adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.482.2731Z (fulltext)
of dust producers (Bolmer et al. 2018 ; Zafar et al. 2018b ) could also change the effect of total Fe on extinction. It is now simple to compute the average total column density of Fe along a sightline back to a given redshift, and then use the fit in Fig. 4 to convert this to an average ‘cosmic dust dimming’ CDD(). In Fig. 6 , we provide this quantity, CDD(), back to a redshift of = 5. Figure 6. The average ‘cosmic dust dimming’, CDD(), is computed as the integrated A V from redshift 0 to . The A V is computed from the average amount of dust along the line of sight based on the distribution of Fe (blue points) shown in Fig. 5 . 4.2 Impact on high-redshift surveys Fig. 6 shows the accumulated effect of dust in random sightlines out to cosmological distances. It is seen that up to redshifts of about 3 the CDD() is smaller than the typical uncertainties on individual A V measurements, and the effect can be ignored without too much impact. At redshifts in excess of 4, in the current paradigm, the CDD() continues to rise despite the dropping metallicity, causing the cumulated effect to become significant. It should be recalled that the curve shows the average, but the effect is highly stochastic. Therefore, corrections and predictions should probably only be considered for statistical samples while the effect on individual objects will be stochastic. 4.3 Inclusion of 2175 Å bump The shape of the extinction curve tells us about the dust grains in the ISM (Draine 2003 ). Carbonaceous dust grains are responsible for the 2175 Å bump and silicates induce the steepness in the extinction curve (Weingartner Draine 2001 ; Draine 2003 ). We here aim to see if different extinction curves carrying different dust grain properties could affect our results. Our sample mostly has preference towards featureless Small Magellanic Cloud (SMC)-type extinction curve. However, 21 cases (3 GRBs and 18 QSO-DLAs) are reported to contain a 2175 Å bump, suggesting carbonaceous dust enriched environments. These cases are highlighted by stars drawn around the data points in Figs 1 – 4 . De Cia et al. ( 2013 ) presented a sample of 18 GRBs (11 measurements and 7 limits) which included a single burst with a 2175 Å extinction feature (GRB 070802). That single burst did not fall on the A V and N (Fe) dust relation of their limited sample, and De Cia et al. ( 2013 ) suggested that objects with the 2175 Å bump might follow a different relation and therefore should not be included. Our larger sample includes 19 sightlines with bumps, and is reaching extinctions 1.8 mag higher than the De Cia et al. ( 2013 ) sample, but we see no evidence that the 2175 Å bump cases follow a different relation. We also find that inclusion of the 2175 Å bump cases strengthens the relation especially at higher extinctions. Depending on the amount of metals available to form dust, different grain compositions and sizes could provide a range of optical extinction laws. 4.4 Dust-to-gas ratios and
Paper: 2019MNRAS.485..356A (fulltext)
power of erg s −1 , very similar to that shown by PSR J1908+0839, but its distance is ∼30 times smaller ( d Geminga = 250 pc). Given the similar spin-down power, the nebula PSR J1908+0839 powers should also have a comparable luminosity with respect to Geminga’s PWN, which would lead to a flux around three orders of magnitude smaller than the flux of Geminga and undetectable by HAWC. Therefore, it is unlikely that 2HWC J1907+084* and PSR J1908+0839 are associated with one another. Finally, the closest pulsar to the source 2HWC J1852+013* is PSR J1851+0118, offset by only 0.10°. This pulsar lies at a distance of 5.6 kpc and has a characteristic age of ∼100 kyr (Yao, Manchester Wang 2017 ). If both objects are related the required pulsar velocity is a reasonable value of ∼100 km s −1 , though this may be as high as ∼245 km s −1 when considering the largest offset given by 0.10° ± 0.10 0.13. However, this pulsar has a relatively low spin-down power, erg s −1 , which along with its high characteristic age make it unlikely to accelerate particles that can emit gamma rays in the TeV regime. To quantitatively test this scenario, we use the Naima software 3 to model the relativistic parent population of the non-thermal gamma-ray emission accounting for different radiative models (see Zabalza 2015 ). To obtain the emissivity of the electron population that gave rise to the gamma-ray emission seen by HAWC, we assume that IC is the dominant radiative process and that the electron spectrum is defined by a simple power law. The target photon field for this process is expected to be a combination of cosmic microwave background (CMB) and infrared (IR) photons. The assumed energy densities in each case are standard Galactic values of u CMB = 0.25 eV cm −3 and u IR = 0.30 eV cm −3 . With such features, the total energy carried by electrons above ∼10 TeV needed to explain HAWC detection would be W e (> 10 TeV) ∼ 6.0 × 10 46 erg. Alternatively, we consider the cooling time of these electrons ( t cool ), which is computed as follows (Aharonian 2004 ): where E e = 10 TeV is the electron energy and u is the total energy density of the medium. In this case, we account for IC and synchrotron losses and hence, assuming a temperature of ∼25 K for the IR photon field (Moderski et al. 2005 ), u can be described as where B is the magnetic field. We use the aforementioned energy densities for the CMB and IR photon fields, and for the magnetic field, we assume the minimum possible value given by the interstellar magnetic field, B = 3 μG, as there is no measured value for the source. Under these assumptions, the cooling time is t cool ∼ 57 kyr. Given the spin-down power of PSR J1851+0118, erg s −1 , the total energy released by the pulsar during the t cool period would be erg. Consequently, even assuming that all the energy released by the pulsar was used to accelerate electrons above 10 TeV, there would not be enough energy to power a PWN with the gamma-ray brightness
Paper: 2019MNRAS.490...50D (fulltext)
Herschel /HIFI guaranteed time CHESS key program (Ceccarelli et al. 2010 ). On the scale of individual envelopes, N 2 D + is seen only towards A (Jørgensen et al. 2011 ). Most recently, the distribution of N 2 D + has been analysed by Murillo et al. ( 2018b ) on the scale of the individual protostars and the circumbinary envelope. C2.31 Glycolonitrile (HOCH<sub>2</sub>CN), <italic>m</italic> = 57 Glycolonitrile was detected towards source B by Zeng et al. ( 2019 ). C2.32 Aminoacetonitrile (NH<sub>2</sub>CH<sub>2</sub>CN), <italic>m</italic> = 56 An upper limit was derived towards source B by Zeng et al. ( 2019 ). C2.33 Formyl cyanide (CHOCN), <italic>m</italic> = 55 An upper limit was derived towards source B by Zeng et al. ( 2019 ). C2.34 Ethenimine (CH<sub>2</sub>CNH), <italic>m</italic> = 41 An upper limit was derived towards source B by Zeng et al. ( 2019 ). C2.35 Nitrous acid (HONO), <italic>m</italic> = 47 Coutens et al. ( 2019a ) detected HONO towards source B and derived an accurate column density. C2.36 Nitrogen dioxide (NO<sub>2</sub>), <italic>m</italic> = 46 Coutens et al. ( 2019a ) derived an upper limit for NO 2 towards source B. C2.37 Nitrosyl cation (NO<sup>+</sup>), <italic>m</italic> = 30 Coutens et al. ( 2019a ) derived an upper limit for NO + towards source B. C2.38 Nitric acid (HNO<sub>3</sub>), <italic>m</italic> = 63 Coutens et al. ( 2019a ) derived an upper limit for HNO 3 towards source B. C3 S-bearing molecules C3.1 Hydrogen sulphide (H<sub>2</sub>S), <italic>m</italic> = 34 H 2 S was detected on envelope scales (Blake et al. 1994 ), as was HDS (van Dishoeck et al. 1995 ; Caux et al. 2011 ). It was also observed towards the two protostars individually (Chandler et al. 2005 ; Jørgensen et al. 2011 ). On large cloud and core scales, H 2 S is also observed, but at lower abundances than closer to the binary source (Wakelam et al. 2004 ). H 2 S was detected on envelope scales in the infrared with the Herschel /HIFI guaranteed time CHESS key program (Ceccarelli et al. 2010 ). Baryshev et al. ( 2015 ) reported a line of H 2 S in absorption from the foreground cloud. HDS and HD 34 S were detected towards source B by Drozdovskaya et al. ( 2018 ) with accurate column densities provided. C3.2 Carbon monosulphide (CS), <italic>m</italic> = 44 C 32 S was the second molecule studied in IRAS 16293-2422. Its emission line profile showed strong self-absorption and prominent asymmetry, which was interpreted as a signature of infall (Walker et al. 1986 ; van Dishoeck et al. 1995 ). C 34 S was observed soon thereafter (Menten et al. 1987 ). CS, like CO, is present on the large cloud and core scales; and the smaller circumbinary envelope and disc-A and -B scales (Mizuno et al. 1990 ; Walker, Carlstrom Bieging 1993 ; Blake et al. 1994 ; Narayanan et al. 1998 ; Caux et al. 2011 ; Jørgensen et al. 2011 ; Takakuwa Kamazaki 2011 ; van der Wiel et al. 2019 ). It also traces the outflows (Walker et al. 1990 ; Girart et al. 2014 ), and shocks,
Paper: 2017ApJ...848...23K (fulltext)
3 . The amplitudes of the PCs have been artificially normalized to unity for easier comparison. We find in general that at a particular redshift, an individual PC tends to be smooth and positively correlated along k , and at a particular k shows negative and positive correlations across redshift. This is a reflection of the underlying smoothness of the power spectra across k , and the fact that physical processes such as reionization, X-ray heating, and Ly α coupling tend to produce redshift-dependent peaks and troughs in the power spectrum (see, e.g., Figure 3 ). The reason why the PCs lose strength at high k is because our rescaling matrix downweights our data covariance matrix at high k . As we will see in Section 4.1 , the bulk of a 21 cm experiment’s sensitivity to the power spectrum is located at lower k . Figure 1. Top: scree plot showing the eigenvalues of the 30 principal components formed from the training data of . Bottom: the first nine principal components of the power spectrum data at each unique k – z combination. The color scale is artificially normalized to [−1, 1] for easier comparison. 2.3. GP Regression For the purposes of emulation, we are not interested in merely reconstructing our original training set data at their corresponding points in parameter space j , but are interested in constructing a prediction of a new data vector, new , at a new position in our parameter space, new . We can construct a prediction of the data vector at this new point in parameter space by evaluating Equation ( 7 ) with new ; however, we do not know this weight vector a priori. To estimate it at any point in parameter space, we require a predictive function for each element of spanning the entire parameter space. In other words, we need to interpolate over our parameter space. To do this, we adopt a GP model, which is a highly flexible and non-parametric regressor. See Rasmussen Williams ( 2006 ) for a review on GPs for regression. A GP is fully specified by its mean function and covariance kernel. The GP mean function can be thought of as the global trend of the data, while its covariance kernel describes the correlated random Gaussian fluctuations about the mean trend. In practice, because we center our data about zero before constructing the PCs and their weights (Equation ( 2 )), we set our mean function to be identically zero. For the covariance kernel, we employ a standard squared-exponential kernel, which is fully stationary, infinitely differentiable, produces smooth realizations of a correlated random Gaussian field, and is given in multivariate form aswhere and ′ denote two position vectors in our parameter space, is a diagonal matrix containing the characteristic scale length of correlations l across each parameter, and σ A is the characteristic amplitude of the covariance. is a tunable hyperparameter of the kernel function that must be selected a priori. We discuss how we make these choices in Section 2.3.1 . We set σ A = 1, and
Paper: 2018MNRAS.478L..99M (fulltext)
, spflg , and rflag were utilized for flagging bad data. Minimal flagging was necessary for L and X bands, however the S -band data were severely hampered by RFI and five of the eight IFs were almost completely flagged. Los Alamos (LA) was used as the reference antenna, with Pie Town (PT) used when LA was unavailable. Imaging and self-calibration was performed using difmap (Shepherd, Pearson Taylor 1994 ). 3 RESULTS The core of 0313−192 is detected at S band with the LBA, and L, S , and X band with the VLBA. The LBA observations detected an unresolved point source with a flux density of 53.6 ± 8.4 mJy. The longer baselines of the VLBA enabled higher resolution images that were able, at all three observed frequencies, to resolve the radio structure. Table 2 provides a summary of the VLBA data. Table 2. Beam size and flux density measurements for the VLBA observations using different imaging weightings. We assume 10 per cent errors, which is standard for VLBI measurements due to the sparse uv -coverage. 3.1 VLBI detection and chance alignment Keel et al. ( 2006 ) were able to constrain the optical and radio positions to within 0.2 arcsec. The X -band VLBA data provide the best positional information due to the smaller beam size. The error in the radio astrometry is dominated by phase calibration errors, resulting in a positional accuracy of approximately 0.48 mas. As stated in Keel et al. ( 2006 ), the coincidence of the VLBI detections of a compact source with the optical centre of 0313−192 effectively eliminates any possibility of the spiral host and DRAGN being in a chance alignment. A ∼30 mJy compact source detected at L band using the VLBA has a brightness temperature of T b > 10 8 , which can only be attributed to the presence of an AGN. The integrated flux of the naturally weighted S -band source agrees within the errors with the S -band LBA observations from 2009 suggesting little variability on this time-scale. The compact radio detection at the position of the optical host, and the detection of jet components, confirms the presence of an active AGN. 3.2 VLBA-detected core and jet Ledlow et al. ( 2001 ) report an inverted spectral index 1 of the radio core of 0313−192 with , which is common for AGN cores. They also note the steeper inverted spectrum between 1.4 and 4.9 GHz, likely due to either free–free or synchrotron self-absorption. The new VLBA observations confirm the inverted nature of the spectral index of the core. We measure a core spectral index of using the L -, S -, and X -band VLBA measurements of the unresolved radio core. Previously published VLA data of 0313−192 at L and X band suggest the presence of a radio jet to the South of the host galaxy (Ledlow et al. 2001 ). Although the diffuse radio lobes are detected on either side of the host galaxy, the putative jet suggests that the radio jets are oriented with the Southern jet emitting towards the line of sight. We detect components of the Southern jet at L, S , and X
Paper: 2020ApJ...891...96J (fulltext)
reliable results (e.g., Henry 1999 ). Lastly, the number of genuinely SLS bright enough to measure with modest (meter-class) telescopes is small. As measured by the number of stars in a meaningful volume of hyperspace similar to the Sun (Radick et al. 1998 ), considerable work would be needed to identify new, dimmer targets. There remains the nagging question of whether the Sun is different from other SLS (Gustafsson 1998 ). Radick et al. ( 2018 ) conclude “it may be unusual in two respects: (1) its comparatively smooth, regular activity cycle, and (2) its rather low photometric brightness variation relative to its chromospheric activity level and variation...” These authors speculate that facular brightening may nearly balance sunspot darkening, explaining the second point. Egeland ( 2017 ) pointed out that the Sun has the most regular cycle of all SLS measured so far. The question of whether the Sun acts (magnetically) as other SLS is difficult to answer. If all such stars are indeed magnetically similar, it implies that stars have a consistent magnetic variability over timescales of several Gyr (the age range of our sample) to ∼100 million years. The latter is close to the uncertainty in ages of older main sequence stars obtained using the best available methods. It is impossible to verify or refute the question for the Sun, even using a cosmogenic proxy record, which presently stretches only 0.01 million years into the past (Wu et al. 2018 ). Certainly, the most Sun-like of the stars found so far, 18 Sco (HD 146233) has clear differences in metallicity and starspot cycle length. Nevertheless, there is hope that a carefully selected stellar ensemble can represent the activity of the Sun in middle and old age. van Saders et al. ( 2016 ) demonstrated that rotation rates of middle-aged and old GV stars converge as a result of weakened magnetic breaking. Unlike younger stars, there is perhaps a good physical reason to believe that magnetic dynamos of older Suns, and their effects, should be similar. 3. Time Series from a Solar “Reconstruction” The limits of our analysis due to the lack of longer time series can be illustrated through a comparison of our results with a “reconstruction” of solar variations with extraordinary and significant forcing of ≈6 W m −2 from 1600 to 2010 (Shapiro et al. 2011 ). Figure 5 highlights two extended periods of near-monotonic large changes predicted over 15 and 50 yr. The first is compatible with several stars (HD 126053, 52711, 50692, 143761; Table 2 ). The second (50 yr) period is compatible with about half of the stars listed. Figure 5. Variations from a model by Shapiro et al. ( 2011 ) are shown, in which variations in radiative forcing since 1750 are of the order of 3 W m −2 , far larger than the IPCC estimate of −0.30 to +0.10 W m −2 Myhre et al. ( 2013 ). (The relationship between Strömgren magnitudes and irradiance is linear, taken from the model computations. The factor differs from the values
Paper: 2016A&A...596A..48K (fulltext)
as follows. The observations are described in Sect. 2. Sections 3 and 4 give reduction techniques and results of analysis of the obtained spectroscopic and photometric data. Discussion on the derived results can be found in Sect. 4. Brief conclusions are presented in Sect. 5. 2. Observations 2.1. Photometry December, 2006. Spectrophotometric observations were carried out on December 12, 2006 with the multi-mode focal reducer SCORPIO attached to the prime focus of the 6 m Big Telescope Alt-azimuth (BTA) operated by Special Astrophysical Observatory (SAO) of the Russian Academy of Sciences (Russia). The focal reducer provided both photometric mode and long-slit spectroscopic mode ( Afanasiev Moiseev 2005 ). A CCD chip EEV-42-40 of pixels was used as a detector. The focal reducer converts the focal ratio to /2.6, providing a pixel scale of 018 per pixel and field of view of . To increase the signal-to-noise ratio (S/N) of measured signal, on-chip binning of 2 in the spatial direction was applied. s image of the comet was obtained through a broadband filter (center wavelength nm, full width at half maximum ( FWHM ) nm); s images were taken through a broadband Rc filter ( nm, nm). A field of standard stars (PG 0231) was observed at approximately the same airmasses as the comet to perform the photometric calibration ( Landolt 1992 ). Reference zero points and color terms for transformation to the standard photometric system were calculated using the standard stars with a scatter of less than 0.03 mag on average. The difference between the comet and standard field airmasses was considered to be small and was compensated for with the mean monochromatic extinction coefficients measured for Peak Terskol Observatory, which is located in the same mountain region ( Kulyk et al. 2004 ). The twilight sky was observed through the filters used to construct flat field images. November, 2007. Another data set was obtained on November 9, 2007 with the 6 m Telescope. The observation set-up was similar to that described in the previous paragraph. s images were acquired with a broadband filter ( nm, nm). The spectrophotometric standard star Feige 34 was observed at the similar airmasses as the comet airmasses to carry out absolute photometric calibration ( Oke 1990 ). We used the mean monochromatic coefficient to compensate the first order of extinction, neglecting the color member, which is small in the -band and comparable to the uncertainty of the main extinction itself. A set of exposures from the evening and morning twilight sky was obtained to create a flat field image. October, 2008. To collect images of the comet, we used the 2 m RCC Telescope at Peak Terskol Observatory operated by the International Center for Astronomical, Medical and Ecological Research (IC AMER, Terskol, Kabardino-Balkaria, Russia). A focal reducer mounted at the prime focus converted the initial focal ratio of to , providing an image scale of 099 per pixel and useful field of view of 94.
Paper: 2018A&A...610A..40R (fulltext)
are needed to perform the analysis of cTTS. In this investigation we analyse rather weak spectral lines, with equivalent width (EW) of about 10 mÅ, so we need high-resolution, high S/N spectra of template stars of different spectral type. In order to minimize the errors of measurements, we prefer to use a grid of synthetic spectra. This way we avoid possible spectral peculiarities from the real stars that could affect our results. 3.1 Template synthetic spectra The synthetic spectra were produced by the software package Spectroscopy Made Easy (SME; Valenti Piskunov 1996 ). It was initially used to confirm the spectral type of each cTTS, with the estimation of T eff , as well as for the determination of v sin i and v rad . Two sets of synthetic spectra with spectral resolution R = 50 000 were produced. The first set corresponds to a grid of narrow-band spectra withseveral values of stellar parameters, which was used in the determination of spectral type, as well as v sin i and v rad . Then, specific spectra for each star in a wider wavelength band between 4500 and 7000 Å were produced. This second set of synthetic spectra corresponds to our templates and was used to measure EWs of hundreds of spectral lines for comparison with the same lines in the spectra of cTTS. The spectral type determination, described in detail in Sect. 3.2 , allowed us to gather information mainly about T eff . The usual criteria of surface gravity (log g ) can hardly be used in the case of highly veiled spectra with strong lines in emission. Due to that we are assuming the value log g = 3.75 dex for these stars (see Sect. 3.2 ). We also assume a priori a solar-like metallicity ([M/H] = 0.0). Besides the mentioned stellar parameters, SME also needs input information about the microturbulence velocity ( v mic ) and macroturbulence velocity ( v mac ), as well as the instrumental resolution. For the v mic parameter we assume a value that is typical for cool stars and wTTS (e.g. Padgett 1996 ; Rojas et al. 2008 ; James et al. 2006 ; Taguchi et al. 2009 ): v mic = 1.0 km s −1 . For the v mac value we assume a value based on the T eff value, as reported by Valenti Fischer (2005) . The three stars have spectral types between K1 and G5 corresponding to temperatures between 4900 and 5500 K for sub-giant stars ( Table 1 ). For this range of temperatures we assume a value of v mac = 2.8 km s −1 , consonant to an average T eff = 5000 K. To create the synthetic spectra we use the Kurucz grids of stellar atmospheric models, considering local thermodynamic equilibrium (LTE) conditions. The line list used by SME to create the synthetic spectra was extracted from the Vienna Atomic Line Database (VALD) database using the “Extract stellar” option ( Ryabchikova et al. 2015 ). Since we measured several hundred spectral lines, we did not apply any corrections to the log gf and Van der Waals damping parameters. 3.2 Spectral type determination The spectral types of our targets were determined