adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.468.1420F (fulltext)
SDSS DR7 and DR13 galaxy samples in two redshift bins for the different models of the LF described in the text. The quoted errors represent marginalized 68percent confidence intervals. Considering total bulk flow amplitudes, the DR13 estimates are generally smaller than the DR7 ones. Averaging the results over the different LF models yields values of and 48595 in units of km s 1 for the low- and high-redshift bin, respectively. In the high- z bin, the flow amplitudes are reduced by about 50200kms 1 compared to DR7, where the most significant changes appear for the Schechter model of the LF. The bulk flow components found from estimators using different LF models are in reasonable agreement, typically consistent within the quoted uncertainties. Assuming the results based on the hybrid model, the estimated bulk flows from DR13 are pointing towards ( l , b ) (315, 17)(52, 15) and (304, 22)(14, 11) for the first and second redshift bin, respectively. Similar directions are obtained for the other estimators. To better assess these measurements, we repeated the analysis for the 269 mock galaxy catalogues introduced in Section 3.2 . In contrast to the study presented in Feix etal. ( 2014 ), these mocks do not account for the photometric tilt of the SDSS DR7 calibration that severely contaminates bulk flows measured through the luminosity approach. The results based on our mocks suggest that the individual flow components obtained with our methods are not statistically independent, but subject to correlations at the level of 0.10.3. If the joint distribution of bulk flow components is approximately given by a multivariate Gaussian with covariance matrix B , the quadratic form should follow a 2 -distribution with k 3 degrees of freedom. To test the validity of this assumption, we computed directly from the SDSS mocks. For the Schechter LF model, the resulting distributions, plotted as histograms in Fig. 2 , are indeed well matched by the 2 -distribution (solid curves). Adopting the other models of the LF yields very similar results. Using the estimate of B from the mock catalogues, we may therefore assign probabilities to the bulk flow measurements listed in Table 1 . As customary, we express these in terms of confidence limits based on normally distributed data. Figure 2. Distribution of the quadratic form in two redshift bins derived from the SDSS mock catalogues, assuming the Schechter model of the LF: the histograms represent the observed (normalized) distribution and solid lines correspond to the 2 -distribution with k 3 degrees of freedom. The corresponding event levels (in units of the standard deviation ) for the inferred bulk flow amplitudes are presented in Table 2 . Compared to the DR7 results, the probability estimates of the DR13 measurements are on average considerably larger, and thus consistent with an improved photometric calibration. The most prominent change is found in the high- z bin for the Schechter model of the LF, where the
Paper: 2016MNRAS.458L..69B (acknowledgments)
RDB is grateful to S. Raimundo for the discussion which inspired this work. We thank the referee for constructive comments/suggestions to improve the manuscript. EB received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 655324, and from the I-CORE program of the Planning and Budgeting Committee (grant number 1937/12).
Paper: 2017MNRAS.472.1152R (acknowledgments)
Acknowledgements We thank the anonymous referee for their helpful comments. We are grateful to Phil Uttley for his suggestion to look into this and to Yuri Cavecchi, Daniela Huppenkothen, Anna Watts and Ralph Wijers for very useful discussions. AP acknowledges support from a Netherlands Organization for Scientific Research (NWO) Vidi Fellowship. PTO would like to acknowledge funding from STFC. This work makes use of data supplied by the UK Swift Science Data Centre at the University of Leicester and the Swift satellite. Swift , launched in November 2004, is a NASA mission in partnership with the Italian Space Agency and the UK Space Agency. Swift is managed by NASA Goddard. Penn State University controls science and flight operations from the Mission Operations Center in University Park, Pennsylvania. Los Alamos National Laboratory provides gamma-ray imaging analysis.
Paper: 2018A&A...618A.183S (acknowledgments)
IPGP contribution NS 3968. Data processing and statistical procedure described in Sect. 3 was performed by A. Shapoval and M. Shnirman under support of the Russian Science Foundation (project No 17-11-01052)
Paper: 2019MNRAS.489.4378S (fulltext)
stars within 10 arcmin of 30 Dor. In this resulting spectroscopically selected pseudo-BBS sample, the early B-type stars outnumber the O-type stars roughly 3–1, although the implications of this are unclear since we don’t know the level of incompleteness for either. Fig. 2 shows an HR diagram with the spectroscopic OB stars plotted without (left) and with (right) an average reddening correction of E ( B − V ) = 0.13 mag applied (the average value for OB stars in the LMC adopted by A19). Also shown for comparison are LBV stars in the LMC and SMC, plotted using T eff and L Bol values from the literature (see Smith et al. 2019 ), and single-star evolutionary tracks (Brott et al. 2011 ). OB stars are placed on this HR diagram by taking their apparent or reddening corrected B − V colour as a proxy for temperature, and the luminosity comes from the V mag and a bolometric correction for the corresponding T eff value, with relations adopted from Torres ( 2010 ) and Flower ( 1996 ). These are by no means intended to be taken as accurate T eff and L Bol values; they are meant to illustrate the range of these values one might infer from apparent magnitudes and colours when only photometry is available. The red diagonal lines in Fig. 2 indicate where the V mag cut-off resides. Even though these are not the same stars as in the photometric BBS sample, there are several salient points that one can glean from Fig. 2 . Figure 2. HR diagrams comparing LBVs to inferred properties of OB stars derived from photometric colours. The T eff value used to plot each OB star is the temperature one would infer by converting the apparent B − V colour to a temperature, and the luminosity comes from the bolometric correction for that T eff value (relations adopted from Torres 2010 and Flower 1996 ) and its apparent V magnitude. The left-hand panel (a) shows the result when no reddening correction is adopted, whereas the right-hand panel (b) shows the values after applying a single average correction for E ( B − V ) = 0.13 mag to all OB stars. The black unfilled circles are spectroscopically confirmed O-type stars (similar to the sample in ST15), and the blue filled circles are spectroscopically confirmed early B-type stars, with spectral types between O9.5 and B2 (see text). Both samples are limited to spectroscopically confirmed O or early B stars brighter than V = 13.9 mag (to be consistent with the value adopted by A19), corresponding roughly to an absolute V magnitude of M V = −5. The diagonal red line in each panel shows the cut-off of V = 13.9 mag, and the four red hash marks show temperatures corresponding to different values of ( B − V ) = −0.32, −0.30, −0.20, and −0.10 mag. In both panels, the orange dashed lines are single-star evolutionary tracks from Brott et al. ( 2011 ), with initial masses in M ⊙ labelled in orange. Light yellow and blue shaded areas indicate expected effective temperature and luminosity ranges for O-type and early B-type stars
Paper: 2018MNRAS.480.1870S (fulltext)
the difference to Cuzzi et al. ( 2010 )’s assumption that the grains would be sized to optimally couple to the smallest eddies and questioned whether the pebble concentrations in Cuzzi et al. ( 2010 )’s semi-analytic model exceeded what is physically plausible. If the assumptions in Cuzzi et al. ( 2010 ) are unrealistic and if Hopkins ( 2016 )’s modelling and assumptions capture all the important physics, then larger numbers of, and better known orbits for the ultra-wide binaries could constrain whether the turbulent concentration mechanism produces excessively large planetesimals that would have disrupted the TNOs. Pebble Accretion is a promising mechanism for growing planetesimals and protoplanets (Ormel Klahr 2010 ; Lambrechts Johansen 2012 ). It substantially enhances the cross-section for accreting small particles that are partially coupled to the gas and have their relative velocities damped by gas drag (see Johansen Lambrechts 2017 , for a review). Johansen et al. ( 2015 ) considered the evolution of a population of planetesimals growing by pebble accretion. They began with a size distribution of large bodies derived from simulations of the Streaming Instability (Youdin Goodman 2005 ; Johansen et al. 2007 ), which produces q ∼ 2.8 with a maximum mass of (Simon et al. 2016 , 2017 ). Johansen et al. ( 2015 ) considered the subsequent evolution of the planetesimals in this environment. They performed simulations of a narrow ring at 25 au and found that a single body underwent significant growth, reaching roughly an Earth mass, amounting to roughly one-third of the mass in that ring. If this result for a single 3 M ⊕ ring can be extended to a 30 M ⊕ disc spanning 20–30 au, the disc would produce ∼10 Earth mass bodies, inconsistent with the limits derived here. Whether this result is generally applicable to Pebble Accretion depends on the sensitivity of final mass-number distribution to factors such as the initial mass-number distribution, which is yet to be broadly explored. Similar to Pebble Accretion, the collisional pebble agglomeration model of Shannon, Wu Lithwick ( 2016 ) also produces a top heavy q ∼ 2 size number distribution, but with the largest bodies in the 10 s to 100 s of kilometers in size, dependent on the composition of the pebbles. Here, we use the code and general setup from that work, but consider a belt from 20 to 30 au that begins with 30 M ⊕ . With little mass in bodies larger than ∼100 km, it is compatible with the limits we have found. In addition to testing planetesimal formation models, our limits offer constraints on the dynamical history of the solar system. Several other works have invoked large bodies within the pre-Scattered disc to explain features of the Solar system. As described in Section 1 , Nesvorný Vokrouhlický ( 2016 ) invoked 1000–4000 bodies of mass or 1000 objects of mass in the pre-Scattered disc to produce the inferred capture rate of resonant objects. These choices would be incompatible with the
Paper: 2015ApJ...805..120M (acknowledgments)
We thank the anonymous referee for comments and suggestions. This paper was partially funded by the PRIN INAF 2014 grant. M.M. thanks M. Dadina for discussions about the X-IFU instrumental background.
Paper: 2018ApJ...853..137V (acknowledgments)
The authors would like to thank Janet Weiland and Chuck Bennett for providing the ILC data in a compatible format, Tom Dame and Chris Salter for helpful discussions, and the anonymous referee for guidance for improving an early version of this manuscript. No thanks are due Hurricane MARIA, which made our task that much more difficult.
Paper: 2021MNRAS.500.5515Y (fulltext)
with gravity modes or motions caused by subsurface convection, the cause of these variabilities is not properly understood. Thus, with the motivation to understand the dominant variability of 2.7 d in κ Cassiopeiae, we shall present here a linear non-adiabatic stability analysis of models for κ Cassiopeiae. The result of instabilities will then be determined by following them into the nonlinear regime for selected unstable models using a fully conservative numerical scheme. This paper falls into five sections where a description of the models used is given in Section 2 , and the linear stability analysis together with its results are discussed in Section 3 . The nonlinear simulations and their results are presented in Section 4 . A discussion and conclusions follow in Section 5 . 2 MODELS FOR κ CASSIOPEIAE Although the star κ Cassiopeiae has been the subject of several studies (see e.g. Underhill 1979 ; Percy 1981 ; Hayes 1984 ; Katushkina et al. 2018 ), its fundamental parameters (in particular its mass) are not precisely known. For the present investigation, we adopt the effective temperature ( T eff = 24 600 K) and luminosity (log L /L ⊙ = 5.69) estimated by Simón-Díaz et al. ( 2018 ) using high-resolution spectra of κ Cassiopeiae. These values are close to those determined by Kudritzki et al. ( 1999 ), Smartt et al. ( 2002 ), and Evans et al. ( 2004 ). Due to the uncertainty of the mass, we consider a range of models with masses between 27 and 44 M ⊙ . This mass range covers the mass of 33 M ⊙ suggested for κ Cassiopeiae by Searle et al. ( 2008 ) on the basis of stellar evolution calculations. Evolutionary tracks for models having solar chemical composition and masses of 30, 35, 40, and 45 M ⊙ respectively are shown in Fig. 1 where a thick dot corresponds to the observed position of κ Cassiopeiae in the Hertzsprung–Russell (HR) diagram. These tracks have been generated by the ‘mad star’ stellar evolution code. 1 The position of κ Cassiopeiae in the HR diagram suggests that models representing this star have masses below and close to 40 M ⊙ (see Fig. 1 ). Figure 1. Evolutionary tracks of stars with solar chemical composition and having masses in the range between 30 and 45 M ⊙ . The observed location of κ Cassiopeiae in the Hertzsprung–Russell diagram is marked by a thick dot. The amplitudes of radial eigenmodes decay exponentially from the stellar surface to the core. Thus, the latter may be disregarded when studying stellar stability (see e.g. Glatzel Kiriakidis 1993 ; Glatzel, Kiriakidis Fricke 1993 ; Saio 2011 ; Yadav Glatzel 2016 , 2017a ). Accordingly, our present study is restricted to envelope models of κ Cassiopeiae thus disregarding any influence of nuclear energy generation and nucleosynthesis. The envelope models with solar chemical composition ( X = 0.70, Y = 0.28, and Z = 0.02; where X , Y , and Z represent the mass fraction of hydrogen, helium, and heavy elements, respectively) are constructed by integrating the stellar
Paper: 2019A&A...624A.139B (acknowledgments)
I am grateful to Gabriel Martínez-Pinedo for providing stellar weak rates tables on a fine grid. Thanks are due to Robert Fisher, for interesting discussions concerning the floatability of hot bubbles, and to Ivo Seitenzahl and Friedrich Röpke for clarifying the origin of the differences between the present results and those of Parikh et al. The referee, Chris Fryer, has made interesting suggestions to improve the presentation of this paper. This work has been supported by the MINECO-FEDER grant AYA2015-63588-P.
Paper: 2017A&A...608A.100E (acknowledgments)
This research has been funded by the Belgian Science Policy Office under contract BR/143/A2/STARLAB and the Fonds voor Wetenschappelijk Onderzoek Vlaanderen (FWO). A.E. acknowledges support from the FWO. S.V.E acknowledges the Fondation ULB. L.S. and D.P. are Senior FNRS Research Associates. D.K. acknowledges support from BELSPO. The authors want to acknowledge the referee, Claudio B. Pereira, for his interest and very valuable input. 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. This research has also made use of the SIMBAD database, operated at CDS, Strasbourg, France.
Paper: 2019ApJ...882L..27D (fulltext)
1 ). We extracted photometry for point sources in each individual image to construct light curves of Sgr A* and calibration stars. The brightness and position of all sources were measured using the point spread function (PSF)-fitting software StarFinder (Diolaiti et al. 2000 ). Details on running StarFinder on individual frames for Galactic center observations can be found in Do et al. ( 2009 ). Sgr A* is detected in almost every frame of observations on three nights. There are fewer detections of Sgr A* on 2019 May 19 due to lower-quality AO correction and faint flux levels for Sgr A*. We calibrate the point source photometry using reference stars defined by Gautam et al. ( 2019 ). We convert between K ′ magnitudes to absolute fluxes F , using the relationship mJy (Tokunaga Vacca 2005 ). To convert from K ′ fluxes to K s fluxes, we use the filter transformation computed for the color of Sgr A* (Chen et al. 2019 ). We will mainly use observed fluxes in this work to avoid confusion with the value of extinction to apply. To de-redden the flux measurements, one can use the relationship . To compare to fluxes from (Witzel et al. 2012 ), use (Schödel et al. 2010 ). We estimate the Sgr A* relative photometric uncertainties by using the relationship between flux level and flux uncertainty of nearby stars. The photometric uncertainties for stars on each night were determined by using the standard deviation of their individual flux measurements. We find that the reference stars are stable during the nights with 3% photometric errors. Following Do et al. ( 2009 ), we fit a power-law to the relationship between flux level and flux uncertainty for stars within 1′′ of Sgr A*. We then use this relationship to infer the flux uncertainty for the all flux measurements of Sgr A*. The Sgr A* relative photometric uncertainties are typically less than 5% at high flux levels and about 15% for faint flux values. 3. Results Our observations show Sgr A* to be highly variable in 2019. On 2019 May 13, Sgr A*'s flux level changed by a factor of 75 within 2 hr (from 6.19 ± 0.08 mJy to 0.08 ± 0.01 mJy). The maximum observed fluxes occurred during the beginning of the observations, suggesting that Sgr A* was likely even brighter earlier in the night. On the night of 2019 April 20, Sgr A* flux also shows large variations with measurements ranging from 1.74 ± 0.04 mJy to 0.07 ± 0.01 mJy. The two other nights of our study (2019 April 19 and 2019 May 23) show less variation at the level of one to three magnitudes of change during the night. Figures 1 – 3 show the observed light curves. Figure 2. K ′ light curves of Sgr A* (black) and a comparison star, S0–17 (white, located about 02 from Sgr A*), on four nights of observations in 2019. We use stars within 1′′ of Sgr A* to characterize the photometric error at different Sgr A* brightness levels. The photometric uncertainties are typically less than 5% at high flux levels. Observations on 2019 May 13 also include H -band
Paper: 2020ApJ...898L..38R (fulltext)
be well fit by rigid body rotation at a rate of 0216 ± 0016 yr −1 . Taking into account of SPHERE’s true north uncertainty, the updated motion rate isThis pattern speed points to a driver located at au, or , from the 1.9 M central star (Figure 3 ). Figure 3. Dynamically constrained, assumed circular orbit of a single arm driver. For a 1.9 M central star, the best-fit ±1 σ orbit has a radius of ( au). Our best-fit measurement of the companion-driven spiral pattern speed is consistent with the R18 measurement within 3 σ , 18 18 Only 3 σ uncertainties are well-constrained in R18 . while our derived uncertainty is ∼40 times smaller, thanks to the use of the same instrument and the maps that are the least biased by postprocessing methods. In numerical simulations, an approximately five Jupiter mass arm driver located at ∼09 has been proposed by Baruteau et al. ( 2019 ), which is within 2 σ from our best-fit companion location assuming a 1.9 M central star, or 1 σ assuming a 1.56 M central star. 4. Discussion 4.1. Direct Imaging Constraints We obtain the direct imaging constraints on the mass of the putative planet orbiting MWC 758 with hot-start evolutionary models (i.e., Sonora, Bobcat; M. Marley et al. 2020, in preparation) using 6.73 h of Keck/NIRC2 -band archival observations: 2025 s on 2015 October 24 (Program ID: C220N2, PI: E. Serabyn, Reggiani et al. 2018 ), 11,160 s on 2016 February 12 (Program ID: U131N2, PI: E. Chiang), 3200 s on 2016 October 24 (Program ID: C221N2, PI: G. Ruane, Reggiani et al. 2018 ), and 7830 s on 2017 February 2 (Program ID: U072, PI: E. Chiang). Following the method described in Ruffio et al. ( 2018 ) and taking into account of the orbital period uncertainty for the driver, we obtain an apparent L -band magnitude of 18 at 99.9% confidence level (i.e., a 3 σ equivalent upper limit). Adopting an age of 10.9 Myr (Garufi et al. 2018 ), this corresponds to a mass of 5 M Jupiter . Using the contrast curve of Keck/NIRC2 in the M s -band (Mawet et al. 2019 ) and assuming Gaussian noise, we expect that a 5 M Jupiter planetary driver, whose mass has been predicted by Dong et al. ( 2015b ) and Baruteau et al. ( 2019 ) with the semimajor axis updated in this study, can be detected at 5 σ level if it is a hot-start planet using four half-nights of NIRC2 M s -band high contrast imaging observations (or at 3.5 σ with the Spiegel Burrows 2012 cold-start model using the same observation). 4.2. Eccentric Driver If the driver has a non-zero eccentricity e , its semimajor axis cannot be uniquely determined from an assessment of the instantaneous angular frequency due to the unknown orbital phase. Giant planets of several Jupiter masses interacting with a gaseous disk are expected to have their eccentricities quickly damped to below the disk aspect ratio, here about 20% (Dunhill et al. 2013 ; Duffell Chiang 2015 ). Simulations have also shown that density waves excited by planets with e ≳ 0.2 develop wiggles and bifurcations, as the
Paper: 2019ApJ...878...10T (acknowledgments)
We are grateful to the CSO staff for very efficient support, both on-site and during the later remote observations. Y.-W.T. is supported by the Ministry of Science and Technology (MoST) in Taiwan through grant MoST 108-2112-M-001-004-MY2. P.M.K. acknowledges support from MoST 107-2119-M-001-023 and from an Academia Sinica Career Development Award. A.D.C. acknowledges the support from the UK STFC consolidated grant ST/N000706/1. Facilities: CSO.
Paper: 2018MNRAS.475.1190Y (acknowledgments)
Acknowledgements We appreciate Paul Wiita, James Guillochon, Tinggui Wang, Wenfei Yu, Bin Liu, Morgan MacLeod, and Dacheng Lin for helpful discussions, the referee for suggestions, and Dimitris Emmanoulopoulos for providing us with the RXTE data of NGC 7213. This work was supported in part by the National Key Research and Development Program of China (2016YFA0400804), the Youth Innovation Promotion Association of CAS (id. 2016243), the Natural Science Foundation of Shanghai (No. 17ZR1435800), and the National Natural Science Foundation of China (grant Nos. 11403074, 11773055, 11333005 and 11350110498). This research has made use of data and/or software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory.