adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019A&A...628A..64P (fulltext)
dex, log ( g ⋆ ) = 4.25 ± 0.14 (cgs), and V sin i ⋆ = 11.9 ± 0.3 km s −1 . We found no evidence of a double-lined binary or any blends of spectral components. Fig. 2 Transit light curves of EPIC 212036875 b obtained with MuSCAT2 (TCS) in g ′ ( upper left ), r ′ ( upper right ), i ′ ( lower left ), and z ′ ( lower right ) filters. The black solid line is the best-fit model accounting for the de-trending and transit components. The residuals are plotted in the lower portion of each plot. 3.3 Radial velocity follow-up with FIES The RV follow-up was performed with the FIbre-fed Échelle Spectrograph (FIES; Telting et al. 2014 ; Frandsen Lindberg 1999 ) mounted on the 2.56 m Nordic Optical Telescope (NOT) at the Roque de los Muchachos Observatory. We observed nine high-resolution ( R ≈ 67 000) spectra between 9 April and 22 May 2018 as part of our CAT and TAC programmes 57-015, 57-206, and 57-210, and OPTICON programme 2018A-044. To account for the RV offset caused by a major instrument refurbishment that occurred on 30 April 2018, we treated the RV taken between 9 and 26 April, and between 6 and 8 May as two independent data sets. In addition, 14 intermediate-resolution ( R ≈47 000) FIES spectra were also acquired between 12 May 2018 and 26 Feb. 2019, as part of the OPTICON programme 2018B-052 and the Spanish-Nordic programme 58-301. Depending on the sky conditions and scheduling constraints, we set the exposure times to 1800 – 3600 s for both resolutions. To trace the RV drift of the instrument we followed the strategy outlined in Gandolfi et al. (2015) and Buchhave et al. (2010) and bracketed the science exposures with long-exposed (60 – 90 s) ThAr spectra. We used the standard IRAF and IDL routines to reduce the data. The S/N of the extracted spectra ranges between ~35 and 75 per pixel at 5500 Å. Radial velocities were extracted via multi-order cross-correlations with the spectrum of the RV standard star HD 168009, for which we adopted an absolute RV of −64.650 km s −1 ( Udry et al. 1999 ). The FIES RVs are listed in Table A.1 . Figure A.2 shows the generalised Lomb-Scargle periodogram of the offset-corrected Doppler measurements (combined by subtracting the systemic velocities listed in Table 5 ). We found a very significant peak at the orbital frequency of the transiting BD with a false alarm probability FAP ≪ 10 −6 , proving that the Doppler reflex motion of the star induced by the orbiting companion is clearly detected in our data. 3.4 Subaru/IRCS AO imaging In order to obtain high-contrast, high-resolution images of EPIC 212036875, we performed AO imaging with the InfraRed Camera and Spectrograph (IRCS, Kobayashi et al. 2000 ) mounted on the Subaru 8.2 m telescope on 14 June 2018. The target star was used as a natural guide and AO correction was applied to obtain high-contrast K ′ -band images of the target. We used the fine-sampling mode (1 pix ≈ 21 mas), and implemented a five-point dithering to minimise the impacts of bad pixels and
Paper: 2019MNRAS.487.2961M (fulltext)
is σ = 0 km s −1 (so any apparent velocity dispersion would be due entirely to binaries and measurement error); in scenario 2, we assume an intrinsic dispersion of σ = 2.7 km s −1 , which is close to our best-fitting dispersion of Ret II for the mean period assumed here. For each realization, we first calculate the uncorrected dispersion using 3σ-clipping: we find the maximum likelihood values of μ, σ, then remove stars that lie beyond 3σ, and repeat as needed until no such outliers are present. In order to find cases that roughly resemble Ret II, we repeat many random realizations and keep three cases for which the uncorrected dispersion σ clip lies between 2.5–3.5 km s −1 . This required several realizations before three such cases were found for either scenario. In scenario 2 (σ = 2.7 km s −1 ), we did choose one realization with σ clip = 2.3 km s −1 to see if a non-zero intrinsic dispersion can be recovered even for a lower σ clip value. After applying our analysis to these mock data, the resulting posteriors in the intrinsic dispersion are plotted in Fig. 5(a) for the scenario where σ = 0 km s −1 . In all three cases we infer that σ 1.5 km s −1 at greater than 90 per cent confidence; the most probable intrinsic dispersion is well below 1 km s −1 in all cases. Thus, our method appears unlikely to find a peak at σ > 2 km s −1 if the apparent velocity dispersion is dominated by binaries. In Fig. 5(b) we plot the resulting posteriors for the cases where the true σ = 2.7 km s −1 . Here we see that σ = 0 km s −1 is ruled out in all cases; in none of these cases do we find a most probable intrinsic dispersion lower than 2 km s −1 . This builds confidence that, despite the relatively small sample size, our binary-corrected velocity dispersions can be trusted. Figure 5. Posteriors in velocity dispersion for simulated data sets, where the velocity measurements are randomly resampled for each star in Ret II. In (a) we simulate a ‘galaxy’ with intrinsic dispersion σ = 0 km s −1 , while in (b) the simulated galaxy has intrinsic dispersion σ = 2.7 km s −1 . Although each case has a high uncorrected dispersion σ clip determined from sigma-clipping, our method can clearly differentiate between these two scenarios despite the relatively small sample sizes. As we mentioned above, despite Ret II having a most probable intrinsic dispersion of 3 km s −1 in our analysis, there is still a non-zero probability of σ = 0 km s −1 . What are we to make of this? First keep in mind that if the K15 measurements are not systematically offset from the higher resolution J15 measurements by ≈1–3 km s −1 , the probability of Ret II having σ = 0 km s −1 essentially vanishes. If we retain the possibility of a large offset between the K15 and J15 data sets, then there are three possible explanations: either (i) Ret II’s true intrinsic dispersion is in fact somewhere between 0–3 km s −1 , and the peak appearing at 3 km s −1 is merely due to the small sample size; (ii) Ret
Paper: 2019ApJ...880...70G (fulltext)
Sky Survey (RASS). For A478 and Ophiuchus, we corrected the MCXC luminosities by factors of 1.34 and 1.25, respectively, to account for the different N H derived from our Chandra fits, which were factors of 2.0 and 1.8 higher than the database values adopted for the RASS catalogs. This makes the luminosities internally consistent with those for the central regions that were derived directly from Chandra observations. For Perseus, even the 0.15 R 500 radius is not fully covered by one Chandra ACIS-I observation, so we used the Chandra mosaic from Aharonian et al. ( 2017 ) to derive the luminosity within 0.15 R 500 . To convert the 0.1–2.4 keV luminosities to the more physically meaningful bolometric ones, we need gas temperatures, which we have derived from Chandra spectral fits in the same regions for most clusters. The temperature fits are summarized in Table 6 ; their errors include the statistical uncertainty and a 10% uncertainty on the value of N H . For Perseus, Ophiuchus, and 2A 0335+096, Chandra coverage is too small to probe an average temperature within R 500 , so we used average cluster temperatures from the literature instead, which come from instruments with a larger field of view (see Table 6 ). For Perseus and Ophiuchus, whose total mass is estimated from the M 500 − T X relation (the 2A 0335+096 mass comes from Planck ), this should result in slightly underestimated masses and R 500 , because the cool core biases the average temperature low. This will not affect our conclusions in any significant way. For a further eight clusters, marked with a in Table 6 , the Chandra field of view is smaller than R 500 but covers enough of it to derive a reliable temperature for the region, considering the steeply declining radial brightness profiles and the relatively small contribution of the missing emission to the spectrum. For these clusters, we use Chandra temperatures fitted in smaller regions given in the notes for Table 6 . For Perseus, for simplicity, we used one ACIS-I pointing (Table 5 ) out of many available to derive a temperature for r 0.15 R 500 , as it almost covers the whole region. The statistics of that data subset are more than sufficient. The above simplifications are adequate because we are using the temperatures only for the luminosity conversion. For each cluster region, we converted the 0.1–2.4 keV luminosities to the bolometric luminosities (Table 6 ) using a conversion factor appropriate for the corresponding temperature (Böhringer et al. 2004 ). The luminosity errors in Table 6 include the uncertainty on the temperature and a 10% ROSAT – Chandra flux cross-calibration uncertainty, added in quadrature. The latter was evaluated by comparing the R 500 0.1–2.4 keV luminosities derived from Chandra and those determined from ROSAT for the 10 clusters in our sample with both the Chandra and the MCXC measurements. We found that our Chandra luminosities are, on average, 6% higher than the RASS ones, with a scatter of ∼10%.
Paper: 2020ApJ...898..139W (fulltext)
observed data. However, it is worth noting that the end frequency may be difficult to detect as LIGO is not currently very sensitive to frequencies above ∼1000 Hz. Figure 11. Example of GW quantities considered in the correlation matrix for a 15 M ⊙ progenitor with α Λ = 1.25, which does not explode successfully. The blue line is the early slope between 500 and 1000 Hz, and the orange line shows the slope just prior to collapse , between collapse to a BH and 500 Hz below the final frequency. These quantities will be used to compare between simulations and explore correlations with properties of the progenitor and explosion. Green line is the function fit of the peak GW frequency after 0.5 s postbounce. Although the parameters we consider seem rudimentary, we experimented with using various arbitrary functional fits to the frequency evolution with time, but found that as with the neutrino evolution, the fitting parameters were not well correlated with the progenitor and explosion process, especially in comparison with the parameters we have adopted. For example, Morozova et al. ( 2018 ) used a quadratic fit of the form to characterize the peak GW frequency between 0.5 and 1.5 s postbounce. However, we have found that although this functional fit captures well the shape of the peak frequency at early times, it fails to capture the behavior as the peak frequency levels out at late times ( t > 1.5 s postbounce). We propose instead a fit of the form , which better captures the behavior at later times. An example of such a fit is shown in Figure 11 . The coefficients of both functional fits are not well correlated with properties of the progenitor or explosion. Nonetheless, such fits may be useful for studies of detectability, as priors in LIGO data searches, etc., and thus the fit coefficients can be found online, 11 11 Publicly available at [doi: 10.5281/zenodo.3822555 ]. but we will not discuss them further in this work in our exploration of correlated quantities. It is easiest to see how these GW parameters change with the properties of the progenitor and explosion by looking at the correlation matrix. Figure 12 shows the correlation matrix between progenitor, explosion, and GW properties for simulations that explode successfully. Again, there are many additional parameters that could be included, but all showed weak correlations with the GW emission and thus we do not include them here. The GW eigenfrequencies are intimately related to the PNS mass and the core compactness ξ 2.5 , because they are sensitive to the structure of the core and its evolution. Figure 12. Correlation matrix for observable GW quantities and underlying progenitor and explosion properties for simulations that successfully explode. The quantities considered are core compactness ( ξ 2.5 ), PNS mass () at 1 s postbounce, explosion energy ( E expl ), and early peak GW frequency slope () between 500 and 1000 Hz. The lower-left half shows the correlation strength, ranging
Paper: 2020A&A...633A..71P (acknowledgments)
CP and VFC are funded by Italian Space Agency (ASI) through contract Euclid – IC (I/031/10/0) and acknowledge financial contribution from the agreement ASI/INAF/I/023/12/0. We acknowledge the support from the grant MIUR PRIN 2015 Cosmology and Fundamental Physics: illuminating the Dark Universe with Euclid .
Paper: 2018A&A...614A..73F (acknowledgments)
Acknowledgements The authors thank Jaime de la Cruz Rodríguez for his helpful comments on an early version of the manuscript. Financial support from the Spanish Ministry of Economy and Competitivity through projects AYA2014-55078-P, AYA2014-60476-P and AYA2014-60833-P is gratefully acknowledged. The authors wish to acknowledge the contribution of Teide High-Performance Computing facilities to the results of this research. TeideHPC facilities are provided by the Instituto Tecnológico y de Energías Renovables (ITER, SA). URL: http://teidehpc.iter.es . This work was also supported by computational resources provided by the Australian Governmentthrough the Pawsey Supercomputing Centre under the National Computational Merit Allocation Scheme.
Paper: 2016MNRAS.457.2554C (acknowledgments)
This work is partly supported by the DST-India grant no. Sr/S2/HEP-048/2012 and an India–Israel joint research grant (6-10/2014[IC]). We thank Matt Kunz for sharing the code that was used in Latter Kunz ( 2012 ). We also thank Ramesh Narayan for useful discussions on the global linear stability analysis.
Paper: 2020A&A...639A..91L (fulltext)
</inline-formula> and <inline-formula id="FI40"> <alternatives> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="mml_eq62"> <mml:mover accent="true"> <mml:mrow> <mml:mi mathvariant="sans-serif">B</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="false"> ̄</mml:mo> </mml:mrow> </mml:mover> </mml:math> <tex-math id="tex_eq62"></tex-math> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" id="img_eq62" mime-subtype="gif" mimetype="image" xlink:href="aa37995-20-eq62.gif"></inline-graphic> </alternatives> </inline-formula><sup>sCOLA</sup>) Poisson solver (). To make sure that differences between Φ sCOLA ( x , a ) and Φ( x , a ) are as small as possible, we make use of a Poisson solver with Dirichlet boundary conditions, instead of assuming periodic boundary conditions. Such a Poisson solver uses discrete sine transforms (DSTs) instead of FFTs, and requires the boundary values of Φ in six planes (west, east, south, north, bottom, top) surrounding the PM grid (see Appendix C ). These planes have a thickness of N ghost cells (depending on the value of the FDA used to approximate the Laplacian); they are represented by hatched regions in Fig. 2 , right panel. At each scale factor a K when the computation of accelerations is needed, the Dirichlet boundary conditions are extracted from the precomputed Φ BCs ( x , a K ) (step C.3., see Sect. 3.3 ). Ideally, Φ BCs ( x , a K ) should be the exact, non-linear gravitational potential in the full volume at a K , Φ( x , a K ). However, knowing this quantity would require having previously run the monolithic simulation in the full volume, which we seek to avoid. In this paper, we rely instead on the linearly-evolving potential (LEP) approximation ( Brainerd et al. 1993 ; Bagla Padmanabhan 1994 ), namely The idea behind this approximation is that the gravitational potential is dominated by long-wavelength modes, and therefore it ought to obey linear perturbation theory to a better approximation than the density field. In Eq. ( 23 ), we have assumed that the linear growth factor D 1 is normalised to unity at the scale factor corresponding to the initial conditions. The precomputation of Φ BCs in step C.3. is therefore an interpolation from the LPT grid to the PM grid and a simple scaling with D 1 ( a K ). The output of the Poisson solver is the gravitational potential Φ sCOLA ( x , a K ) on the PM grid, in the interior of the sCOLA box (dark green grid points in Fig. 2 , right panel). Consistently with the treatment above, Φ sCOLA ( x , a K ) is padded using the values of Φ BCs ( x , a K ) in 2 N ghost cells around the PM grid, in each direction (light green and yellow regions in Fig. 2 , right panel). Therefore, the only difference between and Δ x resides in using the LEP instead of the true, non-linear gravitational potential at the boundaries of the sCOLA box. Accelerations ( and sCOLA ). Given the gravitational potential Φ sCOLA ( x , a K ), accelerations are computed
Paper: 2017ApJ...838...40M (fulltext)
3000 au × 3000 au region of the densest collapsed core in each simulation (projections for the entire simulation domain are shown in Figure 2 ). The mean field points in the horizontal ( x ) direction. The strong-field case () shows highly elongated structure (perpendicular to the mean-field direction) and a classical hourglass magnetic field morphology aligned with the mean-field direction. The weak-field simulations () show more chaotic magnetic field morphology dictated by turbulence, with pinches and clumps. Figure 3 shows the deviation in the mean magnetic field () from its large (parsec) scale direction as a function of scale: alignment becomes poorer with weaker mean-field strengths. The degree of alignment can remain tight to the smallest scales in the strong mean-field regime. Figure 3 also shows a related quantity: the (volume) fraction of gas within a sphere of radius r (the length scale) that has a magnetic field aligned within 30° of the large-scale mean-field value (). Figure 1. Density-averaged line-of-sight magnetic field and projected gas densities zoomed in on a core in each of the simulations. The arrow in the top left represents the initial magnetic field. Figure 2. Density-averaged line-of-sight magnetic field and projected gas densities of the entire simulation domain (5.2 pc) for the four simulations. Each box is centered on the corresponding prestellar cores shown in Figure 1 . Figure 3. Top: deviation of the mean magnetic field averaged over some length scale around the core from the large (parsec) scale field. Bottom: fraction of gas inside a sphere of radius the length scale with the magnetic field aligned within 30° of the large-scale mean-field value. 4. Density Scaling of the Magnetic Field The relationship between density and magnetic field is important for theoretical models and observations. B ∝ ρ 2/3 is predicted for collapsing clouds with weak magnetic fields (i.e., gravitational energy dominating magnetic energy). The same scaling is also predicted for flux-frozen, isotropic collapse in general. Lazarian et al. ( 2012 ) interpreted the observed B ∝ ρ 2/3 from Zeeman measurements as a signature of reconnection diffusion being most efficient at lower densities (i.e., before collapse proceeds) in the weak-field limit. In contrast, theoretical predictions for anisotropic contraction models, often expected in the strong-field limit, predict a weaker scaling: B ∝ ρ 1/2 (Tritsis et al. 2015 ). We test the density magnetic field scaling relationship using our high dynamic range Arepo simulations. We plot the occupation of the gas in the density/magnetic field phase diagram for our simulations in Figure 4 . The collapse occurs where the gas is compressed by turbulence above the critical density , which is defined as the density at which the background level of turbulent pressure is subdominant to the gas pressure (Krumholz McKee 2005 ; Li et al. 2015b ). That is, the critical pressure is obtained by equating with .
Paper: 2019A&A...627A.161P (acknowledgments)
Acknowledgements We thank an anonymous referee for helpful comments that improved the quality of this paper. CARMENES is an instrument for the Centro Astronómico Hispano-Alemń de Calar Alto (CAHA, Almería, Spain). CARMENES is funded by the German Max-Planck-Gesellschaft (MPG), the Spanish Consejo Superior de Investigaciones Científicas (CSIC), the European Union through FEDER/ERF FICTS-2011-02 funds, and the members of the CARMENES Consortium (Max- Planck-Institut für Astronomie, Instituto de Astrofísica de Andalucía, Landessternwarte Königstuhl, Institut de Ciències de l’Espai, Insitut für Astrophysik Göttingen, Universidad Complutense de Madrid, Thüringer Landessternwarte Tautenburg, Instituto de Astrofísica de Canarias, Hamburger Sternwarte, Centro de Astrobiología, and Centro Astronómico Hispano- Alemán), with additional contributions by the Spanish Ministry of Economy, the German Science Foundation through the Major Research Instrumentation Programme, and DFG Research Unit FOR2544 “Blue Planets around Red Stars”, the Klaus Tschira Stiftung, the states of Baden-Württemberg and Niedersachsen, and by the Junta de Andalucía. This work is based on data from the CARMENES data archive at CAB (INTA-CSIC). We acknowledge financial support from the Agencia Estatal de Investigación of the Ministerio de Ciencia, Innovación y Universidades, and the European FEDER/ERF funds through projects AYA2018-84089, ESP2017-87676-C5-1-R, ESP2016-80435-C2-1-R, AYA2016-79425-C3-1/2/3-P, AYA2015-69350-C3-2-P, and the Centre of Excellence “Severo Ochoa” and “María de Maeztu” awards to the Instituto de Astrofísica de Canarias (SEV-2015-0548), Instituto de Astrofísica de Andalucía (SEV-2017-0709), and Centro de Astrobiología (MDM-2017-0737), and the Generalitat de Catalunya/CERCA programme. Some of the calculations presented here were performed at the RRZ of the Universität Hamburg, at the Höchstleistungs Rechenzentrum Nord (HLRN), and at the National Energy Research Supercomputer Center (NERSC), which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098. We thank all these institutions for a generous allocation of computer time. P.H.H. gratefully acknowledges the support of NVIDIA Corporation with the donation of a Quadro P6000 GPU used in this research. This work has made use of the VALD database, operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna. This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
Paper: 2017ApJ...847..124H (acknowledgments)
We thank the anonymous referee for careful consideration of this manuscript and helpful comments. J.K.T. thanks G. Valori and F. Zuccarello for helpful discussions. This study was supported by the Austrian Science Fund (FWF): P25383-N27, P27292-N20, V195-N16; and by the Österreichischer Austauschdienst (OeAD), the Slovak Research and Development Agency (SRDA): SK 01/2016, SK-AT-2016-0002; the Scientific Grand Agency: VEGA 2/0004/16, the Thousand Young Talents Plan (a sub-program of the 1000 Talent Plan); and the Joint Research Fund in Astronomy (U1631242) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and the Chinese Academy of Sciences (CAS). SDO is a mission for NASA’s Living With a Star (LWS) Program. SDO data are courtesy of the NASA/ SDO and HMI science team. RHESSI is a NASA Small Explorer Mission. GOES is a joint effort of NASA and the National Oceanic and Atmospheric Administration (NOAA).