adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.456.3253Y (fulltext)
with the MW (Mayer et al. 2006 ; see also Section 4 ). Crucial to the identification of t sat through features of Δ F ( t ) are the uncertainties in ψ( t ). Ideally, the permitted smooth ψ( t ) should be obtained from the raw observational data by taking into account correlated errors in each time bin. Such an analysis is beyond our scope here. Upon advice from T. J. L. de Boer, we give instead an approximate treatment as follows. We first make a Monte Carlo realization of the data by sampling for each time bin according to a Gaussian distribution with the mean and standard deviation specified by the corresponding mean and 1σ uncertainty shown in Fig. 1 . The realization is accepted only if it gives (3.12 ± 0.05) × 10 7 M ⊙ for the total mass of stars formed (personal communications from T. J. L. de Boer). Out of a total of 10 4 realizations, only 3806 satisfy the constraint. For each accepted realization, we obtain a quadratic-spline fit to ψ( t ) and use it to calculate M g ( t ) and Δ F ( t ) from equations ( 15 ) and ( 16 ), respectively. 3.1 Baseline case We first discuss our baseline case of α = 1.5 and λ * ( t sat ) = 10 − 14 M ⊙ yr −1 . The M g ( t ) calculated from the smooth ψ( t ) shown in Fig. 1 assuming that λ * ( t ) = λ * ( t sat ) is shown as the top curve in Fig. 2 . The corresponding Δ F ( t ) is shown as the curve in Fig. 3 (a). The shaded region in this figure indicates the 68 per cent confidence interval for Δ F ( t ) estimated from the Monte Carlo simulations constrained by the total mass of stars formed. Figure 2. Total mass M g for cold gas in Fornax's star-forming disc as a function of time t derived from equation ( 15 ) and the smooth ψ( t ) shown in Fig. 1 for the baseline case of α = 1.5 and λ * ( t sat ) = 10 − 14 M ⊙ yr −1 . The top curve assumes a disc of fixed size with λ * ( t ) = λ * ( t sat ). The middle (bottom) curve is for a disc growing until t sat ≈ 4.8 (7.8) Gyr. The middle and top (bottom) curves have the same M g ( t ) for t > 4.8 (7.8) Gyr. Note that all curves reach M g = 0 at t ≈ 13.6 Gyr, the end of star formation in Fornax. Figure 3. Rate Δ F of net gas flow to or from Fornax's star-forming disc as a function of time t estimated from equations ( 15 ) and ( 16 ) and the data shown in Fig. 1 for the baseline case of α = 1.5 and λ * ( t sat ) = 10 − 14 M ⊙ yr −1 . A net inflow or outflow corresponds to Δ F > 0 and 0, respectively. In each panel, the curve is calculated from the smooth ψ( t ) shown in Fig. 1 and the shaded region indicates the 68 per cent confidence interval. Panel (a) assumes a disc of fixed size with λ * ( t ) = λ * ( t sat ). Panels (b) and (c) are for a disc growing until t sat ≈ 4.8 and 7.8 Gyr, respectively. We attribute the onset of suppression of the net gas inflow at t ≈ 4.8 or 7.8 Gyr to effects from Fornax's becoming an MW satellite. See text for details. We take t sat to coincide with the onset of suppression of the net gas inflow. Based on the Δ F ( t ) shown in Fig. 3
Paper: 2017A&A...603L...1C (fulltext)
of the blue curves with the (ortho+para) HO abundance profile deduced by Keto et al. (2014) (black curve in Fig. 3 ) shows that the total NH is about 30 times more abundant than HO within the central 1000 AU, while it is two orders of magnitude less abundant than HO from about 8000 AU to 20 000 AU. Fig. 4 MOLLIE radiative transfer results using the o-NH fractional abundance profiles from Fig. 3 , overlapped with the observed spectrum: ( top ) blue solid and dashed curves refer to the Crapsi et al. (2007) profiles, assuming an ortho-to-para NH ratio of 0.7 and 1.0, respectively. ( Bottom ) The red thick curve refers to the Sipilä et al. (2015a , b) profile; the red thin curve is the same as the thick red curve, but divided by 2. The o-NH profile has a very different shape when the chemical model of Sipilä et al. (2015a , b) , which includes spin-state chemistry, is considered (red curve in Fig. 3 ). The chemical abundance gradients in L1544 were simulated using the same procedure as described in Sipilä et al. (2016) : the physical model for L1544 ( Keto Caselli 2010 ; Keto et al. 2014 ) was separated into concentric shells and the chemical evolution was calculated separately in each shell. The gas was initially atomic with the exception of H (all in H) and D (in HD). The o-NH abundance profile (Fig. 3 ) was extracted from the model at the time when the CO column density was comparable to the observed value (; see Caselli et al. 1999 ), taking into account telescope beam convolution. Other details of the chemical model are extensively discussed in Sipilä et al. (2015a , b) . The predicted o-NH profile (red curve in Fig. 3 ) presents a broad peak between 5000 AU and 15 000 AU, it drops at larger radii because of photodissociation and at lower radii because of freeze-out. The o-NH() spectra simulated with MOLLIE are shown in Fig. 4 for the three o-NH abundance profiles in Fig. 3 , together with the observed spectrum. This figure shows that the empirical abundance profile of Crapsi et al. (2007) reproduces the observed spectrum better than the abundance profile predicted by Sipilä et al. (2015a , b) , although the simulated spectrum underestimates the brightness temperature at the lowest velocities of all groups of hyperfine components, as also found in the case of o-HO ( Caselli et al. 2012 ), which suggests that the contraction velocity within the core centre is slightly higher than the velocity used in our model. The red spectrum (based on the chemical model of Sipilä et al. 2015a , b ) is significantly brighter than the detected line and the ratios between the low- and high-velocity peaks of the various hyperfine components are not well reproduced (as better shown by the red thin curve in Fig. 4 , which is the red thick profile scaled down by a factor of two). It is interesting to note that the red curve is shifted to lower velocities than the blue curves. This is due to the large abundance of NH predicted at radii in the range of 400012
Paper: 2017ApJ...849...59B (fulltext)
been performed) remain candidate stellar cloaks in the recoil scenario. A2261 has two Chandra exposures of 10 and 25 ks (ObsIDs 550 and 5007, respectively), which do not conclusively reveal any accretion onto an SMBH. The X-ray flux in those data is dominated by 0.5–2 keV emission from hot cluster gas, showing a cooled core (Bauer et al. 2005 ). Above 2 keV, there is some emission, possibly a point source consistent with knot 4, although it is too faint to reliably distinguish it from cluster gas emission. 4.2. Assessment of the Radio Relic in the A2261-BCG Core The age of the relic radio source can potentially be understood as a time marker for an event related to the disturbance of the central SMBH, and thus we are interested in understanding the age and nature of this relic. We limited the timescale of last electron energization in the radio source to . The existence of a relic in this BCG is interesting in itself and has a number of potential origins, as discussed in Section 3.3 . To investigate how atypical A2261-BCG’s radio source properties are among BCGs, we compare it with the sample of Hogan et al. ( 2015 ), who systematically studied the radio properties of a large sample of BCGs (including A2261-BCG). In comparison with that sample, we find that the 1.4 GHz radio luminosity is typical of ∼50% of BCGs. Strikingly, we appear to not detect a distinct core in this object down to a 3 σ limit of at 10 GHz. Although the non-detection of a core component in the Hogan ( 2014 ) sample was relatively common, their typical upper limit on core emission was on average a factor of >1000 less stringent than our limit. Nevertheless, they found that ∼30% of their BCGs had clear spectrally identified radio cores, while 30% consisted of only non-core components (i.e., had upper limits on core emission, as in our case), and 30% were complete radio non-detections; see their Table C.1. The most unusual thing about the radio relic in A2261-BCG is its central position combined with its small size and steep spectral index. We show the spectral index distribution in Figure 9 of 30% of the non-core BCGs in the Hogan sample. There are only a handful of targets in the sample with spectra comparable to or steeper than the central relic in A2261-BCG, and it is likely that given the large selection in Hogan ( 2014 ), there is some contamination from BCG-unrelated cluster relics included in this distribution. The distribution of core-dominated BCGs tend to center on indices closer to . Therefore, it seems that the radio spectrum of A2261-BCG lies at the extremity of implied activity and jet ages for BCGs. Figure 9. Distribution of the spectral index for a subset of the Hogan ( 2014 ) BCG sample. Here we show only the ∼30% of BCGs in that sample that are similar to A2261-BCG, in that only a non-radio-core component has been spectroscopically identified in the BCG. Note that Hogan et al.'s measurement of the A2261-BCG is included in the histogram. Our measurement is
Paper: 2020MNRAS.492.4005P (fulltext)
absorption or emission-line features can be present alongside smoothly varying blackbody or other continuum radiation (see e.g. Tennyson 2019 ). In each of these general settings, we observe a signal plus noise and would like to denoise the signal as accurately as possible. Indeed the set of statistical tools available for addressing this general problem is quite vast. Commonly used non-parametric regression methods include kernel smoothers (e.g. Hall et al. 2002 ; Croft et al. 2002 ), local polynomial regression (LOESS; e.g. Maron Howes 2003 ; Persson et al. 2004 ), splines (e.g. Peiris Verde 2010 ; Contreras et al. 2010 ; Dhawan et al. 2015 ), Gaussian process regression (e.g. Gibson et al. 2012 ; Aigrain, Parviainen Pope 2016 ; Gómez-Valent Amendola 2018 ), and wavelet decompositions (e.g. Fligge Solanki 1997 ; Theuns Zaroubi 2000 ; Golkhou Butler 2014 ). A rich and elegant statistical literature exists on the theoretical and practical achievements of these methods (see e.g. Györfi et al. 2002 ; Wasserman 2006 ; Hastie, Tibshirani Friedman 2009 for general references). However, when the underlying signal is spatially heterogeneous , i.e. exhibits varying degrees of smoothness, the power of classical statistical literature is quite limited. Kernels, LOESS, smoothing splines, and Gaussian process regression belong to a broad family of non-parametric methods called linear smoothers , which has been shown to be uniformly suboptimal for estimating spatially heterogeneous signals (Nemirovskii, Polyak Tsybakov 1985 ; Nemirovskii 1985 ; Donoho Johnstone 1998 ). The common limitation of these methods is that they are not locally adaptive; i.e. by construction, they do not adapt to local degrees of smoothness in a signal. In particular, continuing with the example of a smoothly varying signal with occasional sharp features, a linear smoother will tend to oversmooth the sharp features and/or overfit the smooth regions in its effort to optimally balance statistical bias and variance. Considerable effort has been made to address this problem by locally varying the hyperparameter(s) of a linear smoother. For example, locally varying the kernel bandwidth (e.g. Muller Stadtmuller 1987 ; Fan Gijbels 1992 , 1995 ; Lepski, Mammen Spokoiny 1997 ; Gijbels Mammen 1998 ) irregularly varying spline knot locations (e.g. De Boor 1974 ; Jupp 1978 ; Dimatteo, Genovese Kass 2001 ), and constructing non-stationary covariance functions for Gaussian process regression (e.g. Schmidt O’Hagan 2003 ; Paciorek Schervish 2004 , 2006 ). However, since hyperparameters typically need to be estimated from the data, such exponential increases in the hyperparameter complexity severely limit the practicality of choosing the hyperparameters in a fully data-driven, generalizable, and computationally efficient fashion. Wavelet decompositions offer an elegant solution to the problem of estimating spatially heterogeneous signals, providing both statistical optimality (e.g. Donoho
Paper: 2016MNRAS.458..733P (fulltext)
; Grimes et al. 2009 ; Tripp et al. 2011 ; Tumlinson et al. 2011 ; Muzahid 2014 ). This is not the case for either of our O vi absorbers. The sub-solar metallicity, which we obtain for the low- and high-ionization gas phases, is also not usually found in metal-rich outflows (Muzahid 2014 ). Thus, there is no definitive evidence for the absorbers discussed in this paper being directly associated with a galactic-scale wind. These warm absorbers may be tracers of ancient outflows (wind age >1 Gyr), which, as the simulations of Ford et al. ( 2014 ) suggest, can end up diffusely distributed at galactocentric distances of d ≳ 500 kpc where absorption from the ambient intergalactic medium also dominates. With the limited SDSS sampling of galaxies discussed in this paper, it is difficult to distinguish an outflow-generated O vi from the several other possible scenarios that can give rise to warm O vi in galaxy haloes (see Heckman et al. 2002 ; Sembach et al. 2003 ; Maller Bullock 2004 ; Tripp et al. 2008 ). Based on models of structure formation, it has been hypothesized that galaxy groups can have intragroup gas at T ∼ 10 5 –10 6 K, temperatures too low to discriminate their emission from the soft X-ray background (Mulchaey et al. 1996 ). Presently, such a medium can be probed only through high-ionization absorption lines such as O vi , Ne viii and thermally broadened Ly α against the light from background QSOs. Exploring this gas reservoir is important, as it potentially harbours as much as ∼4 per cent of the total baryons in the low- z Universe, on a par with the fraction of baryons trapped inside galaxies (Savage et al. 2014 ). A recent example is given by Stocke et al. ( 2014 ), whose sample of BLA–O vi warm absorbers is more likely to be from intragroup gas than individual galaxy haloes. The warm O vi is hypothesized to arise in conductive interface layers between cold T ≲ 3 × 10 4 K clouds and a hitherto undetected diffuse T ∼ 10 6.5 K intragroup medium. Hydrodynamic simulations also predict that a substantial fraction of baryons are to be found along large-scale filaments connecting galaxy overdensity regions. In Narayanan et al. ( 2011 ), a BLA–O vi absorber is identified as tracing T ∼ 1.4 × 10 5 K intergalactic gas along a nearby galaxy filament ( v HELIO = 3081 km s −1 ), with several loose groups of galaxies beyond ρ ∼ 1.5 Mpc of projected separation. Another example of baryonic reservoirs along galaxy filaments is given by Wakker et al. ( 2015 ). About 85 per cent of the Ly α systems from their sample of 15 absorbers, which includes BLAs, appear to be tracing intergalactic filament gas that lies far outside the virial radii of the nearest galaxies that trace the filament. Along similar lines, Tejos et al. ( 2016 ) found a ∼4–7 times higher covering fraction for BLAs near galaxy overdensities, compared to random fields. In that study also, the warm gas was identified as being away from individual galaxy haloes, but possibly along cluster
Paper: 2021A&A...650A.128G (fulltext)
core-helium burning in the blue part of the HRD or crosses the HRD and starts core-helium burning as a RSG. This depends on a variety of parameters, which can be interdependent, such as the following: rotation (and its implementation), mass-loss rates, chemical gradients in the radiative zones, activity of the intermediate H-burning shell ( Langer Maeder 1995 ; Maeder Meynet 2001 ). Models at low metallicity tend to remain in the blue region of the HRD longer (e.g. Georgy et al. 2013 ), and this is also what we observe in the computations used in this work. A similarity between our models at both metallicities is that the luminosity of the hydrogen burning-shell at the onset of shell hydrogen-burning is systematically higher for models computed with the Schwarzschild criterion compared to models computed with the Ledoux one (see Fig. 4 ). This is due to chemical gradients in the region which was previously occupied by the hydrogen burning-core during the MS (the region below the dot-dashed line in Fig. 3 ), which prevent a convective zone from appearing at the same location where hydrogen is burnt. Since hydrogen-shell burning starts in a radiative condition with the Ledoux criterion, there is no refueling in fresh hydrogen by convection and then the energy production remains lower. In the Ledoux models, the convective zone appears higher inside the star, where no burning occurs. It then grows at a deeper level by eroding the chemical gradient below the convective zone. The difference between solar and LMC metallicity is that at solar metallicity, the first crossing of the HRD can be fast: All the Ledoux models and the Schwarzschild 25 M ⊙ models ignite central helium burning on the RSG branch after a quick crossing of the HRD. Only the Schwarzschild 20 M ⊙ model starts burning its helium as a BSG before slowly crossing the HRD for the first time: The luminosity of the intermediate hydrogen-burning shell is sufficient to maintain the model on the blue side of the HRD, and it remains almost constant; whereas in other cases, it decreases over time. Figure 4 shows the time-evolution of the shell luminosity for 3 × 10 5 years after the end of the MS. Three different kinds of behaviours can be seen. The first type are models with an abrupt decrease in the shell luminosity shortly after the shell ignites (Ledoux models at solar metallicity). These are the models which cross the Hertzsprung gap very quickly after the end of the MS. The second type are models with a slow decrease in the shell luminosity over time (models at Z = 0.006 and Schwarzschild solar metallicity 20 M ⊙ model). These models ignite core-He burning in the blue part of the HRD and cross the gap slowly. The third type is the model with an intermediate behaviour (Schwarzschild solar metallicity 25 M ⊙ model). This model crosses the HRD quite quickly, but not as fast as the first type of models. For the models that are quickly crossing the HRD, the intermediate convective zone of the
Paper: 2019MNRAS.482.3227N (fulltext)
we refer to the subset of haloes that share the same ΛCDM companion across all the simulations as the common sample. For geometrical reasons, we set the limit value for to be 0.5: this represents the case in which two haloes with the same major axis a have centres separated exactly by the same amount a . The configurations that are selected by point ( i ) are the ones for which the distance between the halo centres is less or equal to the smallest major axis between the two. A higher value for would include genuine small haloes that have been more subject to dynamical QP drifting but would also result in a spurious match of bigger haloes. For these reasons, we adopt , checking that the selected sample gains or loses of components if values 0.45 and 0.55 are used, without modifying the overall statistical properties of the sample itself. With respect to at point ( ii ), instead, we applied the matching algorithm using several values, each denoting a specific threshold of the minimal value allowed for the mass ratio of halo couples in order to be considered as a match. As reported in Table 3 , more than of all the matching haloes across LCDM and FDM simulations without mass selection case have a mass ratio in the ratio range and almost in the range. In order not to spoil our matching catalogue, especially with very close but highly different in mass halo couples, we choose the limiting value of . Table 3. Number of common matches across LCDM and FDM simulations, using different values of the parameter representing the minimum allowed ratio between the minimum and maximum masses of each candidate couple. 4 RESULTS In this section we present the results obtained from our simulations in decreasing order of scale involved, starting from the matter power spectrum, to the simulated Lyman α forest observations, to the statistical characterization of halo properties, and their density profiles. 4.1 Matter power spectrum The relative difference of the matter power spectrum of the various FDM models with respect to ΛCDM at four different redshifts is displayed in Fig. 2 . Figure 2. Matter power spectrum of FDM models contrasted with LCDM at different redshifts. As already found in the literature (see e.g. Marsh 2016a ; Nori Baldi 2018 ), the evolution of the matter power spectrum shows that the initial suppression encoded in the transfer functions used to build up the initial conditions is restored at intermediate scales to the unsuppressed level, eventually, by the non-linear gravitational evolution. At the redshifts and scales that are relevant for Lyman α forest observations, however, the relative suppression with respect to ΛCDM is still important and ranges from for the lowest FDM mass considered. The relative difference of the matter power spectrum, displayed in Fig. 3 , shows an additional suppression with respect to ΛCDM when the QP is included in the dynamical evolution in the comparison between the FDM and FDMnoQP simulations, e.g. comparing Figs
Paper: 2018ApJ...868..151D (fulltext)
2 (Lanzafame et al. 2018 ). The extended Kepler mission, K2 (Howell et al. 2014 ), has now observed more stars than the original Kepler sample, potentially doubling the number of high precision stellar rotation periods available. Similarly, by the end of the Gaia mission, Lanzafame et al. ( 2018 ) estimate that a sample of 3–20 million rotation periods will be recovered. The era of statistical studies in stellar rotation has therefore begun. Stellar rotation has long been noted as a means to possibly age-date stars due to their constant angular momentum loss via winds (Skumanich 1972 ). While studies of open clusters give hope that this “gyrochronology” model broadly works for solar-type and lower-mass stars, many uncertainties exist about the details of this spindown and its utility as a clock. These include questions about the initial rotation period distribution for stars (e.g., Barnes 2010 ; Matt et al. 2015 ), the specific analytic prescription for modeling the spindown (Angus et al. 2015 ), and exploring the efficiency of this angular momentum loss mechanism at older ages (van Saders et al. 2016 ). One of the most compelling results from the rotating star sample in Kepler is the discovery of a bimodal period distribution. McQuillan et al. ( 2013 ) first found a bimodality in the distribution of field M dwarfs with periods between ∼10 and ∼50 days. This feature was also found in the Kepler field K dwarfs in McQuillan et al. ( 2014 ), but was not seen in the bluer stars. Using Gaia DR1, Davenport ( 2017 ) was able to remove contaminating subgiants from the rotation sample, and found the bimodality extended to the G dwarfs as well. This bimodal surface rotation period distribution is either a new short-lived transition or instability phase of rapid angular momentum loss, or a signature of star formation history imprinted in the present-day rotation period distribution. However, as Davenport ( 2017 ) notes, this feature has only been observed in the Kepler rotation period catalog, and most critically only for stars within ∼300 pc. The faint M dwarfs in the Kepler sample studied in McQuillan et al. ( 2013 ) have an average distance of 250 pc. Gaia DR1 was also only able to provide precise distances for the nearest stars, and Davenport ( 2017 ) report a median distance of 285 pc for the blue stars in their sample. In this paper, we extend the work of Davenport ( 2017 ) in studying the Kepler rotation period distribution as a function of distance from the Sun using new astrometric data from the Gaia mission. By matching the McQuillan et al. ( 2014 ) rotation period catalog to the newest data from Gaia Data Release 2 (Gaia Collaboration et al. 2018b ), we can use precise distances for essentially every star in the McQuillan catalog to select the most-likely main-sequence dwarfs out to distances >2 kpc. Importantly, this filters out both subgiants, the main contaminant noted by Davenport ( 2017 ), and unresolved binary stars. Here we demonstrate