adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...819L..20B (fulltext)
by Binggeli et al. ( 1985 ) in the Virgo Cluster Catalog (VCC), although no redshift has been published. Indeed, Binggeli et al. ( 1985 , p. 1756) classified VCC 1287 as a dwarf irregular galaxy “ .. of very large size and low surface brightness. ” GCs were identified exploiting all five MegaCam bands. A catalog of sources was extracted using SExtractor (Bertin Arnouts 1996 ). Only objects detected in all five bands were considered, selecting point sources by imposing the i -band class star parameter of SExtractor to be >0.5. This removes most extended objects from the catalog, owing to the excellent (06) seeing of the images. We selected as GC candidates all point sources within the following color ranges: 0.6 ( g − z ) 1.2, 1.1 ( u − g ) 1.8, and 0.7 ( g − i ) 1.0. We selected a fairly narrow color range in order to minimize contamination from background sources (in particular, red background galaxies). However, these color ranges are consistent with the distributions seen in other studies of Virgo dwarf galaxies (e.g., Beasley et al. 2006 ; Peng et al. 2006 ). In order to exclude ultra-compact galaxies, we also imposed a magnitude cut, i > 19.5 mag, equivalent to the magnitude of the Milky Way GC ω Cen at the distance of Virgo. To determine the total number of GCs in VCC 1287, we constructed the surface density profile of the GC system and found it to extend to ∼175′′ (∼13 kpc), beyond which is a constant background with 0.2 objects per arcmin 2 . Next, all point sources within 175′′ of the galaxy center and that satisfy our selection criteria were flagged as GCs—returning 18 candidates. Star and galaxy contamination were calculated by the random placement of 1000 circles with 175′′ radii in the 1 deg 2 field surrounding VCC 1287. We found a mean of 6 objects expected within such a circle, with a standard deviation of 2.5 counts using our selection criteria, with no sky apertures with 18 objects. The identification of 18 GC candidates represents an overdensity with respect to the background level at ∼5 σ significance. Subtracting this contamination rate left 11 GC candidates down to the GC turnover magnitude ( i = 23.1, using I -band values of Kundu Whitmore 2001 and an i -band transformation from Faifer et al. 2011 ). Assuming that the GC luminosity function is bell-shaped (Harris et al. 2000 ), we doubled their number to obtain a total GC population of 22 ± 8 GCs, where the uncertainties come from the quadrature sum of the Poisson uncertainties and the background contribution. This may not sound remarkable, but when normalized to the host galaxy magnitude using the “specific frequency” ( S N ; Harris van den Bergh 1981 ), we find VCC 1287 has S N = 80 ± 29. This is a significant overabundance of GCs for VCC 1287's stellar luminosity (Figure 4 ). 2.2. GC Spectroscopy GC candidates were selected for multi-object spectroscopy with the OSIRIS instrument on the Gran Telescopio de Canarias (GTC) in La Palma. We observed this spectroscopic
Paper: 2019MNRAS.483.3792K (fulltext)
on the velocity–luminosity relation for RSGs derived by Mauron Josselin ( 2011 ), we obtain a wind velocity of ∼35 km s −1 during the RSG state of ρ Cas. Considering that the enhanced mass-loss within the RSG phase took place about 10 000 yr ago, the material would have travelled over a length of roughly 0.36 pc, which corresponds to an angular distance of ∼24 arcsec at the distance of 3.1 kpc for ρ Cas (Lobel et al. 2003 ). This angular distance is too small to be seen on, e.g. WISE images, on which ρ Cas is saturated. This distance is also much smaller than the size of a regular wind-blown bubble resulting from the previous blue supergiant phase, so that we can exclude that the material is located within the astrosphere or shock region with the interstellar medium. On the other hand, this distance is too large to be seen on the HST images presented by Schuster et al. ( 2006 ), and too large to guarantee that the material is dense and hot enough to produce measurable amounts of CO band emission. Therefore, we believe that the post-RSG wind, which is faster but much less massive, interacts with the material released during the RSG phase. This interaction can lead to a compression and heating of the old material (with the appropriate carbon isotope ratio) and hence give rise to the observed, static CO band emission. The temperature of the CO gas is much higher than the dust sublimation temperature. In the region of CO band emission, the physical parameters within the environment might hence prevent the efficient formation of dust, which could be another reason for the lack of a detectable dusty nebula around ρ Cas. For its spectral type, and considering that ρ Cas is presumably a very slow rotator, it displays extremely broad absorption lines. These were suggested to be broadened by macroturbulence. This type of line broadening is typically found in early-type (OB) supergiants in which pulsations play a significant role together with stellar rotation (see e.g. Ryans et al. 2002 ; Aerts et al. 2009 ; Simón-Díaz et al. 2010 ; Kraus et al. 2015 ), and is one of the criteria to discriminate YHGs from YSGs (de Jager 1998 ). While ρ Cas is known to pulsate, not much is known about its real rotation velocity, although Lobel et al. ( 1998 ) derived a value of v sin i ≃ 25 km s −1 from line-profile fitting. To investigate the possible contribution of rotation to the line profile broadening of ρ Cas, we analysed photospheric lines using the method of Fourier transformation. This method only works properly for symmetric profiles. However, symmetric line profile shapes are rather rare in our data of ρ Cas and basically only seen in two nights. Also, given the spectral resolution of our data, the Fourier transformation can recover only rotation velocities projected to the line of sight higher than v sin i ∼ 15 km s −1 with high confidence. The results from our analysis are shown in Fig. 8 for different elements within the same night (top panel) and for the
Paper: 2017MNRAS.469.2933B (fulltext)
stars (FRMS) has been already discussed by several authors (e.g. Decressin et al. 2007 ). This FRMS self-enrichment scenario has no problem associate with later SNe that can severely suppress secondary star formation. However, it is not clear in this FRMS scenario how SG star formation can be completed well before SN explosion can brow away the cold gas of the molecular cloud (MC). We here do not discuss how to avoid this potentially serious problem, because it is beyond the scope of this paper to investigate the FRMS scenario in detail. 4.3 Metallicity spreads in GCs Although Lee et al. ( 2009 ) investigated the colour magnitude diagrams ( V versus b − y or hk ) of the Galactic GCs and found evidence of Ca abundance spreads in the 7 GCs, Carretta et al. ( 2010 ) showed that Ca abundance spreads among FG and SG stars in 17 Galactic GCs are less than 0.02–0.03 dex. The most massive Galactic GC ω Cen and 8 GCs (e.g. M22) have been observed to show [Fe/H] spreads among GC stars so far (e.g. Freeman Rodgers 1975 ; Marino et al. 2015 ). The apparent lack of internal [Fe/H] spreads among GC stars in most GCs implies that self-enrichment by SNe did not proceed efficiently at GC formation for some physical reasons. Since these GCs show anticorrelations between light elements, which could be due to self-enrichment by AGB stars, the physical mechanisms that suppress self-enrichment by SNe in GC formation need to be understood clearly. Nakasato, Mori Nomoto ( 2000 ) investigated SFHs of proto-GC clouds with the masses of 10 6 M ⊙ , sizes of 150–300 pc, and initial temperature of 10 4 K using their original SPH simulations with feedback effects chemical enrichment by SNe. They found that although star formation in shell-like gaseous structures formed through compression of gas through SNe feedback effects is possible, self-enrichment is not seen to occur in all of their models. This study have demonstrated that although new star formation from gas contaminated by SNe is possible, the mass fraction of such stars is quite small. Furthermore, most of such stars can be formed mostly in the shocked gas that are distant from the main GC-forming regions, and therefore they cannot be finally within the central regions of GCs. As a result of this, the mass fraction of such (SG) stars with [Fe/H] by more than 0.05 dex larger than (FG) stars formed from original cold gas of GC-forming GCs is quite small within the central 10pc of GCs. This implies that the apparent lack of [Fe/H] spreads in typical GCs is due largely to SN feedback effects in GC-forming MCs. Baumgardt, Kroupa Parmentier ( 2008 ) show that the stellar masses of GCs required for self-enrichment by SNe is more than 10 7 M ⊙ , which means that original GC-hosting MC should be very massive (∼10 8 M ⊙ ) for a reasonable SFE (∼0.1). Therefore, typical GCs are unlikely to have [Fe/H] spreads. The most massive Galactic GC ω Cen has been suggested to originate from a nucleated dwarf galaxy (e.g. Freeman 1993
Paper: 2019MNRAS.482..639F (fulltext)
timing of the first transit. As such, the mean anomaly of 159c was different for each simulation but was not a free parameter. This leaves eight unknown parameters, plus seven for the presumed third planet. This is a 15-dimensional parameter space to search. To further reduce computational requirements, we reduced this to 11 parameters. This reduction was accomplished by presuming 159b was in a circular orbit, using its orbital plane as the reference for the system, and using a constant estimate of its mass. Thus, 159b’s eccentricity, argument of periastron, longitude of the ascending node, and mass are removed from the priors. This is discussed in more detail in Section 3.4.2 . 3.2 TTVFast and MultiNest We used TTVFast (Deck et al. 2014 ), a symplectic numerical integrator, to compute the transit times for each hypothesized set of orbital parameters. These times were converted to TTVs, then compared to the observed TTV data from Holczer et al. ( 2016 ). Another set of parameters would then be chosen by MultiNest, and the values compared again. To search the parameter space efficiently, we used the Bayesian Inference algorithm MultiNest (Feroz et al. 2009 , 2013 ). This iterative process can find solutions far more quickly than searching the entire parameter space. We used the Python interface for MultiNest, PyMultiNest by Buchner et al. ( 2014 ) for this project. Due to the size and complexity of the parameter space, finding the best fits required an iterative process and dozens of sets of runs. Each ’run’ produces a single result from MultiNest, estimated from millions of samples of the parameter space and as many simulations from TTVFast. Repeated runs, even with the same priors, would often return different best-fits. This is indicative of multiple maxima in our parameter space, particularly associated with different resonances between 159d and 159c. It is known that resonances produce strong TTVs and that there can be degeneracy between resonances in this regard (Boué et al. 2012 ). To examine this effect, we performed additional runs with periods of 159d constrained to be near the resonances (periods within 10 days of resonance) of 159c. This narrowing of ranges allowed us to determine the best-fits associated with each resonance independent of the others. We used the default parameters of PyMultiNest with the exception of the evidence tolerance, which we set to 0.15 (from default of 0.5). This reduced tolerance value allows for a more fine-tuned search across the peaks of this rapidly varying landscape. 3.3 Likelihood The Likelihood, L we used is based on the usual χ 2 statistic and is given by: where x i is the calculated TTV, μ i is the observed TTV, and σ i is the error in the observed TTV for point i , respectively (Ivezic et al. 2014 ). The log-likelihood is the value we aim to maximize in the simulation In this case, summation includes transit data from both 159b and 159c. 3.4 Priors The priors used for the search were constrained
Paper: 2018A&A...613A..11W (fulltext)
individual column density slices along the filaments (shown in the first three panels of Fig. E.1 ). Although a Plummer may reasonably well describe slice 50 in the north-west filament (and perhaps even the centre-most portion of slice 54 in the north-east filament) the extended emission in the majority of slices renders fitting them with Gaussian or Plummer profiles inappropriate. We have evaluated further the impact of constraining the Gaussian fit parameters on the goodness of the fit itself. While setting the peak position of the Gaussian profiles to lie on the spine (i.e. x = 0) does not improve the produced fits, restricting the fitting to the inner portion of the filaments does result in better Gaussian fits of the central portion of the filament. However, filament properties (width and line mass) derived from such fits are very much dependent on the exact fitting range used. Such properties calculated from Trapezoidal integration of the asymmetrical profiles (as shown in Table 3 and Fig. 14 ) are less affected by such restrictions, and therefore more robust. Characterising filaments is fraught with difficulty, and caution must be observed whilst deriving global properties from their mean profiles (e.g. Panopoulou et al. 2017 ). Fig. E.1 Results of fitting Gaussian (red line) and Plummer (blue dashed line) profiles to individual radial column density slices (orange circles in the first , second and third panels of each row ) and the mean radial column density profile (black circles in the fourth panel of each row) of (a) the north-east filament ( top row ) and (b) the north-west filament ( bottom row ). The grey points in the background of each panel plots all of the radial column density slices simultaneously. Plummer profiles may on occasion well describe the inner regions of the filaments only, whilst Gaussian profiles fail to fit any of the distributions. Gaussian profiles are only found to reasonably fit some of the data once the fitting range is restricted tothe inner portion of the filament. Appendix F Derivation of the analytical expression of the virial ratio Consider a uniform density filament fragmenting at the length scale equal to the separation of a core, λ core . In an idealised scenario, energy is conserved during collapse whilst fragmentation is happening. However, there is a fraction of energy converted from gravitational potential energy ( E g ) into kinetic energy ( E k ) at the time the filament fragmentation starts. This conversion may be expressed in the virial equation as an efficiency ε , where if no energy is converted, ε = 0. If energy conversion does occur, this can be expressed as a fractional increase in E k , expressed as E k = E k ,0 + Δ E k , where E k ,0 is the initial kinetic energy of the fragmenting core before collapse. This fractional increase in kinetic energy can be expressed as , where E g ,0 is the initial gravitational potential energy. Substitution into the standard virial ratio of α vir = 2
Paper: 2017ApJ...850L..13B (fulltext)
placed in very dense environments near the Galactic center. The number of BH-LCs decreases with increasing natal kick magnitudes, as larger kicks disrupt a larger fraction of primordial binaries. For the same reason, natal kicks also affect the distribution of P orb of BH-LCs. While BH-LCs are disrupted in the Zero-kick model only if the change in mass during BH formation is severe enough to reduce the orbital binding energy to the point of unbinding, the NS-kick model leads to disruption of BH-LC progenitors with . We find a significant population of BH-LCs with P orb longer than the nominal Gaia mission in all of the models except the NS-kick model. This population exclusively contains BH-LCs that have not experienced a common envelope evolution. The population with orbital periods in the Gaia -observable range and lower contain BH-LCs that have experienced a common envelope, as well as BH-LCs that have not. Our NS-kick model does not allow formation of BH-LCs with and , while other models with lower natal kicks allow formation of these systems (Figure 2 ). By extending the Gaia mission beyond the nominal , our condition could be relaxed, and Gaia may constrain highly uncertain BH natal kicks from the properties of detected BH-LCs at longer orbital periods. 3.2. Gaia-observable Population Figure 3 shows the number of systems observable by Gaia () as a function of limiting G magnitude. The number of observable systems is inversely related to the BH natal kick strength because the likelihood of unbinding a BH-LC progenitor increases with kick strength. Figure 3. Mean number of observable BH-LCs as a function of limiting G magnitude computed from 500 Galactic realizations. Shades show regions above and below the mean denoted by the lines. Dashed (solid) lines denote our estimates using pessimistic (optimistic) astrometric cuts (Section 2.4 ). Blue, red, and black shading denote Zero-kick, FB-kick, and NS-kick models, respectively. Table 1 shows the result of our astrometric cuts. The total number of BH-LCs formed in our models is roughly consistent with the estimates in Mashian Loeb ( 2017 ). However, the majority of the systems formed lie above our upper cut (Figure 2 ). In addition to the natal kick prescription, the Galactic component has a noticeable effect on the number of BH-LC systems. Because the thick disk is modeled as a single star burst ago, all of the LCs in the thick disk at present are low mass. Large natal kicks for BHs such as our NS-kick model affect BH-LCs with low-mass LCs more (Figure 2 ; Section 3.1 ). As a result, in the thick disk, the NS-kick model yields a very low . The adopted continuous SFR in the thin disk keeps relatively high even for the NS-kick model by forming high-mass luminous stars very close to the present epoch. Table 1 Average Number of BH-LCs Observable by Gaia from 500 Galactic Realizations with for Our Optimistic and Pessimistic Cuts (Section 2.4 ) We estimate 3800–12,000 BH-LCs in the MW are
Paper: 2017ApJ...845..159G (fulltext)
AO1 Guest Observer program. NuSTAR consists of two co-aligned X-ray telescopes, with the corresponding focal plane modules FPMA and FPMB that provide 18′′ FWHM imaging resolution over a 3–79 keV X-ray band and a characteristic spectral resolution of 400 eV FWHM at 10 keV (Harrison et al. 2013 ). The reconstructed NuSTAR coordinates are accurate to at the 90% confidence level. The nominal timing accuracy of NuSTAR is ∼2 ms rms, after correcting for drift of the onboard clock, with the absolute timescale shown to be better than ms (Mori et al. 2014 ; Madsen et al. 2015 ). Although this is insufficient to resolve the 3.05 ms period of PSR B1821-24, we devise a method, presented herein, to correct the photon arrival times to better than accuracy. NuSTAR data were reprocessed and analyzed using FTOOLS 09May2016_V6.19 ( NUSTARDAS 14Apr16_V1.6.0) with the NuSTAR Calibration Database (CALDB) files of 2016 July 6. The resulting data set provides a total of 140.7 ks and 55 ks of net good exposure time for the two pointings, respectively. For the timing analysis, the nominal NuSTAR clock correction was applied to the photon arrival times which are converted to barycentric dynamical time (TDB) using the DE405 solar system ephemeris and radio timing coordinates of Ray et al. ( 2008 ). For all subsequent analyses we merged data from both FPM detectors. Although the globular cluster M28 hosts at least 11 other pulsars (Bégin 2006 ), they are all over a factor of ∼100 fainter than PSR B1821-24 in X-rays (Bogdanov et al. 2011 ) and thus cannot be studied effectively with NuSTAR . 2.1.2. RXTE PSR B1821-24 has been observed extensively in hard X-rays with RXTE during the mission lifetime, primarily for the purpose of absolute spacecraft time calibration and to measure the X-ray-to-radio phase lags (Rots et al. 1998 ; Ray et al. 2008 ). In anticipation of the NuSTAR observations, we collectively analyzed all available archived RXTE data on PSR B1821-24 for the first time. A total of 180 ks of exposure times were accumulated during three programs: P20159 (1997 February 12–13, 16.9 ks), P90053 (2005 February 28–March 1, 14.9 ks), and P92008 (2006 July 6–8, 48.4 ks; 2006 October 9–10, 51.2 ks; 2007 April 25-26, 48.5 ks). These observations were made with the Proportional Counter Array (PCA; Jahoda et al. 1996 ) on board RXTE . The PCA consists of five collimated xenon proportional counter units (PCUs), each having a front propane anticoincidence layer, with a total effective area of ∼6500 cm −2 over a 1° field of view (FWHM). Each PCU is sensitive to photons in the energy range of 2–60 keV, with an energy resolution of 18% at 6 keV recorded in 256 channels. Data were collected in GoodXenonwithPropane mode with two to five PCU modules in use at any given time, with an average of 3.7 PCUs active overall. In this mode, photon arrival times are recorded with resolution. We analyzed events from the top xenon layers of each PCU only. Including the second layers resulted
Paper: 2016MNRAS.458.3314C (fulltext)
of our problem. Since a series of earlier works have studied this topic (Rees 1988 ; Kochanek 1994 ; Khokhlov Melia 1996 ; Strubbe Quataert 2009 ; Kasen Ramirez-Ruiz 2010 ), we only summarize here the main results that are most relevant to our work, including the kinetic energy, internal velocity dispersion and the opening angle with respect to the central SMBH. An UDS originates from the part of a star which at the time of tidal disruption has a positive energy. This unbound part contains about half of the mass of the star if the star initially approaches the SMBH along a parabolic orbit with a pericentre distance R p comparable to R t – an probable case in real galaxies (Rees 1988 ; Magorrian Tremaine 1999 ). In this case, the unbound material distribute in a range of specific energy between and (Guillochon Ramirez-Ruiz 2013 ; Stone, Sari Loeb 2013 ). As a result, the total kinetic energy E k of the UDS is where m * ≡ M * /M ⊙ , r * ≡ R * / R ⊙ . Here we have assumed an even distribution of unbound material in specific energy, i.e. , which was proposed by Rees ( 1988 ) and later confirmed by smooth-particle hydrodynamics simulations (Evans Kochanek 1989 ). Although we made a couple simplifications to derive equa-tion ( 2 ), the result agrees well with the median value computed from a statistically representative ensemble of UDSs (Guillochon et al. 2015 ). Using this equation and the fact that for main-sequence stars at m * > 1 (Demircan Kahraman 1991 ; Gorda Svechnikov 1998 ), we further derive . This scaling relation indicates that more massive stars generally give rise to more energetic UDSs. It is worth noting that equation ( 2 ) does not include the additional gain of kinetic energy during the compression and bouncing of the star at the orbital periapsis (Brassart Luminet 2008 , and references therein). We neglected it because the bouncing energy , about β 2 GM * / R * per unit mass (Guillochon et al. 2009 ), is usually much smaller than , where β ≡ R t / R p is the so-called penetration factor. Take tidal disruption of a solar-type star ( M * = M ⊙ and R * = R ⊙ ) for example. When M • = 4.3 × 10 6 M ⊙ which is the mass of the SMBH in the Milky Way (MW; Genzel, Eisenhauer Gillessen 2010 ), the penetration factor β cannot be greater than 4.3; otherwise, the star will enter a radius smaller than the ‘innermost bound circular orbit’ (twice the Schwarzschild radius for a non-rotating black hole) and eventually be swallowed as a whole by the SMBH. Because of such a limit to β, the ratio is small, in fact in the range (0.0063, 0.12) for the SMBHs in the mass range of 10 6 M • /M ⊙ 10 8 . We notice that our E k differs by a factor of β 2 from that derived by Khokhlov Melia ( 1996 ), Strubbe Quataert ( 2009 ) and Kasen Ramirez-Ruiz ( 2010 ). This is because those authors calculated using the formula , i.e. they assumed that the energy spread is determined at R p instead of at R t . However, recent studies of the tidal-disruption