adsabs/WIESP2022-NER huggingface dataset:
Paper: 2015MNRAS.450.2132H (acknowledgments)
We thank the anonymous referee for their thorough comments which helped to improve the method and the manuscript. We gratefully acknowledge the support of the UK's Science Technology Facilities Council (STFC Grant ST/H00260X/1 and ST/J500914/1). The calculations for this paper were performed on Cray XT4 at Center for Computational Astrophysics, CfCA, of the National Astronomical Observatory of Japan and the DiRAC facilities (through the COSMOS consortium) including the COSMOS Shared Memory system at DAMTP, University of Cambridge operated on behalf of the STFC DiRAC HPC Facility. This equipment is funded by BIS National E-infrastructure capital grant ST/J005673/1 and STFC grants ST/H008586/1, ST/K00333X/1 ST/J001341/1. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton. We would also like to thank PRACE for the use of the Cartesius facility. This work was carried out, in part, through the Gaia Research for European Astronomy Training (GREAT-ITN) network. The research leading to these results has received funding from the European Union Seventh Framework Programme ([FP7/2007-2013] under grant agreement number 264895). DK acknowledges the generous support and hospitality of the Kavli Institute for Theoretical Physics (KITP), University of California Santa Barbara during the ‘Galactic Archaeology and Precision Stellar Astrophysics’ programme, where some of this research was performed. This research was supported in part by the National Science Foundation under Grant No. NSF PHY11-25915. We would also like to thank Mercè Romero-Gómez and Francesca Figueras for providing the subroutine to calculate the Gaia performance errors, including the update to post-launch estimates, Sanjib Sharma for providing the galaxia extinction maps and isochrones and Sami Niemi for the suggestion of using KDE's to visualize the velocity distributions.
Paper: 2016ApJ...816L..19G (fulltext)
Joung Mac Low ( 2006 ), we assign 20% of the explosions to SN Ia and 80% to SN II with scale heights of and , respectively (Tammann et al. 1994 ). However, estimates of the displacement of runaway OB stars suggests that the effective scale height of SNe II might be larger (Li et al. 2015 ). We separately vary the thermal energy injection per SN, , and the CR energy injection, , with . We discuss three different simulations: purely thermal SNe (, TH1-CR0 ), purely CR SNe (, TH0-CR1 ), and the physically most realistic setup with both energy injections (, TH1-CR1 ). 3. MORPHOLOGICAL EVOLUTION In all simulations, the SNe induce the formation of filaments, clumps, and voids. The presence of CRs leads to a smoother morphological appearance. An overview of the disk structure after is depicted in Figure 1 with cuts through the center of the box for the density edge-on (top) and face-on (middle) and the CR energy density (bottom). From left to right, we present TH1-CR0 , TH0-CR1, and TH1-CR1 . Both runs with CRs quickly push gas away from the midplane and form an extended atmosphere, whereas in the simulation with only thermal SN feedback, the disk remains compact without a noticeable extended layer of gas above the disk. The CR energy density shows relatively small spatial variations of about one order of magnitude. High CR energy peaks correspond to recent SNe and disappear quickly due to diffusion. Figure 1. Structure of the disk after of evolution. The left panel shows the run without CRs, the middle panel the run with purely CR feedback, and the right panel the simulation with both feedback energies. In the top and middle row, we plot the density edge-on and face-on in cuts through the center of the box. The bottom row depicts the CR energy density in the midplane. The disk in the run including CRs is significantly thicker compared to the purely thermal run. The thermal energy input of the SNe is primarily important in the denser part of the disk. CRs show their main impact in generating an extended atmosphere. The CR energy density is smooth in the midplane. Local variations are due to recent SNe and the resulting CR injection. Those variations disappear after less than a Myr due to diffusion. In Figure 2 , we show the vertical profile of the density for all simulations at . The arrows indicate the heights of 90% enclosed mass. The inclusion of CRs increases from to with similar values for both runs including CRs. The additional thermal energy injection from SNe in run TH1-CR1 changes the profile up to a height of . At larger altitudes, the profiles for TH0-CR1 and TH1-CR1 are basically indistinguishable. For comparison, we include the vertical density profile of the MW (Dickey Lockman 1990 ) showing reasonable agreement with both CR runs. Figure 2. Vertical profiles of the total gas density for all simulations. The arrows indicate the height of 90% enclosed mass. A fit to the observed density profile of the solar neighborhood (Dickey Lockman
Paper: 2019MNRAS.486.2050K (acknowledgments)
ACKNOWLEDGEMENTS The contribution of JK has been funded by ERDF postdoctoral grant No. 1.1.1.2/VIAA/I/16/194 ‘Chemical effects of cosmic ray induced heating of interstellar dust grains’. The contribution of JRK has been supported by ERDF project ‘Physical and chemical processes in the interstellar medium’, No 1.1.1.1/16/A/213. Both projects are being implemented in Ventspils University College. We thank Ventspils City Council for the support.
Paper: 2018MNRAS.477.1639Z (fulltext)
modelling of the full-shape anisotropic correlation function of: The systematic errors between the three-multipole and three-wedge methods are similar and the errors reported are always the largest of the two possibilities. For the analysis of the final eBOSS quasar sample, further work on improving the fidelity of the OuterRim-based mocks and understanding the difference in the bias models is needed. In particular, Vlah, Castorina White ( 2016 ) extended the CLPT-GS formalism to take into account contributions from effective field theory and additional bias terms. They showed that the effects of the biasing scheme are as important as higher order corrections to the theoretical predictions. Therefore, it would be interesting to see how this model performs for the analysis of the final eBOSS sample. Improvements in the model to account for the shape of the redshift error distribution would also be valuable. 5 ANALYSIS This section reviews the weighting scheme applied to the data to treat the potential systematic effects. We denote the total weighting scheme by , where the subscript specifies the different methods to compute the total weight. With this notation, the total weighting scheme used for the DR14 quasar BAO analysis (Ata et al. 2017 ) is: The first term, FKP = 1/(1 + n ( z ) P 0 ), is the FKP weight (Feldman, Kaiser Peacock 1994 ) that corrects for the variations of the observed quasar density n ( z ) across the redshift range and also depends on the amplitude of the power spectrum at the scale at which the FKP weights optimize the measurements (here we choose k = 0.14 h Mpc which is the typical scale at which the BAO signal is optimally detected which gives P 0 = 6 × 10 3 h −3 Mpc 3 ). The second term, photo , is a photometry weight which corrects for the variation of the depth across the survey; cp , is a weight that accounts for the quasar targets that could not be measured due to fibre collision; and noz is a weight that addresses for the number of confirmed quasars for which a secure redshift could not be determined. We will show that the use of the redshift efficiency, ε( x, y ), across the focal plane as a weight, focal = 1/ε( x, y ) is more appropriate than noz . We adopt the following definition of the total weight : Each quasar in the DR14 catalogue is thus weighted by to correct for any spurious variation of the quasar densities and to provide a more isotropic selection. For the random catalogue, we apply the FKP weight alone as it corresponds to a Poisson sampling which should not be affected by inhomogeneities in the selection. In tests defined throughout the following subsections, using the ability to test against unbiased samples, we will demonstrate that this new weight reduces systematic effects on the quadrupole by a factor of three. Furthermore, the region close to μ = 1 is responsible for the remaining systematic shift, and we propose a method to take this into account. The fits are performed with the CLPT-GS model.
Paper: 2017MNRAS.469..916B (acknowledgments)
Acknowledgments This paper is written in memory of our friend James R. Cohen (19482006) with whom this project was started. WAB has been supported as a Visiting Professor of the Chinese Academy of Sciences (KJZD-EW-T01). WAB thanks the Shanghai Astronomical Observatory staff for their hospitality during the tenure. TA has been supported by the NSTC 973 Program (2013CB837900) and Shanghai Rising Star programme. The e-MERLIN is a National Facility operated by the University of Manchester at Jodrell Bank Observatory on behalf of the UK Science and Technology Facilities Council (STFC). This research has made use of the NASA/IPAC Extragalactic Database (NED) that is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Paper: 2019A&A...627A..56M (fulltext)
Pillet et al. (1993) , Baumann Solanki (2005) , Lagrange et al. (2010) , and Meunier et al. (2010a) . Faculae properties. The faculae properties were similar to those used in Borgniet et al. (2015) , which included the ratio distribution between plage and spot sizes, and decay rates (here we considered the plages that were produced each time we generated a spot). Network properties. Network properties were similar to those used in Borgniet et al. (2015) , which include the diffusion coeficient from Schrijver (2001) , the fraction of plage flux that was used to build the network, and the decay rate. The diffusion coefficient was scaled with the amplitude of the convective blueshift, as discussed in Sect. 2.7 . 3 From structures to observables 3.1 Filling factor and photometric and radial velocity time series Because of the huge number of simulations, it is not possible to compute the observables as done in our previous work ( Lagrange et al. 2010 ; Meunier et al. 2010a ; Borgniet et al. 2015 ), in which we computed maps, then spectra, and finally RVs (and other observables) in the same way as for stellar observations. We therefore simplified the computations as follows: we directly summed the contribution from each structure to the RV, photometry, and astrometry. The filling factors were also computed. This implies that we assumed that the structures are point-like, which means that we did not need to check for superimpositions. This is different to what was done in Borgniet et al. (2015) . This also means that we neglected any geometrical effect that would be due to a large area covered by a structure, which is a good assumption because we modeled relatively quiet stars with moderate structure sizes. The assumption is very good for spots because the maximum size of a spot would correspond to a radius of about 3° (which would be a very rare case; typical spots are much smaller, see Table E.1 ). Plages may cover a larger area, which is expected to add a second-order distortion to an ideal time series: in most cases, the extreme sections of a structure (east and west) are expected to produce a signal that is very similar to the central part of the structures on average; in addition, we considered at each time step an irregular decay of the structures that has a random amplitude, so that the distortion produced by the assumption would not be identifiable given this other source of irregularity. The same structures were used for all stellar inclinations. The formulae are provided in Appendix C . We recall that we chose to study different laws for several of our parameters (as summarized in Fig. 1 ), so that several time series of spots, plages, and network features were produced for each of the 141 points of the 2D grid in ( B – V , Log ), each corresponding to a different parameter set ( Table E.1 ). This leads to 11 421 time series, or 22 842 when the two levels for Tspot are considered for each inclination and observable, hence a total of 228
Paper: 2016MNRAS.456.4441C (fulltext)
to estimate the duty cycle and constrain the outburst recurrence time for a large sample of CVs. These properties will help constrain the mass-transfer rate and AML rate in the overall population. Better-constrained AML rates will improve evolutionary models and our understanding of the post-bounce population, and help to reconcile the observed and predicted position of the period minimum. Our catalogue was constructed with a goal towards answering these, and related, questions that require large samples of CV properties to answer. Additionally, it is meant as a reference for individual systems and a means of selecting CVs for targeted campaigns, based on their outburst properties. In Section 2 , we discuss the CRTS survey in detail. Section 3 describes the classification script, and Section 4 outlines the contents of our catalogue. The catalogue is then characterized in Section 5 . 2 CATALINA REAL-TIME TRANSIENT SURVEY The CRTS identifies transients in the data from the Catalina Sky Survey 1 (Larson et al. 1998 , 2003 ) – a photometric survey that searches for Potentially Hazardous Asteroids and Near Earth Objects. Three subsurveys constitute the Catalina Sky Survey, namely the original CSS (Catalina Schmidt Survey), the MLS (Mt Lemmon Survey) based in Arizona, and the SSS (Siding Spring Survey) in Australia, which ended on 2014 July 5. Each has a dedicated telescope with a 4k back-illuminated, unfiltered CCD camera (Djorgovski et al. 2010 ). The field of view and typical limiting magnitude for each survey (at ∼30 s integrations) are 82 and V ∼ 19.5 mag for the CSS, 11 and V ∼ 21.5 mag for the MLS, and 4and V ∼ 19 mag for the SSS. Together these surveys cover 30 000 deg 2 (, see Drake et al. 2014 for more details). The Galactic plane () is avoided due to overcrowding, as are the Magellanic Clouds. Each observation consists of four images (frames) that are separated by ≈10 min. The observing cadence is typically one to four times per lunation (depending on the subsurvey and field). Aperture photometry is performed using SExtractor software (Bertin Arnouts 1996 ) and converted to V -band magnitudes using standard stars as described in Drake et al. ( 2013 ). The CRTS began processing CSS data on 2007 November 8, MLS data on 2009 November 6 and 2010 May 5 for SSS data (Drake et al. 2014 ). Although there is Catalina Sky Survey data preceding these dates, the CRTS only use these data in the transient classification process – any object that was only variable during this time will not have been identified as a CRTS transient. To identify variability, the CRTS makes catalogues of the objects in each image and compare these to previously recorded magnitudes; they do not use image subtraction. An object needs to pass a number of tests to be classified as a transient. In each set of four frames (one epoch), it needs to be positionally coincident in at least three of the frames. This eliminates high proper motion objects (movement of > 0.1 arcmin min
Paper: 2018ApJ...863..156H (fulltext)
to fit the blue wing of the bulk water absorption. The original dOH band and the shifted one are each fit using a Gaussian function. This fitting scheme is applied to each spectrum collected in the whole experiment to trace the diffusion of N 2 in the ASW. The band area of the shifted dOH band at 3672 cm −1 and the original dOH band at 3694 cm −1 during the isothermal experiment at each target temperature are shown in Figures 3 and 4 , respectively. It is evident that the shifting of dOH strongly depends on the target temperature. Figure 2. RAIR Spectra of: (1) 200 ML pure water ice deposited at 10 K; (2) after annealing at 70 K for 30 minutes; (3) after cooling down to 10 K and deposition of 16 ML N 2 on top; (4) after the isothermal experiment. The dashed lines are the fit while the solid circles are the experimental data. Spectra are offset for clarity. Figure 3. The band area of the 3672 cm −1 peak during isothermal experiments of N 2 on ASW at different target temperatures. The lines are fits using the model described in the Section 4 . Figure 4. The band area of the 3694 cm −1 peak during the isothermal experiment of N 2 on ASW at different target temperatures. 4. Modeling Fick’s law of diffusion was used to obtain the diffusion energy barrier as well as the pre-exponential factor D 0 , similar to other studies (Mispelaer et al. 2013 ; Karssemeijer et al. 2014 ; Lauck et al. 2015 ). The one-dimensional Fick’s law of diffusion is described by the following equation:where C ( z , t ) is the coverage (unitless) at depth z into the ice. If the amount of N 2 at depth z is just enough to cover the whole pore surface at that depth, then C ( z , t ) = 1. D ( T ) is the temperature dependent diffusion coefficient. We assume that 1 ML of water corresponds to a thickness of 0.3 nm. Therefore the total water ice thickness is h = 60 nm. Right after N 2 deposition at 10 K, N 2 should only cover the top few layers. We denote this initial penetration depth as d . After sufficient diffusion, N 2 penetrates into the ASW and evenly covers the pore surfaces. We have the following equations: The values of d , C 0 , and C inf can be obtained from Figures 3 and 4 . We denote the area of the original dOH band at 3694 cm −1 before and after isothermal experiment (such that N 2 covers the pore surface evenly) to be O i and O f , respectively. Similarly, we denote the area of the shifted dOH band at 3672 cm −1 before and after sufficient diffusion to be S i and S f , respectively. Figure 5 illustrates how the two dOH bands change with the fraction of pore surface covered by N 2 . Simple calculations show that In the model, we do not consider desorption, because in all of the isothermal experiment, the ice temperature is always below the temperature at which desorption occurs. Solving Equation ( 2 ) yieldsFigure 6 shows the solution at t = 0, 200, and seconds. At t = 0 s, N 2 molecules penetrate only to depth d . The shifted dOH band area should be proportional
Paper: 2020ApJ...894L..21R (fulltext)
current. We have also modified the weighting of the grid points in the velocity domain to check whether models that put less emphasis on small-scale dynamics in the corona could still resolve the bubbles. In all cases, the bubbles created were identical. However, changing the starting atmosphere will change whether a bubble is identified. This is due to the change in the transition region location with respect to the hydrogen column density. The beam will therefore not penetrate at the same location into a different starting atmosphere, which will not trap a portion of the chromosphere. It may very well be that the bubbles can be created in other starting model atmospheres but with a different set of beam parameters. In the example shown in Figure 2 , the initial bubble accelerates upward into the corona initially due to the pressure gradient caused by the beam heating in the chromosphere. However, a shock forms at T = 10 s, which highly accelerates the upper atmosphere, including the bubble. By T = 15 s into the simulation, this velocity is already over 300 km s −1 . The value of temperature, density, and electron density are all stationary within the bubble over time, which is essentially the mass motion of the material. Interestingly, due to the gas pressure increasing at the edges of the bubble, the overall width of the bubble decreases over time. For the bubble in Figure 2 , 45% of the mass is lost between 10 and 15 s due to redistribution of the bubble mass into the overall wave front. This compression can be most easily seen from T = 5 s to T = 10 s in Figure 2 . Individual well-defined bubbles were identified by investigating the temperature of each atmosphere. The location of the transition region was defined to be the lowest point in the atmosphere where the temperature gradient exceeds 5000 K between grid cells, while having a temperature value of at least 50,000 K. Any point above the transition region that has a temperature below 40,000 K was deemed to be a part of the bubble and logged. This resulted in 21/72 atmospheres with bubbles. Of these 21, all had beam fluxes above 1.0e11 erg cm −2 , and low-energy cutoff values above 15 keV. The most common value of low-energy cutoff being E c = 15 keV. This value provides the optimal penetration depth to efficiently trap a portion of the chromosphere while accelerating it upward (see Figure 1 ). The E c = 10 keV models mainly heated the upper chromosphere and some of the transition region, and did not lead to the creation of bubbles. The E c = 25 keV models only resulted in bubbles for the highest spectral indices ( δ > 5) with the highest levels of beam flux (3.0e11 erg cm −2 ). This is due to the extremely high energy density that needs to be applied to the lower chromosphere in order to heat it sufficiently and create a bubble. Harder beams tend to have a larger spread of energy into the denser portions of the photosphere and lower chromosphere. The bubble lifetimes can be as short