adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021A&A...650A.188A (fulltext)
of photometric long-term cycles in M dwarfs). Fig. 10 GLS periodograms of CARMENES activity indicators of G 264–012 in the VIS ( top panels ) and NIR ( bottom panels ) channels. Horizontal FAP lines and vertical signal indications are the same as in Fig. 3 . Since the CARMENES spectral range does not include the Ca II HK lines, we carried out an analysis of all available archival data covering this spectral region, namely the HARPS and HIRES spectra used in the RV analysis of this work (Sect. 4.1.2 ), 1 spectrum acquired by FEROS ( Kaufer et al. 1999 ), 3 spectra from UVES ( Dekker et al. 2000 ), and 10 from XSHOOTER ( Vernet et al. 2011 ). In order to extract from spectra acquired by all five instruments in a uniform manner, we rectified the spectra with a grid of PHOENIX spectra ( Husser et al. 2013 ) by extracting a set of narrow band passes around both the H and K lines. The approach will be described in detail in an upcoming publication (Perdelwitz et al., in prep.). The top panel of Fig. 12 shows the resulting time series. The smaller periodicity in the bottom panel of Fig. 12 , with a period of P = 36.6 ± 1.9 d, where the error was determined via a Monte Carlo approach, is in agreement with the stellar rotation period derived from Kepler K2 photometry (Sect. 3.1 ). Fig. 11 GLS periodograms of CARMENES VIS, HARPS, and HIRES activity indicators for Gl 393. Horizontal FAP lines and vertical signal indications are the same as in Fig. 4 . Fig. 12 Top panel : time series of Gl 393. The source of the archival data is denoted by the colour coding. Bottom panel : generalised Lomb-Scargle periodogram of the time series of Gl 393. The dashed vertical lines denote the periods of the activity cycle ( P ≈ 1226 d) and stellar rotation ( P ≈ 36.6 d) determined from the time series. FAP levels of 10 −3 and 10 −9 are displayed as horizontal lines. 4.5 Gaussian process analysis of RV data 4.5.1 G 264–012 All indicators gathered (Sect. 4.2 – 4.4 ) point towards activity as origin for the RV signal found at a period of P d = 92.70 d in G 264–012. There is, however, no indicator contradicting the planetary nature of the other two signals, P b = 2.30 d and P c = 8.05 d. In order to accurately determine their orbital parameters and masses, we account for stellar activity in our models. We choose a model with two circular Keplerian orbits (no evidence for eccentricity was found in these two very-short period planets, whose orbits should be almost certainly already circularised, see Sect. 4.1 ) for the planets combined with a Gaussian process (quasi-periodic kernel; see Eq. ( 1 )) for the activity signal. We present the fits in Fig. 13 , a summary of all parameters used in the model in Table 5 and the MCMC posterior distribution plot in Fig. A.1 . When subtracting the model from the VIS RV data we derived an rms of 2.22 m s −1 , which is consistent with the errorbars. Fig. 13 G 264–012. Panel a : GP fit (black solid line) and 1-sigma uncertainties (grey
Paper: 2020A&A...641A..82R (fulltext)
of magnetic variability. We demonstrate here that using multiband photometric light curves contemporaneous to transit observations, we can overcome this problem, at least partly. The advantage of using several photometric bands is that, as discussed in the previous section (see also Mallonn et al. 2018 ), it is possible to independently estimate the zero point with respect to the unspotted photosphere ( z ), and thus the absolute spot filling factor (including any persistent level of spottedness during the rotation cycle), and the temperature contrast. This provides extremely valuable information to correct transit depth variations due to spots. We can actually use our StarSim simulations of WASP-52 to investigate at which level of accuracy we can correct the effects of starspots on transit data. The StarSim simulations of the WASP-52 planetary system discussed in the previous section reveal that, without additional information allowing us to infer the distribution and filling factor of spots during the transit, the planet-to-star radius cannot be determined with an accuracy better than a few percent. This is because in that case, we do not know at which rotation phase of the star the transit is observed, and therefore, we can only estimate a range of possible filling factors based on the amplitude of the photometric modulation. However, if we know the rotation phase at the time of transit, and we can estimate the spot properties and distribution from light curves, we can infer the correction that needs to be applied to the relative radius of the exoplanet (or transit depth) with an accuracy of order 10 −4 , as shown in Table 4 . Figure 10 illustrates the sequence of spot corrections on the transit depth as a function of time. We plot the planet radius variation due to unnoculted spots, Δ r SP ∕ R * , following our StarSim model describing the photometric light curve in Fig. 8 . Each panel corresponds to a different wavelength band of interest for the Ariel mission, as an example. To test all possible filling factors, we consider transits occurring at any time, but actual WASP-52 b transits are indicated as filled dots. The average value of all the simulations performed is displayed as a solid line. It is clear from this figure that the variation of the apparent planet radius depends on the projected spot filling factor and follows the stellar rotation period. The relative effect on the radius diminishes towards longer wavelengths due to the lower flux contrast of spots, from an average of ~4 × 10 −3 to ~8 × 10 −4 at 550 nm (VIS-Phot) and 6 μ m (AIRS-Ch1), respectively. In all cases it is possible to estimate the unocculted spot correction to the planetary relative radius with a precision close to 10%, estimated as the standard deviation ( σ ) of the sample of 21 optimal fits to the light curves. As explained above, this residual uncertainty is due to the variance of the stellar parameters and the randomness of the determination of the active
Paper: 2019MNRAS.486.5709L (acknowledgments)
ACKNOWLEDGEMENTS ZL thanks the support from the China Scholarship Council. This work is supported by the Strategic Pioneer Program on Space Science, Chinese Academy of Sciences, Grant No. XDA15052100. PTOB acknowledges support from STFC. JPO, PAE, and KLP acknowledge support from the UK Space Agency. This work made use of data supplied by the UK Swift Science Data Centre at the University of Leicester. This work is based on observation obtained with XMM–Newton , an ESA science mission with instruments and contributions directly fund by ESA Member States and NASA, based on observations made with the NASA/ESA Hubble Space Telescope , and obtained from the Hubble Legacy Archive, which is a collaboration between the Space Telescope Science Institute (STScI/NASA), the Space Telescope European Coordinating Facility (ST-ECF/ESA) and the Canadian Astronomy Data Centre (CADC/NRC/CSA) The Chandra data are obtained from the Chandra Data Archive . This research has made use of software provided by the Chandra X-ray Center (CXC) in the application packages ciao .
Paper: 2018ApJ...866...98K (fulltext)
appropriate choice of the arbitrary function t 0 ( R ). Let us now find the general expressions for t sc ( R ) and t hor ( R ) for each type of solution ( 14 )–( 16 ). For further purposes, it is convenient to rewrite the solution in dimensionless unitsIn the rest of our paper, we shall use these dimensionless units and thus prefer to omit the tilde over the letters for brevity. The solution ( 14 )–( 16 ) in dimensionless units ( 26 ) reads: 1. The case of flat space: 2. The case of closed space: 3. The case of open space: By solving Equations ( 23 ) and ( 24 ) successively for each case ( 27 )–( 29 ), we obtain: 1. For k = 0 2. For k = 1 3. For k = −1 Here, in our calculations, we have used the fact that β is very large for realistic objects (estimative size of the universe is about m, and r g of a typical black hole in the galaxy center is about ∼10 10 m, i.e., one has β ∼ 10 16 ). Thus, we have employed the Taylor expansion for large β to get rid of transcendental equations for the open and closed spaces. Analyzing the expressions ( 30 )–( 32 ) and applying the condition ( 25 ), we come to the following form of the arbitrary functions t 0 ( R ): It is easy to see that these functions have the same limit at small R and the result ( 17 ) remains valid. To give an example of the behavior of the obtained model, we present in Figures 2 – 3 the profiles of the energy density and the Hubble parameter for the parabolic case of our solution ( 14 ). The energy density is given by ( 19 ). The Hubble parameter describing the rate of expansion of the inhomogeneous universe is generally defined according to Ellis ( 2009 ) as followswhere l is some characteristic length that corresponds to the scale factor a ( t ) in Friedmann models, and τ is proper time given in the standard way:Θ is the volume expansion given bywhere u α is usual 4-velocity of the dust particles. Figure 2. Energy density profile for the parabolic case of our solution given in dimensionless units. (a) At the initial (Big Bang) singularity there is an infinite value of the energy density that is decreasing with time, while in the center of symmetry there always holds ε ( R = 0, t ) = 0. (b) The structure of the profile at the initial singularity in the vicinity of the central point . There is a punctured infinite energy density cusp, which is rapidly decreasing with time. (c) The character of the energy density dependence on time for different values of R . The dashed curve corresponds to the parabolic Friedmann solution. The bigger is R , the closer is ε ( R , t ) to the Friedmannian line. (d) The picture at the epoch of the Milky Way birth. The number of each curve corresponds to the index of the value t (or R ) listed in the legend. Figure 3. The Hubble parameter profile for the parabolic case of our solution given in dimensionless units. (a) The line t = t +0( R ) marks the line of the initial singularity. (b) The structure of the profile at the initial singularity in the vicinity
Paper: 2015MNRAS.449.3827K (acknowledgments)
This research has made extensive use of data and/or software provided by the HEASARC, which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. We also acknowledge the use of NASA's Astrophysics Data System (ADS) and of the SIMBAD data base, operated at CDS, Strasbourg, France. In this work, we analysed observations performed with INTEGRAL , an ESA project with instruments and science data centre funded by ESA member states (especially the PI countries: Denmark, France, Germany, Italy, Switzerland, Spain) and with the participation of Russia and the USA, and we also analysed observations obtained with XMM–Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and the USA (NASA). Moreover, this research has made use of data obtained from the Chandra Data Archive.
Paper: 2020MNRAS.496.1598R (fulltext)
by stochastic kicks as seen by the torque fluctuating about zero, resulting in small inward and outward migration. The bottom panel is in a constant β disc, where it is clear that despite stochastic kicks being present, the torque, on average, is slightly negative, resulting in inward migration. Figure 14. Total z -component of the torque exerted by the disc material on a 1 M ⊕ planet is shown on the left-hand panels. The migration tracks are shown on the right-hand panels. The top five panels are simulations modelled with a variable β , whilst the bottom panel is one of the simulations modelled with a constant β . The migration is dominated by stochastic kicks, which results in random outward or inward motion, as seen in the torques oscillating about zero. 5.2 Comparison with previous work We compare our results to those of Baruteau et al. ( 2011 ) and Malik et al. ( 2015 ), who considered giant planet migration in self-gravitating discs. There are a couple of differences in this work. They perform their simulations using a 2D grid-based code, whereas the simulations in this work are performed using a 3D SPH code. The initial conditions of the disc are also slightly different. Their disc surface density and temperature decreased as R −2 and R −1 , respectively, whereas in this work, the disc surface density and temperature decreases as R −1 and R −0.5 , respectively. To ensure that the different initial conditions and codes used do not influence the results, we initially performed a set of simulations with a constant value of β = 15 to compare with Baruteau et al. ( 2011 ) and Malik et al. ( 2015 ). Despite the differences, we agree with their findings that using a constant β results in the giant planets rapidly migrating towards the inner edge of the disc. However, the crucial development in our work is how the disc is cooled: Whilst we also utilize the β -cooling approach, we allow β to vary with radius to mimic a more realistic disc such that our discs remain gravitationally stable in the inner regions, and the spiral structure only forms in the outer regions. The planets are able to slow down their rapid inward migration before reaching the inner disc edge. Using radiative transfer, Stamatellos Inutsuka ( 2018 ) migrate 1 M Jup planets in self-gravitating discs using a 3D SPH code. The initial conditions in their discs are more similar to ours: the same initial disc mass and surface density profile. Their usage of a realistic treatment of the disc thermodynamics meant that like in the simulations presented here, the planets are evolved in discs that are only gravitationally unstable in the outer regions. Using realistic thermodynamics to allow the gas to control its cooling/heating based on its properties, the thermodynamics of the circumstellar and circumplanetary disc is better captured. This enhances the mass growth of the planet causing the planets to grow to beyond the brown dwarf limit. This resulted in their planets opening up
Paper: 2018ApJ...857..115Y (fulltext)
feature tracking. 3. Results The polar crown prominence in the northwest limb (N63W72) had been stable for over two weeks before our observation began on 2016 June 4. Figure 1 shows the prominence of interest in the H α , AIA 131, 171, and 304 Å images. The prominence is bright in H α , but dark in the AIA images. Note that the intensity of the H α image is reversed, and that darker features in the image are actually brighter. The dark structure in the AIA images is wrapped in the bright emissive gas, indicating that the cool material is wrapped inside the hot coronal gas. The main body of the polar crown prominence lies vertical to the plane of the sky, and an arch-shape prominence newly emerged in the northern region of the polar crown prominence after 04:00 UT. The arch-shape prominence was brighter than the main prominence in the AIA images, but it was fainter in the H α images. The observed arch-shape prominence looks similar to the hedgerow prominence overlying a cavity observed by Ryutova et al. ( 2010 ) except for the horizontal flow within it. We detected a horizontal flow, which is highlighted in the next paragraph. Figure 1. Polar crown prominence observed on 2016 June 4, with the newly emerged arch-shaped prominence in the region north of it. The SDO/ AIA 131, 171, 304 Å data, and H α images (upper-left, upper-right, lower-left, and lower-right panels, respectively) show the formation of a vortex on the arch-shaped prominence. Note that the intensity of the H α image is reversed. The dashed boxes in the H α image represent the FOV of the Figure 3 (small red), Figure 4 (big red), and Figure 8 (blue). A full sequence of the images, covering from 08:09:43 to 09:31:46 UT, is provided in the animation. In the animation, 08:09:43 UT is taken to be the epoch of time t . (An animation of this figure is available.) Figure 2 shows H α images of the observed region at the moment of the rising of the arch-shape prominence and the formation of a 5′′-size vortex. The arrows plotted on the H α images represent the velocity vectors of the stream on the plane of the sky. Near the left-hand side leg of the arch-shape prominence (690′′, 680′′), the dominant direction of the plasma flow is upward along the arch. It rose up slowly (20 km s −1 ) and reached about a height of 20′′. Then it streamed to the right along the arch toward the polar crown prominence with a speed of 20–60 km s −1 . The stream can be clearly identified in both panels of Figure 2 . The measured collimated flow speed of about 20–60 km s −1 is higher than the typical speed of the prominence plasma (10–20 km s −1 ; Zirker et al. 1998 ; Ahn et al. 2010 ; Vial Engvold 2015 ). The full dynamics of the evolution can be seen better in the animation of Figure 1 . Very luckily, the flow lies parallel to the plane of the sky considering the narrow bandpass width of the filter. Figure 2. Velocity vectors superposed on H α images at the moment of the rising of the arch-shape prominence (left)
Paper: 2018A&A...616A..47T (acknowledgments)
Acknowledgements Daniel Thun was funded by grant KL 650/26 of the German Research Foundation (DFG). Most of the numerical simulations were performed on the bwForCluster BinAC. The authors acknowledge support by the High Performance and Cloud Computing Group at the Zentrum für Datenverarbeitung of the University of Tübingen, the state of Baden–Württemberg through bwHPC and the German Research Foundation (DFG) through grant no INST 37/935-1 FUGG. All plots in this paper were made with the Python library matplotlib ( Hunter 2007 ).
Paper: 2015A&A...573A..93V (acknowledgments)
M. Vitale is supported by the International Max-Planck Research School (IMPRS) for Astronomy and Astrophysics at the Universities of Bonn and Cologne. This work is partly based on observations with the 100-m telescope of the MPIfR (Max-Planck-Institut für Radioastronomie) at Effelsberg. The authors would like to thank Alex Kraus for support and help during observations and data reduction. The FIRST Survey is supported in part under the auspices of the Department of Energy by Lawrence Livermore National Laboratory under contract W-7405-ENG-48 and the Institute for Geophysics and Planetary Physics. The Sloan Digital Sky Survey is a joint project of the University of Chicago, Fermilab, the Institute for Advanced Study, the Japan Participation Group, Johns Hopkins University, the Max Planck Institute for Astronomy, the Max Planck Institute for Astrophysics, New Mexico State University, Princeton University, the United States Naval Observatory, and the University of Washington. Apache Point Observatory, site of the SDSS, is operated by the Astrophysical Research Consortium. Funding for the project has been provided by the Alfred P. Sloan Foundation, the SDSS member institutions, NASA, the NSF, the Department of Energy, the Japanese Monbukagakusho, and the Max Planck Society. The SDSS Web site is http://www.sdss.org . This work was supported in part by the Deutsche Forschungsgemeinschaft (DFG) via the Cologne Bonn Graduate School (BCGS), and via grant SFB 956. We had fruitful discussions with members of the European Union funded COST Action MP0905: Black Holes in a violent Universe and the COST Action MP1104: Polarization as a tool to study the Solar System and beyond. We received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement No. 312789.
Paper: 2016ApJ...817..168H (fulltext)
) and the 321 GHz H 2 O maser line (Hirota et al. 2014a ). The brightness temperature of the 658 GHz H 2 O line, >2 × 10 4 K, would indicate that the line is emitted via maser action. In contrast, the 336 GHz H 2 O line shows a different distribution (Figure 5 (c)) and a spectral profile with symmetric double-peaks (Figure 6 ). As discussed in Alcolea Menten ( 1993 ) and Hirota et al. ( 2014a ), the 336 GHz H 2 O line could be a thermal line and trace the edge-on disk, unlike emission from the other transitions at 232, 321, and 658 GHz. We note that via Menten et al. ( 2006 ) the 336 GHz H 2 O line toward the red supergiant VY CMa also favors a thermal nature for this line’s excitation. Recently, theoretical model calculations for the excitation of the 658 GHz H 2 O line have been presented (Richards et al. 2014 ; Nesterenok 2015 ) for the red supergiant VY CMa and for asymptotic giant branch (AGB) stars. The 658 GHz H 2 O maser is predicted to be pumped efficiently for H 2 densities of 10 9 –10 10 cm −3 , a fractional H 2 O abundance of 10 −4 –10 −5 , and gas temperatures of 1000–1500 K (Nesterenok 2015 ). Although the physical and chemical properties of the 658 GHz H 2 O maser emitting region associated with Source I would be expected to be different from that of a late-type star, these parameters are roughly consistent with those for the disk-outflow system of Source I associated with SiO masers and other submillimeter H 2 O lines (Reid et al. 2007 ; Goddi et al. 2009 , 2011 ; Hirota et al. 2014a , 2015 ). 4.4. Relationship between the 22 GHz H<sub>2</sub>O Supermaser We note that our ALMA observation was done during an active phase of the 22 GHz H 2 O maser burst in Orion KL, called a “supermaser” (Hirota et al. 2011 , 2014b ). The 22 GHz supermaser is located at the phase tracking center of the ALMA observation, corresponding to the Compact Ridge region (Hirota et al. 2011 , 2014b ). While the total flux density of the 22 GHz H 2 O supermaser was decreasing on 2012 August 25, it was still Jy (Hirota et al. 2014b ). In spite of the extremely high flux density of this maser, there was no significant emission of the 658 GHz H 2 O line in the Compact Ridge region in our ALMA data with the 1 σ noise level of 2 Jy beam −1 in the relevant region. Thus, the 658 GHz H 2 O line is not related to the 22 GHz supermaser phenomenon, as has also been reported for other transitions at 321 and 336 GHz (Hirota et al. 2014b ). 4.5. Continuum Emission at ALMA Band 9 To measure the position of the 658 GHz H 2 O emission, we also made a map of the ALMA band 9 continuum by applying the self-calibration solutions. Continuum emission is detected at the position of Source I, although it is outside the primary beam of the 12 m antenna. The flux density and the peak intensity are derived from Gaussian fitting, respectively 1.04 ± 0.04 Jy and 0.77 ± 0.03 Jy beam −1 (Figure 2 (b)). If we correct the primary beam attenuation by a factor of 8.6, the absolute flux