adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...873..124W (fulltext)
CGs spanning all redshifts, the only available path forward is to utilize cosmic simulations of galaxy evolution. The Millennium Simulation (Springel et al. 2005 ; Lemson Virgo Consortium 2006 ) provides a tool to study the history of CGs in the universe. It used 2160 3 particles with mass M = 8.6 × 10 8 h −1 M ⊙ to trace the evolution of a comoving cube with side length 500 h −1 Mpc, where h is the Hubble constant parameterization such that H 0 = 100 h km s −1 Mpc −1 . The Millennium Simulation used a ΛCDM model with cosmological parameters Ω M = 0.25, Ω b =0.045, Ω Λ = 0.75, and h = 0.73, consistent with observational results from the Carnegie Hubble Program (Freedman et al. 2012 ) and the WMAP (Hinshaw et al. 2013 ) mission. 10 10 In this paper all distances and masses are scaled to h = 1. The Millennium Simulation used only dark matter particles to which associated properties were later assigned. Dark matter halos were identified using a friends-of-friends algorithm as described in Springel et al. ( 2005 ). Galaxies were added to the simulation post facto using semianalytic techniques (e.g., De Lucia Blaizot 2007 ; Guo et al. 2010 , 2011 , 2013 ). Galaxies are initially associated with individual dark matter halos, which may become subhalos of larger structures over time. The techniques of De Lucia Blaizot ( 2007 ), for example, expanded on the methods of Croton et al. ( 2006 ) and incorporated the effects of rapid star formation and gas loss in galaxy mergers, changes in galaxy properties with varying stellar mass functions and stellar populations, and attenuation due to dust. The simulation results were saved in a collection of 64 redshift “snapshots.” The identified dark matter halos were then assigned galaxy properties using the semianalytic techniques discussed in the references above. The snapshots, combined with the high resolution of the simulation and the sophisticated semianalytic galaxy formation and evolution models, provide an excellent tool for studying the history of CGs over cosmic time. Throughout this paper, we adopt the De Lucia Blaizot ( 2007 ) catalog of galaxies created through this semianalytic approach. 1.2. Previous Work A number of previous studies have utilized the Millennium Simulation galaxy catalogs to investigate CGs. These studies have been limited to relatively low redshifts to facilitate comparison to observations, such as the well-known Hickson catalog (Hickson 1982 ; Hickson et al. 1992 ). By using a light cone in the Millennium Simulation galaxy catalogs (Henriques et al. 2012 ), it has also been possible to create “mock” catalogs of CGs, which can be compared to observations and are subject to analogous limitations (e.g., interloping foreground or background galaxies; McConnachie et al. 2008 ; Díaz-Giménez Mamon 2010 ; Díaz-Giménez Zandivarez 2015 ; Farhang et al. 2017 ). By comparing mock CGs in projection in the Millennium Simulation to observed Hickson compact groups (HCGs), McConnachie et al. (
Paper: 2018MNRAS.480.5226E (fulltext)
are not very important for this discussion. The effect of number counts magnification is small compared to WL, RSD, or BAO. For F x B :All magnification only increase FoM γ with , while the effect is a 15 per cent increase for F x B :Counts. This discussion therefore focuses on the number counts results (Counts). In F x B -〈 FB 〉 the surveys overlap, which gives sample variance cancellations, but it does not include the Bright–Faint cross-correlations. This reduces the bias constraints and therefore the magnification constraints. The non-overlapping surveys (F+B) does not have sample variance cancellations from overlapping volumes and there the ratio drops further. Finally, could the low magnification effect be caused by a strong covariance between the counts–shear and magnification signals? The F x B -〈δ γ〉 line is F x B :All, but without all counts-shear cross-correlations or equivalently F x B :Counts adding shear–shear. The effect of magnification is also low when only including the shear-shear lensing observables. A lower Faint bias amplitude improves the forecast. One can understand this from a signal-to-noise argument. The variance (Δ 2 ) of the magnification signal when assuming Gaussian fluctuations (equation 3 ) is where i and j are the two redshift bins. The auto-correlation terms dominate for well separated bins in photo-z space, which leads to the approximation in the second line. If ignoring RSD, the magnification–magnification correlations and the shot-noise, the magnification signal-to-noise is where α j and b j are, respectively, the magnification slope and the galaxy bias in redshift bin j . The and terms are the matter auto-correlations in bin i and j , respectively. This approximation is invalid for low Faint bias amplitudes. Equation ( A4 ) shows two important criteria for magnification. A higher magnification slope increase the signal, while a lower bias of the background population decrease the errors. This criteria can be useful if focusing solely on magnification. However, one should focus on RSD, BAO, and WL for the combined constraints, since these effects give stronger cosmology constraints. APPENDIX B: PHOTOMETRIC REDSHIFTS AND GALAXY DENSITIES B1 Photometric redshifts The bias measurements are also affected by redshift uncertainties. Our two fiducial galaxy samples are: a photometric (Faint) and a spectroscopic (Bright) with respectively 0.05(1 + ) and 0.001(1 + ) Gaussian redshift uncertainties. The Faint sample is analysed in broad redshift bins since the Faint photo-z is broad. Improving the Faint photo-z would only substantially change the forecast if also using narrow bins for the Faint sample. We focus instead on the Bright redshift uncertainty and fix the Faint photo-z to the fiducial value. Fig. B1 shows the F x B :All Bright bias error ratios between the case of using spectroscopic (Bright) sample and the case of including redshift uncertainties (photo-z) in the Bright sample. A vertical line (0.0035)
Paper: 2017ApJ...849....8M (fulltext)
When some of these minor planets are perturbed to orbits with proximity to the WD, they are thought to be tidally disrupted and form a circumstellar disk (Veras et al. 2014 , 2015 ), which eventually accretes onto the WD (Jura 2008 ; Rafikov 2011 ; Metzger et al. 2012 ). The spectroscopic analysis of WD atmospheres (Wolff et al. 2002 ; Dufour et al. 2007 ; Desharnais et al. 2008 ; Klein et al. 2010 ; Gänsicke et al. 2012 ), as well as infrared spectroscopy of the debris disks themselves (Reach et al. 2005 , 2009 ; Jura et al. 2007 , 2009 ; Bergfors et al. 2014 ), are typically consistent with “dry” compositions, characteristic of inner solar system objects. Studies that analyzed the atmospheric composition of large samples of nearby He-dominated WDs, inferred their collective water mass fraction to be of the order of ∼1%, and no more than a few percent even for particular subsets (Jura Xu 2012 ; Jura Young 2014 ; Pietro Gentile Fusillo et al. 2017 ). On the other hand, an increasing number of individual WDs (Farihi et al. 2013 ; Raddi et al. 2015 ; Xu et al. 2017 ) have now been inferred to contain a large water mass fraction in their atmospheres, ranging between 26% and 38%. Polluted WDs are therefore the only current means to understand exoplanetary composition. However, linking the inferred quantity of water in WD atmospheres to the origin of polluting planetary material is not trivial, and requires additional assumptions and calculations. Several studies have previously focused on water retention inside minor planets, as they evolve through and off the main-sequence of their host stars. Understanding how water survives inside minor planets as a function of their intrinsic characteristics, is a necessary first step in beginning to understand the origin and nature of polluting planetary material. The seminal work of Stern et al. ( 1990 ) introduced a simple sublimation model that considered the initial orbital distance and size of minor planets as they evolve around a post-main-sequence 1 star. Jura Xu ( 2010 ) considered a more advanced model, taking into account conductive heat transport to the interior, and also the minor planets’ orbital expansion during the post-main-sequence stellar evolution of a 1 and 3 host stars. Malamud Perets ( 2016 ; hereafter MP16 ) utilized a modern and more sophisticated code that consistently accounts for the thermal, as well as physical (rock/water differentiation), chemical (rock hydration and dehydration), and orbital evolution of icy minor planets. Contrary to previous models, it starts at the beginning of the main sequence (of a 1 host star), and considers new parameters: the formation time of the minor planet (which affects its early evolution through radiogenic heating and thus its internal differentiated state) and also the variation in the initial minor planet composition. Malamud Perets ( 2017 ; hereafter MP17 ) finally investigated multiple host star masses, ranging from 1 to 6.4 (corresponding to
Paper: 2017A&A...603A..85B (fulltext)
0.2 and 10.0 . Although one of the main problems of this survey was the poor and sparse sampling and the small number of observations per star, which complicates the statistical analysis and increases the uncertainty on the frequency of Jupiters in our survey, we find a total occurrence rate of Jupiter-mass planets in the M 67 sample of 18.0% (precisely 15.8% for the MS stars and 23.0% for the evolved stars). It is worth noting that on average we need a minimum number of 20 observations per star to exclude Jupiter-mass planets at high confidence when a periodogram analysis is applied. The Jupiter-mass planets in our sample imply that our results are consistent with the planet frequency of much larger surveys carried out for field star samples. For example, from the ELODIE survey, Naef et al. (2005) estimated that a fraction of 7.5 1.5% stars host giant planets with periods shorter than 10 yr, while Cumming et al. (2008) derived a frequency of 12 1.6% from the Keck survey and Mayor et al. (2011) 13.9 1.7% from the HARPS/CORALIE survey. As a general conclusion, our simulation study seems to confirm the recent finding ( Meibom et al. 2013 ) that the frequency of massive planets around stars of open clusters agrees with the frequency of these planets around field stars. However, we note that when we only investigate the frequency of hot Jupiters around MS stars of our survey, we find a hot Jupiter occurrence rate (5.7%) that is substantially higher than what is observed in field stars (see also Brucalassi et al. 2016 ). We have shown that the search for planets in open clusters is a powerful test benchmark for the theory of planet formation and stellar evolution. The new generation of spectrographs such as ESPRESSO ( Pasquini et al. 2009 ) will extend this search more effectively. It is clear, however, that to efficiently perform a planet search survey in star clusters, a multi-object capability, even with relatively low multiplex (10–50), is desirable. Unfortunately, no such facility is currently planned. Appendix A: Accompanying plots and table Table A.1 Observed targets in M 67. Notes. Object name, basic stellar parameters, number of observations for each instrument (H: HARPS, S: SOPHIE, C: CORALE, HET: HRS at Het, H-N: HARPS-N), total number of observations, mean stellar RV, average measurements error, average corrected measurements error, observed RV dispersion. Binary candidates are indicated in bold face. Fig. A.1 Contours of the planet detection efficiency for the MS stars in the domains of 1–1000 days for planet period and 0.2–10 for planet mass. A 0.01 FAP level has been used as detection planet threshold in the periodogram analysis. A filled circle indicates the planet position. Fig. A.2 Contours of the planet detection efficiency for the evolved stars in the domains of 1–1000 days for planet period and 0.2–10 for planet mass. A 0.01 FAP level has been used as detection planet threshold in the periodogram analysis. A filled circle
Paper: 2018A&A...620A.120W (fulltext)
test the stability of our reconstruction and the sensitivity of the model to various free parameters, we have considered different combinations of parameters. In particular, we either fixed , B sat, n , and c e as listed in Table 1 , or let them be free parameters within ranges 0.2–0.8, 50–850, and 0.2–0.5, respectively. These tests showed that the resulting reconstructions of TSI differed by up to 0.1 W m −2 on decadal timescales. The overall secular change always lies in the range 0.90–1.0 W m −2 . Consequently, we conclude that the reconstruction is independent of the exact choice of the free parameters (see also Krivova et al. 2007 ; Dasi-Espuig et al. 2016 ). 3.3. SATIRE-M reconstruction on millennial timescales Keeping all the parameters fixed, we now employ the SATIRE-M model to reconstruct solar total and spectral irradiance back to 6755 BC from the cosmogenic isotope data. Figure 7 a shows the TSI reconstructions over approximately the last 9000 years based on the three isotope series, as introduced in Sect. 2.3 (U16-14C in blue, U16-10Be in green, Wu18 in black). Figures 7 b and c are enlargements over the periods 1000 BC–2000 AD and 1400 AD–2000 AD, respectively. The decadally averaged TSI reconstruction based on the SN v2 series is also shown (solid red) for comparison. The reconstruction based on the sunspot record by Chatzistergos et al. (2017) is shown by the dashed red curve. Yellow shading highlights the difference between these two reconstructions. Fig. 7. Three TSI reconstructions based on different isotope records: U16-14C (blue), U16-10Be (green) and the composite Wu18 (black) plotted over the periods: panel a) 6755 BC–2000 AD, panel b) 1000 BC–2000 AD, and panel c) 1400 AD–2000 AD. The uncertainty range of the reconstruction based on the Wu18 composite is indicated by the grey shaded area. Decadal averages of the reconstruction based on the directly observed SN are shown in red after 1639. The solid line is the reconstruction based on the WDC-SILSO series while the dashed line is based on the record by Chatzistergos et al. (2017) . The yellow shaded area highlights the difference between them. It is worth noticing that the reconstructions based solely on either 14 C (U16-14C) or 10 Be (U16-10Be) show temporal discrepancies due to differences in the relevant geochemical production processes and local climatic influences (Sect. 2.3.1 and Wu et al. 2018 ). The reconstruction based on the composite (Wu18) of one global 14 C and six regional 10 Be series has remedied this (see Fig. 12 in Wu et al. 2018 ), and shows the common solar signal extracted from seven individual isotope series. We therefore consider this latter reconstruction to be more robust than reconstructions based on individual isotope data. On the millennial timescale, the variability of TSI () obtained from all three isotope records is very similar (≈0.11%). Since in the second half of the twentieth century, the activity of the Sun was comparatively high (see
Paper: 2018MNRAS.473.2622J (fulltext)
but decrease in an old stellar population (hence, the increase in the abundances of α elements). In Fig. 15 , α-rich stars are highlighted with triangles. However, the AMR for these stars seems to have an unexpected positive trend. To investigate this, we slice [α/M] into six portions and fit a straight line to illustrate the evolution of [M/H] with age in every [α/M] slice, which is shown in Fig. 16 . As the derived age may suffer from a marginal effect, we exclude the oldest stars in the fitting process. They are marked with crosses in Fig. 16 . Figure 16. AMRs for different [α/M] slices. Straight lines are fitted to illustrate the relation between [M/H] and age. Crosses denote the oldest stars, which are not used in the fitting. All six [α/M] slices have a positive slope in the AMR. All the slices have an unexpectedly positive gradient in the AMR with the exception of the highest-α slice, which has a negative gradient of −0.004 dex Gyr −1 . The average of all the gradients is about 0.01 dex Gyr −1 . Notice that there are few stars in the 0.15 > [α/ M ] > 0.10 slice, which is just the watershed between the high-α population and the low-α population. We combine this slice with the highest-α slice, which finally gives a negative gradient of ∼−0.01 dex Gyr −1 . We examined whether the positive gradient does exist in the APOGEE-DR12 data set. In this data set, the three low [α/M] slices ranging from 0.05 to 0.15 also have a positive relation, but the other three [α/M] slices have a negative relation. If these positive correlations in some of the [α/M] slices are real, this may indicate that there is fresh metal-poor gas inflowing during the evolution of the thin Galactic disc. The infall of pristine gas dilutes the gas reservoir, lowering the subsequently formed stars’ metallicities but retaining the [α/M] ratio. These stars contribute a positive trend in the AMR in a given [α/M] slice. Thus, the AMR in different [α/M] slices may give us an opportunity to measure the gas infall rate. Actually, Chiappini, Matteucci Gratton ( 1997 ) and Chiappini, Matteucci Romano ( 2001 ) proved the power of the inflow chemical evolution model. In these references, two main inflow episodes are assumed to form the thin and thick discs. The time-scale for the formation of the thick disc is much shorter than that of the thin disc. The model-predicted metallicity distribution function generally agrees with the observations in the solar neighbourhood [fig. 7 in Chiappini et al. ( 1997 ) and Hayden et al. ( 2015 )]. However, without the aid of a dynamic process to build the Galactic chemical evolution model, it is hard to measure the detailed gas infall rate in this way because it is impossible to confirm which stars are born in situ. Even worse, the potential biases involved in our adopted sample are difficult to measure and they probably play a crucial role when determining the AMR in different [α/M] slices. High-quality spectroscopic data with wide coverage
Paper: 2019ApJ...871..176X (fulltext)
we find τ m ≈ 12 days, and we obtain a similar value for SN 2017ein. Therefore, the different M ej values inferred for SN 2007gr and SN 2017ein are mainly a result of different opacity values adopted in the analysis. Moreover, the discrepancy in the ejected mass could also be related to the fitting methods and wave bands of the light curve used in the analysis. SN 2017ein is one of the two SNe Ic showing possible evidence for early shock breakout cooling. Another candidate is LSQ14efd, with its V -band light curve showing a bump feature at early times, which was also interpreted as cooling of the shocked stellar envelope (Barbarino et al. 2017 ). In comparison with SN 2017ein, LSQ14efd is more luminous and energetic, with an R -band peak absolute magnitude of −18.14 mag, kinematic energy E kin = 5.3 B, and ejecta mass M ej = 6.3 M ⊙ , making it stand between normal SNe Ic and SNe Ic-BL. 7. <italic>HST</italic> Progenitor Observation 7.1. Pre- and Post-explosion <italic>HST</italic> Images and Data Reduction We searched the pre-explosion HST images from the Mikulski Archive for Space Telescopes (MAST) 29 29 http://archive.stsci.edu/ and the Hubble Legacy Archive 30 30 http://hla.stsci.edu/ and found publicly available F555W and F814W WFPC2 images taken on 2007 December 11 (PI: W. Li). An F438W-band image at the location of SN 2017ein was taken by HST on 2017 June 12 with the WFC3/UVIS configuration (PI: van Dyk). This image was released and publicly available immediately after the observation. We use this image as a post-explosion image to identify the position of the SN progenitor. Table 8 lists the information of these images. Table 8 Pre- and Post-explosion HST Images on the Site of SN 2017ein The pre- and post-discovery HST images around the SN position are shown in Figure 16 , where the zoomed-in regions centered at the SN site are also shown. There is clearly a point-like source near the SN position in both the F555W and F814W images. The position of this point-like source is obtained by averaging the measured values with four different methods (centroid, Gauss, o -filter center algorithm of IRAF task phot, and IRAF task Daophot). On all pre-explosion images, the separation between the position transformed from the post-explosion SN site and the center of the nearby point-like source is within the uncertainty of the transformed position, suggesting that this nearby point source is the progenitor of SN 2017ein. The coordinates of the SN on the post-explosion F438 image are found to be (539.302 ± 0.002, 564.516 ± 0.003) with different methods (centroid, Gauss and o -filter center algorithm of IRAF task phot, IRAF task imexamine, and SExtractor) and are converted to (798.70, 1766.77) and (798.38, 1766.67) on the pre-explosion F555W and F814W images, respectively, as the coordinates of the progenitor. Figure 16. Top: regions around SN 2017ein showing the prediscovery HST F555W (left) and F814W (middle) images and the post-explosion HST F438W
Paper: 2019MNRAS.486.4638U (fulltext)
what is the driving mechanism of photoevaporation between FUV, EUV, X-ray, and what are the exact values of mass-loss rate is still open. In order for the photoevaporation to start the accretion rate have to drop below the photoevaporation rate. As soon as it starts, a cavity free from gas and dust is generated, with the very large grains rapidly migrating inward (Alexander Armitage 2007 ). CQ Tau was considered by Donehew Brittain ( 2011 ) and Mendigutía et al. ( 2012 ) to have a high accretion rate ( yr −1 ), which can be considered high for disc dispersal to be the main mechanism acting in the formation of the CQ Tau cavity (Owen 2016 ). A combination between the clearing mechanism and photoevaporation can, instead, be considered as a possible option (e.g. Williams Cieza 2011 ; Rosotti et al. 2013 ; Rosotti, Ercolano Owen 2015 ) to be further investigated. Dead zones . The suppression of magnetorotational instability inside the disc can create regions of low disc ionization, the so-called ‘dead zones’ (e.g. Flock, Henning Klahr 2012 ). In these regions the rate of gas flow decreases with an accumulation of gas in the outer edge. At the same time the pressure maxima is able to trap large particles with a consequent ring-like feature in both gas and dust components (Flock et al. 2015 ). The ring is in general located at about the same radius (Pinilla et al. 2016 ) in both the components. However, when dead zones and MHD wind are present at the same time, a difference in the inner radii position can be observed. The presence of dead zones can increase the turbulence present in the disc and generate asymmetric vortices in the disc (Ruge et al. 2016 ) at (sub)mm wavelengths. Such structures can be also created by disc–planet interaction if the α viscosity is low. We cannot exclude this mechanism by our observations. Higher resolution data are need to better constrain the profile of the ring-like disc, mainly its symmetry and variability (Pinilla et al. 2016 ). However, dead zones can explain ring-like structures, but not cavities such as the one clearly present in the CQ Tau system. A combination between the mechanism described above can be also a possible solution, which, however, we will not address in this paper. 7 CONCLUSIONS Discs where it is possible to clearly distinguish a cavity are of particular interest in the study of protoplanetary disc evolution and of the planet formation process. In particular, we have studied the disc around the CQ Tau pre-main-sequence star. We have made use of spatially resolved ALMA observations of the dust continuum and of three CO isotopologues, 12 CO, 13 CO, and C 18 O, together with the observed SED of CQ Tau. We employed the chemical–physical code DALI to model the CQ Tau disc and to derive the surface density profile of the dust (small and large grains separately) and of the gas. Finally, we ran 3D SPH simulations to test whether an embedded planet could be able to create the observed disc structure.