adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...638A..37W (fulltext)
) 6 . Analysis suggests that FRB 121102’s DM local could be small ( Tendulkar et al. 2017 ). Such studies have potential implications for progenitor models, however FRB 121102 may not necessarily be representative of all FRBs. More accurate modelling of DM local may arise as further FRBs are localised. In this work we assume DM local = 0. 3 Excess electron model In this section we present the framework for combining the PDFs of DM exc components to retrieve a PDF, which describes the likelihood of an FRB at source redshift z s to have an observed extragalactic DM component DM exc , contributing to a measured time delay Δ t obs,exc between two observing frequencies ν 1,obs and ν 2,obs on Earth. We demonstrate this using our simulated IGM and progenitor models. Increasingly, accurate component models developed using FRB discoveries (or via other means) may allow these PDFs, which we refer to as excess electron models, to become increasingly useful tools for DM-redshift analysis. In Sect. 3.1 we describe our method and in Sect. 3.2 we apply Bayes’ theorem to the excess electron model and discuss advantages of doing so. 3.1 <italic>P</italic>(DM|<italic>z</italic>) methodology and results Following Eq. ( 1 ), an FRB emitted from a source at redshift z s has a DM exc formed from the sum of its extragalactic components. Assuming these components to be random independent variables drawn from their respective underlying PDFs, P (DM IGM | z s ) and P (DM host | z s ), the convolution of these PDFs, describes the likelihood of an FRB of given source redshift z s to have an observed extragalactic DM component DM exc . This DM exc results in a measured time delay Δ t obs,exc between two observing frequencies ( ν 1,obs and ν 2,obs ) on Earth. Example excess electron models generated using our IGM (Sect. 2.2 ) and host galaxy (Sect. 2.3 ) PDFs are shown in Fig. 4 . Comparisons between projections of these models for different values of z s and P (DM IGM | z s ) illustrate the initially significant (and diminishing with redshift) influence of the host galaxy on P (DM exc | z s ). These comparisons show that the potential host galaxy contribution to DM exc increases the likelihood of an FRB to have a DM exc in excess of that predicted by conventional analysis. This excess may be particularly large for sources at low redshifts. In some cases (see e.g. subplots (a) and (c), which trace YPSR distributions in spiral galaxies and elliptical distributions in elliptical galaxies, respectively), the influence of the host diminishes significantly by z s = 3, signified by the convergence of the P (DM exc ) curves with their IGM counterparts. In other cases (see e.g. subplots (c) and (f), where FRBs are confined to galactic regions of high electron density), the host’s potential influence is still significant at z s = 3. Differences between P (DM exc | z s ) profiles are also apparent between different progenitor models, particularly for sources with low redshifts.
Paper: 2018ApJ...862..107B (fulltext)
produce 2 mag of variation in peak luminosity between the polar and equatorial directions. Polarimetric observations of SN 1998bw were also consistent with asymmetry in the ejecta of 1998bw. Polarimetry studies suggest that CCSNe, particularly those with relatively small envelope masses, may exhibit significant asymmetry in their ejecta (see, e.g., Wang et al. 2001 ). If the ejecta of ASASSN-15nx were highly asymmetrical and viewed at a favorable angle, the actual amount of synthesized might be substantially reduced from the current estimate and produce its high peak luminosity. On the other hand, there is no strong evidence of asymmetry in the [O i ] ( 6300, 6364) nebular lines, as suggested by Taubenberger et al. ( 2009 , 2013 ). However, quantitative modeling is needed to investigate whether an asymmetric model could explain the prolonged linear light-curve and spectroscopic features of ASASSN-15nx. Synthesizing a large amount of radioactive normally should also lead to nebular-phase spectra rich with iron-group elements, as seen in Type Ia SNe, but this doesn’t appear to be the case with ASASSN-15nx. This could pose a serious challenge to the high mass scenario, and nebular-phase spectral modeling would be needed to examine it further. 7.2. CSM Interaction An alternative to the radioactive decay model is strong CSM interactions. In these models, the structure and distribution of the CSM can determine the shape of the light curves. As in SNe IIn (Schlegel 1990 ), which are characterized by narrow emission lines (FWHM ∼ few hundreds of ) in the spectra, strong interactions are thought to be responsible for powering their prolonged and often luminous light curves (e.g., Smith et al. 2008a ; Rest et al. 2011 ). Sometimes ejecta–CSM interactions can lead to steeply declining light curves, as has been proposed for SN 2009jp (jet–CSM interaction; Smith et al. 2012a ) and ASASSN-15no (Benetti et al. 2018 ). PTF10iam (Arcavi et al. 2016 ), which has early light curve features similar to ASASSN-15nx, may also be explained by interaction. The density profile of the CSM can be sculpted to produce the observed light curve of ASASSN-15nx. In ASASSN-15nx, we do not see narrow- or intermediate-width emission lines from the expanding shock driving photo-ionization of the remaining CSM. It is possible to miss such emission lines if those were only present before our spectroscopic observations began at 53.0 days. However, this seems unlikely, as the continuous and linear decline of the light curve indicates a single dominant powering source throughout the evolution, which should produce similar narrow emission lines at later phases if they were present earlier. On the other hand, sometimes CSM signatures may also remain hidden if there is asymmetry or the CSM has a velocity of several thousands of . Some of the spectral features in ASASSN-15nx can be attributed to indirect signatures of CSM interaction. The synow model could not reproduce the broad and
Paper: 2017ApJ...851...69S (fulltext)
magenta boxes in Figure 3 ). In particular, S3 has a best-fit temperature similar to that of the gas within the interface and is in pressure equilibrium with the external gas. We note that the temperature gradient to the east is much smoother than that in the west (Figure 7 ). This difference may be due to more evident KHI to the east, which may have disrupted a previously existing cold front. KHI accelerates the turbulent mixing of gas of different phases, as we expect from theory and simulations (ZuHone et al. 2011 ; Roediger et al. 2015a , 2015b ). Large KHI rolls (above certain wavelengths, l max ) are expected to be suppressed by gravity (Chandrasekhar 1961 ). We do not expect to observe KHI rolls larger thanwhere U is the shear velocity at the front and g is the gravitational acceleration (Roediger et al. 2012b ). We assume ρ c = 1.5 ρ h and U ( r ) = 0.3 c s ( r ); c s and g ( r ) were calculated from the best-fit deprojected temperature profile and the X-ray hydrostatic mass profile (Paper 1 ). We present the maximum KHI length scale as a function of radius at the cluster center in Figure 10 . The size of these features stays below this upper limit. A potential ghost bubble of ≳5 kpc diameter resides at r = 13 kpc ( r = 10 kpc in projection) to the northwest (Figure 2 , see Paper 1 for details). Interestingly, Walker et al. ( 2017 ) propose that such giant X-ray surface brightness decrement may be due to KHI instead of AGN bubbles. The position and size of this ghost bubble candidate are also in the allowed parameter space for a giant KHI roll. Figure 10. Maximum wavelength for KHI rolls as a function of radius in NGC 1399 (black solid line). Gravity suppresses KHI above this length scale. Blue, green, magenta, and red circles mark the sizes and radii of S1, S2, S3, and a ghost bubble candidate, respectively. Alternatively, S1, S2, and S3 may be thermally unstable X-ray filaments resulting from gas cooling (David et al. 2014 , 2017 ). The growth time of KHI can be estimated from (Roediger et al. 2012a ) We calculate τ KH for the maximum KHI length scale, the maximum growth time, at each radius as shown in Figure 8 . The maximum growth time of KHI is shorter than the cooling time by two orders of magnitude. KHI is thus somewhat favored as their origin. The timescale of KHI is also significantly shorter than the dynamical timescale (free-fall time or buoyant rising time), implying that KHI, coupled with gas sloshing, may have played an important role in regulating the thermal state of the hot plasma at cluster centers. 5. Conclusions The Fornax Cluster is a nearby low-mass cool-core cluster. It displays prominent sloshing structure wrapping around the cluster center. We analyzed a mosaic XMM-Newton observation and a total of 250 ks Chandra observations centered on its BCG NGC 1399. We present the properties of the hot gas over a radial range of 1–250 kpc. We found the following. 1. Four sloshing fronts are visible at radii of 200 kpc,
Paper: 2020A&A...635A.192P (fulltext)
the impact of model errors on the performance of the PW+EFC correction loop. We consider the same errors as for PW in Sect. 3.3 : influence function size, translation and rotation of the DM. We use the assumptions of Sect. 3.1 except for the amplitude aberrations. Here they are assumed to be at 10% rms error and their PSD distribution is almost flat so that we may approach the testbed environment of the THD2. The two probes for the PW technique are considered to be the bumps of the actuators 466 and 498. We fix the loop gain at g = 0.5. We simulate 578 actuators in the pupil to calculate the matrix G and we select 550 modes after the EFC SVD. As we study the correction of both amplitude and phase aberrations by a single DM, the correction is done within a half DH spreading from 2 λ / D to 13 λ / D on the horizontal axis and −13 λ / D to 13 λ / D on the vertical axis. After the tenth iteration, the contrast level C is computed as the 1 σ azimutal standard deviation of the intensity in the coronagraphic science image divided by the maximum of the non-coronagraphic point spread function (PSF). The results are shown in Fig. 6 . In each plot, the full line is the performance with no model error. Model errors can strongly impact both the WFS (Sect. 3.3 ) and the WFC. In order to reach a 10 −7 contrast level, the size of the influence function has to be known with less than 25% error, the translation of the modeled DM relative to the true one should be less than 0.5 pitch and the orientation of the modeled DM should be better than 1°. Fig. 6. RMS contrast as a function of the angular separation for different cases of model errors when implementing PW+EFC. Model errors simulated are the size of the influence function ( left ), a lateral translation of the DM ( center ), and a rotation of the DM ( right ). 5. Lab performances: wavefront sensing and control on the THD2 bench 5.1. Bench description We compared the two wavefront sensing and control techniques described above on a HCI testbed developed at LESIA (Observatoire de Paris). The optical testbed, called THD2 for trés haute dynamique , is located in an ISO7 pressurized clean room. It is described in detail in Baudoz et al. (2018a) and its layout is shown in Fig. 7 . In this paper, we used the following components: Fig. 7. Layout of the THD2 bench presenting different optical components. – An optical single mode fiber providing a monochromatic light source of wavelength 783.25 nm with a bandwidth less than 2 nm. The focal length of the first off-axis parabola (500 mm) flattens the Gaussian output of the fiber over the pupil diameter. The resulting amplitude aberration allows to reach a contrast level below 10 −7 at 1 λ / D . – An entrance pupil of 8.23 mm diameter. – 32 × 32 Boston-Micromachine (DM3) settled in pupil plane 2. – A FQPM located at the focal plane 3 in Fig. 7 . – A Lyot-stop of 8 mm diameter in the pupil plane 3 (corresponding to a Lyot filtering of 97.2%). In this Lyot plane, a
Paper: 2017A&A...607A...8P (acknowledgments)
This work is supported by the European Research Council under the SeismoSun Research Project No. 321141 (D.J.P., V.M.N., C.R.G.), the STFC grants ST/L000733/1 (V.M.N.) and ST/L000741/1 (P.J.A.S., L.F.), the Russian Foundation of Basic Research under grants 15-02-01089-a, 15-02-03835-a, 16-32-00315-mol-a (SAA), and by the Federal Agency for Scientific Organisations under project II.16.3.2 Non-stationary and wave processes in the solar atmosphere, CITIS No 01201281650 (S.A.A.). The data is used courtesy of the SDO/AIA team. This research has made use of NASA’s Astrophysics Data System.
Paper: 2021A&A...646A..85G (fulltext)
part of the diagram to the bottom-right, stable nuclear burning of hydrogen is not possible and this region should be populated with other types of soft X-ray sources. Indeed, Fig. 5 shows that in late-type galaxies (M 83, M 51, M 101 and M 81), there is a distinct group of rare soft and luminous sources located above these line. These sources have temperatures of approximately several tens of eV and seem to clearly stand out in the populations of soft sources in these galaxies. When interpreting the results shown in Fig. 5 , one should bear in mind that it shows absorbed luminosities in the 0.3−2.0 keV band and that absorption and bolometric corrections increase roughly exponentially with decreasing color temperature, and therefore the softest sources are truly luminous. For this reason, we classify sources to the left of the red line in Fig. 5 as classical SSSs. We also include those sources that are located outside the stable nuclear burning boundary but whose error bars cross this boundary. In addition, we added to this list two sources in M 101 that are located outside but close to the stable nuclear burning boundary (the two sources in the upper right panel of Fig. 5 with kT ≳ 100 eV and L X ≳ 10 37 erg s −1 ). The catalog of SSSs is presented in Table C.1 , and their numbers in galaxies from our sample are listed in Table 2 . 6.2. Specific frequency of super-soft X-ray sources across morphological types From Fig. 5 , it is evident that SSSs are considerably more abundant in late-type galaxies than in early-type galaxies. However, for a statistically sound conclusion one needs to take into consideration the completeness limits of the various data sets. To this end we used the sensitivity limit for M 51 corrected for the difference in the absorption column density as described in Sect. 5.2 to select SSSs. In each galaxy, we selected sources to the left of the thick red line and above the dashed blue line in Fig. 5 . This selection is within the 20 counts flux limit (solid black line in Fig. 5 ) in all galaxies, except for M 83 where at the low temperature end the 20 counts limit for this galaxy is higher by a factor of about two. This may result in slight underestimation of the specific frequency of super-soft sources in late-type galaxies. In Centaurus A, we used different criteria for sources located in and outside the dust lanes (cf. dotted line in Fig. 5 ), although this does not change the final result. In order to estimate specific frequencies of SSSs, we group the data separately for early- and late-type galaxies. There are 29 super-soft sources in late-type galaxies (M 83, M 51, M 81, M 101), which, for the total stellar mass of 13.9 × 10 10 M ⊙ yield the specific frequency of In computing statistical errors we used Gehrels approximation ( Gehrels 1986 ). Counting lenticular and elliptical galaxies together (Centaurus A, NGC 3115, NGC 3379, NGC 4278, NGC 4697), we find seven SSSs for the total stellar mass of 28.3 × 10 10 M ⊙ ,
Paper: 2016ApJ...819..121P (acknowledgments)
We thank the referee for constructive comments that helped improve the manuscript. This research made use of the data obtained from HEASARC provided by the NASA’s Goddard Space Flight Center. Part of this work is based on archival data, software, or online services provided by the ASI Science Data Center (ASDC). This research has made use of the XRT Data Analysis Software (XRTDAS) developed under the responsibility of the ASDC, Italy. Use of Hydra cluster at Indian Institute of Astrophysics is acknowledged.
Paper: 2018MNRAS.480.5550S (acknowledgments)
ACKNOWLEDGEMENTS We are grateful to Miklós Slíz (software engineer, Graphisoft, Budapest, Hungary) for the development of the computer simulation software. We thank Tatiana Salnikova for her valuable comments on an earlier version of our manuscript. Author contributions . Substantial contributions to conception and design: JSB, AB and GH. Performing experiments and data acquisition: JSB and AB. Data analysis and interpretation: JSB and GH. Drafting the article or revising it critically for important intellectual content: JSB and GH.
Paper: 2018MNRAS.481.2851R (fulltext)
their subsequent dynamical mass-loss. This range represents an order of magnitude departure from the commonly adopted initial enriched fractions in MBL models. Figure 4. Required initial fraction of enriched stars as a function of the present cluster mass. Data points correspond to old () clusters born in high-pressure environments (), coloured by their gas pressure at birth. The grey area corresponds to the commonly assumed initial enriched fraction range of 5–10 per cent (Bastian Lardo 2018 ). The disparity between the commonly used range and our results indicates that either different polluters have to be considered (i.e. that produce greater amounts of ejected material per unit cluster mass, e.g. SMSs, see Gieles et al. 2018 ) or some yet-to-be-understood physical processes in stellar evolution and/or star formation lead to the large (>0.4) enriched fractions. 5 DISCUSSION We investigate whether dynamical mass-loss from GCs under realistic conditions satisfies the requirements from current multiple population models for reproducing the observed fractions of enriched stars in GCs. To do so, we use all surviving clusters present in the 25 present-day Milky Way mass galaxy simulations from the e-mosaics project (Kruijssen et al. 2018 ; Pfeffer et al. 2018 ). We determine the initial-to-present cluster mass ratios and study their dependence on cluster properties in Fig. 1 . We find a steep decrease of the amount of mass lost with present cluster mass. For intermediate mass (10 3 – M ⊙ ) clusters, we also find significant trends of increasing mass-loss towards smaller galactocentric radii, higher gas pressures at birth, and higher cluster metallicities. More massive clusters show no dependence between their median mass ratio and these quantities due to their small amount of mass lost. Assuming all dynamical mass-loss is in the form of unenriched stars, as posited by current multiple population models, we determine the enriched fraction of our stellar clusters. We study its dependence on cluster mass, metallicity, and galactocentric radius and compare it to observed enriched fractions from Milone et al. ( 2017 ) in Fig. 2 . These mirror the dependencies found for the amount of mass-loss and fall below the observed fractions by a factor of 2–20, with an opposite dependence on GC mass. These severe discrepancies rule out cluster mass-loss as the driving mechanism behind the high observed enriched fractions. In order to reconcile our results with observations, we consider what can be changed in models to match the observed enriched fractions. The addition of stellar evolution-driven expansion mass-loss, which is the preferred disruption mechanism suggested in the literature to solve the mass budget problem, predicts an even steeper trend between the numerical enriched fractions and galactocentric radius, in clear contrast with observations. These can only be reproduced if we consider an unphysically steep cluster mass-size relation, which
Paper: 2018MNRAS.476.4372P (fulltext)
least three distinct components, when stars are grouped by their spectral types and kinematics (e.g. Genzel et al. 2010 , section II). We choose to examine the limiting case of a Bahcall–Wolf cusp with γ = 0, which is analytically tractable and roughly matches the observed, slightly flatter density profile of the entire visible stellar population (e.g. Genzel et al. 2003 ; Bartko et al. 2010 ), as well as the observed distribution of diffuse light (e.g. Schödel et al. 2017 ), emanating from stars that could plausibly be old enough to be dynamically relaxed. Figure 3. The on-sky trajectories of the stars S1, S2, S9, and S13 over the time of simulated observations (solid and dashed lines), starting in the year 2000 and covering exactly one orbital period of S2 [approx. 16 yr, see Gillessen et al. ( 2017 ) for measurements of the orbital elements], overlaid on a snapshot of a sample realization of the distribution of background stars (shown as points) and their average light distribution (shown in colour). In the inner region of the nuclear cluster, stars move to good approximation like test particles in a point mass potential Φ( r ) = − GM 0 / r created by the central supermassive black hole, which has a canonical mass of M 0 ≈ 4 × 10 6 M ⊙ , and is located at a distance of R 0 ≈ 8 kpc from the Sun. To be in a stable configuration, these stars must be on bound, effectively Keplerian orbits, which can be defined by six orbital elements. The required distribution of the semi-major axes a = GM 0 /2ε of the background stars, to generate a realization of a Keplerian Bahcall–Wolf cusp, is (Schödel et al. 2003 ; Alexander 2005 ) The corresponding number density profile is . To ensure numerically that in our simulations the correct surface density profile is reproduced within the central square arcsecond, an upper cut-off of this distribution at a = 2 arcsec is sufficient. Any stars with larger semi-major axes are unlikely to be found very close to the central black hole, even in projection. We do not impose a strict lower cut-off, but exclude very few stars with a minimum (pericentre) distance r p = a (1 − e ) r t from the black hole, where r t is the approximate tidal radius of a typical observed B-type star in the Galactic Centre (e.g. Habibi et al. 2017 ): The required distributions of the eccentricity e , the inclination of the orbital plane i , the angle of the ascending node Ω, and the argument of pericentre ω, the specific forms of which encode the assumption of isotropy for the stellar velocities and positions, are the following: The time of pericentre has to be sampled uniformly over the duration of one orbital period, which, in turn, depends on the semi-major axis: The reference epoch t 0 = 2000 is arbitrary, but chosen to coincide roughly with the beginning of Galactic Centre observations using advanced high-resolution observing techniques, specifically adaptive optics imaging and integral field spectroscopy. Magnitudes are assigned to the