adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.484.5484E (acknowledgments)
ACKNOWLEDGEMENTS We thank D. González Caniulef for helpful discussions. SWKE acknowledges support from the Science and Technology Facilities Council (STFC; grant number ST/N504488/1). MJP acknowledges support from STFC (grant number ST/N000811/1). MJP, AAB, and NPMK acknowledge support from the United Kingdom Space Agency (UKSA; grant number ST/P002323/1). This research has made use of data from the High Energy Astrophysics Science Archive Research Center (HEASARC) and the UK Swift Science Data Centre, provided by NASA’s Goddard Space Flight Center and the Department of Physics and Astronomy, Leicester University, UK, respectively.
Paper: 2018MNRAS.480.4702P (fulltext)
where each DLA is detected. The purity of the catalogue should increase with CNR, so any effect of impurities should decrease rapidly with the threshold we impose on CNR. We show this in Fig. 4 , where we see again an increased variation of DLA bias with metal strength as the minimum required CNR is increased. Figure 4. Bias of the DLAs against S (left-hand panel) and (right-hand panel) for different cuts in CNR. Red squares (CNR > 4) and blue triangles (CNR > 2) have been horizontally shifted to avoid overlap. Furthermore, in this case we see a larger variation of the bias for the smallest value of S : a higher threshold in CNR results in a lower bias factor for the DLAs with the smallest metal strength. The variation is highly significant: taking into account that the sub-samples with CNR > 4 contain 70 per cent of the same DLAs in the sub-samples with CNR > 2, the expected statistical fluctuation in the difference of bias factors is 0.4σ, where σ stands for the error in the largest sample, with CNR > 2 (indicated by the blue error bar in the figure). The expected fluctuation comes from the fact that the sub-sample with CNR > 2 can be considered as the combination of two independent samples (one with CNR > 4, and another one with 2 CNR ≤ 4) that are combined with weight to construct the final estimator. This implies that the typical fluctuation is δσ CNR > 2 , where . This variation is opposite to our expectation if impurities caused only by noise were present in the catalogue, in which case false DLAs would have metal strength and bias consistent with zero. Our interpretation of this result is that the contaminants that are included in the catalogue as the CNR threshold is lowered are mostly regions of broad Ly α absorption in the forest, which correspond to absorbers of lower column density than DLAs but with sufficiently broad velocity dispersions to create a broad absorption line that is consistent with a DLA in the most noisy spectra. These absorbers generally have weaker associated metal lines because of their lower column densities and higher ionization level (most of the lines used to measure the metal strength are of low-ionization species; see Arinyo-i-Prats et al. 2018 ), and they are more highly biased than the DLAs with the weakest metal lines because they are associated with collapsed regions, or regions in the process of collapsing, with high velocity dispersion. This can explain why the presence of contaminants increases the bias factor, and actually decreases the variation we measure of the bias factor with metal strength. These type of absorbers and their average metal lines are likely similar to the systems studied by Pieri et al. ( 2014 ); a detailed study of the bias factor of these strong Ly α absorbers is separately being done (Blomqvist et al. in preparation). 4.3 Correction for the effect of metal strength measurement errors The errors in the measurement of mix the samples , and , as each DLA is classified to one of
Paper: 2016ApJ...824...80A (fulltext)
not removed, but their orbits were significantly changed due to a significant perturbation caused by an encounter with a giant planet. In Figure 2 , we present an example of the effects of such an encounter with Jupiter. The plots show the maximum final variation in semimajor axis and eccentricity among the hundred particles that share the same initial radial position. As expected, the values increase with the radial distance. In the region of the rings (indicated by the vertical lines) the semimajor axis changes by more than 30 km and the eccentricity grows up to more than 0.5. Figure 2. An example of a disturbing close encounter caused on the ring by a close encounter with Jupiter. (a) Maximum final change in eccentricity. (b) Maximum final change in semimajor axis (km). The plots show the maximum final changes among the hundred particles that share the same initial radial position. The vertical lines indicate the boundary of the ring region. The encounter was performed with a minimum distance of 6.6 planet radii, with a relative velocity of km s −1 . The encounter resulted in a critical radius of R C = 460 km, meaning that no particles in the region of the rings were removed. The results of the encounters that produced at least some noticeable variations (Δ e ≥ 0.01) on the orbits of the rings are presented in Table 4 for backward and forward integrations, excluding the encounters followed by ejection. Table 4 List of Significant Disturbing Encounters (Δ e ≥ 0.01) with the Giant Planets for both Forward and Backward Integration within the Time Span of 10 Myr Notes. The immediate ejection cases were excluded. a The minimum encounter distance given in terms of the radius of the planet in question. b Survival time after a disturbing encounter. For the forward integration, we observed only five encounters with Jupiter and two with Saturn that resulted in eccentricities larger than 0.1 for the ring particles. None of the encounters with Uranus or Neptune showed significant orbital variation of the particles of the rings. Similarly, if we look backward, we see fewer close encounters that could have increased the eccentricity of the particles of the rings for values larger than 0.1 (five with Jupiter, one with Saturn, and one with Uranus). Since Chariklo, as observed now, has well-defined narrow circular rings (Braga-Ribas et al. 2014 ), this might mean that it did not suffer any of these disturbing encounters or, if they did occur, it was so long ago that the rings had time to evolve, damping the eccentricity and reshaping the rings. Both possibilities are compatible with our results and, as shown in Table 3 , the probability of catastrophic or disturbing encounters is very low, and probably occurred several million years ago. 7. THE EJECTION CASES The close encounters of Chariklo with any of the giant planets followed by ejections are particularly interesting. If we look forward, these would be the encounters responsible for removing Chariklo
Paper: 2019MNRAS.483.3036O (acknowledgments)
ACKNOWLEDGEMENTS We thank an anonymous referee for the helpful suggestions.
Paper: 2017ApJ...839...53L (acknowledgments)
The work is jointly supported by the National Natural Science Foundation of China (41231068, 41304137, 41531073, and 41504132, 11575203) and the Specialized Research Fund for State Key Laboratories. X.L. acknowledges the support of the post-doctoral programme of the North-West University in South Africa. M.S.P. acknowledges the financial support of the South African National Research Foundation (NRF) under the Incentive and Competitive Funding for Rated Researchers, grants 87820 and 68198. M.Z. was supported in part by NASA Grants NNX14AJ53G and NNX15AN72G. This material is based upon work supported by the US National Science Foundation under Grant No. CNS 09-23050.
Paper: 2019MNRAS.487.1570S (fulltext)
other secondary biases, since it shows that the fact that it increases with mass was an artefact of the mass scales that were being probed. Our results place updated constraints to models addressing the connection between galaxies and haloes. The wide dynamical range that we probe encompasses haloes hosting from emission-line galaxies at the low-mass end to massive quiescent galaxies and clusters at the high-mass end. In the context of the halo-galaxy connection, it has become an observational challenge to prove the existence of galaxy assembly bias , a hypothesis that states that the clustering properties of galaxies, at fixed halo mass, depend on secondary halo properties such as the accretion history (or age) of haloes (see e.g. Lin et al. 2016 ; Miyatake et al. 2016 ; Montero-Dorta et al. 2017 ; Niemiec et al. 2018 ). For luminous red galaxies (LRGs), indications of the existence of galaxy assembly bias have been reported by Montero-Dorta et al. ( 2017 ) and Niemiec et al. ( 2018 ), from a sample extracted from the Baryon Oscillation Spectroscopic Survey (BOSS; Dawson et al. 2013 ). The typical mass of the haloes inhabited by these galaxies is estimated in ∼10 12.7−13 h −1 M ⊙ (Niemiec et al. 2018 ), but a large scatter is expected, so that the most massive LRGs could inhabit haloes above ∼10 14 h −1 M ⊙ . These results are, in principle, compatible with both the halo assembly bias and the concentration bias signal reported in Fig. 4 . Our measurement of halo assembly bias at the high-mass end can be properly connected to the results presented in Montero-Dorta et al. ( 2017 ) and Niemiec et al. ( 2018 ) through the technique of ‘age matching’ (Hearin et al. 2016 ). In addition, the large scale of the spin bias signal for cluster-size haloes, of almost a factor 2 at ∼10 14.5 h −1 M ⊙ , suggests an alternative route towards an observational proof of secondary bias. Probing the effect of spin bias requires measuring rotation, or a proxy for it, in a large sample of galaxy clusters. These measurements, which are still extremely challenging, might be possible in the near future thanks to techniques such as kinetic Sunyaev–Zel’dovich effect (see e.g. Baldi et al. 2018 ). We have applied, for the first time, a fully multitracer approach to the measurement of secondary bias. Here, different subsets of haloes are viewed as different LSS tracers. We show that this method is capable of reproducing all features observed from the standard measurement. In addition, we have obtained statistical uncertainties which are comparable to other bias estimates from N -body simulations that minimize cosmic variance by relying on direct knowledge of the density field, e.g. Gao White ( 2007 ); Chue et al. ( 2018 ). However, in contrast to those methods, which in effect compute δ α /δ m at each point (in configuration or Fourier space), the technique used in this work can be applied both to simulations as well as to actual data, in real or redshift space. The
Paper: 2020MNRAS.492.1180R (fulltext)
and UV-illuminated (afterwards) gas condensation of the massive star-forming cluster that formed the Solar system. In this condensation, after a sudden decrease of the temperature due an increase in density and efficient dust cooling, PO condensed on grains along with other molecules, including complex species also detected in the comet (Goesmann et al. 2015 ; Altwegg et al. 2017b ). Under these physical conditions, deuteration increased, producing heavy water and other deuterated species detected in the comet (Altwegg et al. 2017a ). PO does not react easily with atomic H, which is the only mobile species on the dust grains at low temperature. This allowed a relatively high abundance of PO in the ices of the comet. 4.2.2 From Solar system bodies to primitive Earth: providing prebiotic P The confirmation of the presence of PO in the comet 67P/C–G has important implications for the prebiotic chemistry that occurred on the early Earth. The possible contribution of prebiotic material from comets is a long-standing topic (e.g. Chyba et al. 1990 ). Recent works have pointed out that comets could have contributed significantly to the chemical reservoir of our planet. O’Brien et al. ( 2018 ) claimed that of Earth’s water could have its origin in a cometary source, while Marty et al. ( 2017 ), based on ROSINA measurements of Xenon isotopes in 67P/C–G, showed that comets might have provided the of the Earth’s atmospheric Xe. In sight of our findings of P in 67P/C–G, we explore here the possible contribution of comets to the P reservoir on the early Earth. The amount of P in the Earth crust is 930 ppm (Yaroshevsky 2006 ), which means ∼2.6 × 10 22 g. However, most of this P is not available for biological processes, being locked in insoluble minerals. In the astrobiology community, there is an intense debate (known as the ‘phosphate problem’, see e.g. Menor-Salvan 2018 ) about the availability of inorganic phosphate at the dawn of our planet. Orthophosphate (PO), which is the most common form of P on Earth, is mainly found in apatite minerals. The insolubility of these minerals locks P, which is not available for (pre)biotic chemistry. To solve this scarcity of P, alternative sources of P should be explored. One possibility comes from meteorites. It has been measured that CI chondrites, a type of stony meteorites, contain a nearly solar P abundance (Lodders 2003 ). This suggests that an important fraction of P likely came to the Earth in rocks. However, as mentioned before, most of this P is not easily accessible to trigger prebiotic chemistry due to low solubility in water. As a possible solution, Pasek ( 2008 ) proposed that schreibersite, (Fe,Ni) 3 P, the major carrier of P in iron meteorites, which is more soluble in water, may be the main contributor of P in the early Earth. These authors claimed that that impacts of meteorites probably delivered between 10 18 and 10 21 g of P. Another extraterrestrial possibility would be the contribution from
Paper: 2016A&A...596A..54D (fulltext)
simulations showed similar results. However, Chambers (2013) evidenced some differences related to the masses and the time-averaged eccentricities of the final planets. We believe that a more realistic treatment of the collisions should be included in future -body simulations. Such improvements will allow us to determine the orbital and physical properties of the planets formed in the simulations in greater detail. On the other hand, Marcus et al. (2010) presented results from smoothed particle hydrodynamics simulations of impacts between differentiated water(ice)-rock planets. From this, the authors analyzed the effects of late giant impacts on the final bulk abundance of water in super-Earths. Marcus et al. (2010) derived general laws, which should be incorporated into future -body calculations of planet formation to track the composition of growing planets in more detail. In the present work, we consider a snow line with a fixed position for each star. However, several works such as Ramirez Kaltenegger (2014) and Tian (2015) , calculated how the orbital distance of the HZ and the position of the snow line change throughout the pre-main-sequence (PMS) stage of the host star. In particular, for a 0.5 star, Ramirez Kaltenegger (2014) found that the snow line in the PMS moves inwards in the protoplanetary disk from 2 AU to 0.5 AU in a period of 200 Myr. Regardless of star type, all planets located at the inner edge of the main-sequence (MS) HZ receive stellar fluxes that exceed the runaway greenhouse threshold for at least part of the PMS stage, which results in the rapid evaporation of surface water. Triggering a runaway greenhouse does not ensure that a planet could not later become habitable because water could recondense afterwards. Planets orbiting at distances corresponding to the inner and outer MS HZ around a 0.5 star would lose up to 25 EOs and 0.5 EOs, respectively, and about 0.1 EOs are lost at the MS snow line. Assuming that a HZ and a snow line evolving in time may lead to changes in the final results obtained in the present work. Future works based on -body simulations should incorporate such effects to test the sensitivity of the results concerning the physical and dynamical properties of the HZ planets. It is also important to note that all our simulations were integrated for 150–200 Myr. Thus, the planetary systems obtained in the present work should be treated as systems of potential interest. It would be very interesting to analyze the long-period dynamical stability of the planets surviving in the HZ to clarify the distribution of these kind of exoplanets around low-mass stars. We believe that the results obtained in the present work lead to a better understanding about the planetary formation processes around M3- and M0-type stars in systems without gas giants. Future observational evidence will be necessary to test our theoretical models and to strengthen our knowledge about the population of terrestrial-like planets
Paper: 2018MNRAS.481L..16R (acknowledgments)
ACKNOWLEDGEMENTS The calculations for this Letter were performed on the ‘Eureka’ supercomputer at the University of Surrey. RRR and JIR would like to acknowledge support from STFC consolidated grant ST/M000990/1.
Paper: 2015A&A...579A..70C (acknowledgments)
This work is based on observations collected at the European Southern Observatory, Cerro Paranal, Chile, using the Very Large Telescope under programs 182.A-0886 and partly 070.A-9007. Also based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT), which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is based in part on data products produced at TERAPIX and the Canadian Astronomy Data Centre as part of the Canada-France-Hawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS. The VIPERS web site is http://www.vipers.inaf.it/ . We acknowledge the crucial contribution of the ESO staff for the management of service observations. In particular, we are deeply grateful to M. Hilker for his constant help and support of this program. Italian participation to VIPERS has been funded by INAF through PRIN 2008 and 2010 programs. D.M. gratefully acknowledges financial support of INAF-OABrera. L.G., A.J.H., and B.R.G. acknowledge support of the European Research Council through the Darklight ERC Advanced Research Grant (#291521). A.P., K.M., and J.K. have been supported by the National Science Centre (grants UMO-2012/07/B/ST9/04425 and UMO-2013/09/D/ST9/04030), the Polish-Swiss Astro Project (co-financed by a grant from Switzerland, through the Swiss Contribution to the enlarged European Union), and the European Associated Laboratory Astrophysics Poland-France HECOLS. K.M. was supported by the Strategic Young Researcher Overseas Visits Program for Accelerating Brain Circulation (#R2405). O.L.F. acknowledges support of the European Research Council through the EARLY ERC Advanced Research Grant (#268107). G.D.L. acknowledges financial support from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement # 202781. W.J.P. and R.T. acknowledge financial support from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement #202686. W.J.P. is also grateful for support from the UK Science and Technology Facilities Council through the grant ST/I001204/1. E.B., F.M. and L.M. acknowledge the support from grants ASI-INAF I/023/12/0 and PRIN MIUR 2010-2011. L.M. also acknowledges financial support from PRIN INAF 2012. Y.M. acknowledges support from CNRS/INSU (Institut National des Sciences de l’Univers) and the Programme National Galaxies et Cosmologie (PNCG). C.M. is grateful for support from specific project funding of the Institut Universitaire de France and the LABEX OCEVU. SdlT acknowledges the support of the OCEVU Labex (ANR-11-LABX-0060) and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the “Investissements d’Avenir” French government program managed by the ANR.
Paper: 2016ApJ...832..156K (fulltext)
observing epochs. Figure 5 shows that the parameter constraints are much tighter when we average over more observing epochs, emphasizing the importance of sampling the intrinsic source variability when attempting to constrain the model parameters with EHT observations. Figure 5 (a) has the poorest ( u , v ) coverage by far, but achieves the tightest constraint, while fixing both ( u , v ) coverage and sensitivity in Figures 5 (b) and (c) again show that simply sampling more epochs will tighten the parameter constraints significantly. Figure 5. Parameter estimation test with the same underlying parameters as Figure 4 . The three panels show the averaged likelihoods for 20 realizations each in three separate setups. The number of observations in each setup is chosen such that the raw overall sensitivity is held fixed across the three setups. The three panels correspond to (a) 920 epochs on the SMA–CARMA–SMT baseline, where previous EHT observations were done; (b) 61 epochs on baselines including SMA, CARMA, SMT, LMT, APEX, and SPT; (c) 4 epochs including the same baselines as panel (b) but with ALMA replacing APEX. These figures show that sampling the source variability is more important for parameter estimation than the raw sensitivity of the array or the ( u , v ) coverage. 3.4. Effect of Time Averaging In the parameter estimation test, we calculated likelihoods over parameter space with both time-dependent and averaged GRMHD images to compare the effect of time averaging. Figure 6 contrasts the likelihoods of time-resolved (black contours) and time-independent (red contours) inputs to our Bayesian method when the same number of mock observations are analyzed. Figures 6 (a) and (b) are the likelihoods assuming the early three-station baselines and the six-station EHT with ALMA, respectively. The figure shows that a much smaller region of parameter space is allowed when comparing the mock data to a time-averaged image. However, the best-fit parameters in the time-averaged case are offset from the true parameters, as is most clearly evident in Figure 6 (b). This demonstrates the false certainty implied by the small allowed parameter region. The time-resolved analysis, by contrast, delivers allowed parameter ranges that are consistent with the input simulation. Figure 6. Effect of time averaging in the parameter estimation test for (a) the early EHT baselines and (b) the full EHT baselines including ALMA. We use the identical parameters for the mock data as Figure 4 . The likelihoods of the averaged GRMHD image are overplotted in red contours. The likelihood using the averaged images underestimates the uncertainties of the inferred parameters. The best-fit parameters derived from the time-dependent and averaged images are offset from each other as well. 3.5. Model Selection We now use the Bayesian method to identify the relative posterior likelihood among several models, as discussed in Section 2.2 . For this test we generated mock visibilities