adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...604A..96M (fulltext)
interaction with one galaxy twice as high than with all others) will be considered as bound ( F92 ). This is only valid in a statistical way and may be wrong for a particular galaxy. Ferguson ’s IP clearly does not take into account the cancellation of gravitational forces by different neighbors nor the three dimensional structure of the cluster. Also, the analysis of is always performed up to separations of , while according to the definition of in Eq. ( 5 )the ‚nearest neighbor’ of a galaxy might have a larger separation. We explore alternative IPs that take more than one influential neighbor into account. First, a substructure IP which compares the gravitational interaction for a galaxy with all its neighboring galaxies within () to the interaction with the rest of the cluster (, separations ): This IP should show a significantly higher fraction of galaxies with than Ferguson ’s IP. As a second variant we define a group IP by comparing the three most strongly interacting galaxies (if those galaxies are closer than , ) with the interaction by the rest of the cluster: where “” denotes that those maxima are only computed if the corresponding galaxies are located closer than to the th galaxy. This IP should be sensitive to the presence of subgroups within the cluster. We assume that if such subgroups exist, should show approximately the same values for the galaxies contained in a group. Therefore the number of galaxies with should range somewhere between the number of and . As hitherto only gravitational interaction is considered, a last IP is introduced which is sensitive to tidal interaction. It is almost the same IP as the one in Karachentsev (2005, hereafter Ka05) : The only difference is that in Ka05 a constant was added which allows to identify isolated galaxies (). 3.4.2. Direct position shuffling method The pseudo-clusters to calculate IPs which then can be compared with the real IPs based on the MA08 and WLG11 catalogs are created by a direct position shuffling. In this method the positions of two randomly chosen galaxies and are swapped directly. As in F92 this exchange is performed times where is the total number of galaxies in the sample. In this way it can happen that a galaxy changes its position several times in one run, while another may stay in place. 4. Results 4.1. Results for the <inline-formula specific-use="simple-math">Σ</inline-formula>(<italic>r</italic>)-analyses of the MA08 catalog The settings for the MC simulations analyzed in this section are given below. They are valid throughout this paper, unless mentioned otherwise. Primary-secondary discrimination at (74 giants and 399 possible companions at the outset). Primaries have to lie outside an ellipse with minor axis 250 kpc centered on the cluster center (45 primaries left). Distance to Coma: , , , , Distance modulus and therefore a scale of ( Adami et al. 2009 ). To calculate the absolute magnitudes, a small correction of is added to leading to a value of
Paper: 2016MNRAS.456.2457A (fulltext)
for eccentric and radial orbits in the case M g = 3.2 × 10 11 M ⊙ . In the case e = 0.7, it is worth noting that at every passage at pericentre some stars are stripped away from the GC and tend to move on precessing ellipses whose semi-major axes increase with the number of passages at pericentre. On the other hand, when the GC experiences a ‘head-on’ collision with the SMBH, stars are scattered backwards at different velocities, depending on the impact parameter that characterizes each star–SMBH interaction. This peculiar interaction leads to the formation of a wake behind the former GC, with a structure similar to the propagation of a wave on a water plate. Figure 4. Snapshots at different times (labelled) of the cluster on an eccentric orbit in the case M g = 3.2 × 10 11 M ⊙ . The central black dot represents the BH with mass M BH = 5 × 10 8 M ⊙ . The x–y plane is the GC orbital plane. Figure 5. Same as in Fig. 4 , but for a cluster on a radial orbit. Therefore, although it is quite difficult to identify the ‘core’ of the GC, the interaction between an SMBH and an infalling GC produces debris that hides much informations about the initial orbital parameters of the GC. However, due to the fact that it is almost impossible to identify a centre for the cluster in these cases, we limited our analysis to the estimate of the amount of GC mass which remains confined to an inner region of the galaxy, around the SMBH. When considering the simulations with M g ≥ 10 11 M ⊙ , we found that eccentric and radial orbits allow a more efficient transport of mass, towards the Galactic Centre, than circular orbits do. On these orbits, the GC can carry about 20 per cent of its initial mass to the Galactic Centre, while, on circular orbits, such percentage is limited to few per cent. On the other hand, for lower galaxy masses, M g 10 11 M ⊙ , we found that the major contribution to the deposited mass comes from GC on circular orbits. We will deepen the discussion about these results in Section 4 . 4 DISCUSSION In this paper, we have presented several N -body simulations to study the interaction between a single GC and an SMBH in the Galactic Centre. The results shed light on the actual mechanisms of mass accumulation in the central region of a massive galaxy ( M g ≥ 10 11 M ⊙ ). Fig. 6 shows the GC cumulative mass profile as a function of the distance from the SMBH after the orbital decay process, for all the orbits considered and in the case of M g = 10 10 M ⊙ and M g = 3.2 × 10 10 M ⊙ . Considering our lightest galaxy model, we found that circular and radial orbits are both efficient in the transport of mass towards the Galactic Centre. In such cases, the mass left to the Galactic Centre exceeds 80 per cent of the initial mass of the GC. On the other hand, in heavier galaxy models, only GCs moving on initial nearly radial orbits deposit a non-negligible amount of their mass (∼20 per cent of the initial mass of the GC) in the inner 20 pc of the galaxy.
Paper: 2016A&A...596A.100P (fulltext)
available to be detected by standard methods. These proto-clusters, likely lying at , are assumed to be in an active star-forming phase, but not yet fully virialized. To investigate these earlier evolutionary stages we need different approaches, such as the one presented in this paper. During the past decade, more and more proto-cluster candidates have been detected through different techniques, using X-ray signatures, stellar mass overdensities, Ly emission, and association with radio galaxies (e.g., Brodwin et al. 2005 ; Miley et al. 2006 ; Nesvadba et al. 2006 ; Doherty et al. 2010 ; Papovich et al. 2010 ; Hatch et al. 2011 ; Gobat et al. 2011 ; Stanford et al. 2012 ; Santos et al. 2011 , 2013 , 2014 ; Brodwin et al. 2010 , 2011 , 2013 ). However, only a few detections have been done in random fields (e.g., Steidel et al. 1998 , 2005 ; Toshikawa et al. 2012 ; Rettura et al. 2014 ), and most of these detections are biased towards radio galaxies or quasars ( Pentericci et al. 2000 ; Kurk et al. 2000 , 2004 ; Venemans et al. 2002 , 2004 , 2007 ; Galametz et al. 2010 , 2013 ; Rigby et al. 2013 ; Wylezalek et al. 2013 ; Trainor Steidel 2012 ; Cooke et al. 2014 ; Noirot et al. 2016 ), or obtained over very limited fractions of the sky, e.g., in the COSMOS field, which is 1.65 ( Capak et al. 2011 ; Cucciati et al. 2014 ; Chiang et al. 2014 ) and in the Hubble Space Telescope Ultra-Deep Field ( Beckwith et al. 2006 ; Mei et al. 2015 ) with its area. Since the expected surface density of such massive proto-clusters is fairly small, a few times ( Negrello et al. 2007 , 2010 ), performing an unbiased analysis of this population of sources requires us to explore much larger regions of the sky. This has been initiated, for example with the Spitzer SPT Deep Field survey covering 94 and has yielded the detection of 300 galaxy cluster candidates with redshifts ( Rettura et al. 2014 ). The submm and mm sky has proved to be an efficient window onto star-forming galaxies with redshifts between 1 and 6, since it allows us to detect the redshifted modified blackbody emission coming from the warm dust in galaxies. Taking advantage of the so-called negative k-correction in the submm ( Franceschini et al. 1991 ), which compensates for the cosmological dimming at high redshift in the submm, many samples of high- galaxies and also proto-cluster candidates have been identified or discovered in this frequency range in the last two decades (e.g., Lagache et al. 2005 ; Beelen et al. 2008 ; Smail et al. 2014 ). As predicted by Negrello et al. (2005) , Clements et al. (2014) showed that the proto-cluster population can be efficiently detected in the submm as overdensities of dusty star-forming galaxies, which has been made possible thanks to the the observations of larger fields in the submm and mm range. The first discoveries of strongly gravitationally lensed galaxies at very high redshift (e.g., Walsh et al. 1979 ; Soucail et al. 1987 ) opened another window onto the
Paper: 2016MNRAS.462S..33Y (fulltext)
of them had a clear impact. RPC-LAP senses a large increase of plasma during each CME passage on September 11 and 19, with elevated plasma density after. We can differentiate the spiky CME signatures in Fig. 4 from the oscillations in September which correspond to seasonal effects. The spikes also do not manifest until approach into the 30 km orbits. By factoring in the CMEs studied by McKenna-Lawlor et al. ( 2016 ), we can remove two of the large spikes in the September data so they do not appear in Fig. 6 . Another possibility relates to excessive variations of the solar EUV radiation, since the current intensities collected by RPC-LAP sweeps depend on the illumination and ultimately on the solar EUV flux. Thus, a variation in EUV flux should be seen as a variation in the collected photoelectron current density by the probe and qualitatively follow each other. Until the end of 2014 August, the solar EUV flux variations match those of the RPC-LAP current measurements, with a slow increase of the photoelectron emission that can be linked directly to the increase in the flux, as the heliocentric distance between Rosetta and the Sun decreases. The 27-d periodicity in the EUV flux, due to the Sun's rotation (e.g. Woods Rottman 2002 ), has an impact on the photoelectron emission yield of the probes, resulting in the EUV flux and the I 1 current density being in qualitative agreement. After the end of 2014 August, this trend starts to be significantly altered, with the I 1 current density exhibiting large variations that do not correlate any more with the solar EUV flux variation. At the time of the transition into the HDR, the solar EUV flux variations stop driving the evolution of the I 1 currents. Thus, some other effects begin to take precedence on 2014 September 9, and the transition found in the RPC-LAP photocurrent measurements is probably not a temporally isolated artefact due to a sudden variation of the solar flux. On the temporal scale that we study in this paper, we see oscillations of the knee potential, as well as traces of this in the ion attraction current I 1 . These signals correspond to the envelopes of the 6.2-h longitudinal oscillations reported by Odelstad et al. ( 2015 ) and Edberg et al. ( 2015 ), and relate to repeated crossings of more active regions on the comet (with increased neutral outgassing). The envelopes correspond to latitudinal variations, where the Northern hemisphere of 67P is the more active one, and can be seen for electrons with RPC-IES as early as in August (Clark et al. 2015 ), but at a smaller energy and density compared to 2014 September. Although part of the reason for the spread seen at 27–29 km from the nucleus is the oversampling of the projections of active regions once Rosetta enters into the 30 km orbit, this does not explain the sudden change of magnitude upon approach to this orbit. Rosina's Double Focusing Mass Spectrometer (DFMS) measurements show increased density of O 2 following entry
Paper: 2016MNRAS.463.1297Y (fulltext)
metallicity, we illustrate the corresponding comparison in Fig. 11 . It shows that the two samples are similar in metallicity, spanning from −1.4 dex to 0.5 dex. Comparing with LAMOST, KIC metallicities of both samples were underestimated in the metal-poor regime but overestimated in the metal-rich region. In panel (b), we clearly observe this trend with a mean and standard deviation of −0.061 and 0.210 dex, respectively, in the sense of KIC minus LAMOST. This is consistent with the metallicity trend found by Dong et al. ( 2014 ). Figure 11. Same with Fig. 9 but for metallicity. We also checked if the lack of seven independent colours (derived from eight filters: KIC griz , D51 and 2MASS JHK ) contributes to the incorrect KIC-predicted ν max values. However, only three stars do not have a full set of colours and therefore we excluded this effect as a dominant cause for the larger errors in log g . In Fig. 12 , we compare Kepler magnitudes, KIC reddening, asteroseismic masses and spatial distribution for our sample (red data) with the samples from Huber et al. ( 2011 , grey data) and Stello et al. ( 2013 , blue data). Note that we have restricted both comparison samples to seismic gravities in the same ranges as our sample (from 2.2 to 3.5 dex). Figure 12. Histograms for Kepler magnitude K p [panel (a)] and KIC reddening [panel (b)], spatial distribution in Kepler field of view [panel (c)] and histogram of asteroseismic mass [panel (d)]. The red data represents targets of our entire sample, the grey data indicates those from Huber et al. ( 2011 ) while the blue data are targets from Stello et al. ( 2013 ). From Fig. 12(a) , we can see, due to the preferential selection of bright stars by the Kepler Asteroseismic Science Consortium, our sample is systematically fainter by nearly 2 mag, compared to Huber et al. ( 2011 ). We note that Huber et al. ( 2011 ) used data spanning from Q0 to Q6 for long-cadence light curves and Q0 to Q4 for short-cadence light curves, whereas we used data from the entire nominal Kepler Mission (Q0–Q17). Longer time series enhance the probability of detecting oscillations in fainter stars. Both our sample and the Stello et al. ( 2013 ) sample show a sharp cut at K p = 14 mag due to the Kepler selection function (Batalha et al. 2010 ). Panel (b) shows most targets in our sample have reddening around 0.13 mag, which is consistent with the sample of Huber et al. ( 2011 ) but shifted by 0.03 mag compared to the Stello et al. ( 2013 ) sample. Inspection of panel (b) also reveals there are a population of stars with lower reddening peaking at 0.035 mag. To understand if this population of stars reside in special location, we plotted the spatial distribution of targets for our sample (red dots) and Stello et al. ( 2013 , blue dots) in the entire Kepler field of view. We see fewer targets located in the three modules at the bottom-right corner, which are closest to the Galactic plane and have less stars because of the Kepler
Paper: 2017ApJ...851L...2L (fulltext)
range of 1.58, 1.60, and 1.57, respectively. However, the power spectra have flattened in the dissipation range for the whole ICME and the previous AF-rich region, with the spectral indexes of 1.12 and 0.82, respectively, which may be aliased by the instrument noises. The power spectrum for the previous AF-lack region still has a steepening in the dissipation range, with the spectral index of 1.84. Similarly, the break frequencies are also comparable to the frequency corresponding to the ion inertial length. Figure 3. Power spectral density of magnetic fluctuations inside the ICME: (A) the whole ICME observed by Wind at 1.0 au; (B) the AF-rich region of the ICME at 1.0 au; (C) the AF-lack region of the ICME at 1.0 au; (D) the whole ICME observed by Ulysses at 5.4 au; (B) the AF-rich region of the ICME at 5.4 au; (C) the AF-lack region of the ICME at 5.4 au. The dashed colored lines are the power-law fitting results. The red ones are in the inertial range, while the green ones are in the dissipation range. The vertical dashed lines represent the proton gyro-frequency, f pc , the break frequency, f b , and the frequency corresponding to the ion inertial length, f di . The energy cascade rate then can be calculated from Equation ( 1 ). For the whole ICME, the energy cascade rates at 1.0 au are derived to be about 2688.6 J kg −1 s −1 , which is close to the lower limit (2970 ∼ 65,320) estimated by Vasquez et al. ( 2007 ) when the solar wind speed is between 350 and 400 km s −1 . When the ICME propagates to 5.4 au, this value reduces to 274.7 J kg −1 s −1 , suggesting that the capacity of turbulence cascade reduces as the ICME propagates outward. For the AF-rich region, the energy cascade rate drops from 3312.2 to 89.7 J kg −1 s −1 accompanied with the disappearance of AFs, when the ICME propagates from Wind to Ulysses . For comparison, the energy cascade rates for the AF-lack region can be maintained at a certain level, and the values at 1.0 and 5.4 au are 526.9 and 737.3 J kg −1 s −1 , respectively. In addition, Smith et al. ( 2006 ) have found the dependence of the dissipation range spectrum on the rate of energy cascade through the inertial range. Our results are also in agreement with their findings that the steeper spectral in the dissipation range forms from greater cascade rates. Liu et al. ( 2006 ) have taken into account the Coulomb energy transfer between protons and alphas, and derived the equations of the heating rate required for protons ( ε p ) and alphas ( ε α ) to produce the observed temperature profile. The specific expressions are as follows where k B is the Boltzmann constant, T p and T α are the temperature of protons and alphas, m α is the alpha mass, τ pα and τ αp are the Coulomb collision timescales and have a relationship of , n α is the alpha number density, and τ e is the expansion time of the plasma. For more details, please refer to Appendix A in Liu et al. ( 2006 ). The total required heating rate ( ε re ) is
Paper: 2018MNRAS.475.3485D (fulltext)
within the assumed cosmological prior for the parameters . As specified above, in this investigation we assume very conservative prior h ∈ [0.6, 0.85] and Ω m ∈ [0.04, 1] (fixing ). Therefore, F − ( z ) = 0.6[0.04(1 + z ) 3 + 0.96] 1/2 and F + ( z ) = 0.85(1 + z ) 3/2 . Secondly, because of peculiar velocities v p , the apparent redshift z obs of the GW host might be different from the cosmological redshift z determined by the Hubble flow, thus adding a further uncertainty term to the estimated redshift. To account for this effect, for each event defined by D l ± Δ D l , we assign a possible host redshift range . We assume v p = 500 km s −1 , consistent with the standard deviation of the radial peculiar velocity distribution observed in the Millennium run (Springel et al. 2005 ). We neglect additional contributions to the apparent redshift of the GW event coming from kicks at the compact binary formation, which can be as high as 360 km s −1 (Kelley et al. 2010 ), thus comparable with our choice of the peculiar velocity. The support Θ that enters in the evaluation of the galaxy likelihood function in equation ( 8 ) is the error-box defined by . To perform the proposed statistical D l – z reconstruction, we now need to attach a realistic distribution of galaxy to each of those error-boxes. We use the simulated observed light cones of Henriques et al. ( 2012 ) constructed from the Millennium run. The simulation assumes a Λ CDM cosmology with (which justifies the cosmological parameters assumed above to construct the BHB population). Because of the resolution of the simulation, galaxy distributions extracted from it are reliable only down to M * = 10 9 M ⊙ . We checked that at the magnitude cut imposed by Henriques et al. ( 2012 ), their reconstruction of the all-sky galaxy map is complete down to this mass limit out to z ≈ 0.15. The all-sky galaxy map provides sky location, stellar mass, cosmological redshift z , and observed redshift z obs (i.e. inclusive of peculiar velocities) for each galaxy. To select galaxy counterpart candidates to each GW event, we proceed as follow: we fix the cosmological parameters to the values of the Millennium run and we select all galaxies with a cosmological redshift (i.e. excluding peculiar velocities) in the volume shell z ± Δ z consistent with the LISA measurement D l ± Δ D l ; we randomly choose one of those galaxies to be the ‘true host’ of the GW signal. Note that this amounts to say that the true host can be anywhere in the interval D l ± Δ D l , i.e. we are not centring the error-box around the true host; we consider the error-box defined by , where ΔΩ is centred at the sky location of the selected ‘true host’; and we list all the galaxies with M * > 10 9 M ⊙ that fall within the error-box. In doing this, we make sure that their observed redshift z obs lies within the selected range. Each galaxy z obs is the relevant z j that enters in equation ( 8 ). In fact, in a real cosmological measurement one can
Paper: 2016MNRAS.461.3982B (fulltext)
collisions in this specific case. Table 1. Percentage of collision, escape survivors and the cases of secondary disruption for every mass ratio q . This percentage is out of 90 different initial obliquity θ. Survivors mean cases that the lifetime is larger than 200 yr. We simulated 1260 different initial conditions and found that secondary fission should occur in 16 per cent of the cases and collisions in 22 per cent of cases. So, a significant part of the results are candidates to become a stable binary system. Interesting results are the spin fission phenomena in cases with q > 0.20. The cases with q > 0.20 have at least a few percentage of secondary disruption. Jacobson Scheeres ( 2011 ) did not find secondary spin fission in cases of q > 0.20 and their conclusion is that the chaotic systems with q > 0.20 can only become a stable binary through tidal process or BYORP process, but with our results we show that for the non-planar case this is not true. In other words, it is possible get a stable binary for q > 0.20 through a dynamical process (secondary disruption), but with low probability. It means that non-planar systems with large mass ratio can become a stable binary following the small ratio mechanisms outlined in Jacobson Scheeres ( 2011 ), expanding this route to forming binaries. We also analysed the rotational mode of the secondary characterized by the dynamic inertia I D (Scheeres et al. 2000 ) for all escape cases, where I D = H 2 /2 E k , H is the magnitude of rotational angular momentum and E k is the rotational kinetic energy. The parameter I D tells whether the body is in a tumbling or non-tumbling state, and in the tumbling case whether it is in a long-axis mode (LAM) or short-axis mode (SAM). Uniform rotation (non-tumbling) can be about the largest moment of inertia I D = C or about the smallest moment of inertia I D = A , and for all values of I D between A and C the body is tumbling and will have precession and nutation. The condition for a body to be in a SAM rotation mode is ( B I D C ), while the condition for it to be in an LAM rotation mode is ( A I D B ). We choose nine equal regions between the values of A and C and computed the percentage of cases that the values of I D finished in each region, so we made a plot of these percentage of cases as a function of for all mass ratios studied, as shown in Fig. 14 . We can conclude that all of the bodies are in some state of tumbling after escape. Most of them are in SAM, and are closer to uniform rotation about the maximum moment of inertia. The bodies between B and A are in a LAM, and will likely take longer to settle into the principal axis rotation. We note that the most probable state seems to be close to rotation about the intermediate axis, followed by a SAM state and followed by a LAM state. In contrast we find all of the primary body spin states to be in a SAM state, with small excitation. Figure 14. A bar graph of percentage cases as a function of . The vertical