adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.481.1251C (fulltext)
follows: the second Fisher matrix is computed from all ’s except for the two entries at the extrema of the anti-diagonal, which are set to zero; and so on until we compute the 10th Fisher matrix, where is non-zero only for i = j . The inset in Fig. 4 illustrates this procedure, with shading going from black (only diagonal elements considered) to the lightest of grey (all the cross-correlations between thin bins considered). The larger figure shows the structure of the full tomographic matrix of our hybrid method for this test of the importance of off-diagonal correlations. In this case, white means no correlation included. Figure 4. General structure of the full angular power spectrum tomographic matrix, , in our hybrid approach. Block diagonal matrices represent the thick bins, which we consider as independent. The inset illustrates the structure of one of those thick bins, with the thin bins shown. Shades of grey refer to the tests described in Section 4.5 . Now, we consider two extrema: the first and the last of the thick bins. Referring to the inset of Fig. 4 , for both of these thick bins, we start from the full angular power spectrum tomographic matrix and at each step we remove one off-diagonal, from the lightest grey until we remain only with the black, main diagonal. In Fig. 5 , we illustrate the result of this test, by plotting the absolute bias on cosmological parameters induced by neglecting lensing magnification (normalized to the reference values of Table 2 ), as a function of how many cross-correlations between thin bins we include. Apart from the first point(s), for which the bias may be lower, but just because the Fisher matrices are very noisy, it appears clear that the behaviour is very flat. As expected, the trend is flatter at low redshift in the first thick bin. Figure 5. Absolute bias on cosmological parameters induced by neglecting lensing magnification, as function a of off-diagonal elements included, for an analysis carried out either in the first or in the last of the thick redshift bins only, normalized to what is obtained by including all cross-correlations between thin bins in the given thick bin. Fig. 6 shows the bias on cosmological parameters due to neglecting lensing, as a function of how many off-diagonal spectra are considered in the full angular power spectrum, i.e. the larger matrix in Fig. 4 . On the horizontal axis, 1 means black elements in the larger matrix, all the way to 10, representing the inclusion of all elements up to the lightest grey shade. Each point is normalized to its previous value, e.g. the seventh blue bullet point tells us that the bias on Ω b will increase by if we include up to the seventh off-diagonal, with respect to what we would guess by considering only up to the sixth. We notice a net trend: the bias on cosmological parameter best-fit values induced by neglecting lensing may be significant if we considered only the main diagonal or the first few off-diagonals, but then it does
Paper: 2019ApJ...870...53B (fulltext)
al. 2012 ; E. Torresi et al. 2018, in preparation) to estimate a number of physical parameters, such as the number of ionizing photons, covering factors, and mass of ionized gas, with the aim of better understanding the nature of the ionizing source and the properties of the ionized gas itself. We organize this paper as follows. Section 2 presents a description of the sample and the corresponding observations, while the reduction process and calibration of the data are presented in Section 3 . Section 4 shows the catalog of H α +[N ii ] and [O iii ] images and the corresponding flux measurements. In addition, we show [O iii ]/H α +[N ii ] emission-line ratio images. Section 4 presents the results of measuring the physical parameters mentioned before. In Section 5 , we discuss all of the previous results in light of the two AGN classes (LEGs and HEGs). A summary and potential future observations are presented in Section 6 . Throughout this paper, we use H 0 = 71 km s −1 , Ω M = 0.27, and Ω Λ = 0.73. 2. Sample Selection and Observations The sample selection and observations are described in detail in Tremblay et al. ( 2009 ). To summarize, we present a sample of 19 3CR radio sources from the low- z ( z 0.3) extragalactic radio-source Revised Third Cambridge Catalogue (3CR; Bennett 1962a , 1962b ; Spinrad et al. 1985 ). The sample comes from the original HST proposal for 98 3CR sources that was awarded for observations prior to the ACS side 2 electronics failure in 2007 January. Although only 19 sources were observed (Table 1 ), these objects represent an unbiased sample in selection due to the nature of the HST snapshot mode (to fill gaps in the HST schedule, sources are randomly picked from the target list). Moreover, the observed 19 objects span almost the whole redshift range (0.0075 z 0.224) of the original sample and show a wide variety of emission-line ratios, radio power, or spectroscopic properties. With regard to their radio morphologies, the sample consists of four FR Is and 14 FR IIs, taking the double-lobed 3C 314.1 apart (Table 2 ). In terms of their optical spectroscopic classification, eight of the objects in our sample are classified as LEGs and 10 are HEGs, of which four have broad lines, named broad-line objects (BLOs). Of particular interest is 3C 314.1, an RG whose spectral classification is extremely LEG (Capetti et al. 2011 ) interpreted as relic RG (Capetti et al. 2013 ). Table 2 summarizes the radio and spectroscopic classification of the sample. This table also collects the total radio luminosities at 178 MHz, the radio core powers (at 5 GHz), and the X-ray luminosities for the sample. The uncertainties on the radio luminosities are negligible with respect to those of the X-ray luminosities, which amount to ∼0.2 dex. Table 1 Observing Log Note. Log of the HST /ACS WFC observations (Program 10882, PI: W.B. Sparks). Col. (1): 3C source name. Col (2): redshift. Cols. (3)–(4): R.A. and decl. Cols. (5)–(6): ramp filters used
Paper: 2021MNRAS.500..576L (fulltext)
because of missing short spacings. There can be small-scale structure in Stokes Q and U that the interferometer detects, and that can in principle become visible as a component in the Faraday depth spectrum. These are sometimes called Faraday ghosts and are independent of position on the sky (e.g. Shukurov Berkhuijsen 2003 ). Oppermann et al. ( 2012 , 2015 ) have created a map of the smoothly varying Galactic Faraday component, φ gal , by utilizing all-sky Faraday RMs of compact background sources from the literature. We list φ gal at the locations of each of our targets in Table 5 . The effect of the Galactic ISM on an extragalactic source is mainly as a foreground Faraday-rotating screen. The correction for this effect is to subtract φ gal from detected components. In this work, we note that this effect does exist and has possibly affected our measurements but we do not correct for it as it is not adequately constrained. In an effort to analyse Faraday complexity introduced by the ionosphere at the different observation epochs, we look for rotation in the polarization angle (χ) as a function of time. This we do by producing full Stokes images for each scan of the phase calibrator for each epoch. We then restrict our analysis to those scans that have Stokes V flux and p 0 that are below the leakage, and also Stokes I that is above 3σ. We then analyse the resulting q, u , and χ as functions of scan, i.e time. However, the resulting images prove to be too noisy to lead to a conclusion on ionospheric effects. Typical Faraday RM variations from the ionosphere are up to ∼1 rad m −2 . Typical variations are of order 1 rad at 1 m wavelength as χ(λ 2 ) ∼ λ 2 . Variations of this order are seen at 327 MHz at the VLA (Erickson et al. 2001 ). At our lowest frequency even a 90° change at 327 MHz scales to only about 6° at 1300 MHz, and at 1900 MHz. This is small enough to ignore. Note also that it is only the time variation during an epoch that will affect the observations. A constant offset from the ionosphere will be accounted for by the calibration. In the absence of an absolute polarization calibrator, the solution is referenced to the mean feed position angle that is not expected to change as it is hardwired into the instrument. We therefore conclude that the expected ionospheric rotation is negligible and thus not corrected. 4 DISCUSSION Our RM Synthesis analysis shows Faraday complexity, of varying degrees, in four of the six sources in our KAT-7 sample. The most polarized source, J0240−231, displays the most complex Faraday spectrum with several emission components (see Fig. 3a and Table 5 ). The presence of Faraday thick components can be seen in the Faraday spectrum as well. Our analysis clearly resolves this complex spectrum both near the main peak and the smaller peaks. This source also shows the most rapidly changing polarized intensity that depolarizes toward larger wavelengths (see the bottom panel of Fig. 2a ). The behaviour of q
Paper: 2018A&A...609A..15T (fulltext)
intensities of the rotational lines at their ground states. For example, for CHOH, the expected peak intensity of its line at 44 069 MHz transition, nm MHz) calculated by MADEX ( Cernicharo 2012 ) is only 0.3 K. For the 46 980 MHz ethanol line (7 transition of the trans conformer, nm MHz), the expected peak intensity is 0.2 K for the conditions of the experiment. The first experimental run was held in October 2015 using the quartz windows. To obtain the spectra we performed ON/OFF scans, according to Eqs. ( 4 ) and ( 7 ), with ON: chamber filled with the gas, and OFF: empty chamber. However, no lines were observed. Fig. 8 Observed lines of methanol and ethanol at 44 069 and 46 980 MHz, respectively. The problem arose from the quartz windows which reduce the sensitivity (see Fig. 6 ) and, in addition, introduce baseline artefacts due to internal reflections. We have also noticed that thermal emission from people around the chamber leads to significant degradation of the baseline. In the following experiments we installed the Upilex windows which have a much better transmission (see Sect. 2.4 ). Figure 8 shows the lines of CHOH and CHCHOH after 10 min of ON-OFF observing time at a total static pressure of 0.3 mbar when the chamber was filled with each of these gases. From just a single measurement of the ethanol line we could estimate a broadening coefficient of 20 MHz/mbar. Figure 9 shows the methanol line observed at different pressures (baseline ripples can be seen in the 0.1 mbar spectrum). As pointed out in the previous section, in the pressure broadened regime, the peak height remains constant while the linewidth changes with pressure. For pressures below 0.02 mbar the line reaches its Doppler width (see Fig. 10 ). Typical integration times were 10 min during these measurements except for the lowest pressure for which we integrated during 40 min. All spectra were fitted to Voigt profiles, with the Doppler width fixed to the expected value at 300 K (97 kHz), and peak intensities, collisional linewidths (FWHM) and integrated intensities where derived from the fit. Figure 10 shows these experimental values versus pressure, and the linear fits that show the linear dependence of collisional width and integrated intensity with pressure. The self-broadening coefficient thus derived is MHz/mbar (HWHM). The upper panel of Fig. 10 also shows the expected evolution of the peak intensity using the derived broadening parameter. Fig. 9 Observed line of methanol at 44 069 MHz (7 transition) for different values of the total gas pressure. The integration time was 40 min for the narrower feature. The spectral resolution is 180 kHz obtained by a five channel smoothing of the original data. A shift by K is introduced in the vertical scale of each spectrum with respect to the previous one, as pressure decreases. The Upilex windows were adopted for all the other observing periods. In the second observing run in April 2016 we used HCOOH and OCS as probe
Paper: 2019MNRAS.489.2313S (fulltext)
in Fig. 2 correspond to ≈48 per cent of the total number of initial simulated objects, which survived during the integration time. In panels (c) and (d) of Fig. 2 , the distribution of the final osculant orbital elements, a s , e s , and i s of the ploonets with respect to the star, are shown. We observe in a peaked distribution of low-eccentric orbits with similar size than that of the planet (nearby ploonets). As expected, non-nearby ploonets are distributed in the osculating a s − e s plane following the characteristic pattern of constant Tisserand parameter. The periapsis argument is almost uniformly distributed (not shown in Fig. 2 ) and thereby no preferred orbital orientation is found. Finally, the distribution of the mean orbital motion of the surviving exomoons is plotted in panel (d) of Fig. 2 . There are three recognizable peaks close or at resonances (planet:moon) 1:2 (inner ploonets), 3:2, and 5:2 (outer ploonets). It is worthy to mention that the resonances found are transient states of the system. Moreover, almost all the orbits resulted chaotic/unstable according to the assessing of the Mean Exponential Growth factor of Nearby Orbits (MEGNO) parameter (Cincotta, Giordano Simó 2003 ), according to the implementation provided by REBOUND (Rein Tamayo 2015 ). This calculation is not shown in the plot. 3 PLOONET DETECTABILITY If according to the previous analytical and numerical models, a population of small ploonets could arise in young planetary systems hosting close-in giant planets, and we additionally assume that some of them survive to the long-term orbital evolution of the system, the next question we should address is what are the observable signatures that evidence the presence of these objects?. Besides the direct photometric or indirect stellar radial-velocity detection (that could be hard to distinguish from the observational signature of an actual small planet), we consider and evaluate here, two additional and distinctive observational signatures of ploonets: (1) the transit of rapidly evaporating planetary embryo-sized objects with an orbit closely related to an already detected close-in giant planet and (2) characteristic TTVs of the parent planet transit signal. The very existence of ploonets depends on two basic conditions: (1) planetary moons around giant close-in planets are formed before or during the migration process when abundant circumstellar gas and dust is accreted by the planet (Namouni 2010 ; Heller Pudritz 2015 ); and (2) the moons survive planetary migration (Namouni 2010 ; Spalding et al. 2016 ). Although it is hard to ensure that both conditions will be fulfilled under general circumstances, the characteristic time-scales of moon formation (10 3 –10 5 yr, Canup Ward 2006 ) and planetary migration (10 3 –10 7 yr, Armitage 2010 ) seem to favour the idea that some migrating giant planets could arrive to their final orbit, carrying along several volatile rich moons. So far, we have studied the
Paper: 2020MNRAS.496.5414H (fulltext)
will be composed. 3 NUMERICAL ANALYSIS In this analysis all notable features of the apertures such as spiders and gaps are accounted for in order to achieve more comprehensive results. The quality of the main mirrors is assumed to be perfect, without any optical or mechanically induced aberrations such as inter-segment piston or tip-tilt errors. The diffraction-limited PSFs are computed using the fast Fourier transform (FFT) method with a data matrix of size 4096 × 4096 elements in the aperture plane, which is chosen as a compromise in consideration of the required computational power and desirable linear resolution. Actually, a two-fold oversampling is sufficient to comply with the Nyquist criterion. Application of the FFT in this condition would result in nearly three data points per λ/ D of the PSF, which is rather crude for S/N calculations at the diffraction limit. In order to obtain more convenient PSF data sampling, here the central 1024 × 1024 part of the overall matrix is attributed to the pure aperture with the final linear scales of ≈ 11.0, 26.2, 32.2 and 39.4 mm px −1 for the Keck, GMT (also HDRT), TMT and ELT apertures, respectively. This leads to nearly six data points per λ/ D , which is sufficient to adequately represent the PSF for further processing. At the same time, these linear scales are still too low to sense for all notable features of the apertures as we intended to do initially. For example, the inter-mirror non-reflective gaps in the hexagonal segmentation are set to 4 mm, which is of the order of 2.5 times less than the scale for the Keck aperture. In order to resolve this problem the grey pixel approximation method (Troy Chanan 2003 ) is applied. The simple idea is to rescale the numerical intensity of a pixel in accordance with the portion of area that it covers on the corresponding feature. The OTFs are then calculated through FFT of the corresponding PSFs. The main parameters of the apertures adopted in these calculations are given in Table 1 . Table 1. Main parameters of the apertures. Notes . a The central obscuration is determined as a ratio , where S obs and S eff are the areas of central obstruction and clear aperture, respectively. b The original size of the sub-pupils in HDRT was proposed to be 6.5 m, presumably as an optimal maximum in consideration of technological issues. But here we consider 8.4 m mirrors, as practically proven and also in order to make a direct comparison with the GMT. c The value is calculated assuming an obstructing circle of size 8.4 m. The long-exposure atmospheric OTFs are determined following the corresponding expressions (equations 4 and 5 ). Relative ICs are then constructed directly by using equation ( 3 ). In these calculations, for each case, the values of spatial parameters (i.e. r, r 0 , L 0 ) were normalized to the aperture size D , and the spatial frequencies ( u , v ) to the cut-off limit D /λ. This allows us to conveniently present the final graphs purely as a
Paper: 2020ApJ...900L..30C (fulltext)
for excision of data affected by radio frequency interference. After the initial data editing, and gain and bandpass calibration using observations of the compact source 0204+152, a standard self-calibration procedure was carried out for each target source, using line-free channels. The imaging was done with the task tclean , with the w-projection algorithm (Cornwell et al. 2008 ). In the case of J0229+0053, where the observing bandwidth was large, we additionally used the MT-MFS algorithm (second-order expansion; Rau Cornwell 2011 ) to image the target field. The synthesized beams of the final continuum images have full widths at half maximum (FWHMs) of (J0229+0044) and (J0229 + 0053). Both sources were found to be unresolved in our continuum images, with flux densities of mJy (J0229+0044, at ≈640 MHz) and mJy (J0229+0053, at ≈657 MHz). The quoted errors include the error measured from a two-dimensional Gaussian fit to a small region around each source and the typical error in the GMRT flux density scale, ≈10% at these frequencies. The task uvsub was then used to subtract out all detected continuum emission from the self-calibrated visibilities of each field. For each source, a spectral cube was made from these continuum-subtracted visibilities in the barycentric frame, using natural weighting. The H i 21 cm spectrum was extracted by taking a cut through the cube at the AGN location. The final spectrum for each source was then obtained by fitting a second-order polynomial to the line-free regions of each spectrum, and subtracting this out. We further smoothed the spectra by seven channels (J0229+0044) and two channels (J0229+0053), to velocity resolutions of ≈26.6 km s −1 and ≈11.2 km s −1 , respectively. The final optical depth rms noise values are ≈0.12 per 26.6 km s −1 (J0229+0044) and 0.0068 per 11.2 km s −1 (J0229+0053). Our final H i 21 cm spectra toward J0229+0044 and J0229+0053 are shown in Figures 1 (left) and (right), respectively. The velocity-integrated H i 21 cm optical depths are km s −1 (J0229+0044) and km s −1 (J0229+0053). Assuming K and a covering factor, f = 1, these integrated H i 21 cm optical depths imply H i column densities of N hi = (1.35 ± 0.14) × ( T s /100) × 10 22 cm −2 (J0229+0044) and N hi = (1.429 ± 0.015) × ( T s /100) ×10 22 cm −2 (J0229+0053). Note that these are conservative assumptions: a lower covering factor or a higher spin temperature would imply even higher H i column densities. The observational results are summarized in Table 1 . Figure 1. The H i 21 cm spectra toward (left) J0229+0044 and (right) J0229+0053, at velocity resolutions of ≈26.6 km s −1 and ≈11.2 km s −1 , respectively. The solid horizontal line in each panel indicates the AGN continuum flux density at the redshifted H i 21 cm line frequency. The 1 σ rms noise values on the spectra (the horizontal dashed lines) are 0.41 mJy per ≈26.6 km s −1 (J0229+0044) and 0.29 mJy per ≈11.2 km s −1 (J0229+0053). The velocity axes in both panels are
Paper: 2020ApJ...900..123G (fulltext)
processes. Interestingly, the relation between av Q C and av Q O of the ICME materials is different from that of the carbon-cold materials inside the ICMEs reported by Feng et al. ( 2018 ). The carbon-cold materials are defined by the carbon ionic charge state, which should be relatively lower than the solar wind mean value by three times standard deviation, with the ionic temperature lower than 10 6.05 K. It supports the distinct source or heating process of prominence materials and coronal materials in ICMEs. Further, there are three types of charge distributions inside the studied ICMEs excluding the cold material. Similar results are reported by Rivera et al. ( 2019 ) from their case study, who identified and studied the different types from the different sources in depth. Our statistical results support the stable existence of the different types of charge distributions of heavy ions during most years of a solar cycle. And the fractions of these charge distributions show clear dependence on the solar cycle. This means that the combination of the materials from different sources is common in ICMEs, but the fraction of them would change according to the solar activity. It also implies that the acceleration, the heating process, and the freeze-in height in CMEs may be different between the solar maximum and solar minimum. The source of heavy ions and the heating processes are possibly under modulation by the status of the solar magnetic field. As the most abundant heavy ions in the solar atmosphere (Asplund et al. 2009 ), oxygen ion flux in the solar wind has been systematically studied by von Steiger et al. ( 2010 ). It distributes from 10 4 to 10 6 cm −2 s −1 in the slow solar wind, and from 10 4.5 to 10 5.5 cm −2 s −1 in the fast solar wind. The peaks of the two distributions are a bit less and a bit more than 10 5 cm −2 s −1 , respectively. In the 281 ICMEs, the oxygen flux varies from 2 × 10 4 to 1.5 × 10 5 cm −2 s −1 . And it fluctuates around 10 5 cm −2 s −1 during the solar maximum, when the annual mean SN is more than 60. It could be identified that the oxygen flux in ICMEs is similar to that in the solar wind. Since the SN represents the status of the solar magnetic field, the quasi-linear relation between the O flux in the ICMEs and the SN, when the SN is less than 60, indicates that the O flux is possibly related to the status of the solar magnetic field. 5. Conclusion This statistical work analyzes the abundances and the charge states of heavy ions in the 281 ICMEs from 1998 to 2011. The mean proton speed of the ICMEs and the average charge states of heavy ions are highly related to the solar cycle. Since the SN represents the state of the solar magnetic field by nature, the proton speed and heavy ion composition of ICMEs are possibly related to the solar magnetic field. Another interesting result is the classification effect of the speed on the abundances and charge states of heavy ions. Similar to the solar wind, ICMEs could