adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.471.1321R (fulltext)
gravitational influences, it is possible to include the Yarkovsky drift. The orbfit has additional programs for determination of the orbits of Solar system bodies and could compute their positions on the celestial sphere. Also, orbfit has tools for the computation of Lyapunov exponents and proper elements (analytic and synthetic). 2.2 <sc>mercury</sc> The mercury package (Chambers Migliorini 1997 ) includes different algorithms, and it is written using the fortran 77 programming language. The mercury package is publicly available at https://github.com/4xxi/mercury . mercury has the following N -body algorithms: second-order mixed-variable symplectic integrator, general Bulirsh–Stoer integrator, conservative Bulirsh–Stoer, Everhart's RA15 (RADAU) algorithm and Hybrid symplectic/Bulirsh–Stoer integrator. For the purpose of our analysis, we chose the explicit Hybrid symplectic integrator (Chambers 1999 ) that could compute close encounters. Symplectic integrators (for complete theory of symplectic integrators, see e.g. Sanz-Serna 1991 ; Yoshida 1993 ) do not have a long-term build-up of energy error, and they are significantly faster with problems in which most of the mass is contained in a single body. The idea behind symplectic integrators is to split the Hamiltonian () of a system into pieces, each of which can be solved on its own. Symplectic integrator use a fixed time-step that makes calculating close encounter difficult due to the necessity of changing the step size. One of the possible solutions is to use a smaller step size or to split the perturbing terms, and to set a different step size for each term (SYMBA integrator; Duncan, Levison Lee 1998 ). Chambers ( 1999 ) described an alternative solution – a hybrid integrator that has symplectic and non-symplectic components. That alternative solution keeps the best properties of both symplectic and non-symplectic integrators. 3 METHODOLOGY AND DATA To study the limitations of the BIM, first a fictional family is going to be created based on the Veritas asteroid family. In this respect, all adopted values of the fictitious family are the same as of Veritas. Families are formed from the disruption of the target asteroid during a high-velocity impact. Escape speed ( v esc ) is much smaller than orbital speed ( v orb ≈ 15–20 km s −1 ), and, consequentially, all the orbits of family members are similar immediately after their creation. To create the initial orbital distribution of the simulated family members, we have used Gauss equations. These equations describe changes in the osculating elements due to the components of the ejection velocity: where a , e , i , f and ω are the semimajor axis, eccentricity, inclination, true anomaly and argument of perihelion, respectively; na is the mean orbital velocity; and δ V T , δ V R and δ V W are the components of ejection velocity along the direction of the motion, the radial direction and perpendicular to the orbital plane, respectively. The
Paper: 2016A&A...595A..22R (fulltext)
value . This gives 3785 proper motion slices. A slice then contains all stars for which holds. The number of stars per slice strongly varies and depends on the total proper motion. The maximum number of 44 000 stars is at (, ) = (), whereas the number of stars is less than 150 at the borders of our square. About 90% of all slices contain less than 6000 stars. These slices represent a seven-fold overlap of the proper motion plane, with the effect that the same over-density on the sky may appear in neighbouring slices, but not necessarily with the same signal-to-noise ratio. (b) In the second step we searched for over-densities in the proper motion slices. For each star in a proper motion slice, we counted all its neighbours within a radius of 0.5 deg (called sky circle) and determined the surface density (stars/square degree). Surrounding each star’s sky circle we chose an area of 9 sq. deg, where we determined the background surface density . We only retain stars if / of their sky circle is larger than 3.0 and their is larger than 5 per square degree. If the angle between two stars is less than 0.5 deg, each of them is in the other’s sky circle. We discard the one with the lower . This cleaning goes on until only “isolated” sky circles are left, which we call Level-0 centres. As our system of proper motion slices represents a seven-fold overlap of the sky, a Level-0 centre in slice could have the same or a neighbouring Level-0 centre in slice . If they are within the radius of a sky circle and the difference of their actual proper motions is less than 1.5 mas/yr, only the Level-0 centre with higher / is retained. These survivors are called Level-1 centres; they are disjunct and are possible candidates of new open clusters. Of course, many of the Level-1 centres could be related to known open clusters. Therefore, we cross-matched them with the MWSC clusters ( Kharchenko et al. 2013 ) and with the most recent cluster list DAML02 VERSION 3.5 of 2016 Jan. 28 ( Dias et al. 2002 ). We discarded all the Level-1 centres within 0.7 deg of a known cluster, if the radius of the known cluster was larger than 0.2 deg. If the radius of a cluster in MWSC or in DAML is smaller than 0.2 deg, it cannot be identical to one of the objects found here. About 200 Level-1 centres surviving this process (called Level-2 centres) were then subject to a photometric check of their existence as clusters. Table 1 New open clusters. 3.2. Photometric steps. (a) We first made a quick and coarse check in the CMD of the stars in a sky circle around a Level-2 centre using TOPCAT 2 ( Taylor 2005 ). A Level-2 centre was selected for a more careful treatment if stars with showed a tendency towards building a part of a main sequence or a turn-off region. The Level-2 centres were immediately rejected if, on the CMDs, their stars showed a random distribution, mostly at the limiting magnitude of Tycho-2. So, we could quickly check the Level-2 centres found in the previous step. Fig. 1
Paper: 2020A&A...637A..68Y (fulltext)
to be the main reason for our different conclusions. 7.4. [α/Fe]–[Fe/H] relations As shown in Fig 7 , the intrinsic scatter of the [ α /Fe]–[Fe/H] data is large as a result of the complex galactic gas and metal distribution, and stars may form outside the main progenitor halo ( Jeon et al. 2017 ). Our mean galactic metal evolution track assumes that all the gas is always well mixed and thus cannot explain the metal abundance of individual single stars. The best-fit model that applies the IGIMF theory is comparable with the solutions given by Lacchin et al. (2020) , which are shown as the dashed and dash-dotted lines in Fig 7 , especially for metal-rich stars. The highest [ α /Fe] value at the metal-poor end of the plots from Lacchin et al. (2020) is lower than our results and fits the data not as well as our model, however. The highest [ α /Fe] value should be the IMF-weighted [ α /Fe] value of all the massive stars with Z = 0 and a lifetime shorter than the first time step, δ t = 10 Myr, that is, the IMF-weighted thin solid line within the red shaded region of Fig. 8 . Because the IMF-weighted value should be higher than the lowest value in the red region, it is clear that the initial [Mg/Fe] should be higher than 0.5, which agrees with our result and disagrees with Lacchin et al. (2020) . Fig. 8. Stellar metal yield ratios of stars with different mass and metallicity given by Kobayashi et al. (2006) . The yields from Kobayashi et al. (2006) for massive stars are connected to the yields for low- and intermediate-mass stars as given by Marigo (2001) . The yields for M * > 40 M ⊙ are the same as the yields for M * = 40 M ⊙ stars. The red shaded region with log 10 ( M *, initial [ M ⊙ ]) > 1.3073 indicates zero-metallicity stars with lifetimes shorter than 10 Myr, according to Yan et al. (2019) . The [ α /Fe] plateau can be enhanced by a more complicated gas-flow model, a larger stellar-mass bin, or a larger model time step. A non-simultaneous star formation combined with local stellar wind pollution can also complicate the model prediction, which requires more elaborate hydrochemical simulations. We note that the metal-poor part of the [ α /Fe]–[Fe/H] relations is affected by the small number of stars that are formed at the earliest time steps. This shows that the metal-poor part is not reliable because of the small number of massive stars that affect it, the uncertainty of the stellar winds of these massive stars, and whether or not they explode as supernovae. Stars that are more massive than 40 M ⊙ share the same adopted stellar yield table, which is certainly not the ideal setup to discuss the chemical abundance of the extremely metal-poor era of a galaxy. 7.5. Star formation history The tested SFT in Lacchin et al. (2020) is generally longer than our best-fit models. Because the gas flow and star formation criteria in the chemical evolution model are uncertain, the best-fit SFH is not conclusive. Here we compare it with the independently
Paper: 2017ApJ...834..175O (fulltext)
PSFs (right). Both images are on the same logarithmic scale with a lower threshold of . The theoretical PSF is calculated for the central monochromatic wavelength of 3.94 microns. The retardance in the simulation was set to radians, thereby creating the leakage PSF “0.” The observed image is saturated on the first diffraction ring and on the central core but is corrected to the unsaturated flux level. A small asymmetry of the intensities of the wind-driven halos of the star inside the dark holes is seen between the two coronagraphic PSFs (“+” and “−”). Instrumental ghosts are indicated with arrows and are not related to the coronagraphic optic. Further investigation showed that these ghosts can be removed from the dark hole by setting the rotator to an angle of 30°. 3.1. Data Reduction To remove hot, dead, or flaky pixels in each image, we subtracted a median-filtered image with a 3 × 5 pixel box from the cleaned and centroided image cube to generate an image where the outliers clearly stand out. The 3 × 5 box is chosen because the outlying pixels tend to have structure in the direction of the readout and not perpendicular to it. The data points that deviate more than 1000 counts are replaced by the local value of the 3 × 5 median. Four sets of sky reference frames are taken between on-target observations. The sky frames were median-combined for every one of the four sets. Each of the previously taken frames on target were subtracted by the first consecutive median-combined master sky frame. After the background subtraction, the median of a cosmetically clean part of the chip is subtracted in order to remove any residual background offset. A theoretical diffraction pattern consistent with the geometry of the telescope and wavelength of observation is used as a fiducial. This theoretical PSF is then fit to the central leakage PSF by minimizing the chi-squared residuals between the theoretical PSF and the leakage PSF, with x , y , radius (), and intensity as the free parameters of the fit. All images of the data cubes are coregistered by shifting the images to the central pixel of the frame with the previously fit x and y values using a bilinear interpolator. The radius of the leakage PSF fit is used as a subframe quality indicator. The few images (39) that have fitted radii significantly smaller ( pixels) or larger ( pixels) than the diffraction limit are excluded. The best 91% (5200) of the images are selected after sorting the frames by radius from smallest to largest. Both criteria remove the frames where the seeing conditions temporarily worsened or where the AO system lost its lock. After these selections, the images are reordered to original chronological order and binned by four frames, corresponding to 4 s integration time per binned frame to reduce memory consumption and computational time. Instrumental ghosts due to internal reflection of the refractive optics are present in the image; see Figure 2 . Several of these ghosts are
Paper: 2019A&A...624L...2M (fulltext)
on different methods. A simple radial profile (black line within gray boundaries) nicely fits our results and the previous estimates in the range 1.1−2.0 R ⊙ . 3. Coronal magnetic field The upstream Alfvénic Mach number M A and the magnetic field strength B can be derived from the standoff distance Δ r ≡ r sh − r cme between the shock front r sh and the CME leading edge r cme by employing a model adapted by Gopalswamy Yashiro (2011) but originally developed for the bow shock of the Earth. Physically, the shock Mach number M and the radius of curvature r cme of the nose of the driver control the standoff distance. The ratio δ between Δ r and r cme can be expressed as δ ≡ Δ r / r cme = 0.81[( γ − 1) M 2 + 2]/[( γ − 1)( M 2 − 1)], (e.g., Farris Russell 1994 ) where γ is the adiabatic index (we assume γ = 4/3 and γ = 5/3 as two possible values). This expression allows the applicability of the model to the low- M regime provided that M is substituted for the fast magnetosonic Mach number ( Fairfield et al. 2001 ). In the inner corona, v A substantially exceeds the sound speed meaning that M can be confidently replaced by M A . It then follows that , implying that M A (and thus the coronal magnetic field strength B , given the upstream electron density profile n e ) can be obtained at any given time by measuring δ . We point out that the above method is based on a model by Farris Russell (1994) , further extended by Russell Mulligan (2002) , which was specifically designed for the bow shock of the terrestrial magnetosphere. As such, the evaluation of the standoff distance in that model is intended to be strictly valid for the nose of a CME. It is then important to discuss how appropriate such a model is for a coronal CME-driven shock, for which, moreover, as in our case, the standoff distance is calculated not at the nose but at the flanks of the shock. In order to check the validity of the model in the coronal environment, Schmidt et al. (2016) used a 3D magnetohydrodynamic code to test the accuracy of the Gopalswamy Yashiro (2011) method by applying it to a simulation of a real CME event, where the standoff distances between the driven shock and the CME could be determined within the simulation box. As a result, the predicted magnetic field strength profile agreed very well (within 30% between 1.8 and 10 R ⊙ ) with the magnetic field strength profile found in the simulation domain, thus providing strong support for the Gopalswamy Yashiro (2011) method in a coronal environment. As for the second issue, that is, the reliability of the model when the standoff distance is evaluated at the flanks of the shock surface, it is obvious that in the general case of a fast moving CME the shock surface would be better represented by a 3D oblate surface. In that case, our model would yield unreliable results away from the CME nose. In our specific case however, since the translational motion of the CME away from the Sun is negligible with respect to its
Paper: 2020MNRAS.497.3451W (fulltext)
obtained by using (green), (red), and (blue). We make the following observations from this plot: β BF versus f / b falls nicely on a straight line with slope 1 (the solid line). However, note that the relation β BF = f / b (the dashed line) does not exactly hold: we have checked that the actual value of β BF depends on the s min used, decreasing as smaller s min is used; the results here are for . The trend suggests that, roughly speaking, the reconstruction simply spits out the ‘input’ β value, and therefore has no predictive power regarding β, unless we look beyond R ( s ). The two ( f, b ) pairs that give similar β = f / b ≈ 0.375 produce very similar β BF : this is a consequence of the f – b degeneracy mentioned above. For comparison, we include the best-fitting β BF values obtained using R ′ ( s ) as filled circles, using the same colour scheme. Because there is no reconstruction and therefore no input ( f, b ), we simply plot the points at a fixed horizontal coordinate f / b = 0.376 (the theoretical value). We note that β BF obtained in this way is indeed larger than the theoretical value, but this is well within the 1σ uncertainty indicated by the grey error bar; at this stage, we cannot accurately assess the impact on β BF due to the small sample size or the simulation resolution, which is beyond the scope of this work. As mentioned above, a small (per cent-level) error in R ′ ( s ) leads to a larger error in β BF . Figure 12. Upper left : The best-fitting β BF values obtained using the estimator R ( s ) and the reconstructed galaxy catalogues, for seven choices of input parameters f and b supplied to the reconstruction pipeline (crosses). R ( s ) is measured as the weighted average between and s max = 98.9 (green), 90.2 (red), and (blue), respectively, and the error bars are the standard deviations obtained from 15 realizations for the case of as the error bars for remaining s max values are similar. The filled circles are the same, but obtained from the estimator R ′ ( s ): since there is no reconstruction in this case, these data are plotted against the theoretical value β = f / b = 0.376. The dashed line represents β = f / b , whilst the solid line is a line of slope 1.0 to guide the eyes. Lower left : , the constant value fitting ξ 2 [ x ( k ) ]( s ) between and the three s max values s max = 98.9 (green, densely dashed), 90.2 (red, dashed), and (blue, sparesly dashed), respectively, for the same seven choices of input ( f, b ). The vertical dotted lines mark the best-fitting β BF at which is closest to zero, and the error bars are again the standard deviations of the 15 realizations. Right-hand panels : the estimator E4 (upper) and E3 (lower) for the seven choices of reconstruction parameters ( f, b ), with their corresponding values of β indicated in the legend and increase with the sparsenesses of the curves [β increases from to the top (bottom) to the bottom (top) line in the upper (lower) right-hand panels]. Note that
Paper: 2016ApJ...816L..18G (fulltext)
domain to a 4 kpc × 4 kpc × 24 kpc region of the Galaxy. The Galactic magnetic field model of Jansson Farrar ( 2012a ) has azimuthal variations in strength and direction of the field. We chose the galactocentric azimuthal angle to roughly agree with that of the point of impact 70 Myr ago shown in Nichols Bland-Hawthorn ( 2009 ), Figure 3 . The minihalos are necessarily constrained by the size of the domain and fill spheres of 2 kpc radius (the domain half-width). In Figure 1 , the minihalo barycenters are indicated by the “ X ” markers, and their simulation domain half-mass radii (distinct from r e ) are shown as black circles. Figure 1. Initial conditions for each simulation along slices through the y = 0 plane. log 10 of hydrogen number density is represented by the continuous color scale. log 10 of magnetic field strength is represented by contours. The dark matter barycenter is marked with an “ X ” and a circle at the half-mass radius. From left to right, the panels show Runs 1–6, as in Table 1 . 3. RESULTS All six clouds behave similarly as they fall through the halo. This can be seen in Figure 2 which shows the clouds at epoch t 1 (35 Myr), just before impact. As they pass through the halo, the clouds lose material due to ram pressure stripping. By epoch t 1 the clouds with minihalos have lost 16%–18% of their original baryonic mass, while those without have lost ∼36% (see Table 1 ). During this stage, the effect of the weak magnetic field is merely to become swept up and wrapped around the cloud. This is similar to the behavior in the 2D MHD simulations of Konz et al. ( 2002 ). Figure 2. Same as Figure 1 , but for epoch t 1 , equivalent to t = 35 Myr for all runs. Figure 3. Same as Figure 1 , but for epoch t 2 , equivalent to t = 52 Myr for Runs 1 and 2 and t = 45 Myr for Runs 3–6. Each of the clouds approaches the disk with a similar velocity (∼300 km s −1 ). However, the dark matter confined clouds are more compact and contain more baryonic mass and so carry larger kinetic energy densities than the dark-matter-free clouds. The collision of the cloud with the Galactic disk occurs at 36 Myr for those runs with a minihalo, and 37 Myr for the dark-matter-free runs. At the moment of collision, the minihalo separates from the cloud gas and continues, unhindered, through the Galactic disk. During the collision, the gas cloud ceases to exist as it mixes with and transfers momentum to the disk gas. This results in a hole in the disk and a long-lived, bubble-shaped structure filled with moderately dense (∼0.1 to ∼3 H cm −3 ), cool gas (∼1000 K) below the disk, as shown in Figure 3 at 52 Myr for Runs 1 and 2 and 45 Myr for Runs 3–6 (epoch t 2 ). These structures are composed of a mixture of gas from both the cloud and the disk, with the majority of their mass originating from the disk. Tenorio-Tagle et al. ( 1986 ) found similar structures in 2D hydrodynamic simulations of dark-matter-free clouds colliding with the Galactic thin disk. Over
Paper: 2016ApJ...833..237M (fulltext)
750 and HARP 1028. These flare-productive active regions produced at least one flare of intensity greater than M5. Middle panel: power index evolution for HARP 1464 and HARP 1621. These flare-productive active regions produced flares of intensity between M1 and M5. Lower panel: power index evolution for HARP 1019 and HARP 1318. These active regions showed no flare productivity. Vertical lines in the graphs indicate the occurrence of flares with intensity greater than M1. In addition to exhibiting, on average, a lower value for the power index, flare-quiet regions tend to show much more variability in the temporal evolution of the index. Figure 5 (bottom) shows one such trend—where the index tends to drop off over several days. A second trend is shown in Figure 7 , where NOAA 11274 shows periods when the power index rises to 1. However, these periods of time are short. In contrast, there is no sudden spike in the index value for any of the flare-productive regions as they track across the solar disk. Instead, flare-productive regions exhibit a large power index for many days, both before and after they produce solar flares. This suggests that the flare potential of any active region could be determined a few days in advance of the production of a large solar flare and the occurrence of solar flares does not reduce the future flare potential. Figure 6 shows the results in the form of histograms for all 53 HARPs. The range of index values (1.0–2.5) is similar to that in previous studies (Abramenko Yurchyshyn 2010 ). The dotted histogram shows the power index values taken by flare-quiet active regions. The power index distribution for these flare-quiet regions is quite broad and peaks around 1.6. The dashed histogram indicates the results for flare-productive active regions that produced flares of magnitude M1–M5. This distribution is much narrower, with fewer values below 1.5, and peaks around 1.8. However, the high-value tail does overlap with the flare-quiet distribution. Finally, the solid histogram shows the power index values taken by flare-productive active regions that produced at least one flare of intensity greater than M5. In this case, the power index mostly lies above 2 with a maximum at around 2.2. Figure 6. Histograms of power index values for all 53 HARPs. The dotted line indicates a histogram for 25 flare-quiet active regions. The dashed line shows a histogram for 19 flare-productive active regions that produced flares of intensity between M1 and M5. The solid line indicates a histogram for nine flare-productive active regions that produced at least one flare with intensity greater than M5. Figure 7. Power index evolution of a non-flaring active region HARP 803 (NOAA 11274). Flare-quiet regions tend to show much variation in the power index values. The above classification of HARPs was performed based on the maximum intensity of any flare from the region. However, the flare productivity can be measured more carefully in terms of