adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.504.5671E (fulltext)
will require not only a large number of telescopes but many person-hours to plan observations and process the data. Citizen astronomers, citizen science, and educational outreach offer an excellent opportunity to support future space missions. Given the brightness of the host stars of planets found by TESS , even modestly sized telescopes can be used to re-observe these systems, reducing the errors on their ephemerides. The ability of small ground-based telescopes to contribute to exoplanet science is well known (e.g. Kabath et al. 2019 ). The Next Generation Transit Survey (NGTS; Wheatley et al. 2018 ) has shown that sub-millimag precision is achievable by simultaneously observing the same transit with many identical small telescopes and combining data. Using such methods could expand the number of exoplanets that are observable from the ground. Here we present a project to refine the ephemerides of eight exoplanets using a fully robotic telescope network, observations from citizen astronomers, and data from TESS ; a significant portion of the work has been completed by high school students via the Original Research By Young Twinkle Students (ORBYTS) programme. 2 OUTREACH AND CITIZEN SCIENCE PROJECTS The observations and analysis presented here have been achieved via engagement with several citizen science and outreach initiatives and a brief description of each of these is given below. 2.1 Original Research By Young Twinkle Students ORBYTS is an educational programme in which secondary school pupils work on original research linked to the Twinkle Space Mission under the tuition of PhD students and other young scientists (McKemmish et al. 2017a ; Sousa-Silva et al. 2018 ). The ORBYTS programme has been run since 2012 and is jointly managed by Blue Skies Space Ltd. (BSSL) and University College London (UCL). The Twinkle Space Mission 1 is a new, fast-track satellite designed for launch by 2024 and has been conceived for providing faster access to space-based spectroscopic data. While the satellite will also survey Solar system objects (Edwards et al. 2019a , b ), a key science case for Twinkle is the characterization of a population of extrasolar planets via transit and eclipse spectroscopy (Edwards et al. 2019c ). ORBYTS offers school pupils the chance to enrich our understanding of these new worlds by improving our knowledge of the molecules they are made of, their orbits, and their physical properties. This provides a unique opportunity for pupils to undertake cutting-edge science, which has a meaningful impact on a future space mission. To achieve this, ORBYTS partners dynamic, passionate science researchers with secondary schools, where, through fortnightly school visits over an academic year, the researcher teaches the students undergraduate-level physics. The goal of every partnership is that school students will have the opportunity to use this new knowledge to contribute towards publishable research. Pupils get hands on
Paper: 2016ApJ...826L..12E (fulltext)
simple power-law component does not achieve a formally acceptable fit to either spectrum. The fit to the lower-sensitivity spectrum from Obs. 1 measured a power-law index of , but only achieved (where ν is the number of degrees of freedom in the fit). The spectrum of Obs. 2 is steeper, with in this simple fit, and a very fit statistic result owing to the improved S/N: . These simple fits are shown in Figure 2 . The data/model ratio from each fit shows residuals that are consistent with disk reflection, including Fe K emission and a Compton back-scattering excess peaking in the 20–30 keV range. The ratio from Obs. 2 is consistent with blurred reflection from an inner disk that extends close to the black hole, similar to the features observed in the low/hard state of GRS 1915+105 (see Miller et al. 2013a ). Figure 2. NuSTAR spectra of GS 1354−645, fit with a fiducial power-law continuum. The FPMA is shown in black, the FPMB is shown in red, and the model for the FPMA is shown in blue. The data in each panel have been rebinned for visual clarity. Left: the spectrum of Obs. 1 is shown. Right: the spectrum of Obs. 2 is shown. This ratio reveals evidence of strong, broadened reflection consistent with illumination of the inner accretion disk. We next considered fits with relxill (version 0.4c; Garcia et al. 2013 ; Dauser et al. 2014 ). This model includes the power law that illuminates the disk and also incorporates a Kerr blurring function to translate from the frame of the accretion disk to the frame of the observer. It is this function that measures the strong Doppler shifts and gravitational redshifts imprinted on the reflection spectrum. Spin is measured directly using relxill , within the bounds . The inclination of the inner disk—not necessarily the same as the binary system—is also measured directly. For these parameters, values obtained in fits to Obs. 2 were assumed in fits to Obs. 1, owing to its lower sensitivity. Relxill also allows the inner disk to deviate from the ISCO for a given spin parameter, so we also allowed the parameter R in to vary (the model has a hard upper limit of ) in fits to both observations. The emissivity of the disk is described as a broken power law in radius (e.g., ), giving three parameters: q in , q out , and R break . Any corona with an energetic profile defined by the underlying potential and linked to the disk is likely to have a time-averaged emissivity that falls with radius, so we require in fits to both observations. The limited sensitivity of Obs. 1 does not permit more detail to be discerned, so we simply fixed , obviating the meaning of R break . For Obs. 2, however, additional constraints can be imposed based on theoretical and observational expectations. Wilkins Fabian ( 2012 ) have calculated the emissivity profiles expected for idealized point source scenarios, including a “lamp-post” geometry. All calculations assume a disk that extends to the ISCO. The predicted profiles are complex and a
Paper: 2017ApJ...838....6H (fulltext)
of 90%–100% completeness, while the remainder (12%) lies in the region of 50%–90% completeness. To place an upper limit on the impact of selection biases, we adopt the lower limits of 90% and 50% on the completeness in these two regions of the R eff – H 160 plane, assign weights 2.0 (i.e., 1/0.5) and 1.1 (i.e., 1/0.9) to the galaxies in our sample in these regions, and then recompute the R eff – R 200 c relations. For R 200 c ≳ 100 kpc, we find negligible corrections to the median R eff – R 200 c relations, while for R 200 c ≲ 100 kpc, we find corrections below 0.1 dex for all galaxy types and redshifts 0 z 3. We conclude from this exercise that selection biases are likely to be subdominant sources of uncertainty in our R eff – R 200 c relations. Based on this assessment of uncertainties, most of the results of this paper appear to be robust. In particular, there is a strong, approximately linear correlation between the sizes of galaxies and their dark matter halos over the full range of redshifts examined here, 0 z 3. The coefficient of proportionality is larger for late-type galaxies than for early-type galaxies, which follow roughly parallel sequences, except possibly at the highest redshifts. For late-type galaxies, the observed R eff – R 200 c relation is generally consistent with simple models in which galactic disks grow with the same specific angular momentum as their dark matter halos. There is some evidence for a slowdown in disk growth at z 0.5, but the apparent deviation from the J / M equality line is only ∼0.2 dex. We have plotted and examined the R eff – R 200 c relations at all redshifts (0 z 3) for all four SMHM relations to determine whether or not they support the four main results discussed in Section 4 . The outcome of this test is recorded in Table 2 by a T (for true) or F (for false) for each combination of SMHM relation and result. All of the entries are Ts. Table 2 therefore reinforces our conclusion that the main scientific results of this study are robust relative to discrepancies among the SMHM relations (because of the weak dependence of R 200 c on M 200 c ). Table 2 Verification of Main Results 6. Discussion We have found that the sizes of galaxies are proportional on average to the sizes of their dark matter halos over a wide range of galaxy and halo masses and over the entire redshift range 0 z 3 studied here: R eff = αR 200 c with α ≈ 0.03. In particular, we confirm the basic relation found by Kravtsov ( 2013 ) at z = 0 with only minor adjustment, some of which is related to the difference between 2D half-light radii and 3D half-mass radii. There is some curvature at the upper end of our overall R eff – R 200 c relation, which is due to the larger abundance and smaller average size of early-type galaxies compared with late-type galaxies of the same stellar mass. Indeed, we find that early- and late-type galaxies follow distinct, roughly parallel R eff – R 200 c relations offset by a factor of ∼2 for the
Paper: 2019A&A...623A.144Z (fulltext)
panel d shows the period from cosine fitting, panels e, f, g present the amplitude, the phase, and the spectral power from the cosine fitting, and panel h shows the H α intensity. 3.2.1. Periods We detect oscillatory motions with periods between 5 and 80 min (Fig. 2 c). The shorter periods (5 to 40 min) have a local significance level of around 99%; they last only two to four times their periods and are visible in small areas. It would be interesting to find out where and when they occur, but this is beyond the scope of this study. Here, we concentrate our work on the long periods between 40 and 80 min which were already detected in Paper I. We find that the prominence does not oscillate as a block at these long periods. We identify four main oscillatory areas with a high spectral power (see Fig. 2 g and Table 1 ). We select these four areas with high spectral power and overplot their contours on all the panels of Fig. 2 . For each area, we calculate the mean value of the oscillatory parameters (see Table 1 ). The value of the spectral power in relative units is around 40. The mean oscillation period for areas A1 and A2 is close to 65 min with the two methods (Scargle and cosine fit methods). For area A3 we obtain a value of 45 min. We exclude area A4 from further analysis because of the non-uniform pattern in period, amplitude, and phase distribution, which is a hint of the bad fit of the cosine curve due to high noise. Additionally, the periods calculated using the two methods are significantly different. We may note that a similar period value of 65 min is commonly observed for longitudinal oscillations in prominences ( Luna et al. 2018 ). In areas A1, A2, and A3, the period is nearly uniform (Fig. 2 c). The mean amplitude of the oscillations is between 0.70 km s −1 and 1.43 km s −1 , values larger than the estimation of the error bars. However, higher amplitude values are present in the selected areas, reaching locally 3 km s −1 (Fig. 2 e). We notice another region with even higher amplitude located near the pixel coordinates ( x = 40, y = 150), but the signal has a relatively low spectral power value and is too small to be treated as an oscillating feature. Table 1. Mean oscillatory parameters calculated for areas with the highest spectral power (see Fig. 2 ). 3.2.2. Phase Fitted phase is presented in Fig. 2 f. In areas A2 and A3 at the bottom of the prominence the phase has the same value in each area. Additionally, in each of these two regions the period values are very similar (see Figs. 2 c and d), and the spectral power reaches local peaks (see Fig. 2 g). On the contrary, in area A1 the period and amplitude are similar but the phase does not have a uniform value; it shows a continuous transition between − π and 0 radians. This may be interpreted as the passage of a wavefront of a MHD wave ( Schmieder et al. 2013 ; Ofman et al. 2015 ). The colour gradient gives the direction of the propagation of the wave in the POS. This is
Paper: 2020MNRAS.492.3940R (fulltext)
accurately. Accounting for the large equivalent widths of the Hβ lines, we use a 600 s integration time for broad-line (i.e. narrow-band) observations. Figure 2. The SDSS-BOSS spectrum for Target-10. The transmission curve for the Hα filter used to measure the flux contained within the Hβ line is shown in orange and the SDSS i -band filter is shown in green. The region between 6820 and 6960 Å used to determine the median rest-frame L 5100 for the SDSS spectrum is shown in grey. Top: The whole spectrum. Bottom: The region between 6400 and 7000 Å which contains both the broad Hβ line and the region used to measure the rest-frame L 5100 . Our targets span a range of redshifts between 0.350 and 0.398. Therefore, for each source, we use the appropriate Hα photometric filter for which the redshifted Hβ line dominates. For Target-10, we use the Hα-6566 Å narrow-band filter. As seen in Table 1 , we obtain the largest number of acceptable exposures with SDSS-J144645.44+625304.0 (referred to as Target-10 hereafter). Indeed, Target-10 is the only QSO for which we have obtained a baseline of observations longer than the recommended 3 t H β (1 + z ) needed to recover a lag. Thus, in what follows, we only discuss the analysis of Target-10 and defer the rest to a future work. Table 1. Our pre-observation targets selected based on their luminosity, redshift, and visibility. t Hβ (1 + z ) is expected observed-frame lag derived from the Bentz et al. ( 2013 ) t rest – L 5100 relation, where L 5100 is estimated from the SDSS spectrum. We quote the length of time each target is visible and the baseline of the observations in units of days and expected observed-frame lag. 2.3 Ensemble photometry and flux calibration In order to estimate lags between the BLR and the continuum-emitting region of the QSO, we must first calibrate the i -band and Hα photometric magnitudes to a common magnitude system. We are then required to calibrate our i -band photometry using the known SDSS DR12 AB magnitudes of sources in the observed field. We calibrate Hα photometry by propagating available SDSS spectra through the transmission curve for the same narrow-band Hα filter (6566 Å) used to observe the Hβ line in Target-10, accounting for the fibre aperture. We perform aperture photometry using source extractor (Bertin Arnouts 1996 ) to estimate Petrosian magnitudes (Petrosian 1976 ; Graham et al. 2005 ) for each detected source in the field for both i -band and Hα exposures. We use Petrosian magnitudes in order to calibrate each exposure to the SDSS catalogue and to easily avoid the effects of differing seeing between our observations without modelling the point spread function (PSF). We consider only those sources which have SDSS clean = True and source extractor flags = 0 for use as reference sources. We can then apply a similar ensemble photometry method to that detailed by Honeycutt ( 1992 ), on the i -band exposures and calibrate those instrumental magnitudes to the SDSS
Paper: 2016ApJ...827...47L (acknowledgments)
J.A.l.R. acknowledges support from NASA grants NNX14AF43G and NNX14AJ53G. J.A.l.R. and G.M.W. were also supported by NASA grant NNX15AI65G. G.P.Z. was supported by NASA grants NNX08AJ33G, Subaward 37102-2, NNX09AG70G, NNX09AG63G, NNX09AJ79G, NNG05EC85C, Subcontract A991132BT, NNX09AP74A, NNX10AE46G, NNX09AW45G, and NSF grant ATM-0904007. This work was also supported in part by a UAH IIDR award to G.P.Z. O.K. was supported by RFBR grants no. 14-02-00769 and no. 16-02-00479.
Paper: 2017MNRAS.467.1024M (acknowledgments)
Acknowledgments We thank the anonymous referee for their comments. MJM acknowledges the support of a UK Science and Technology Facilities Council (STFC) funded PhD. SPL and VSD are supported by STFC grant ST/J001589/1. TRM and EB are supported by the STFC grant ST/L000733/1. VSD and TRM acknowledge the support of the Leverhulme Trust for the operation of ULTRASPEC at the TNT. Support for this work was provided by NASA through Hubble Fellowship grant HST-HF2-51357.001-A. The research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 320964 (WDTracer). The results presented are based on observations made with the WHT , operated at the Spanish Observatorio del Roque de Los Muchachos of the Instituto de Astrofsica de Canarias by the Isaac Newton Group, as well as the TNT, operated at the Thai National Observatory by the National Astronomical Research Institute of Thailand. This research has made use of NASAs Astrophysics Data System Bibliographic Services.
Paper: 2018MNRAS.478.2650J (acknowledgments)
ACKNOWLEDGEMENTS This work was supported by the European Research Council under the European Commission’s Seventh Framework Programme (FP7/2007-2013 grant agreement no. 339248). The calculations discussed in this paper were performed on the DiRAC complexity system, operated by the University of Leicester IT Services, which forms part of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ), and the University of Exeter Supercomputer, Isca. The former equipment was funded by BIS National E-Infrastructure capital grant ST/K000373/1 and STFC DiRAC Operations grants ST/K0003259/1 and ST/M006948/1. DiRAC is part of the National E-Infrastructure.
Paper: 2017ApJ...839..132T (acknowledgments)
We acknowledge support from the Knud Højgaard Foundation, the Villum Foundation (Project No. 13164), the Danish National Research Foundation (DNRF91), the Deutsche Forschungsgemeinschaft under the Excellence Cluster Universe (Grant No. EXC 153), and the European Union under the Innovative Training Network “Elusives” (Grant No. H2020-MSCA-ITN-2015/674896) and under the ERC Advanced Grant No. 341157-COCO2CASA. We thank the Institute for Nuclear Theory at the University of Washington for hospitality and the DOE for partial support during early stages of this work.
Paper: 2017ApJ...850...20G (acknowledgments)
The authors acknowledge support from NewCompstar, COST Action MP 1304. R.O. Gomes and S. Schramm acknowledge support from HIC for FAIR. This work was also partially supported by grant No. BEX 14116/13-8 of the PDSE CAPES and Science without Borders programs, which are an initiative of the Brazilian government.
Paper: 2015ApJ...806...93J (acknowledgments)
We thank the referee for making very helpful comments and suggestions.
Paper: 2019A&A...623A..55M (acknowledgments)
Acknowledgements We thank the anonymous referee for his/her useful comments and suggestions. L.M. acknowledges support from “Proyecto interno” of the Universidad Andres Bello. P.B. acknowledges financial support from the Scientific Council of Observatoire de Paris and from the action fédératrice “Exploitation Gaia”. S.V. gratefully acknowledges the support provided by Fondecytreg. n. 1170518.
Paper: 2015MNRAS.450.2423A (acknowledgments)
We warmly thank our anonymous referee whose comments significantly improved this paper. The authors would like to thank Carolyn Doherty, Simon Campbell, Luca Casagrande and Warrick Ball for useful discussions. We also thank David Nataf and Alessio Mucciarelli for providing us with their data tables. GCA would like to thank the staff at MoCA for their help and support through the duration of his PhD part of the research leading to the presented results has received funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 338251 (StellarAges). VD'O is partially supported by an ARC grant (Super Science Fellow 2011). RPC is supported by the Swedish Research Council (grants 2012-2254 and 2012-5807). RJS is a recipient of a Sofja Kovalevskaja Award from the Alexander von Humboldt Foundation, by ARC DP1095368 and Discovery Grant. This work was supported in part by ARC DP1095368 and Discovery Grant DP120101815 (PI: J. Lattanzio) at Monash University, Clayton, Australia.
Paper: 2019A&A...628A.102K (acknowledgments)
Acknowledgement We thank Patrick Dufour and Simon Coutu for sharing with us a poster presented at the EUROWD-21 workshop in Austin 2018. We have used their results in the belief that they are public. Only very recently we realized that this paper is not available in the public archive of the conference. We acknowledge that Coutu et al. (2018) was the first paper to determine carbon abundances for DQ using Gaia data. Our current study was motivated by their results. This work was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPq), and Fundação de Amparo à Pesquisa do Rio Grande do Sul (FAPERGS) – Brasil. This research has made use of NASA’s Astrophysics Data System Bibliographic Services, the SIMBAD database, operated at CDS, Strasbourg, France, the NIST and VALD atomic databases, and public data from the Sloan Digital Sky Survey and the Gaia Mission.
Paper: 2018ApJ...868...63E (acknowledgments)
The authors thank Judd Bowman, Adam Lidz, Rennan Barkana, Anastasia Fialkov, and Jordan Mirocha for useful discussions, along with Florian Bolgar for correcting several numerical errors. Calculations in this work were performed using the Colossus library (Diemer 2017 ). A.E.W.’s contribution was supported by an appointment to the NASA Postdoctoral Program at the California Institute of Technology Jet Propulsion Laboratory. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. R.A.M. was supported by the NASA Solar System Exploration Virtual Institute cooperative agreement 80ARC017M0006.
Paper: 2016MNRAS.460.3720R (acknowledgments)
This research was supported under the Australian Research Council's Discovery Projects funding scheme (project number DP 120102393). We acknowledge the AAVSO network of variable star observers for providing the optical light curves of SS Cyg. JCAM-J is the recipient of an Australian Research Council Future Fellowship (FT140101082). GRS acknowledges funding from an NSERC Discovery Grant. DA acknowledges support from the Royal Society. KLP acknowledges support from the UK Space Agency. This work was supported by the Spanish Ministerio de Economá y Competitividad (MINECO) under grant AYA2013-47447-C3-1-P (SM). This work has made use of NASA's Astrophysics Data System. This research made use of APLpy, an open-source plotting package for python hosted at http://aplpy.github.com . The International Centre for Radio Astronomy Research is a joint venture between Curtin University and the University of Western Australia, funded by the state government of Western Australia and the joint venture partners. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The Westerbork Synthesis Radio Telescope is operated by the ASTRON (Netherlands Institute for Radio Astronomy) with support from the Netherlands Foundation for Scientific Research (NWO). The European VLBI Network is a joint facility of independent European, African, Asian and North American radio astronomy institutes. This research has made use of data and/or software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory.
Paper: 2017MNRAS.472.4900C (acknowledgments)
Acknowledgements The authors wish to acknowledge the contribution of Henk Spruit, who visited Monash University as part of the Monash Centre for Astrophysics Distinguished Visitor Program in 2014. This project was partly supported by the European Research Council through ERC Advanced Grant No. 320478–TOFU.