adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.473L.121K (fulltext)
confined the jet to propagate along high-density walls (similar to model B simulations in BTG17 ). Our simulations, performed in both 2D and 3D, follow jets from the compact object to a scale 10 3 larger. These large scale simulations allow us to follow the jet acceleration through conversion of its magnetic energy into kinetic energy. In the context of short GRB jets, as the jet breaks out from the confining medium, it essentially travels through vacuum (or at least very low ambient gas). This is quite advantageous since BTG17 have shown that these type of jets are almost identical in 2D and 3D runs and that they are fairly axisymmetric (see also Bromberg Tchekhovskoy 2016 ). For this reason, we focus our efforts on axisymmetric 2D simulations, which are considerably less computationally expensive and can be better resolved numerically. We mimicked the boundary of the dynamical ejecta by setting the ambient gas density of our previous simulations (see BTG17 model B-2D-vhr for details on jet and ambient gas parameters and numerical resolution) to be essentially zero beyond 100times the size of the compact object as displayed in Fig. 2 , which shows both density and velocity (Lorentz factor) contours. After the jet breaks out from the dynamical ejecta, a rarefaction wave crosses the jet and it spreads sideways and accelerates further (Komissarov etal. 2010 ; Tchekhovskoy etal. 2010 ). For this work, we simulate steady state jets that will maintain the conditions at the central engine constant. This means that the rotation of the black hole and the magnetic field strength is kept constant, so that the jet has a constant power throughout the duration of the simulation. Future simulations will explore short-lived jets. Figure 2. Numerical simulation of a jet that is collimated by and breaks out from the dynamical ejecta. We show 2D cuts of density (left-hand panel) and Lorentz factor (right-hand panel), where r 0 stands for a few times the radius of the central compact object. The jet accelerates as it breaks out from the dynamical ejecta and spread sideways. At large distance the jet turns conical and its lateral structure is fixed. The distance where the radiation from the jet takes place is uncertain; the prompt emission may originate at the transparency radius of the jets or further out, at optically thin conditions (see the next section). The distance at which the jet produces the -ray radiation lies beyond our simulated region. However, we argue that the dynamical range of our current simulations is sufficient for the objectives of our estimates. This is due to the fact that after the phase of lateral expansion and the crossing of the rarefaction wave, the jet becomes approximately conical and its properties freeze out as a function of angle (see Fig. 3 ). Therefore, the lateral structure of the jet, which is the key aspect of our study, is set at this distance. It is no longer necessary to follow the jet beyond this point since its
Paper: 2020A&A...635A.119C (fulltext)
code. We adopt the high- z edition, which includes the modeling in the radio bands, as well as the Lyman absorptions in the rest-frame UV from the intergalactic medium ( da Cunha et al. 2015 ). We plotted the best-fit models in Fig. 6 , along with the photometric measurements, and the fitting results of HYPERZ and those using the infrared templates. Fig. 6. UV-to-radio SEDs for each of our sample SMGs except ALESS66.1 for which the UV-to-NIR photometric measurements are contaminated by a foreground quasar Simpson et al. (2014) . Sources that are identified to host an AGN are marked with an asterisk after the ID. The measurements are plotted as black points and the best-fit models from MAGPHYS , HYPERZ , and the IR templates compiled by Swinbank et al. (2014) are plotted as gray, blue, and red curves, respectively. For clarity, we do not show the uncertainty, however, it is discussed in the text, including the fact that the IR luminosities that were estimated by the two methods are in agreement with each other within the uncertainty. The insets are zoom-ins in the OIR regime from 0.3 to 10 μ m in log–log scale. We find significant differences in higher L IR sources between the best-fit models of MAGPHYS and HYPERZ ; also, in all of these discrepant cases, the HYPERZ modeling provides a better fit with a lower χ 2 . We first compare the infrared luminosities estimated from the SED templates 8 and those from MAGPHYS . We confirm that both values are statistically consistent with each other in all sources. We then turn to A V in which we find significant differences in some sources. As seen in Fig. 6 , ALESS67.1, ALESS75.1, and AS2UDS412.0 appear to have different best-fit models in the UV-to-NIR regime, although the fit from HYPERZ shows better agreement with the data. Interestingly, in these three cases where the A V differ, the values obtained from MAGPHYS are all higher. We note that given the different assumptions of the attenuation curves in these two codes, the A V values from the UV-to-NIR regime should also ideally be deduced from MAGPHYS by turning off the fitting of the MIR-to-radio part. Unfortunately, the public version of MAGPHYS does not support this option. However, the fact that the A V values are in excellent agreement with each other in ALESS17.1 and AS2UDS292.0, which is as expected from a good match shown in Fig. 6 , suggests that the systematic difference between the A V values can be neglected. We also tried to homogenize the two codes by running the HYPERZ fitting that only uses the τ -decay SFHs that are similarly adopted by MAGPHYS , thus definitively removing the bursting SFH in our adopted method for HYPERZ . We find that the output values vary within uncertainties and the conclusions remain unchanged. Since stellar mass is not one of the direct outputs from HYPERZ, we estimated the stellar masses by converting the absolute magnitude, which is one of the outputs from HYPERZ based on some estimates of mass-to-light
Paper: 2017MNRAS.471.3753S (acknowledgments)
Acknowledgements It is a pleasure to thank A. Beresnyak, B. Burkhart and A. Schekochihin, as well as the anonymous Referee, for helpful comments and discussion. JS was funded in part by the Gordon and Betty Moore Foundation through Grant GBMF5076 to Lars Bildsten, Eliot Quataert and E. Sterl Phinney. Support for PFH was provided by NASA Astrophysics Theory Grant NNX14AH35G National Science Foundation Collaborative Research Grant #1411920 and CAREER grant #1455342. Numerical calculations were run on Caltech cluster ‘Zwicky’ (NSF MRI award #PHY-0960291) and XSEDE allocation TG-AST130039 supported by the NSF.
Paper: 2017MNRAS.469S.178G (acknowledgments)
Acknowledgements OSIRIS was built by a consortium of the Max-Planck- Institut für Sonnensystemforschung, Göttingen, Germany, CISAS University of Padova, Italy, the Laboratoire d’Astrophysique de Marseille, France, the Institutode Astrofísica de Andalucia, CSIC, Granada, Spain,the Research and Scientific Support Department of the European Space Agency, Noordwijk, The Netherlands, the Instituto Nacionalde Técnica Aeroespacial, Madrid, Spain, the Universidad Politéchnica de Madrid, Spain, the Department of Physics and Astronomy of Uppsala University, Sweden, and the Institut für Datentechnik und Kommunikationsnetze der Technischen Universität Braunschweig, Germany. The support of the national funding agencies of Germany (DLR), France (CNES), Italy (ASI), Spain (MEC), Sweden (SNSB) and the ESA Technical Directorate is gratefully acknowledged. We thank the Rosetta Science Ground Segment at ESAC, the Rosetta Mission Operations Centre at ESOC and the Rosetta Project at ESTEC for their outstanding work enabling the science return of the Rosetta Mission. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.
Paper: 2020ApJ...905...53L (fulltext)
2020 ). Detailed information about the data can be found at http://shsuyu.github.io/H0LiCOW/site/h0licow_data.html . The galaxies passively evolving on a time much longer than their age difference can be used as a cosmic chronometer (Jimenez Loeb 2002 ). The age difference between two ensembles of old galaxies at different redshifts gives a direct estimation of the Hubble parameter . We use 31 such measurements of H ( z ) (Simon et al. 2005 ; Stern et al. 2010 ; Moresco et al. 2012 ; Zhang et al. 2014 ; Moresco 2015 ; Moresco et al. 2016 ; Ratsimbazafy et al. 2017 ), which are shown in Figure 1 . For comparison, we also plot the H ( z ) prediction of a typical ΛCDM model with Ω m = 0.3, h = 0.7 and its PAge approximation. Figure 1. Cosmic chronometer data. The red solid line is prediction of ΛCDM model with Ω m = 0.3, h = 0.7, and the green dashed line is its PAge approximation. 4. Results We run Monte Carlo Markov Chain (MCMC) calculations with BAO, SLTD, CC and the their combination (All), respectively. Uniform priors are applied on h ∈ [0.55, 0.85], p age ∈ [0.5, 1.5], η ∈ [−2, 1], and r d h ∈ [0, 1 Gpc]. In addition, following Huang ( 2020 ), we use a cosmic age bound t 0 > 12 Gyr in all cases. The chains and analysis tools are uploaded to https://zenodo.org/record/4065034 to allow future researchers to reproduce our results. We first consider a spatially flat universe whose geometry is fully characterized by h , p age , and η . The marginalized constraints on p age and η , with BAO, SLTD, CC, and their combination are shown in Figure 2 . We find that CC alone does not imply cosmic acceleration, while BAO and SLTD both favor cosmic acceleration at ∼2.5 σ level. With all the data combined together, we obtain a derived posterior of the deceleration parameter q 0 = −0.514 ± 0.116, a ∼4.4 σ detection of cosmic acceleration. For the Hubble constant, we obtain H 0 = 70.7 ± 1.9 km s −1 Mpc −1 that is consistent with both the local distance-ladder measurement and the CMB+ΛCDM result. The derived posterior of the baryon acoustic scale r d = 142.2 ± 2.7 Mpc is consistent with CMB + ΛCDM value 147.18 ± 0.29 Mpc (Aghanim et al. 2020 ). Figure 2. Marginalized constraints on PAge parameters. The inner and outer contours enclose 68% and 95% confidence regions, respectively. The dotted violet contours show the effect of switching on the Ω k freedom for the BAO+SLTD+CC case. Proceeding to the nonflat case with a uniform prior Ω k ∈ [−1, 1], we find that the marginalized constraint on p age and η are not much affected by the addition of Ω k freedom, as Figure 2 visually indicates. The detection of cosmic acceleration remains at a ∼4.1 σ level. Because the spatial curvature has almost no impact around the local universe, we can still compare the posterior of the Hubble constant H 0 = 69.4 ± 1.9 km s −1 Mpc −1 with the distance-ladder measurement, and again find no significant discrepancy. When Ω k is allowed to vary, the data do not favor, however, a
Paper: 2021MNRAS.503.2583S (fulltext)
spectrum and 27 have multiple archival spectra. As we are refining our CLAGN candidates by selecting candidates where the archival spectra already show some signs of change, there is the potential to miss CLAGNs where the spectra have changed after the last archival spectra was taken and this will decrease our completeness. Of the AGNs that were not identified by our CLAGN candidate selection methods and have multiple archival spectra, we identified AGNs that appeared to have some small variations in the spectra, however, these variations were not significant enough to require further investigation (i.e. changes in spectral class were 0.1 or less). Thus, while our three candidate selection criteria select many AGNs that are not CLAGNs, our selection criteria are not missing a large number of nearby CLAGNs. 3 SPECTROSCOPIC FOLLOW-UP Once candidates were identified using the colour and flux criteria discussed in Sections 2.2– 2.4 , we inspected the archival spectra of these objects in order to identify potential CLAGNs without obtaining new spectra. It should be noted that we did not follow up candidates where archival spectra from the past two decades showed no variability (irrespective of the selection criteria). We used the Wide Field Spectrograph (WiFeS, Dopita et al. 2010 ) integral field unit (IFU) on the Australian National University’s 2.3-m telescope at Siding Spring to obtain new spectra of our candidates to confirm that they are indeed CLAGN. WiFeS has a field of view of 25 × 38 arcsec 2 , divided into 950 spaxels. The advantage of using an IFU for follow-up observations of candidates is that extraction aperture size can be matched to previous observations (e.g. the 7-arcsec fibre of 6dF, Jones et al. 2009 ). The wavelength coverage is 3800–9200 Å, which spans the H α , H β , and O iii lines at z 0.04, and we expect H α to show the most clear signs of change in CLAGNs. We observed in nod-and-shuffle mode taking at least three frames with 60s on object, 60s on sky, and 10 cycles per frame. This results in 40 min on object, 40 min on sky, and ∼15 min on overheads including telescope nod time, guide star re-acquisition and CCD readout. In total we allow ∼100 min per galaxy. Our observations were taken between 2018 July and 2019 March. We reduced our data with PyWiFeS, Python-based pipeline (Childress et al. 2014 ). We observed 15 CLAGN candidates over multiple observing sessions, the AGNs are presented in Table 4 . Table 4. CLAGN candidates that were observed with WiFeS between 2018 July and 2019 March. a New CLAGN that we have identified. b Known CLAGN. 4 SPECTRAL VARIABILITY AND NEW CLAGNS We next present the spectra of the new CLAGNs that we have identified using the selection criteria mentioned in Sections 2.2 – 2.4 , where we compare archival spectra of the galaxies with spectra taken using WiFeS. We plot archival spectra, where available, with the spectra taken using WiFeS to display the change. Multiplicative scaling has
Paper: 2017A&A...598A..98L (acknowledgments)
J.L. thanks O. Venot, T. Cavalie, S. Guerlet, A. Spiga, and T. Fouchet for helpful discussions. J.L. is also indebted to S. Guerlet for making the radiative data for Saturn available. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 679030/WHIPLASH)
Paper: 2018A&A...618A..58M (fulltext)
time-series, but all data points available in the original time-series are published in DR2. Consequently, all measurements are shown in the figures displaying Gaia light curves in this paper, and the disregarded measurements are identified by magenta crosses. Fig. 3 Skydistribution in Galactic coordinates of all LPV candidates published in Gaia DR2. Each star is drawn with a purple–red–yellow colour-grading scale according to the median ( G BP ) −median ( G RP e ) colour scale shown on the right of the figure. 3 Overview of the <italic>Gaia</italic> all-sky LPV survey We present in this section an overview of the all-sky Gaia DR2 catalogue of LPV candidates, together with light-curve examples. Validations on catalogue subsets according to distance to the Sun or stellar populations are presented in the next sections. 3.1 Sky distribution overview The sky distribution of all DR2 LPV candidates is shown in Fig. 3 , colour-coded with the G BP − G RP e colours 1 of the stars according to the colour-scale on the right of the figure. The Galactic disc, bulge, and halo are clearly visible, as well as the extra-galactic populations of the Magellanic Clouds (the two concentration areas below the Galactic plane at Galactic longitudes of about 285° for the Large Magellanic Cloud (LMC) and 303° for the Small Magellanic Cloud (SMC)) and of the Sagittarius dwarf galaxy (elongated tail below the Galactic bulge). The reddest LPV candidates (yellowish colour in the figure) are seen to be distributed in the Galactic plane, where dust leads to high extinctions. Fig. 4 Histogram of G time-series durations, in days, of all Gaia DR2 LPV candidates. The dashed blue histogram represents the subset of the catalogue published with periods for the sources. 3.2 Light-curve overview The histogram of the G -band time-series durations of all LPV candidates is shown in Fig. 4 , with the subset of sources with published periods shown by the dashed blue histogram. Only 27 sources have durations shorter than one year, while 90% of the sources have observationsover more than 1.5 yr. The time coverage of the measurements over the observation duration is equally important. To compute the time coverage, we divided the time-series into 20 equally spaced sub-time intervals, and computed the fraction of sub-time intervals that have non-zero measurements. The histogram of the resulting time coverage is shown in Fig. 5 . It shows a distribution of the number of stars with time coverages between 35% and 90% that is about equal. Fig. 5 Histogram of the time coverage (see Sect. 3.2 ), in percentage, of measurements in the cleaned G time-series of all Gaia DR2 LPV candidates (grey bars) and of the subset with published periods (blue bars). Fig. 6 Sky distribution of the G -band time-series durations of each DR2 LPV candidate, according to the colour scale shown on the right of the figure. Time-series with durations shorter than 500 d are plotted in yellow (they are mostly located in the
Paper: 2017ApJ...847...66O (fulltext)
and low column densities, which appear to support that the hot core is externally heated. The strong bursting observed in the water masers located in the direction of the HMC in Orion-KL could be caused by the interaction between the explosive outflow and the ambient quiescent gas (Hirota et al. 2011 , 2014 ). In this study, using the tremendous sensitivities, a better uv-plane coverage, and a very broad covering of frequency range at (sub)millimeter wavelengths offered by the recent operational Atacama Large Millimeter/Submillimeter Array observatory (ALMA), we carried out a search for compact and faint continuum sources (in five different continuum bands of ALMA) that are probably associated with (proto)stellar objects and hot molecular gas within the HMC in Orion-KL. We report, in addition to Orion Source I and Orion BN, the detection at submillimeter wavelengths of three new compact continuum sources (ALMA1-3) that are located in the vicinities of the HMC, but not with associated hot molecular gas. Additionally, one of these compact sources seem to be associated with high-mass stars, but it does not account for the internal heating of the HMC. We thus conclude that the hot molecular core in Orion-KL is indeed heated externally, as suggested by many observational works. 2. Observations The observations were carried out with ALMA between 2014 and 2015 as part of the ALMA programs: 2013.1.01034.S (Band 4), 2012.1.00146.S (Band 6), 2013.1.00048.S (Band 8), and 2012.1.00123.S (Bands 7 and 9). The total bandwidth used to estimate the continuum emission in the ALMA observations was more than 2 GHz (see Table 1 ). However, as there are many lines detected in the spectral windows, and there is probably some contamination from very faint lines. In order to search for compact millimeter sources within the hot molecular core in Orion-KL, we constrain the uv-range of the observations (see Table 1 and Figures 1 – 3 ). We chose the uv-range based on a trade-off between removing as much extended emission as possible and keeping enough visibilities to obtain a good map. The resulting synthesized beams are presented in Table 1 . The number of antennas used during the observations varied between 31 and 40 (see Table 1 ). Weather conditions were very good and stable, with an average precipitable water vapor of 6 mm (Band 3), 1 mm (Band 6), 0.3–0.7 mm (Band 7), 0.3 mm (Band 8), and 0.15 mm (Band 9). Figure 1. ALMA continuum band 4 (3.0 mm) contour and color-scale images of the HMC located in Orion-KL. Upper panel: continuum image using the full uv-range from the ALMA observations. The contours start at 15% and go up to 90% in steps of 5% of the intensity peak. The intensity peak is 0.150 Jy Beam −1 . The half-power contour of the synthesized beam of the image is shown in the bottom-left corner. Lower panel: continuum image using only part of the uv-range (>150 kλ ) from the continuum observations. The contours start at 15% and go up to 90% in steps of 5% of
Paper: 2021MNRAS.501.5776M (fulltext)
manuscript, we report on the results of the monitoring of MAXI J1631-472 with the MeerKAT interferometer, complemented with X-ray data from Swift and MAXI . We show the evolution of the radio and X-ray emission throughout its outburst and provide a physical interpretation of the data set. We also compare the behaviour observed with other known outbursting BH systems. The paper is structured as follows: in Section 2 , we describe the observations and details of the data reduction. In Section 3 , we present the results of the two wavebands and highlight some of the interesting behaviour. In Section 4 , we summarize our conclusions. 2 OBSERVATIONS 2.1 MeerKAT The MeerKAT monitoring of MAXI J1631-472 was performed weekly, with the observations carried out at a central frequency of 1.28 GHz and bandwidth of 856 MHz. These observations were taken as part of the ThunderKAT Large Survey Programme on MeerKAT (Fender et al. 2016 ). Each observation consists of a 15-min scan of the target, and a 2-min scan of the phase calibrator, in this case J1726-5529. The primary calibrator used was J1939-6342, which was observed at the start of each observation for 5 min. We used casa (McMullin et al. 2007 ) to perform the data reduction. We performed flagging of radio frequency interference, where we removed the first and last 150 channels (channel width ∼ 209 kHz) of the band. The autoflagging algorithms, rflag and tfcrop , were used for subsequent flagging. We then performed calibration, where the flux density on the primary calibrator was set using the known model. The phase-only and antenna-based delay corrections on the primary calibrator were then solved for, then the bandpass corrections for the primary were then applied. We solved for the complex gains on the primary and secondary, then proceeded to scale the gain corrections from the primary to the secondary and target source. Finally, the imaging was performed using wsclean (Offringa et al. 2014 ). Flux densities for MAXI J1631-472 were extracted from the images using pybdsf (Mohan Rafferty 2015 ); these are presented in Table 1 . We were able to extract an in-band spectral index (α, with the flux density S ν scaling with frequency ν as S ν ∝ ν α ) at the four brightest epochs using a fit to eight uniformly spaced channels across the 856 MHz bandpass. To determine the spectral index, we performed a least-squares fit using the scipy and curve-fit module and function, respectively, in python to calculate the slope and its associated error bar. The results from these fits are presented in Table 2 and indicate that the source was optically thin during the times of strongest radio flux density. Table 1. MeerKAT radio (1.28 GHz) and Swift X-ray (1–10 keV) fluxes of MAXI J1631-472. The spectral states of each of the observations are indicated in the final column: Soft (S), intermediate (I) and Hard (H). Table 2. Spectral indices during the strongest radio flux: S ν ∝ ν α . 2.2 MAXI We used X-ray data from the