adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...885...97O (fulltext)
luminosity function for bright galaxies and numerous papers (e.g., Vogelsberger et al. 2014 ; Crain et al. 2015 ) detail their success. So, whether the CDM paradigm is right or wrong, the physical processes that they implement give them a luminosity function, above and below the L * break, that matches real data. We have looked at the publicly available output from these groups to see if their results do or do not match observations on statistical expectations with regard to the Tremaine Richstone ( 1977 ) t 1 statistics. We use r -band magnitudes throughout this study. 3.1. EAGLE Simulation The EAGLE simulation is a publicly available (McAlpine et al. 2016 ) suite of cosmological hydrodynamical simulations (Crain et al. 2015 ; Schaye et al. 2015 ). It assumes a standard Λ cold dark-matter cosmology from the Planck-1 (Planck Collaboration et al. 2014 ), Ω m = 1 − Ω Λ = 0.307, Ω b = 0.04825, h = 0.6777, σ 8 = 0.8288, and n S = 0.9611. The simulation suite is run with a modified version of the GADGET-3 N -body Tree-PM smoothed particle hydrodynamics (SPH) code (Springel 2005 ), and includes an updated formulation of SPH, new time stepping, and sub-grid physics (see Schaye et al. 2015 , for details). In this study, we use RefL0100, which has a cosmological volume of (100 comoving Mpc) 3 and a baryonic mass resolution of . We refer the readers to the introductory papers Crain et al. ( 2015 ) and Schaye et al. ( 2015 ) for complete descriptions of the sub-grid physics models. We bin EAGLE simulation results into different size galaxy groups from smallest (12–24 objects), with stellar mass greater than , to more massive systems, with 75–150 galaxies (see Table 1 ). The red lines in Figures 1 and 2 show the average magnitude gap between first-brightest and second-brightest galaxies, , in each group and the brightness of the first-brightest galaxy . Overall, simulated galaxies in EAGLE show a ∼1.7 mag gap between first-brightest and second-brightest galaxies, which is similar to the gap reported by van den Bosch et al. ( 2007 ) with 2° Field Galaxy Redshift Survey data. Figure 1. M 12 presented in the EAGLE simulation (red) and in the “randomized” data (blue). Figure 2. Average first-ranked r magnitude as a function of number of galaxy members in the EAGLE simulation. Table 1 EAGLE Simulation Note. a Average halo mass of the first-brightest galaxies in solar mass. Next, we put all the groups in one bin and randomly repopulate each group from the collective bin, keeping the same number of galaxies in each galaxy group or cluster (See Table 2 ). By construction, this keeps the overall luminosity function invariant. Then, we recompute , and σ ( M 1 ) for each of Figures 1 and 2 . Table 2 EAGLE “Randomized” Simulation Note how the gap is systematically larger in the original () than in the randomized groups (), and the first-brightest galaxy is brighter by over a magnitudes in each original set of galaxy groups than in the randomized versions of
Paper: 2019ApJ...884...58S (fulltext)
the case of mass transfer from the primary to the secondary star before the secondary expands. Figure 1. Radii (solid lines) and core masses (dashed lines) of the two stars in the binary system, (blue) and (red) as function of time. We assume that merger takes place when the secondary experiences its large and rapid expansion. Figure 2. Like Figure 1 , but for the (blue) and (red) binary system. In Figures 3 and 4 , we present the stellar models of the stars in the two binary systems we study here when the lower-mass companion experiences its large and rapid expansion. We assume that merger occurs during this phase. Figure 3. Structures of the binary systems (upper) and (lower) when the lower-mass companion experiences its large and rapid expansions at . In Table 1 we present the following stellar parameters for the two stars at the time when the more massive star (primary star) finishes its rapid expansion: luminosity, L , effective temperature, T eff , stellar masses, M * , core mass, M core , stellar radius, R * , core radius, R core , and binding energy of the envelope, E bind . In Table 2 we present the same quantities, but for the case when the secondary star finishes its rapid expansion. We assume that a merger takes place then (later we will discuss alternative merger times). Table 1 Properties of the Giant Primary Star and the Pre-giant Secondary Star When the More Massive (primary) Star Finishes Its Rapid Expansion Note. At this phase, the core of the secondary star is not relevant for our study, and we do not present its properties. The gravitational energy that the two cores release when they merge; namely, when their separation is , is given by Equation ( 10 ). The ratio of this energy to the binding energy of the two envelopes, isThis is the typical ratio for the and binary system (using the data from Table 2 ). For the M ZAMS,1 = 31.5 M ⊙ and binary system this ratio is 0.8 (using the data from Table 2 ). As the efficiency of channeling the gravitational energy of the merging cores to envelope removal is α CE 1, when the cores merge there is still bound envelope, but a large fraction of the envelope is leaving the system, and a fraction might form an extended envelope. Table 2 Properties of the Giant Stars When the Lower-mass Star in the Binary System Finishes Its Rapid Expansion Note. We assume that in many cases merger takes place during this time (see Figures 1 and 2 for the models). 4.3. The ILOT Properties Using these results and Equation ( 10 ), we can crudely estimate the total radiated energy in the ILOT. Very crudely, in many ILOTs the typical ratio of radiated to kinetic energy is E rad ≈ 0.1 E kin (e.g., Kashi Soker 2010 ). We take about half of the envelope to be ejected at the escape speed, , and the radiation to carry a fraction of of that energy. For an ejected mass of , we find . This is the general median value of the radiated energy of ILOTs (e.g., Kashi 2018 ). The photon-diffusion time from an ejected mass of
Paper: 2016MNRAS.457.1456V (fulltext)
of luminosities for the LMC reported in Table 1 . Concerning the mass-loss rates, Riebel et al. ( 2012 ) find that the C-stars in the LMC with the highest degree of obscuration are injecting into the interstellar medium carbonaceous dust with a rate of ∼10 −7 M ⊙ yr −1 . This is translated into an overall mass-loss rate of ∼2 × 10 −5 M ⊙ yr −1 . 2 This quantity is significantly smaller than the result reported in Table 1 , which is in much better agreement with the results published by Gullieuszik et al. ( 2012 ), van Loon ( 2006 ) and Groenewegen et al. ( 2007 ). On the chemical side, we have seen that the conclusions we may draw for the SMC and LMC are different. In the former case, we showed that within the context of our description of the AGB and dust systems, the colours of the C-stars exhibiting the highest degree of obscuration are reproduced only if the amount of carbon accumulated at the surface exceeds ∼0.5 per cent of the total mass. This C abundance corresponds to a C/O ratio above 3. The IR colours of the most obscured LMC C-stars are however less sensitive to the surface carbon abundance than their SMC counterparts. The uncertainties affecting the degree of carbon enrichment are still significant, which is confirmed by the differences between the monash (surface carbon mass fraction slightly below 0.02, C/O ∼ 10) and aton ( X (C) ∼ 0.01, C/O ∼ 3) models. The most obscured C-stars in the MCs are very faint in the optical domain but much brighter in the near-IR. Thus, the best observational way to test the different nature (i.e. progenitor masses) of the obscured C-stars in the SMC and LMC would be to obtain follow-up high-resolution () near-IR ( JHK bands) spectroscopy of these stars. This can be done with actual instrumentation (e.g. CRIRES at European Southern Observatory/VLT). From such spectra, one could get the CNO elemental abundances, which are expected to be different in both types of stars (see e.g. Ventura et al. 2015b ). On the other hand, an analysis of the chemical composition of planetary nebulae (PNe) in the MCs would provide important complementary information on the chemical enrichment of C-stars in these galaxies. This is because only small surface abundance changes are expected from the final AGB phase to the PN stage. For example, Ventura et al. ( 2015b ) showed that the most C-enriched PNe in the LMC display carbon abundances below X (C) ∼ 0.01, in agreement with the 2.5 and 3 M ⊙ aton models and with the 2.25 M ⊙ monash model (see the bottom-right panel in Fig. 1 ). The extension of this type of analysis to a much wider sample of PNe in the LMC (and also to the SMC PNe) will help us to understand whether this upper limit in the carbon abundance is real, or due to the limited sample of PNe examined. 7 CONCLUSIONS We use Spitzer IR data of C-stars in the MCs to improve our understanding of the evolution of AGB stars with masses below ∼3 M ⊙ , which become carbon stars through repeated TDU events. We focus on
Paper: 2016MNRAS.460.2586L (fulltext)
panel, the upper right-hand panel and the lower panel of Fig. 3 , respectively. For simplicity, here we consider only four bins. For low z , β is always consistent with a constant value. For SDSS, the 1σ upper bound of β in the last bin deviates from the results given by the full SDSS subsample at 2σ CL, showing that the SDSS subsample favours a decreasing β at high red shift. This conclusion is consistent with the results of Marriner et al. ( 2011 ). For SNLS, the 1σ upper bounds of β in the third and fourth bins deviate from the results of the full SNLS subsample at 1.6σ and 3.3σ CL, respectively. So compared with SDSS, the SNLS subsample favours a time-varying β with a larger decreasing rate. It should be mentioned that this conclusion differs from the results of the full SNLS3 sample (Wang Wang 2013a ). This means that the SNLS3 data set may contain unknown systematic uncertainties (Betoule et al. 2014 ). Figure 3. The 1σ confidence regions of β given by the three subsamples: low z (upper left-hand panel), SDSS (upper right-hand panel) and SNLS (lower panel). The grey region and the grey dashed line are the 1σ region and the best-fitting result given by the full low- z , the full SDSS and the full SNLS subsamples, respectively. The red, green, blue and yellow regions correspond to the 1σ regions of the first, second, third and fourth bins, respectively. In Fig. 4 , we compare the 1σ confidence regions of β given by the JLA without HST data (left-hand panel) with the results given by the full JLA sample (right-hand panel). We can see that the HST subsample only affects the evolution behaviour of β at high red shift. For the case without HST , the 1σ upper bound of β in the last bin deviates from the results given by the full sample at 3.9σ CL. For the full JLA sample, the 1σ upper bound of β in the last bin deviates from the results given by the full JLA sample at 3.6σ CL. This indicates that the HST subsample can slightly slow down the rate of decrease of β at high red shift. Figure 4. The 1σ confidence regions of β given by the JLA without HST data (left-hand panel) and the full JLA sample (right-hand panel) at red-shift region [0,1]. The grey regions and the grey dashed lines denote the 1σ regions and the best-fitting results given by the full samples, respectively. The red, green, blue and yellow regions correspond to the 1σ regions of the first, second, third, and fourth bins, respectively. Next, we discuss the impact of a varying β on parameter estimation. For simplicity, here we consider the standard cosmological model: the ΛCDM model. As shown in Fig. 2 , β prefers a higher value at low red shift and a lower value at high red shift. So we assume that β is related to the red shift by a simple piecewise function: where β 1 and β 2 are two model parameters. In Fig. 5 , using only the full JLA sample, we plot the one-dimensional (1D) marginalized probability distributions of Ω m 0 for constant β and varying β( z ). It can be
Paper: 2018A&A...620A..17P (fulltext)
groups: good ( LR > 0.25), fair (0.05 LR 0.25), and bad ( LR 0.25). In addition, as a cross-check we calculated the simple probability of chance coincidence, according to Downes et al. (1986) . We then assigned a preliminary rank, rejecting most of the cases with bad scores. A primary single counterpart is either a physical solitary association, a single non-bad association, or exceptionally the best of the rejects that has been “recovered”. When several candidates above the threshold exist, the one with the best estimator is considered the primary counterpart, and all the others secondaries. The detailed procedure can be found in XXL Paper XXVII. 2.4 Optical photometry The BCS covers ~50 deg 2 , fully covering XXL-S. Optical data in the griz bands were obtained with the Mosaic2 imager mounted on the Cerro Tololo Inter-American Observatory (CTIO) 4m Blanco telescope, reaching 10 σ point source depths of 23.9, 24.0, 23.6, and 22.1 mag (AB) in the four bands (for more details see Desai et al. 2012 ). The DECam ( Flaugher et al. 2015 ) mounted on the Blanco telescope also observed XXL-S (PI: C. Lidman) in the griz bands (4850−9000 Å). The limiting magnitudes in each band (defined as the third quartile of the corresponding magnitude distribution) reach 25.73, 25.78, 25.6, and 24.87 mag (AB). More details of the observations can be found in XXL Paper VI and in Desai et al. (2012 , 2015 ). 2.5 Optical spectroscopy A significant effort has been made to obtain spectroscopy of both northern and southern XXL fields, either with large spectroscopic surveys, for example SDSS, VIPERS ( Scodeggio et al. 2018 ), GAMA ( Baldry et al. 2018 ), ESO Large Program ( Adami et al. 2018 , a.k.a. XXL Paper XX), or smaller scale spectroscopic observations, like the William Herschel Telescope (WHT) spectroscopic follow-up program ( Koulouridis et al. 2016 , XXL Paper XII). The southern field has been chosen for this study due to the homogeneity of its spectroscopic follow-up data, which is based uniquely on a number of observing runs with the multifibre 2dF+AAOmega facility on the AAT; instead, the northern field is based on a compilation of different surveys with different instruments, limiting magnitudes, selection biases, and solid angles.The first set of AAOmega runs occurred in 2013, and is described in Lidman et al. (2016 , hereafter XXL Paper XIV). The second set of runs occurred in 2016 and is described in XXL Paper XXVII. As noted in XXL Paper XIV, priority was given to cluster galaxies over AGN in the 2013 observations. In the 2016 set of observations, AGNs had the highest priority. Hence, the coverage of the AGNs is, perhaps apart from the smallest scales, uniform. It should also be remembered that there is always a lower fibre separation limit, which for the AAOmega case is , corresponding to ~0.2 h −1 Mpc at the median redshift of our sample. However, since any given region within the XXL area was targeted multiple times with 2dF, the number of objects lost
Paper: 2021ApJ...908...11L (fulltext)
are able to identify galaxies , which have internal and external properties different from dark matter subhalos in several respects. Stellar components tend to be more compact than their dark matter counterparts, and thus better survive the strong background tidal force exerted by their host halo. The background potential, however, is largely governed by the dark matter component. Sometimes, satellite galaxies may even lose their associated dark matter component through tidal disruption as they orbit within the group. In our search for galaxies, we thus start from the stellar distribution, which is then used as a seed to explore the distribution of the other particle species. We first identify the N s nearest star particle neighbors of each star particle and measure local densities using the W 4 smoothed particle hydrodynamics (SPH) density kernel (Monaghan Lattanzio 1985 ). We build a neighborhood network using chains of N n -nearest neighbors at each particle position. This means that all particles have their own neighboring connections. This coordinate-free method suppresses the ambiguities that arise in building the density field, and it reduces the number of parameters required by the galaxy-finding algorithm. Second, we search for the peaks in the stellar mass density field at each particle position. These peaks are local maxima with respect to their own N n neighbors. To alleviate the identification of spurious local peaks due to Poisson noise, we adopt two approaches. One approach is to merge multiple peaks if their distance is less than l merge . The other approach is to set both the minimum number of stars and the minimum stellar mass in the core region. Here, a core region is a volume identified by progressively lowering the density threshold down to the point where the isodensity surface encloses another peak. This is similar to the watershed method. At this point, particles within the isodensity surface are considered to belong to a galaxy candidate centered on the enclosed peak. Third, after extracting core particles related to a density peak, we apply density cuts to group the remaining (noncore) particles utilizing the watershed method. Around each density core, we search for noncore particles whose densities are between the i th and i + 1th density thresholds. We gather those particles to build a “shell” group. By definition, a shell group should surround other density groups. Accordingly, all particles are split into core and shell groups. Finally, to complete the membership decision, we check the tidal boundary of each galaxy candidate, as well as the total energy of particles within the boundary. Even though a particle may be bound to a galaxy, it is not considered a member unless it is within the tidal radius. Figure 6 shows the most massive halo in HR5 identified using the variable FoF approach at z = 0.76. Even with varying particle mass, the derived distributions of dark matter (top left) and gas (bottom left) are
Paper: 2017ApJ...836..101C (fulltext)
K km s −1 , or 2.3 Jy km s −1 . The flux of the NOEMA observation is 1.1 Jy km s −1 in the same region. The difference could be due to missing short spacings, such that larger structures are resolved out, or to the residual interferometric “bowl”, which has not been completely eliminated by the cleaning. 2.2. CO and IR Data The CO J = 1–0 data were taken with the CARMA array combined with zero spacings from the 5 × 5 Beam Array Receiver System on the Nobeyama Radio Observatory 45M telescope (Koda et al. 2011 ). Schinnerer et al. ( 2013 ) observed M51 with the Plateau de Bure interferometer at high resolution, but their map does not extend to regions as far out as this one. In order to compare with sites of recent star formation, we use the 70 μ m image from the Very Nearby Galaxy Survey, (Mentuch et al. 2012 ) which was accessed through the Herschel Database in Marseille (HeDaM 10 10 http://hedam.lam.fr , 11 11 The Herschel Database in Marseille (HeDaM) is operated by CeSAM and hosted by the Laboratoire d’Astrophysique de Marseille. ). The resolution is only slightly poorer than ours, so we can use the Herschel 70 μ m images to estimate the SFRs (Boquien et al. 2011 ; Galametz et al. 2013 ). 3. Results Figure 2 shows the integrated intensity maps of the HCN, HCO + , and HNC emission lines along with the CO(1−0) image obtained by Koda et al. ( 2011 ). The integrated intensities are measured as , and the emission velocity ranges ( V ) are defined from the data cube and shown in Table 1 . Uncertainties are calculated as ( is the data cube rms, and W and are the velocity range and velocity resolution of the integrated intensity map) and are 5.6, 0.15, 0.14, and 0.13 K km s −1 for CO, HCN, HCO + , and HNC, respectively. Because the HCN, HCO + , and HNC emissions show similar but clearly different morphologies, we choose not to bias our results on any of these tracers and instead use the higher signal-to-noise ratio CO map with slightly better angular resolution to define the giant molecular associations (GMAs). A total of six GMAs have been identified (black numbered polygons in Figure 2 ) by the ClumpFind 12 12 http://www.ifa.hawaii.edu/users/jpw/clumpfind.shtml algorithm (Williams et al. 1994 ). The HCN and HCO + emission peaks are similar at about 1.2 and 1.4 K km s −1 , and the peak positions are consistent with each other at the center of GMA 2. The HNC line is only detected in GMAs 1 and 2, and the emission region is displaced to the south compared to the other lines. GMAs 3, 4, and 6 are weak in HCN but strong in HCO + (and CO). The HCO + distribution is generally broader than that of CO, HCN, and HNC. Figure 2 also shows the H α emission regions (white contours) in order to allow a comparison with the sites of massive star formation. As can be seen from the detail in Figure 1 , the 70 μ m emission distribution is quite similar to that of H α , so the white contours provide a good reference for the sites of star formation in these GMAs.
Paper: 2021MNRAS.502..581C (fulltext)
polynomial to each run. 2.2 APO and prompt Additional observations were obtained on 2017 March 4, using the Agile photometer on the 3.5 m telescope at Apache Point Observatory, and on 2017 May 5 and 2017 May 6 using the 60 cm robotic Prompt8 telescope at CTIO. The APO observations consisted of 515 30 s integrations obtained through a BG39 filter, and the Prompt observations consisted of 253 and 300 30 s integrations obtained through a Baader Clear (C) filter. The APO and Prompt data were reduced following our standard procedures (see e.g. Chote et al. 2014 ; SZ16 ) to extract aperture photometry, with one exception: the polynomial fit to remove long-period trends was omitted as there was insufficient data to separate the effect of differential extinction from the long-period variability intrinsic to GW Lib. 2.3 Hubble Space Telescope Quasi-simultaneous Hubble Space Telescope (HST) far-ultraviolet (UV) spectroscopy of GW Lib was obtained using the Cosmic Origins Spectrograph (COS; Green et al. 2012 ) on the nights of 2017 August 31, September 6, and September 13, near the end of the NGTS observation window. The first two observations occurred during bad weather at Paranal, so simultaneous observations were not possible. The weather was clear during the final run, but GW Lib set past the NGTS elevation limit less than 2 min before the HST observations started. The time-tag COS data were reduced following the procedure from TO16 to create ultraviolet light curves. Data were binned into 5 s sub-spectra, which were then background-subtracted and the Ly α and OI airglow lines at 1216 and 1302 Å were masked. Each spectrum was then binned in the spectral direction to create individual photometric points with a 5 s cadence. We present limited results from this data set where they provide important context to the NGTS observations, but defer the full analysis to a future publication (Toloza et al. in preparation). 3 LONG PERIOD VARIABILITY Photometry demonstrating the long-period behaviour was extracted using a 2.5 pixel aperture and all calibrations. Points more than 5σ from the nightly mean (42 in total) were rejected, and then the data were smoothed with a running 40 min mean (roughly 190 points) to reduce the scatter introduced by noise and the 20 min signal. The resulting 34-week long light curve is presented in Fig. 1 . Figure 1. The full 7.5 month NGTS light curve of GW Lib, smoothed with a 40 min boxcar filter to emphasize the long-period behaviour. Each fortnight is offset vertically by 0.5 units, and the observation duty cycle for each fortnight is listed in parentheses below the start date. The regular gaps in the light curve are due to the day/night cycle; the gaps on February 1–2 and August 11–15 were due to technical issues; the remaining gaps are nights when NGTS was not operating due to cloud. Several nights are marked with indicative periods to highlight the detection of the previously observed long-period modulations. The time windows