adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...635A.192P (fulltext)
translation error of one pitch implies a 20% error in the 7 λ / D × 7 λ / D region around the center, whereas it reaches 60% in the largest DH (28 λ / D × 28 λ / D ). The more uncertainties there are on the positions of the actuators, the narrower the region of correction. We now consider that the modeled DM is not translated relative to the true one but is rotated around the pupil center. We calculate the estimated field and the RMSE for each rotation error. Results are plotted in blue on the right of Fig. 5 . As for the translation error, the larger the DH the more sensitive the PW is to the rotation error. For example, the RMSE is 10% for the largest DH (28 λ / D × 28 λ / D ) and 5% for the smallest one. Actuator 498 is at about two actuators from the pupil center such that a rotation of 10° corresponds to a translation of 0.35 pitch. From the translation error plot (center plot of Fig. 5 ), a 0.35 pitch translation error gives a RMSE of ∼10% for the largest DH and ∼5% for the smallest one. We therefore expect the PW estimation to be more sensitive to an error on the angular position of the DM if the actuator-probes are further away from the center of the pupil. We confirm this statement by executing the same study for a pair of actuator-probes closer to the edge of the pupil: actuators 283 and 251. The results plotted in red on the right of Fig. 5 confirm the following statement: the closer the pair of actuator-probes is to the center of the pupil, the more robust is the PW with respect to a rotational error. 4. Wavefront control Once the electric field E S 0 is estimated, DMs are controlled to minimize the stellar speckle intensity inside a DH. In this section, we present two wavefront control techniques (SCC and EFC) assuming small aberrations ( α and β ) and a single DM placed in the pupil plane. Both techniques use an iterative process and a control matrix. 4.1. SCC and EFC common strategy Classical AO systems measure and minimize the phase aberrations β in the pupil plane. In the context of HCI, this strategy is not optimal because the amplitude aberrations α also induce stellar speckles in the science image. Moreover, DMs cannot control all the high spatial frequencies because of the limited number of actuators. Therefore, even if there are no amplitude aberrations, one DM cannot completely null the phase β . That is why Malbet et al. (1995) proposed to minimize the stellar speckle intensity inside a DH in the science image instead of the phase in a pupil plane. This has two main advantages. The field induced by both amplitude and phase aberrations can be minimized. And a stronger attenuation can be reached using the frequency-folding phenomenon and by decreasing the size of the DH ( Bordé Traub 2006 ; Give’On et al. 2006 ). We assume a single DM placed in the pupil plane and we consider that the focal plane field E DM = iC [ Aφ ] induced on the science detector is a linear combination of the DM actuator voltages : where G is the
Paper: 2017A&A...606A..88T (acknowledgments)
M.T. and L.T. acknowledge support by the DFG cluster of excellence Origin and Structure of the Universe ( www.universe-cluster.de ). This work has been supported by the DISCSIM project, grant agreement 341137 funded by the European Research Council under ERC-2013-ADG. M.T. and L.T. thank Eva Wirström, the Gothenburg Centre for Advanced Studies in Science and Technology and the participants of the workshop Origins of Habitable Planets hosted at Chalmers (Gothenburg) in June 2016 where considerable part of this work was fruitfully discussed. M.T. is grateful to Cathie Clarke for insightful discussions. The fits have been carried out on the computing facilities of the Computational Center for Particle and Astrophysics (C2PAP) as part of the approved project “Dust evolution in protoplanetary disks”. M.T. acknowledges usage of the Max Planck Gesellschaft Hydra computing cluster for code testing, for which is grateful to Paola Caselli. Figures have been generated using the Python-based matplotlib package ( Hunter 2007 ). Staircase plots of PDFs have been generated with a user-modified version of the Python-based triangle package ( Foreman-Mackey et al. 2014 ). This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. This work was partly supported by the Italian Ministero dell’ Istruzione, Università e Ricerca through the grant Progetti Premiali 2012 – iALMA (CUP C52I13000140001). A.N. acknowledges funding from Science Foundation Ireland (Grant 13/ERC/I2907). C.F.M. acknowledges an ESA Research Fellowship. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2013.1.00220.S, ADS/JAO.ALMA#2013.1.00226.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ
Paper: 2020MNRAS.494.2385K (fulltext)
wave. Thus, there is a continuous flux of particles crossing R c from below. These particles get accelerated to high LF and advected with the Alfvén wave as a result of the strong electric field 2 in the charge starvation zone. The continuous loss of particles from the interior of R c causes the e ± density to decrease with time, and the region that was not charge starved before becomes charge starved. We estimate the speed at which R c moves inward. The current amplitude ( I aw ) associated with the Alfvén wave packet along is roughly independent of R; I aw is the integral of current density over the cross-section of the wave packet. Thus, the number of particles that cross R c per unit time and are subsequently advected by the Alfvén wave is ∼ I aw /2 q . The cross-sectional area of the wave packet, A aw , increases as R 3 as the wave follows the dipole field lines. Therefore, d R c /d t ∼ − I aw /(2 qnA aw ). This result can be recast in a slightly more convenient form as d R c /d t ∼ − n c /2 n; I aw / A aw ∼ j , and j can be written in terms of n c and n using equations ( 4 ) and ( 6 ). A corollary of this result is that e ± present in the NS magnetosphere are depleted (advected by the wave) on a time of the order of 〈 n / n c 〉 R c / c , which is a few ms. Thus far, we have discussed how the Alfvén current below R c supplies a flux of particles into the charge starvation region where they are quickly accelerated and advected by the wave. We now describe how particles the Alfvén wave packet encounters at the head of the wave at R > R c also get trapped and advected by the wave. Charge particles of one sign – those that experience the force of the field in the forward direction – are swept up by the wavefront and compressed into a thin sheet of width much smaller than λ aw and forced to move with the wave. Particles of the opposite charge move in the reverse direction, along , at a speed of ≈ c for about half a wavelength and there they start accumulating in a thin sheet; these results follow from equation ( 16 ) for γ. The Coulomb field in the thin sheet increases with time as more charges are swept up by the wave and accumulate in the sheet: where R is the location of the wavefront at time t . The Coulomb field strength exceeds the wave-field E d for , and at that point the sheet starts spreading in the longitudinal direction at close to the speed of light. Since the Coulomb fields at the front and the back sides of the sheet do not decrease as the width of the sheet increases, the spreading continues until the sheet thickness becomes ∼λ aw /2. Particles in the sheet now overlap with particles of opposite charge in the adjoining half-wavelength of the Alfvén packet, thereby partially shielding E cou . The physics of the system is that charge particles of one sign swept up at the front of the Alfvén wave packet, within λ aw of R c , accumulate in a thin sheet near the leading front. The sheet starts to spread longitudinally, after about
Paper: 2016ApJ...816L..21C (fulltext)
the surface, which naturally underestimates the extinction. The curves are shown in Figure 3 (top) for both the truncated and full disks. At a given location, the optical depth of the outer disk is generally lower for the truncated disk with radial drift, as we would expect. Note that it does not move all the way inward to the truncation radius since there are still small grains out to the gas disk radius. Figure 3. Top row: effective opacity () to external FUV radiation for the different small/large dust mass distributions over steradians. The left and right columns show the full model and drift-dominated model, respectively. The dashed and solid lines indicate and . The line color corresponds to the different small grain dust fractions, gray (10%) and black (1%). Bottom row: for the case, the ratio of desorbed molecule flux from photodesorption vs. thermal desorption (). The solid line indicates where the processes are equal. We compute the local flux, , and adopt a value for the yield of photodesorbed CO of molecules photon −1 , which is appropriate for the FUV photodesorption experiments of Fayolle et al. ( 2011 ). Figure 3 (bottom) compares the relative desorption fluxes from the ISRF to thermal processes () for the case. Large values correspond to efficiency in each process, i.e., regions of the disk where is large implies photodesorption dominates thermal desorption and vice versa. In all models, thermal desorption dominates the inner disk and transitions to photodesorption in the outer disk. We find that the transition is substantially further out for the drift-dominated model and that the transition occurs rapidly. In the case with drift, thermal desorption dominates ISRF photodesorption inside of , and for the full disk the transition occurs at ∼80 AU. These results are non-obvious, as radial drift increases outer disk heating but also decreases the opacity to external FUV. We find similar results for 1% small grain mass case, with values of 400 AU (drift) and 70 AU (full), respectively. 4. DISCUSSION Based on these results, we expect radial drift to alter the disk temperature profile and enhance thermal desorption of CO in the outer disk. To approximately estimate the enhancement in CO column density, we take the temperature profile and assign a CO abundance of 10 −4 per H 2 for dust temperatures between 17 and 200 K. We calculate the vertically integrated column density of CO versus radius and then compare the models with and without drift and find the maximum enhancement in CO column is ∼2–2.5 times higher outside of the large grain radius. Recently, observations of IM Lup have shown multiple rings of DCO + , a direct product of CO-driven chemistry (Öberg et al. 2015 ). Furthermore, Tang et al. ( 2015 ) report multiple CO ring structures in both 12 CO and 13 CO in GG Tau. Radial temperature variations increasing CO sublimation in the outer disk may be contributing to these structures, though they should be modeled in detail to
Paper: 2015ApJ...813..130W (acknowledgments)
The authors thank the anonymous referee for constructive comments and suggestions that greatly improved the paper. We would like to thank the telescope operators and supporting astronomers at the Palomar Observatory and the Keck Observatory. Some of the data presented herein were obtained at the W.M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W.M. Keck Foundation. The research is made possible by the data from the Kepler Community Follow-up Observing Program (CFOP). The authors acknowledge all the CFOP users who uploaded the AO and RV data used in the paper. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. J.W.X. acknowledges support from the National Natural Science Foundation of China (grant No. 11333002 and 11403012), the Key Development Program of Basic Research of China (973 program, Grant No. 2013CB834900) and the Foundation for the Author of National Excellent Doctoral Dissertation (FANEDD) of PR China. J.W. acknowledges the travel fund from the Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University).
Paper: 2016ApJ...828...35A (acknowledgments)
The authors thank Adam Riess, Daniel Eisenstein, Eric Linder, Kaisey Mandel, Andrew Friedman, and Raul Jimenez for very useful discussions and suggestions to improve this work. Supernova cosmology at the Harvard College Observatory is supported by National Science Foundation grants AST-156854 and AST-1211196. A.A. acknowledges, also, the Instituto Avanzado de Cosmología of Mexico, and the Mexico-Harvard Fellowship sponsored by Fundación México en Harvard and CONACyT. We acknowledge the use of emcee: The MCMC Hammer (Foreman-Mackey et al. 2013 ).
Paper: 2021MNRAS.505.3982B (fulltext)
and using 16 Gigabytes of RAM takes between 5 to 10 s. Including the pixel-error map calculation increases the computing time to approximately 10 min. For comparison, the Super-Pixel method typically requires several minutes to extract the binary field, where the actual time depends on the image pixel resolution and the number of Super-Pixels employed. The computational speed of our network thus makes it particularly useful as a component in a Bayesian statistical inference framework to perform EoR parameter estimation using tomographic statistics (e.g. Gazagnes et al. 2021 ). We compare the accuracy of our approach with a feature finding method from the field of image processing, the so-called Super-Pixel method, which Giri et al. ( 2018 ) showed to be superior over simple thresholding methods. The results show that our neural network can identify neutral regions in the mock observations at least as well and often much better than the Super-Pixel method. We show a visual comparison and the resulting pixel-per-pixel error map tested on our ‘fiducial’ model. This error map gives valuable insight into the parts of the image that are hard to recover and helps us check for overfitting. We studied the accuracy of a range of derived quantities from the recovered binary fields, comparing the performance of SegU-Net with the Super-Pixel method. These quantities are the volume-averaged ionization fraction – the evolution of which provides the reionization history, the size distribution of the ionized (BSD) and neutral (ISD) regions, the dimensionless power spectra of the recovered binary fields, and the three Betti numbers, which quantify the topology of the segmented data sets. For all quantities, we find that the SegU-Net results are more accurate than the Super-Pixel results, especially for the early and late stages of reionization, where the Super-Pixel method often struggles to produce accurate results. Machine learning methods generally are sensitive to the properties of the training set. Therefore, we tested SegU-Net on input data with different properties than the training set. First, we analysed the performance on data sets with different noise levels than the network was trained. We found that SegU-Net yields accurate results for data sets in which the noise level is characterized by an observing time of , which approximately corresponds to an SNR ≳3. Secondly, we applied the network to mock observations calculated from the results of a fully numerical reionization simulation, rather than the seminumerical simulations used to train the network. We find that SegU-Net achieves the same level of accuracy when applied to this data set and therefore is not sensitive to the type of simulation employed during the training process. We want to point out that similar efforts are being made by Gagnon-Hartman et al. ( 2021 ). They focus on reconstructing the segmented maps of ionized and neutral regions in the context of foreground mitigation using the
Paper: 2017MNRAS.469.3088K (fulltext)
were a ≈ 1730 au and e ≈ 0.99999, corresponding to separation at pericentre d per ≈ 3.8 R ⊙ and the companion filling its Roche lobe 89 per cent (radius-wise), which is sufficient for circularization in all our models. Fig. 3 shows the detailed evolution of the binary orbital parameters a and e , as well as the pericentre separation d per . Even though the binary was affected by more than 5000 encounters, the vast majority of interactions had very little effect on its orbit, with only a few inducing significant changes of orbital parameters. The average mass of fly-by stars in the only 10 encounters during which the orbital energy was changed by more than 10 per cent was ∼13.5 M ⊙ , whereas the average mass of all stars encountered by the binary was ∼0.62 M ⊙ . In fact, it is very typical for the majority of dynamically formed LMXBs in our simulations that the dynamic evolution of a binary is mainly dependent on a few key encounters with, on average, exceptionally massive fly-bys. This is connected to the fact that, on average, the amplitude of a velocity kick induced by a fly-by to one binary component with respect to the other can be analytically estimated as proportional to the fly-by's mass (e.g. Hills 1981 ), i.e. Here, a is the semimajor axis of the binary, M fly-by is the mass of the fly-by, b is the impact parameter and v rel is the fly-by's velocity, relative to the mass centre of the binary. This suggests that the local stellar mass distribution has influence on the time-scale of evolution through dynamical interactions and therefore the effectiveness of the LMXB's dynamical formation channel. We discuss this further in Section 5.2 . Figure 3. Detailed evolution of orbital parameters a and e , as well as distance at pericentre d per = a (1 − e ) for an exemplary, typical binary that has undergone tidal circularization (noticeable as a rapid decrease of both semimajor axis and eccentricity at the very end of evolution). This particular system was subject to over 5000 stellar encounters, most of them occurring during the second half of its evolution. The binary was subject to several major changes of orbital parameters as a result of encounters with exceptionally massive fly-bys. For instance, the most significant change of eccentricity from 0.71 to 0.09 at around 3.7 Gyr (coupled with an increase of the semimajor axis from 345 to 530 au) was caused by an interaction with a ∼4.5-M ⊙ star, whereas the mean mass of fly-bys in our simulations is ∼0.62 M ⊙ . Additionally, the encountered star was passing by with a relatively low speed of ∼45 km s −1 with respect to the binary (the mean relative speed of all fly-bys is ∼125 km s −1 ), which granted an exceptionally long time of gravitational interaction. 4.2 Evolution of orbital parameters The evolution of orbital parameters in Fig. 3 also illustrates a general trend of steadily increasing semimajor axis. This is an average regularity for all of our evolutionary paths leading towards
Paper: 2019MNRAS.485..396V (fulltext)
of their central BH. It is therefore plausible that major merger events triggering the rapid growth phase of BHs could contribute to the formation of anomalously massive SMBHs. Based on hydrodynamical and cosmological simulations we thus expect the existence of observed M BH ( M ⋆ ) outliers with . Observed outliers with such large M BH are plausibly survivors of tidal stripping and it would be interesting to study their environment out to ∼1 Mpc. The positive correlation between the scatter in M BH (σ) and in [Mg/Fe](σ) indicates a strong relation between the M BH and the time-scale of the quenching process for massive galaxies. It would furthermore be interesting for future studies to investigate the relation between merger-driven rapid growth phases at high redshifts ( > 2) and anomalously high-mass SMBHs. APPENDIX A: BMG PROPERTIES In Table A1 , we list all of the relevant properties of the five BMGs and observed outliers. Additionally, we list information on some of the most notable observed M BH ( M ⋆ ) outliers. For NGC 1271 and NGC 1277, the [Mg/Fe] values are the best-fitting values derived by Martín-Navarro et al. ( 2016 ). In column 7, the ‘outlier ’ represents the lower limits of a 500 Myr time-step. Table A1. The five BMGs and their properties. Column 1 shows the BMG ID (for simulated galaxies) or name (for observations). From left to right: BH mass (column 2), stellar mass (column 3), half-mass radii for the simulated galaxies, or effective radius for observations (column 4), one-dimensional stellar velocity dispersion, which is computed from the average of the three-dimensional velocity dispersion (column 5), mass-weighted stellar age (column 6), and [Mg/Fe] abundance (column 7). Column 8 shows the redshift of the snapshot at which each galaxy crossed the a M BH / M ⋆ > 0.01 threshold. These redshifts have an uncertainty of 500 Myr. Column 9 is the distance to the nearest more massive neighbour of each BMG at = 0. The last two columns (10 and 11) show f strip and f age from equations ( 1 ) and ( 2 ), respectively. The last four rows show the observational values for NGC 1271, NGC 1277, and Mrk 1216 as determined by Walsh et al. ( 2015 , 2016 ), Ferré-Mateu et al. ( 2015 , 2017 ), and Martín-Navarro et al. ( 2016 ). In Fig. A1 , we have plotted the stellar mass density of outlier ID E and its surroundings at three different snapshots throughout the simulation. The remaining images are available at https://sites.google.com/view/vson-umbh-galaxies/homepage Figure A1. Stellar surface density distribution of BMG-ID E at three different snapshots. These images are created with the publicly available Py-SPHViewer, which uses the smoothed particle hydrodynamics interpolation scheme. The colours represent the projected density of stellar particles bound to BMG-ID E, with blue/pink corresponding to low density and yellow corresponding to high-mass density. BH particles are indicated with blue dots. The most relevant parameters of the
Paper: 2018MNRAS.476..151E (acknowledgments)
ACKNOWLEDGEMENTS We thank Hiranya Peiris, Benjamin Joachimi, Fergus Simpson, and the referee for helpful comments on the pre-print version of this paper. We thank Frankie Nobis-Efstathiou for help with the early stages of this project. We also thank Anthony Challinor, Steven Gratton, and members of the KiDS team for comments on aspects of this analysis. We also thank members of the Planck Parameters team. PL acknowledges support from an Isaac Newton Studentship at the University of Cambridge and from the Science and Technologies Facilities Council.