro 2002 ; Bullock Johnston 2005 ; Font et al. 2011 ). In addition to classical globular clusters (Harris 1996 , 2010 ) and classical dwarf galaxies (Mateo 1998 ), these modern surveys have also discovered many low-luminosity globular clusters (Koposov et al. 2007 ; Balbinot et al. 2013 ), a large number of ultra-faint dwarf galaxies (Willman et al. 2005a , 2005b ; Belokurov et al. 2006a , 2007 ; Zucker et al. 2006 ; Walsh et al. 2007 ), and stellar streams, which are thought to have originated from the tidal disruption of either dwarf galaxies (see, e.g., the Sagittarius stream; Majewski et al. 2003 ; Belokurov et al. 2006b ) or globular clusters (see, e.g., the Palomar 5 tidal tails; Odenkirchen et al. 2001 ). However, there are other structures that cannot be classified by one of these familiar designations. Modern wide-field deep imaging surveys have enabled for the first time the discovery of large-scale, highly diffuse halo structures that can cover up to several hundred square degrees on the sky. Examples of these recently identified extended stellar distributions include the Virgo overdensity (VOD; Jurić et al. 2008 ) and the Hercules–Aquila (HerAq) cloud (Belokurov et al. 2007 ), both located about 20 kpc from the Sun, and the Pisces overdensity (Sesar et al. 2007 ) at a distance of about 85 kpc. These overdensities have generally been identified by noting excesses of a particular stellar tracer in a restricted area of the sky. For example, excess halo stars were first found in the Virgo constellation (Vivas et al. 2001 ), , by using RR Lyrae stars as tracers. This structure is known as the Virgo stellar stream (VSS) and lies about 20 kpc from the Sun (Duffau et al. 2006 ). In a similar location in the sky, Jurić et al. ( 2008 ), working with a large sample of Milky Way disk and halo stars from SDSS, identified a very extended overdensity that stood out from smooth models of the Galaxy’s thin/thick disks and halo at a heliocentric distance of ∼6–20 kpc. This structure is dubbed the VOD and appears to encompass an area as large as 1000 deg 2 . Bonaca et al. ( 2012 ) used a later data release from SDSS and estimated
Paper: 2015A&A...583A..73B (acknowledgments)
This work was supported by the German Deutsche Forschungsgemeinschaft, DFG project number DFG STR645/7-1. This work is based on data obtained with the STELLA robotic telescopes in Tenerife, an AIP facility jointly operated by AIP and IAC. Detailed comments from an anonymous referee are also appreciated.
Paper: 2018ApJ...868..102V (fulltext)
section’s findings suggest that turbulence is essential for bringing about condensation in a medium with a non-negligible entropy gradient. More sophisticated numerical simulations that solve complete sets of hydrodynamical and magnetohydrodynamical equations will be needed to quantify with greater precision the relationship between turbulence and the critical t cool / t ff ratio for condensation within media with and without dynamically important magnetic fields. Concurrently, simulations of self-regulating mechanical AGN feedback should be analyzed for correlations between the rate at which ambient gas condenses into cold clouds and the values of t cool / t ff and σ t / σ v in the ambient medium. This paper will leave those investigations for the future and instead turn to the question of global self-regulation. 4. Coupling between Precipitation, Feedback, and Turbulence Previous work on precipitation-regulated feedback has assumed that the condensation threshold depends on either the t cool / t ff ratio alone (e.g., McCourt et al. 2012 ; Sharma et al. 2012b ) or the parameter combination , which accounts for the failure of buoyancy damping to suppress condensation when α K is sufficiently small (Voit et al. 2017 ). 3 3 Gaspari et al. ( 2017 , 2018 ) have recently proposed a different criterion for condensation in a turbulent medium that compares t cool with the timescale for eddy turnover. See the Appendix for a discussion of how that criterion relates to the results of Section 3 . However, an additional dependence on σ t / σ v , such as that shown in Figure 7 , can transform the feedback loop into a converging cycle that settles into a particular equilibrium state. This section explores global convergence to thermal balance in a precipitating medium by dropping the thermal balance requirement and developing a toy model that illustrates how turbulence can couple with precipitation. The model illuminates the conditions under which coupling produces a long-lasting equilibrium state in agreement with observations of both the precipitation threshold and the turbulent velocity dispersion in galaxy cluster cores. 4.1. A Toy Model for Coupling In order to keep the overall model as simple as possible, the dynamical timescales of the feedback system are assumed to be much shorter than its thermal and cosmological timescales, so that the ambient gas remains close to hydrostatic equilibrium. The model further assumes that heat input does not change the slope of the ambient entropy profile, which is constrained to be K ( r ) ∝ r 2/3 in a gravitational potential with t ff ∝ r . As a result, the gas temperature remains constant at , with a specific thermal energy that remains invariant while the gas gains and loses heat energy. Instead of causing the temperature to rise and fall, heat input causes the gas to expand and radiative losses cause the gas to contract, while gravitational work keeps kT steady. In such a constrained system, the t cool / t ff
Paper: 2015ApJ...801....4W (acknowledgments)
We thank the anonymous referee for helpful comments that greatly improved this manuscript. We thank Professor Jennifer Patience for kindly providing the SINFONI spectra of CT Cha B. We thank the whole Magellan Staff for making this wonderful telescope possible. We would especially like to thank Povilas Palunas (for help over the entire MagAO commissioning run). Juan Gallardo, Patricio Jones, Emilio Cerda, Felipe Sanchez, Gabriel Martin, Maurico Navarrete, Jorge Bravo and the whole team of technical experts helped do many exacting tasks in a very professional manner. Glenn Eychaner, David Osip and Frank Perez all gave expert support which was fantastic. It is a privilege to be able to commission an AO system on such a fine telescope and site. The MagAO system was developed with support from the NSF MRI and TSIP programs. The VisAO camera was developed with help from the NSF ATI program. Y.L.W.'s and L.M.C.'s research was supported by NSF AAG and NASA Origins of Solar Systems grants. J.R.M. is grateful for the generous support of the Phoenix ARCS Foundation. J.R.M. and K.M. were supported under contract with the California Institute of Technology funded by NASA through the Sagan Fellowship Program.
Paper: 2016MNRAS.459.3711S (acknowledgments)
XS thanks Eiichiro Komatsu for carefully reading the manuscript and giving helpful suggestions, as well as Erwin Lau and Surhud More for discussions and comments, and Susmita Adhikari for explaining the details of their method.
Paper: 2018MNRAS.480.5332W (fulltext)
catalogue, in units of (100 km s −1 ) 2 . The bin width is 500 km s −1 (redshift). The black dashed line shows the original simulation catalogue; the red solid line with error bars is the average of the perturbed catalogues. The error bars show the standard deviation of the perturbed results, which is regarded as the measurement error. The background contours indicate the cosmic variance from Fig. 2 . Figure 5. The dashed line shows the standard deviation of the cosmic variance of the correlation statistic ψ 1 at the redshift separation bin 0–500 km s −1 of the simulation catalogues as a function of sample size. The solid line indicates measurement error of the perturbed sample averaged over all the bins as a function of sample size, in units of 1000 km 2 s −2 . The contours show the 68 per cent and 95 per cent ranges of the results, whereas the error bars are the standard deviations of the measurement errors. 4.3 Measurement error and sample size Once we have a large enough sample size to adequately probe a volume, we do not expect cosmic variance to change with sample size, since in this case it is due to the variations between volumes of a similar size. To decrease the cosmic variance we would need to increase the depth of our sample; larger volumes should vary less as we approach the scale of homogeneity for the Universe. In contrast, we can reduce the effect of measurement errors by increasing our sample size, even if the volume being probed stays the same. To characterize the effect of sample size on measurement errors, we select new mock surveys with up to 20 000 galaxies by changing in equation ( 1 ) while leaving the other selection function parameters unchanged. We then perturb them using the same method described in Section 4.2 . In Fig. 5 , we show how the cosmic variance and measurement errors of the correlation statistic ψ 1 changes with sample size. Increasing the sample size reduces the statistical errors significantly, while, as expected, the cosmic variance doesn’t show significant difference. 5 CORRELATION RESULTS Fig. 5 shows the relationship between sample size and statistical error. Using this relation, we can calculate the statistical error of CF3 surveys, which have many more data points than the CF2 surveys. Fig. 6 shows the correlation function of CF3-galaxy survey with Hubble constant H 0 = 75 km s −1 Mpc −1 ; this is the value given by Tully et al. ( 2016 ) as the best fit to the data. We see that the correlation function of CF3 is larger than the mean value of correlations of mock catalogues. Hellwing et al. ( 2017 ) show that cosmic variance of local group (LG) observer mocks with a Virgo-like cluster is larger than the Copernican observer mocks without Virgo, which explains the smaller mean correlation value from mocks in Fig. 6 . Figure 6. Velocity correlation functions of CF3-galaxy survey with H = 75 km s −1 Mpc −1 . ψ 1 , ψ 2 , ψ ∥ , and ψ ⊥ are in units of (100 km s −1 ) 2 . The solid line shows the