adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...871..149F (fulltext)
analysis of the visibility weights in order to ensure robust uncertainties in the measured flux. Future work on identifying variability in deeply embedded protostars is underway. A Cycle 6 ALMA proposal for 4 epochs of stand-alone Atacama Compact Array Band 7 observations of variables in Serpens identified by the JCMT Transient Survey has been accepted (PI: Logan Francis, project code 2018.1.00917.S ). These observations will complement results from the contemporaneous Transient Survey by observing at 850 μ m with a resolution of 38 (compared to the 146 resolution of the JCMT), sufficient to reach the scale of the inner envelopes (∼1500 au) of protostars in Serpens. Appendix Discussion of Individual Fields in Serpens Main The Serpens Main cluster has been extensively surveyed at a variety of wavelengths and resolutions over the past 30 yr. Our ALMA observations provide some of the highest resolution and most sensitive maps of deeply embedded protostars in the cluster to date. In many of our fields, we are thus able to resolve single sources at the scale of the accretion disks, uncover significant extended structure, and identify previously unknown faint sources. Here, we discuss each field in the context of past and recent observations, and compare the positions of our sources with YSOs identified in the Spitzer “cores to disks” (c2d) and “Gould Belt” (GB) surveys (Dunham et al. 2015 ). Table 14 lists the properties of all c2d/GB YSOs in our fields associated with our millimeter sources in Table 5 . Table 14 Properties of the Serpens c2d+GB YSOs Note. This table adapted from Table 2 of Dunham et al. ( 2015 ). a Classes are defined by the extinction corrected spectral index α ′ as Class 0 + I: α ′ ≥ 0.3, flat spectrum: −0.3 ≤ α ′ 0.3; Class II: −1.6 ≤ α ′ −0.3; and Class III: α ′ −1.6 (Greene et al. 1994 ). The Ser-emb objects targeted by our ALMA and the earlier CARMA observations (Enoch et al. 2011 ) were originally defined from large-scale Bolocam 1.1 mm and Spitzer mid-IR surveys, and classified according to their bolometric temperature (Enoch et al. 2009 ). Most of our targets lie in two dense clusters: the northern Main Cluster (Ser-emb 4, 6, 8) and the southern Cluster B (Ser-emb 3, 7, 9, 11, 17). Three sources are relatively isolated (Ser-emb 2, 5, 15) from either cluster. An overview of the region showing these clusters and the locations of the targeted embedded sources can be found in Figure 1 of Enoch et al. ( 2011 ). Our Figures 9 – 11 show the continuum maps with the full field of view for each of our ALMA pointings, and indicate the positions and IDs of c2d/GB YSOs by green pluses (Class 0 + 1, flat spectrum) and orange crosses (Class II). Red squares show the location of the postage-stamp views in Figures 2 and 3 . Figure 9. Full maps of our ALMA observations of Serpens protostars. Red squares indicate the field of view for the postage stamps in Figures 2 and 3 . The maps are shown with primary beam correction to indicate
Paper: 2018ApJ...855..109L (fulltext)
10 3 ]. In panel (b), C ranges from (0, 200] and γ ranges from (0, 0.2]. For the purpose of cross-validation, we split the entire data set into two subsets: the training set and the test set. Amari et al. ( 1997 ) found the optimal number of the test set as , where l and n are the number of data points and features, respectively. Taking l = 187 and n = 13 in our case, we found that the partition of the entire data set between the training set and the test set should be 80%:20% (145:37). Using the optimal pair of parameters C and γ found above, we fed the training set into the SVM regression algorithm to build a prediction engine. Next, we made a prediction of the CME transit times using the test set and calculating the R 2 score between the predicted and actual transit times. To find the best result with the highest R 2 score, we randomly shuffled the entire data set (the order of events in the data set is shuffled, which is a general practice to avoid bias, see, e.g., Géron 2017 ) and repeated the above steps (i.e., split the shuffled data set into the training and test sets, build an engine using the training set, and calculated the R 2 score of the test set). Theoretically, there are (∼6 × 10 38 ) possible combinations of the training set and test set. This is a huge number and it is impossible to exhaustively test all of the possibilities given the available computer power. Figure 5 shows the variation of the average (blue curve) and maximum (green curve) R 2 scores among all of the test sets with the increasing number of trainings. The average R 2 score increases continuously before the number of trainings reaches 1000, and remains almost unchanged after that. This suggests that when training is performed over 1000 times, the result can reflect the basic distribution of the R 2 scores for all possibilities. The maximum R 2 score increases steeply when the number of performed trainings is less than 100,000 and yields a similar value when it is increased by a factor of 10. This indicates that it becomes more feasible to find the best engine with an increasing number of trainings. Figure 5. Variation of the average (blue curve) and maximum (green curve) R 2 scores with increasing number of trainings. Considering the above results and reasonable CPU time consumption, we repeated 100,000 times of trainings to find the best training set, which results in a highest R 2 score of its corresponding test set, to construct the engine. This could be rather costly. However, via paralleling the process employing the open-source Message Passing Interface (Open MPI, http://www.open-mpi.org/ ), a 100,000-time training only takes ∼25 minutes on an Intel(R) Core(TM) i7-7770K desktop with 8 threads. However, we should note that training the SVM regression 100,000 times cannot always reveal the best result (as shown by the green dashed line in Figure 3 ), because 100,000 is only a fraction of all of the possibilities (). Multiple runs are sometimes needed to
Paper: 2018A&A...614A.108S (fulltext)
of the warp causing reddening extends 1.07 and 0.68 R ⋆ above the disk midplane (for the Jan. 18 and 19 spectra, respectively). The CoRoT light curves indicate that the warp blocks part of the star during about half of the rotation period, which points to a ratio of roughly 3:1 between the azimuthal and vertical dimensions of the warp given the above size estimate. We note that, with the viewing geometry derived by Fonseca et al. (2014) , the star is unobscured only during phases when the disk does not significantly extend above the disk midplane. 4.3. Gas absorption The X-shooter spectra also contain atomic gas absorption lines that include nonstellar contributions, which we later correlate with the dust extinction. The spectral resolution of X-shooter renders it challenging to differentiate interstellar (expected to be narrow) from circumstellar absorption (expected to be broad) directly in the spectra. Hence, we concentrated on the evolution of the observed EW, because any “truly” interstellar (e.g., Pascucci et al. 2015 ) or stellar absorption is expected to stay constant. In this situation, observable changes in the absorption require that inter- and circumstellar column densities have roughly similar values. Circumstellar column density changes that are small compared to the interstellar absorption would remain unnoticed. Conversely, observing EW changes directly implies that the circumstellar column densities are sufficiently large. 4.3.1. Measured line properties The Na and K optical doublets are the strongest absorption lines in our spectra ( Fig. 4 ). They are common interstellar absorption lines (e.g., Welty Hobbs 2001 ), but can also have circumstellar contributions. In fact, accretion emission can also contribute to these lines, but the lack of strong accretion signatures 3 implies that this would affect the EW by≲ ≳ 10mÅ. Similarly, absorption by the accretion funnels has been suggested to cause broad absorption features in CTTS, mainly in the strongly accreting ones. Hence, we regard a funnel contribution to the Na and K absorption insignificant for V 345 Mon due to its very low accretion rate. In all three epochs, we clearly detect absorption in the Na and K optical doublets while the Na UV lines are not significantly detected with a combined EW upper limit of 0.7 Å. The depth of the Na and K doublets is mainly photospheric, the PHOENIX model ( T eff = 4900 K, log g = 4.50, solar metallicity, see Husser et al. 2013 ) gives stellar EWs of 3.6 Å (Na) and 0.6 Å (K). Neverthless, material between us and the photosphere causes extra and variable absorption since the measured EW is higher than the photospheric value for both doublets during all epochs. With the limited spectral resolution of X-shooter, we cannot localize the gas that causes this extra absorption, for example, through its velocity structure. However, some of this extra absorption is likely interestellar and thus constant between the observing nights ( Pascucci et al.
Paper: 2020MNRAS.491.1554W (acknowledgments)
ACKNOWLEDGEMENTS MW would like to thank H. Domínguez Sanchez and M. Huertas-Company for helpful discussions. MW acknowledges funding from the Science and Technology Funding Council (STFC) Grant Code ST/R505006/1. We also acknowledge support from STFC under grant ST/N003179/1. LF, CS, HD, and DW acknowledge partial support from one or more of the US National Science Foundation grants IIS-1619177, OAC-1835530, and AST-1716602. This research made use of the open-source python scientific computing ecosystem, including scipy (Jones et al. 2001 ), matplotlib (Hunter 2007 ), scikit-learn (Pedregosa et al. 2011 ), scikit-image (van der Walt et al. 2014 ), and pandas (McKinney 2010 ). This research made use of astropy , a community-developed core python package for Astronomy (The Astropy Collaboration 2013 , 2018 ). This research made use of TensorFlow (Abadi et al. 2015 ). All code is publicly available on GitHub at www.github.com/mwalmsley/galaxy-zoo-bayesian-cnn (Walmsley 2019 ).
Paper: 2016ApJ...829....1M (acknowledgments)
We thank the referee, Richard Alexander, for his useful and clarifying comments that improved the paper. E.M., G.A., M.O., J.M.T., and J.F.G. acknowledge support from MINECO (Spain) grant AYA2014-57369-C3 (co-funded with FEDER funds). S.L. acknowledges support from DGAPA-UNAM IN105815 and CONACyT 238631. C.C.-G. acknowledges support from UNAM-DGAPA PAPIIT IA102816. L.Z. acknowledges the financial support from DGAPA, UNAM, and CONACyT (Mexico). Facility: VLA.
Paper: 2016ApJS..225...13Y (fulltext)
and magnetic reconnections. By sampling the shock profiles at different times and different locations of the bow shock and tracing the particles, we have shown the following results: 1. We identify different stages in the macroscopic evolution of the bow shock and the entire formation of the magnetosphere in the meridian plane. The macrostructures reach a quasi-stable state after several ion cyclotron cycles, consistent with previous 3D implicit PIC simulations (Peng et al. 2015b ). Furthermore, a relatively high temporal-spatial resolution employed in our simulation provides an opportunity to examine the microstructure of the bow shock and the magnetic reconnection. 2. In the southward IMF condition, the shock around the subsolar point is quasi-perpendicular, and the shock at the wings is oblique. The angle and solar wind speed along the shock normal decrease with the distance away from the subsolar point. At quasi-perpendicular regions of the shock, a self-reformation of the shock front is found and the cyclic period of the reformation is similar to those observed in 1D and 2D local simulations. At the oblique region, the shock becomes weak due to the slow solar wind speed in the shock normal direction. 3. Different from 1D and 2D local simulations, particles in the downstream region are more complicated at the bow shock. By tracing the ions and electrons in the simulation, we find that the solar wind ions injected at the subsolar point will become diffuse and can be convected to the cusp region and the downstream of the oblique region of the bow shock. Electrons are more diffusive than ions in the downstream region. The ions injected at the northern part of the bow shock will fill in the northern part of the magnetosheath. A fraction of electrons injected at the same place will be convected to the southern part of the magnetosheath. The particles injected and reflected by the quasi-perpendicular shock region can affect the local velocity distributions and electromagnetic fluctuations in the transition and downstream regions of the oblique shock at the wings of the bow shock. This feature makes the oblique shock regions become a little different from those observed in local planar shock simulations. 4. The impact of the upstream plasma beta β i and solar wind Mach number M A on the bow shock and the magnetosphere show that the shock front becomes quasi-stationary in the high beta and low Mach number cases. In addition, the magnetotail reconnection disappears in this quasi-stationary case due to the low compression of the magnetotail under a low solar wind pressure condition. If we keep the Mach number of the incident solar wind unchanged and only increase the beta value, the bow shock is still nonstationary. Contrasting the low beta case, the ripple scale becomes larger in the high beta case. 5. To confirm the impact of the solar wind condition on the bow shock front nonstationarity, we also studied the time evolution of shock
Paper: 2016ApJ...829..110B (acknowledgments)
This work is supported in part by a Department of Energy Office of Nuclear Physics Early Career Award, and by the Director, Office of Energy Research, Office of High Energy and Nuclear Physics, Divisions of Nuclear Physics, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, and from NSF grant AST-1206097. Support for M.-R.W. and G.M.-P. is provided in part by the Helmholtz Association through the Nuclear Astrophysics Virtual Institute (VH-VI-417) and the BMBF-Verbundforschungsprojekt number 05P15RDFN1.
Paper: 2016MNRAS.455.2187M (fulltext)
jet emanating from Sgr A*: the jet collides with the Eastern arm of the Sgr A West H ii region, driving a shock front which is observable in infrared and radio, and accelerating relativistic electrons which are seen in X-ray. The long axis of the hypothetical jet path corresponds to an azimuthal angle φ ∼ (−35 ± 2)°. If the jet propagates along the rotation axis, this measurement is consistent with our finding that φ ∼ (−32 ± 23)°. The rotation axis is also similar to that inferred by jet models of Markoff, Bower Falcke ( 2007 ), θ ≳ 75°, φ ∼ −25°, and model-dependent constraints on the spin axis by Meyer et al. ( 2007 ), φ ∼ − 25°. Surprisingly, this rotation axis is also nearly coincident with the Galaxy's rotation axis, φ = −317 (Reid Brunthaler 2004 ). Since we find a rotation axis misaligned with both the stellar clockwise disc (by ∼90°), and with G2 (by ∼60°), our model argues against scenarios in which gas in the accretion flow originates from either of these sources. The rotation axis we identify does align quite closely with the circumnuclear disc, a massive torus of molecular gas with an inner edge ∼1.5 pc from Sgr A* (θ = 118° ± 15°, φ = −25° ± 15°; Jackson et al. 1993 ). Our analysis suggests that the gas in the accretion flow hails from the circumnuclear disc, rather than from the young clockwise stellar disc as is commonly assumed. In addition to constraining the rotation axis of the accretion flow, we find degenerate relationships between the gas density and magnetic field strength, and between the rotation profile and the shape of the G2 cloud. These degeneracies could be broken with further observations, yielding first measurements of the gas density and rotation speed at radii of 100s of au in the Galactic centre. This is an exciting possibility, because these measurements would constrain the thermodynamics and momentum transport within the accretion flow, providing important information about the physics of black hole accretion in low-luminosity systems such as Sgr A*. We find that G2's motion is with the accretion flow rather than against it (Fig. 8 ; cf. Sadowski et al. 2013a ). With a relative inclination of ∼60° between G2's pre-pericentre orbit and the accretion flow, this is similar to the M3 simulation of Abarca, Sadowski Sironi ( 2014 ). This lowers the relative velocity somewhat, and may help explain non-detections of the bow-shock ahead of the cloud (e.g. Bower et al. 2015 ; Tsuboi et al. 2015 ). A potentially important effect we have not included in our analysis is the back-reaction of the drag force on to the accretion flow; since the combined mass of G1 and G2 is comparable to the total amount of gas in the accretion flow, the drag force acting on the clouds could also influence the evolution of the accretion flow. This is a complex process, as the back-reaction depends on angular momentum transport in the accretion flow and could be affected by magnetic fields, turbulent transport, and viscosity; we have
Paper: 2018ApJ...852...17C (acknowledgments)
We thank Cluster Science Archive ( http://www.cosmos.esa.int/web/csa ) for providing the data for this study. The fruitful discussion with A. Vaivads is appreciated. This work was supported by NSFC grants 41574153, 41431071, and 41404133.
Paper: 2016MNRAS.455.4301C (fulltext)
and our results not dominated by these corrections. This is discussed in more detail in Section 7 and Tables 2 and 3 . Table 2. Impact of the different corrections for observational systematic effects on the derived best-fitting bias b for the tomographic bins selected with our template method. The baseline mask is described in Section 4.2.3 and corresponds to all regions where our sample is complete (i.e. 10σ depth i ≥ 22.5). The third column corresponds to an additional masking of regions with high values of potential systematic variables such as seeing, where we observe large decrements in galaxy density (as described in Section 6.1 ). The fourth column refers to further corrections to w (θ) after these masks, in the cases where the data still correlate with maps for potential systematics (as discussed in Section 6.2 ). The fifth column reports the χ 2 /d.o.f. after all corrections applied. Empty entries refer to the cases where such corrections were not necessary. These values were obtained after fitting the ‘linear growth’ model for scales θ > θ min = (0.26, 0.18, 0.12, 0.08, 0.06) deg, from first to last z -bin. Table 3. Same as Table 2 but for the tomographic bins selected with the machine learning method. Different algorithms for photometric redshift estimation use different data quantities (most notably, template-based ones use magnitude errors while most machine learning do not). Thus, one expects a different response to potential systematics. 6.3 Stellar contamination Stellar contamination will affect the measured clustering signal, even if the cross-correlation between the galaxies and stars is negligible. This is due to the fact that rather than modulate the selection function of galaxies, stellar contamination introduces a separate population. In the limit where the stars are unclustered and the stellar contamination is constant, the observed galaxy density is δ o = (1 − f star )δ gal and thus i.e. the measured clustering is diluted by the square of the purity of the galaxy sample. We derive a general expression for the clustering of a galaxy population, given some stellar contamination (full details are shown in Appendix D ) where in this formalism, the number density of contaminating stars is allowed to be a function of not just the total density of stars, but also the survey observing conditions. The cross-correlation tests presented in the previous sections did not find stellar contamination to be a significant systematic contaminant. This suggests that the term in equation ( 26 ) proportional to w star, S is consistent with zero. However, the terms remain, and thus any stellar contamination must be accounted for. We estimate the stellar contamination in each photometric redshift bin by using DES observations in the COSMOS field. This region contains space-based observations which identify matching DES sources as point-like or not (as detailed in Section 4.1 ). Fig. 9 shows the purity of the DES sample in the COSMOS field
Paper: 2017A&A...598A.117B (fulltext)
in marginally excited dynamos, we restrict ourselves in the following to being real. We now also assume that is constant. In that case, oscillatory solutions were previously thought impossible ( Rädler Bräuer 1987 ). Analogously to their approach, we multiply Eq. ( 8 ) by , where the asterisk denotes complex conjugation, and integrate by parts. Using Eqs. ( 9 ) and ( 10 ), we obtain Furthermore, , so Equation ( 8 ) yields altogether four terms, two of which are real and the other two imaginary. We obtain two integral constraints where denotes the value of on the second boundary at . This implies that (negative frequencies for positive ) and if and . Similar integral constraints can also be formulated for the complex magnetic field, . Unfortunately, the perfect conductor boundary condition, , is more cumbersome. Instead, one could formulate the problem for an artificially modified boundary condition, on . Together with the condition on , the problem for becomes equivalent to that for . In either case, the integral constraints are analogous to those of Rädler Bräuer (1987) ; see Appendix A for details. Fig. 1 Plots of a) real, b) imaginary, and c) absolute parts of . In a) and b) , the zero lines are marked in white, while in c) those of are dotted blue and those of are solid red. 4. The solution Given that Eq. ( 8 ) has constant coefficients, it has solutions proportional to where the index denotes one of two independent solutions. The are in general complex and obey the characteristic equation It has two solutions, To satisfy the boundary conditions ( 9 ) and ( 10 ), we write the solution as a superposition of and . Equation ( 9 ) is readily satisfied by writing where we have ignored the possibility of an arbitrary (complex) constant in front of . To satisfy Eq. ( 10 ), we now require that vanishes. The existence of solutions to is demonstrated by looking at a contour plot of ; see Fig. 1 , where we also plot separately the real and imaginary parts of . We see two zeroes in , which is confirmed by the crossing of the lines where and vanish. (At , there is no such crossing, so is not a solution.) The transcendental equation relating to can be written in more explicit form as To find solutions to , it is convenient to introduce the complex variable We seek solutions to via complex interpolation, where subscripts and refer to the current and previous iteration. This yields the first critical value as with the corresponding complex wavenumbers The wavenumbers and obey the relation which follows from Eqs. ( 9 ) and ( 18 ). The critical values of and were first obtained by Jabbari et al. (2017) using explicit time integration. Additional solutions exist in the second and fourth quadrant of the plane; see Fig. 2 . They are all oscillatory, in agreement with the integral constraints; see Eqs. ( 13 ) and ( 14 ) and Table 1 . However, those higher modes would generally be unstable in a nonlinear calculation and therefore only of limited interest (