adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...880...27P (fulltext)
obs , and I obs were then, for the i th value, and“Observed” values of p ′ and were calculated using Equations ( 2 ) and ( 8 ), respectively, for a given value of . We repeated this process 10 4 times. Figure 2 shows a single realization of our Monte Carlo simulations for three cases: α = 1, 0.5, and 0 (constant polarization fraction). In all cases, p 100 mJy beam –1 = 0.025. The results are shown without any debiasing of the polarization fraction and colored according to their S/N in I and p ′. Figure 2. Monte Carlo simulations of the observed polarization fraction p ′ as a function of S/N I / σ QU for three cases: α = 0 (top), 0.5 (middle), and 1 (bottom). In all cases, p 100 mJy beam –1 = 0.025. Results are shown without debiasing. The polarization fraction p ′ is given as an absolute value (not as a percentage), so any value p ′ > 1 (marked in bold on plot axes) is unphysical. Points are colored according to their S/N in I and p ′; gray points meet none of the criteria shown in the legend. The solid black line shows p ′ = μ p , the mean of the Rice distribution, for the given values of α and . The dashed black line shows the true polarization fraction p = ( I / σ QU ) − α . The dotted line shows the null-hypothesis/low-S/N relation . In the cases where α 1, the dotted–dashed line marks the critical value . Figure 2 shows that where I / σ QU ( I / σ QU ) crit , the distribution shows an identical p ′ ∝ I −1 behavior in all cases, and that the underlying power-law behavior does not dominate over the I −1 dependence until I / σ QU ≫ ( I / σ QU ) crit . Thus, unless a reasonable number of data points have S/Ns significantly greater than the critical value, the true power-law behavior is not recoverable. The overall behavior of the recovered polarization fraction is well described by the mean of the Rice distribution. 4.1. Uncertainty on Polarization Fraction In keeping with standard practice in observational polarimetry and the default behavior of the POL-2 pipeline (e.g., Kwon et al. 2018 ), we estimated the uncertainty on the polarization fraction using the relation(e.g., Wardle Kronberg 1974 ). We note that this result is derived using classical error propagation and so assumes that δQ , δU , and δI are small and uncorrelated. We here take and . As discussed above, measurement errors δp calculated using Equation ( 19 ) can only be treated as representative of a Gaussian distribution around p ′ when p ′/ δp is large. 4.2. Fitting Methods We investigated the results of fitting two models to our Monte Carlo simulations. Fitting was performed using the scipy routine curve _ fit . In all cases, we attempted to recover the input values of and α , assuming that σ QU is a fixed and directly measurable property of the data set. We supplied our calculated δp values to curve _ fit as 1 σ uncertainties on p ′. Although this is technically valid only at high S/N, the default behavior of this fitting routine is to consider only the relative magnitudes of
Paper: 2021ApJ...907...83V (fulltext)
the contribution of a magnetic background turbulence, like that provided by an SSD, to α . Here, α M can be obtained already by SOCA (Rädler Rheinhardt 2007 ). Second, it can be interpreted as reflecting the quenching of α by such that with its magnitude growing, α M also grows, but opposite in sign to α K , resulting in a reduced total α . This can be obtained via closure approaches like the τ or eddy-damped quasi-normal Markovian approximations (Pouquet et al. 1976 ). Turbulent diffusivity in incompressible flows is estimated asIf the Strouhal number u rms τ / l , l a characteristic scale of the flow, is assumed to be unity, we can estimate τ = 1/ k f u rms . To obtain the fluctuating fields b ′, j ′, we have removed the large-scale field via filtering out the contributions of the k = 1 mode from the magnetic field snapshots. We estimated the growth rates by first calculating α and η t for individual snapshots using data from runs with high snapshot frequency during exponential growth. Then, we used Equation ( 10 ) to get λ for an individual snapshot and subsequently time-averaged over the growth phase. We find results that are at least approximately aligned with the directly measured values with better agreement at low ; see Figure 18 . Toward high , the results begin to diverge, most strongly at the highest . This appears to indicate that the closure estimates can work surprisingly well but do not catch all of the details. Figure 18. Growth rates ( 10 ) based on closure estimates of α and η t at the exponential growth stage, compared to directly estimated ones. But how does α behave at the saturation stage? Brandenburg et al. ( 2008 ) measured α and η t quenching, with both decreasing as functions of . For comparison, we calculated α K,M from the saturation stages of our runs and discovered similar results; see Figure 19 . The normalized α K approaches a constant, whereas α M keeps getting stronger with . Our results are close to Brandenburg et al. ( 2008 , their Figure 3) within our range of , including substantially more points though. The total α decreases with growing , reaching eventually a tentative plateau. Figure 19. α K and α M at the saturated stage as functions of , normalized by α 0 = u rms /3. Bars represent the fluctuation level of α derived from their standard deviations. The jump at is due to an unclear disagreement between resolutions 128 3 , 64 3 , and 256 3 , while that at is due to different saturation stage lengths in the runs with resolutions 256 3 and 512 3 . 5.5. Magnitude Distribution of the Magnetic Field Next, we investigate how the probability density functions (pdf’s) of the magnetic field differ in the regimes where SSD or LSD alone and SSD and LSD together are acting. While spectra gave us information about the distribution of the magnetic field across different scales, pdf’s can further reveal details of the structure of the field in different spatial directions. Additionally, we calculated kurtoses of these
Paper: 2019ApJ...877...49G (acknowledgments)
We would like to thank the SPIRITS team (PI: Mansi Kasliwal) for assistance with the inclusion of the SPIRITS data in four of our galaxies and Chris Clark for his help with the P – L distance uncertainty calculation. This work is supported by Spitzer via grant GO11041 and the NASA Astrophysics Data Analysis Program grant No. NNX16AT56G. R.D.G. was supported by NASA and the United States Air Force. O.C.J. has received funding from the European Union’s Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement No. 665593 awarded to the Science and Technology Facilities Council.
Paper: 2017MNRAS.469.4221K (fulltext)
did for the Schwarzschild case (Chattopadhyay Kumar 2016 ), but now for Kerr metric. And then using these values, we look for transonic jet solution. We iterate c φ and n c for the same values of Z φ and r b and by estimating the value of from the disc. This goes on until the entropy of the transonic jet is in between the PSD and pre-shock disc or . Once the transonic solution is found, then from Section 2.4 , we compute the , which is fed to equation ( 35 ) to recalculate the r sh . Once the new r sh is obtained, steps (iii)–(v) are repeated but with the new value of , till r sh converges to a value. The resulting jet is the self-consistent jet solution driven by disc properties. While the jet solution is being obtained, shock transition in jets is also studied. 4 RESULTS We are connecting two flows, namely accretion and jet, and since both the flows are quite complicated, we would first present all possible advective accretion solutions, then all possible jet solutions and then self-consistent accretion-jet solutions. 4.1 Accretion In this section, we present only accretion solution, i.e. we follow steps (i)–(iii) of Section 3 . BH accretion disc is necessarily transonic, i.e. the solutions falling on to a BH pass through at least one sonic point. Therefore, a lot of insight can be gained by studying the critical point conditions. For a given value of and λ, the critical points can be uniquely determined. Let us denote and . Therefore, using the critical point conditions (equations 19 and 20 ) in equation ( 16 ), we obtain energy as a function of r c for given values of λ, a s and ξ (equation 21 ). We plot with r c for a s = −0.99 (Fig. 1 a), a s = −0.5 (Fig. 1 b), a s = 0.0 (Fig. 1 c), a s = 0.5 (Fig. 1 d), a s = 0.8 (Fig. 1 e) and a s = 0.99 (Fig. 1 f). Each of the curves plotted from top to bottom is for λ = 1.5–4.0 with interval dλ = 0.5 and with same composition parameter, ξ = 1.0. Critical points are formed because gravity increases the infall velocity, but because accretion is an example of convergent flow, the temperature of the flow also increases during accretion (compressional heating). As temperature increases, sound speed also increases, but in accretion the rise of sound speed is less than the bulk velocity. Therefore, the flow that is subsonic at large distances becomes supersonic within a certain point (critical point). However, if the infalling matter is rotating, then the centrifugal term competes with the gravitation term in a way that MCP may form. As a s increases, frame dragging enhances the centrifugal term significantly, therefore, even matter with low angular momentum exhibits MCP, for example the flow with λ = 2 [dotted in Figs 1 (a)–(f)] exhibits MCP for a s = 0.99 but not for a s ≤ 0.8. On the other hand, if the flow is retrograde, then MCP occur only for higher angular momentum flow. Fig. 1 (c) is similar to fig. 3a of Chattopadhyay Chakrabarti ( 2011 ). This phenomenon is better exhibited with ‘kite-tail’ plot.
Paper: 2016ApJ...825...93O (fulltext)
area enclosed by the dark bubble. Finally, Figure 10 gives an estimate of the order of magnitude of the upflows and also of the delays between them. The upper panel shows Si iv velocities as a function of time for eight pixels within the dark bubble (purple lines). The spatial average of those pixels has been smoothed in time using a five-step running average. This smoothed mean is represented by the black line. For comparison, an average of pixels outside of the flux emergence region is plotted as a cyan line. Note the bump produced in the velocities at around 09:03:00 UT, which is only detected by the pixels belonging to the bubble but not by the pixels outside the magnetic bubble. The bump corresponds to upflows of about 8 km s −1 in the transition region. Note also how some individual pixels within the bubble present transient upflows at other times (e.g., at 08:55:00 UT) that are not captured by the smoothed mean. It is important to realize that the 8 km s −1 upflows found represent the result of a single Gaussian fit to the Si iv spectra. Double Gaussian fits show that the Si iv line is split into two components during the event, as is also evident from Figures 7 and 9 . The more blueshifted component shows velocities of 40–50 km s −1 in bursts, presumably due to reconnection between the emerging bubble and the ambient field (indicating that the bubble has reached the dark appearing canopy of H α fibrils), while the other component is blueshifted by at most a few km s −1 . The Doppler shift derived from the single Gaussian fit of Figure 10 is therefore showing the combined effect of reconnection bursts and the rising bubble, and cannot be considered a real velocity but rather a time marker. Figure 10. Upper panel: Si iv velocities as a function of time for several pixels within the dark bubble (purple lines) and their smoothed mean (black line). Note the bump produced in the velocities at around 09:03:00 UT, marking the existence of an upflow of more than 8 km s −1 in the transition region. Compare to the averaged velocities of pixels outside of the flux emergence region (cyan line). Lower panel: velocities measured in the photosphere (green line and dashed black line), low chromosphere (red line), and transition region (solid black line) as a function of time. Note the successive bumps occuring with a certain delay in the different lines, proving how the upflowing plasma traverses different layers in the solar atmosphere. The Ti ii velocity has been multiplied by 10 for display purposes. The lower panel plots velocities and velocity proxies measured in the photosphere (green and dashed black line), low chromosphere (red line), and transition region (black line) as a function of time. In principle, we would expect that the Fe i intensity darkening and the Ti ii velocity upflow, seen at about 08:55 UT, would occur simultaneously, since both sample the photosphere at similar heights. However, there is a slight delay between the green and
Paper: 2018ApJ...863...44A (acknowledgments)
SPHERE is an instrument designed and built by a consortium consisting of IPAG (Grenoble, France), MPIA (Heidelberg, Germany), LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France), INAF–Osservatorio di Padova (Italy), Observatoire de Gen‘eve (Switzerland), ETH Zurich (Switzerland), NOVA (Netherlands), ONERA (France), and ASTRON (Netherlands), in collaboration with ESO. SPHERE also received funding from the European Commission Sixth and Seventh Framework Programmes as part of the Optical Infrared Coordination Network for Astronomy (OPTICON) under grant number RII3-Ct-2004-001566 for FP6 (20042008), grant number 226604 for FP7 (20092012), and grant number 312430 for FP7 (20132016). HA acknowledges support from the Millennium Science Initiative (Chilean Ministry of Economy) through grant RC130007 and further financial support by FONDECYT, grant 3150643. Part of this work has been carried out within the framework of the National Centre for Competence in Research PlanetS, supported by the Swiss National Science Foundation. HA, SPQ, and HMS acknowledge the financial support of the SNSF. GH-MB acknowledges financial support from CONICYT through FONDECYT grant 3170657. CC acknowledges support from project CONICYT PAI/Concurso Nacional Insercion en la Academia, convocatoria 2015, folio 79150049, and financial support from ICM Nucleo Milenio de Formacion Planetaria, NPF. MB acknowledges funding from ANR of France under contract number ANR-16-CE31-0013 (Planet Forming Disks). CP acknowledges funding from the Australian Research Council (ARC) under Future Fellowship number FT170100040. SC and SP acknowledge financial support from CONICYT FONDECYT grant 1171624. Financial support was provided by Millennium Nucleus RC130007 (Chilean Ministry of Economy). SP acknowledges CONICYT-Gemini grant 32130007. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. This work has furthermore made use of data from the European Space Agency (ESA) mission Gaia ( http://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC; http://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. We thank the staff at the VLT for their excellent support during the observations. We also would like to thank our anonymous referee for comments that helped to improve this paper. Facility: VLT:Melipal (SPHERE) .
Paper: 2019A&A...627A..92G (fulltext)
( 15 ). For θ > 0, all modes with m ≥ 1 are populated, whereas the approximation () contains only m = 2. For θ = π /2, h × vanishes identically and the relative amplitude of the modes m ≠ 2 with respect to the mode m = 2 is the largest one, reaching ∼75% for m = 3 and ∼50% for m = 4 when a = 0.98 M . Some tests of our computations, in particular comparisons with previous results by Poisson (1993a ; a = 0) and Detweiler (1978 ; a = 0.5 M and a = 0.9 M ) are presented in Appendix A . 3. Signal-to-noise ratio in the LISA detector The results in Sect. 2 are valid for any BH. We now specialize them to Sgr A* and evaluate the S/N in the LISA detector, as a function of the mass μ of the orbiting object, the orbital radius r 0 and the spin parameter a of Sgr A*. 3.1. Computation Assuming that its noise is stationary and Gaussian, a given detector is characterized by its one-sided noise power spectral density (PSD) S n ( f ). For a gravitational wave search based on the matched filtering technique, the S/N ρ is given by the following formula (see e.g. Jaranowski Królak 2012 ; Moore et al. 2015 ): where is the Fourier transform of the imprint h ( t ) of the gravitational wave on the detector, h ( t ) being a linear combination of the two polarization modes h + and h × at the detector location: In the above expression, ( t , r , θ , φ ) are the Boyer–Lindquist coordinates of the detector (“Sgr A* frame”), while F + and F × are the detector beam-pattern coefficients (or response functions), which depend on the direction (Θ, Φ) of the source with respect to the detector’s frame and on the polarization angle Ψ, the latter being the angle between the direction of constant azimuth Φ and the principal direction “+” in the wavefront plane (i.e. the axis of the h + mode or equivalently the direction of the semi-major axis of the orbit viewed as an ellipse in the detector’s sky) ( Apostolatos et al. 1994 ). For a detector like LISA, where, for high enough frequencies, the gravitational wavelength can be comparable or smaller than the arm length (2.5 Gm), the response functions F + and F × depend a priori on the gravitational wave frequency f , in addition to (Θ, Φ, Ψ) ( Robson et al. 2019 ). However for the gravitational waves considered here, a reasonable upper bound of the frequency is that of the harmonic m = 4 (say) of waves from the prograde ISCO of an extreme Kerr BH (see Fig. 4 ). From the value given by Eq. ( 5 ), this is f max = 2 × 7.9 ≃ 15.8 mHz, the multiplication by 2 taking into account the transition from m = 2 to m = 4. This value being lower than LISA’s transfer frequency f * = 19.1 mHz ( Robson et al. 2019 ), we may consider that F + and F × do not depend on f (see Fig. 2 in Robson et al. 2019 ). They are given in terms of (Θ, Φ, Ψ) by Eq. (3.12) of Cutler (1998) (with the prefactor appearing in Eq. (3.11) included in them). Generally, the function S n ( f ) considered in the LISA literature, and in particular to present the LISA sensitivity
Paper: 2020ApJ...891...10L (fulltext)
such as the malfunction of satellites, radiation hazards for astronauts, and the interruption of radio communication and electric power (e.g., Schrijver et al. 2014 ; Ponomarchuk et al. 2015 ). Hence, it is very important to establish a reliable system for flare prediction to effectively prevent various types of damage caused by powerful flare events. The triggering mechanism of solar flares is not well understood, thus many flare prediction models have been developed with different methods in recent years. Some of them are based on statistical methods (Song et al. 2009 ; Mason Hoeksema 2010 ; Bloomfield et al. 2012 ; Barnes et al. 2016 ), while most of them are based on machine-learning methods. Traditional machine-learning methods commonly used for flare prediction cover artificial neural network (Qahwaji Colak 2007 ; Ahmed et al. 2013 ; Li Zhu 2013 ; Nishizuka et al. 2018 ), k-nearest neighbors (Li et al. 2008 ; Huang et al. 2013 ), support vector machine (Yuan et al. 2010 ; Bobra Couvidat 2015 ; Nishizuka et al. 2017 ; Sadykov Kosovichev 2017 ), random forests (Liu et al. 2017 ; Florios et al. 2018 ), and ensemble learning (Colak Qahwaji 2009 ; Huang et al. 2010 ; Guerra et al. 2015 ). Because flares originate from the magnetic field around sunspots in an active region (AR), many of the above studies utilize photospheric magnetic observational data of AR for binary class prediction. Both statistical methods and traditional machine-learning algorithms learn from features that are manually selected and extracted from the observational data. The predictive performance of these methods mainly relies on the quality of the features used. Deep learning neural networks (DNNs), which are as a new branch of machine learning, have become a highly reliable technique for solving large-scale learning problems in astronomy and other branches of science (Abraham et al. 2018 ). A famous method of DNNs, convolutional neural networks (CNNs; LeCun et al. 2015 ), have become very popular in the area of image processing and computer vision. It could learn directly from the raw data instead of features. CNNs are usually composed of several convolutional layers, and each convolutional layer is trained to automatically select and extract increasingly complex features from input data. There are some literatures reporting that CNNs have been successfully applied to solar flare prediction. Park et al. ( 2018 ) applied a CNN model to predict a binary class flare within 24 hr, which is based on a combination of GoogLeNet (Szegedy et al. 2014 ) and DenseNet (Huang et al. 2016 ). After trained by full-disk solar line of sight (LOS) magnetograms, their model could only make binary class prediction for a ≥C-class flare, which lacked research on the prediction for a ≥M-class major flare. Huang et al. ( 2018 ) presented a CNN model to predict a binary class flare, which is based on the classic CNN model architecture including two convolutional layers with 64 11
Paper: 2016MNRAS.455..484R (acknowledgments)
The authors would like to thank Bence Bécsy, Gergely Dálya, and Ákos Szölgyén for useful discussions and for valuable comments on the manuscript. Péter Raffai is grateful for the support of the Hungarian Academy of Sciences through the ‘Bolyai János’ Research Scholarship programme. We also acknowledge financial support from the NASA Astrophysics Theory Program under grant no. NNX11AE05G (to ZH) and from OTKA under grant no. 101666 (to ZF).