adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...598A..22B (fulltext)
those observed in Ar, allowing a clear assignment. The most striking observation is that those bands are very weak in comparison to other bands growing in this region (Figs. 2 and 3 ). In the 23002000 cm zone (Fig. 2 ), new bands are formed during the irradiation. Among them, a band at 2079.1 cm can clearly be assigned to the HCN molecule ( Milligan Jacox 1967 ). This assignment is confirmed in other regions of the spectrum where characteristic bands of HCN are observed especially at 3306.4 cm (Fig. 3 ) and 723.5 cm. At longer irradiation times, the HCN dimer is formed with a band at 791.9 cm ( King Nixon 2003 ) and oligomers with a band at 809.3 cm (cf. Table 2 , for detailed assignments). Bands at 3622.2 and 2028.6 cm are characteristic of the HCN photoisomerisation producing the isonitrile HNC. After 75 min of irradiation, the CN radical ( Jacox Thompson 2007 ) is detected with its characteristic band at 2050.8 cm. One additional peak is also detected at 1337.5 cm, which is tentatively assigned to the radical HCN that was already observed in an Ar matrix ( Milligan Jacox 1967 ). The first interpretation of these results is that the CN radical formed by the CN photodissociation subsequently reacts by abstraction of one H atom of H molecules to form the hydrogenated compound. To be sure that the products are formed from the CN radicals reacting with H and not by CN radical reacting with H atoms, we developed a strategy changing the wavelength of the irradiation in addition to using a D matrix. Fig. 2 IR spectra of CN in a H matrix in the range 23002000 cm at different irradiation times using a hydrogen lamp and assignment of the new species. a) deposition; b) 5 min; c) 15 min; d) 35 min; e) 75 min; f) 135 min and g) 195 min of irradiation. D = Dimers of HCN, M = Monomer of HCN, P = Polymers of HCN. Fig. 3 IR spectra of CN in a H matrix in the range 35002800 cm at different irradiation times using a hydrogen lamp and assignment of the new species. a) deposition; b) 5 min; c) 15 min; d) 35 min; e) 75 min; f) 135 min and g) 195 min of irradiation. D = Dimers of HCN, M = Monomer of HCN, P = Polymers of HCN. 3.4. C<inline-formula specific-use="simple-math"><sub>2</sub></inline-formula>N<inline-formula specific-use="simple-math"><sub>2</sub></inline-formula> VUV irradiation in a H<inline-formula specific-use="simple-math"><sub>2</sub></inline-formula> matrix using a sapphire window (<inline-formula specific-use="simple-math"><italic>λ</italic> > 147</inline-formula> nm) and an Ar lamp (<inline-formula specific-use="simple-math"><italic>λ</italic></inline-formula> > 160 nm) The edge of the absorption band of H overlaps with the 121 nm radiation ( Bunch et al. 1958 ) and therefore we have to take into account that an excited state or photodissociation of H could be involved in the previous formation of HCN. To verify that the observed reactivity is exclusively due to the cleavage of cyanogen, we changed the MgF window of our system with a sapphire
Paper: 2016ApJ...826...15F (fulltext)
of TD. In addition, the vector displacement ( of the two spacecraft is (−270.40, −47.21, −30.48) R E . It can be seen that the dot product of the TD normal (−0.098, 0.961, 0.258) and the vector displacement is negative. This means that Wind should have observed the “TD” earlier than ACE . In fact, the ACE spacecraft has observed this “TD” earlier than Wind . Thus, this DD should not be a TD. If we consider the DD to be an IS, the calculated DD propagation speed is 349.1 km s −1 in the rest frame, and the corresponding estimated time difference is 73.6 minutes, which is also different with the observed time difference (93.7 minutes). Even so, we consider that the DD should be an IS rather than a TD. As for the time deviation of the IS model, it may be caused by the following two factors. One is that the shock front may be not flat; the estimated time difference is not accurate. The other may be that not all the parts of the leading boundary of the reconnection exhaust meet the requirements of an IS, and the IS may be formed only somewhere within the boundary, with Wind and ACE crossing different parts of the leading boundary. Figure 3. Magnetic fields measured by Wind and ACE in the GSE coordinate system, where the dotted lines are for ACE , and the ACE time sequences were shifted by 94.1 minutes. 4. SUMMARY Previously, ISs were considered to be unphysical because they do not satisfy the so-called evolutionary conditions based on the theory of hyperbolic systems of conservation laws (Jeffrey Taniuti 1964 ; Kantrowitz Petschek 1966 ). Over the past more than four decades, there have been a large number of numerical investigations for ISs. In spite of evolutionary conditions, ISs are commonly observed to be stable shocks in numerical simulations (e.g., Wu 1987 ; Wu Hada 1991 ; Takahashi et al. 2013 , 2014 ), and two interplanetary ISs have been observed and reported (Chao et al. 1993 ; Feng Wang 2008 ). However, both ISs were identified through fitting the R-H relations based on one spacecraft observation. Our previous study (Feng et al. 2007 ) suggested that the analysis using a single spacecraft observation based only on the R-H relations could misinterpret a TD as an IS. In this study, we reported an IS associated with a magnetic reconnection exhaust, which was detected by the Wind and ACE spacecraft at 1 au on 2000 August 9. We investigated this shock by fitting the R-H relations and referring to the ACE observations. We fitted this discontinuity using the procedure of Lin et al. ( 2006 ) and found that the observed magnetic field and plasma data satisfy the R-H relations well. The normal Alfvén-Mach number is greater than unity in the preshock state and less than unity in the postshock state; the fast-mode Mach number in upstream and downstream regions are less than unity, and both slow-mode Mach numbers are greater than unity. The components of the preshock and postshock magnetic fields in the shock front have opposite signs. Namely,
Paper: 2021A&A...648A..78P (fulltext)
RR Lyrae stars that are likely Orphan members, and we obtained . This is very similar to the value reported by Sesar et al. (2013) and also significant 12 . We explored the existence of a metallicity gradient in the Orphan stream using our nonvariable and RR Lyrae sample 13 . The depiction of the metallicity versus φ 1 can be found in Fig. 5 . In both of our samples (nonvariable and RR Lyrae sample) we do not detect any significant correlation between the sky position φ 1 and metallicity. A similar outcome holds even when we include only stars with a high probability p ( A | B ) > 0.5 for both of our samples. One of the possible reasons for this discrepancy lies in the different metallicity calibrations between our study and Sesar et al. (2013) . In our case, we rely on the new calibration of the ΔS method using metallicities determined from high-resolution spectra ( Crestani et al. 2021 ), while Sesar et al. (2013) relied on the calibration of Layden (1994) which is slightly offset compared to metallicities obtained from high-resolution spectra (see, e.g., For et al. 2011 ; Chadid et al. 2017 ). Another reason could lie in the metallicity scale, where Sesar et al. (2013) values lie on the Zinn West (1984) scale 14 , while our metallicities are on a different metallicity scale ( Chadid et al. 2017 ; Sneden et al. 2017 ; Crestani et al. 2021 ). This could shift the metallicities of Sesar et al. (2013) toward the metal-rich end by up to 0.2 dex ( For et al. 2011 ). To conclude, using our dataset we were unable to confirm the existence of a metallicity gradient in the Orphan stellar stream. Fig. 5. Stream coordinates, φ 1 , versus [Fe/H] ( top panel ), and metallicity distribution function ( bottom panel ) for likely variable andnonvariable Orphan stream members. The color-coding of each nonvariable star represents the probability of association to the Orphan stream p ( A | B ), and the green squares represent RR Lyrae stars associated with the Orphan stream in our study. The histogram in the lower panel represents the metallicity distribution of the entire sample (black line) the RGB stars (red dashed line), BHB stars (blue dotted line), and RR Lyrae stars (green lines). The red solid line represents the kernel density estimate of the metallicity distribution of the RGB stars. 5.2. Grus II as a possible progenitor In the work by K19, the previously considered candidates for the Orphan progenitors, Segue 1 and UMa II ( Fellhauer et al. 2007 ; Newberg et al. 2010 ), were excluded based on their distance and proper motions. One viable candidate for the progenitor of the Orphan stream remained, Grus II, a UFD (found in the DES by Drlica-Wagner et al. 2015 ). Grus II matches with the Orphan stellar stream in the coordinates and proper motion space. Recently, line-of-sight velocities and chemical abundances became available for several stars associated with Grus II UFD ( Simon et al. 2020 ; Hansen et al. 2020 ). The line-of-sight velocities center
Paper: 2018A&A...609A.135S (fulltext)
between the best-fit values obtained from fits of the F99 law to each object’s set of color excesses (see the last column of Tables 4 and 5 ) and the values inferred from individual color excess measurement and the F99 law (see Table 3 ). In doing so these comparisons are made for each of the four color excess combinations: (top-left panel), (top-right panel), (bottom-left panel), and (bottom-right panel). Included in each panel’s sub-plots is the root-mean-square (rms) difference between the two methods. In general the values obtained by the two methods are in good agreement, as indicated by the fact that the data follow the black lines in the plots. Fig. 17 from as given in Tables 4 and 5 versus . The Poznanski et al. (2012) relation is over-plotted as a solid blue line. Empty symbols indicate upper limits on . A linear fit between the two quantities (with intercept fixed to zero) is drawn as a black dashed line and the corresponding fit expression is reported in the top-left corner of the figure. This expression has been obtained by linearly fitting all the points including the limits. We find that the optical and NIR color combinations providing the smallest dispersion among the suite of , , , and color combinations considered in this work are as follows. In the case of and time relative to the and colors exhibit minimal dispersion in , while for and time relative to the and colors exhibit minimal dispersion. Moreover, for and time relative to the and colors exhibit minimal dispersion in , while for and time relative to the and colors exhibit minimal dispersion. Discrepancies between the two methods are largely due to different spectral lines affecting the measurement from a single color combination more than the global fit value. We now compare the value derived for each object in the sample to their measurement obtained from visual-wavelength spectra (Holmbo et al., in prep.). Specifically, average values given in Tables 4 and 5 are plotted in Fig. 17 vs. . Over-plotted in the figure is the relation (solid blue line) found between these parameters by Poznanski et al. (2012) , as well as a linear fit (dashed line) to the data assuming when . The latter of these is given by . As expected there is significant scatter between the two quantities (cf. Poznanski et al. 2011 ). Returning to the CDFs shown in Fig. 14 , it is interesting to note that the SNe Ic consistently exhibit higher extinction values compared to SNe IIb and SNe Ib, and this holds independent of the assume value of . This is consistent with the findings of Anderson et al. (2015) whom used pixel statistics based on emission to study the environments of SNe by tracing on-going (16 Myr old) star formation within SN host galaxies. Anderson et al. found that SNe Ic show a statistically significant preference for being located in regions of on-going star formation, while SNe Ib are located in regions that do not as closely follow ongoing star formation. The implications are that
Paper: 2019ApJ...871...19Z (fulltext)
in Table 1 . In each case, the log-likelihood ratio with respect to a power-law hypothesis, TS, is also given. The maximum energy displayed for each spectrum (see also panel (b) in Figure 1 and Table 3 ) corresponds to the highest energy of photons attributed to CTA 102 at more than 95% confidence level (CL), as evaluated using gtsrcprob . Figure 2. Spectra for dates, where near-simultaneous data by all three observatories are available. The MJDs are indicated. The gray spectrum is the MJD 57670 spectrum for comparison. Table 1 Fermi -LAT Detailed Results of CTA 102 Used for Each Spectrum Shown in Figure 2 Note. The columns give the center of the daily binned time interval in which the spectrum is extracted in MJD, the integrated flux density, the photon index, the curvature index, and the Log-likelihood ratio between the two spectral hypothesis (Log parabola with respect to power law). 2.2. X-Ray Analysis The Neil Gehrels Swift Gamma-Ray Burst Mission (hereafter Swift , Gehrels et al. 2004 ) is a multiwavelength space instrument, which monitors sources in the optical, ultraviolet, and X-ray energy bands. X-ray observations of CTA 102 were possible thanks to the X-ray Telescope (XRT, Burrows et al. 2005 ) onboard. It monitored CTA 102 since 2005 in 144 pointing observations taken in the energy range of 0.3–10 keV. For the goal of this paper, data collected between MJD 57668 and MJD 57821, which correspond to the ObsIDs of 00033509084-00033509120 have been analyzed and are presented in the light curve (Figure 1 (c)). X-ray data analysis was performed using version 6.20 of the HEASOFT package. 7 7 http://heasarc.gsfc.nasa.gov/docs/software/lheasoft The data were recalibrated using the standard procedure xrtpipeline . Spectral fitting was carried out using XSPEC v.12.8.2 (Arnaud 1996 ). Data were binned in order to have at least 30 counts per bin and were fitted using a power-law model, Equation ( 1 ), with the Galactic absorption value of cm −2 (Kalberla et al. 2005 ) set as a frozen parameter. Furthermore, it was tested whether the spectrum can be better descibed with a broken power-law model. According to reduced values, a simple power law is preferred for all X-ray spectra of CTA 102. The observations presented in the SEDs (Figure 2 ) are summarized in Table 2 . While there is some variability in the spectral index, this is mostly minor and most of the X-ray variability comes from a change in the normalization. Table 2 Swift -XRT Observations of CTA 102 Used for the Spectra Shown in Figure 2 Note. The columns give the MJD, the observation ID, the duration of the observation, as well as the spectral parameters, i.e., normalization and Index . The normalization energy is keV. 2.3. Optical/UV Analysis In the optical and ultraviolet regime, CTA 102 was monitored with the UVOT instrument on board Swift . The monitoring was performed in the bands UVW 2 (188 nm), UVM 2 (217 nm), UVW 1 (251 nm), U (345 nm), B (439 nm), and V (544 nm), with the
Paper: 2017ApJS..232....5M (fulltext)
around 65% and 41%, and the uncertainties in their speeds give a likelihood of perfectly inelastic collision of around 98% and 100%, respectively. Similarly, the uncertainties in the propagation directions of the 2012 June and 2011 August CMEs give a likelihood of inelastic collision of around 65% and 77%, the uncertainties in their angular widths give a likelihood of inelastic collision of around 50% and 53%, and the uncertainties in their speeds give a likelihood of inelastic collision of around 94% and 84%, respectively. For the 2013 October CMEs, the uncertainties in the propagation directions, widths, and speeds correspond to a probability of 67% for super-elastic collision, 75% for inelastic collision, and 50% for super-elastic collision, respectively. The uncertainties in the directions of the 2011 February, 2012 March, and 2012 September CMEs give a likelihood of super-elastic collision of around 88%, 38%, and 89%, the uncertainties in their widths give a likelihood of super-elastic collision of around 73%, 43%, and 60%, while the uncertainties in their speeds give a likelihood of super-elastic collision of around 89%, 61%, and 49%, respectively. From the above values of probabilities and the values of e shown in Figures 4 – 11 , we note that the effect of the uncertainties is so large for some cases that it is difficult to ascertain a particular nature of collision for those cases. For instance, under the uncertainties considered, three cases (CMEs of 2010 May 23–24, 2013 October 25, and 2012 September 25–28) of the CMEs show the most probable nature of collision vacillating between the inelastic to super-elastic regime. Such an effect is expected for the CMEs of 2013 October 25 as its observed nature of collision was elastic, i.e., at the boundary of the inelastic and super-elastic. However, the CMEs of 2010 May show huge uncertainties in the e value in their directions, widths, and speeds. The CMEs of 2012 September 25–28 show a large deviation from the super-elastic nature due to uncertainties in the pre-collision leading edge speeds of the CMEs. Such uncertainty in the e value due to a change in the speed of the CMEs was not noted in Mishra et al. ( 2015b ). We point out that in the oblique collision scenario, even a reasonable uncertainty in the observed CME characteristics leads to a different nature of collision. The e value is found to be dependent to some extent on the relative propagation directions of the CMEs. However, this was not recognized in earlier studies that assumed the head-on collision scenario (Mishra et al. 2014 , 2015a ). We envisage that a change in the propagation directions may lead to a change in the relative contribution of the expansion speeds to the centroids speeds of the CMEs along the line joining their centroids. We did not make any attempts to understand whether the different propagation directions of the CMEs, which cause different contact areas between them, have some role in deciding the nature
Paper: 2018MNRAS.473.1359L (fulltext)
Charlot ( 2003 ) 5 for the MILES spectral library (Snchez-Blzquez etal. 2006 ) using a Chabrier ( 2003 ) stellar initial mass function (IMF) over the range 0.1100M . The components for the base models comprised 16 ages from 1Myr to 13Gyr for each of four metallicities ( Z 0.004, 0.008, 0.02, 0.05). We note that tests with somewhat different base model sets and choice of IMF did not significantly affect continuum subtraction and so do not affect the emission-line results on which our analyses rely. Our procedure largely mirrors that performed on MUSE data of supernova hosts elsewhere (e.g. Galbany etal. 2016a ). 3.3 Emission-line fitting Emission-line fitting was performed on the spectra after subtracting the best-fitting continuum, as determined in Section 3.2 . Prior to fitting, the continuum-subtracted spectra were median filtered with a width of 120 this width was chosen in order to remove any broad residuals left by the imperfect continuum fitting, whilst leaving the narrow emission lines unaffected. The emission-line model was a composite model of single Gaussians initially centred at the wavelengths of H , [O iii ] 4959,5007, H, [N ii ] 6548,6583 and [S ii ] 6716,6731 (the two exceptions to this set-up, SNe 2002bp and 2008ge, are detailed later). The fitting was performed via the weighted LevenbergMarquardt least-squares algorithm. Such fitting is, however, susceptible to finding local minima solutions that are dependent on the initial guesses for the Gaussian parameters. In order to circumvent the dependency on initial guesses, and to restrict to physically plausible solutions we added the following caveats and steps to our fitting routine: The velocity offset of the Gaussian means were restricted to within 500km s 1 relative to the galaxys rest frame (i.e. after accounting for the redshift of the galaxy nucleus). This comfortably encloses the limits of rotational velocities seen in late-type galaxies (Sofue Rubin 2001 ) whilst preventing misidentification of lines. The velocity offset of the Balmer and forbidden lines were each tied, i.e. a single velocity offset for H and H was fit and a separate offset was fit for the other lines. The standard deviations of the Gaussians were restricted to 130km s 1 and pairs of lines for [O iii ], [N ii ] and [S ii ] had tied values for the standard deviation (in km s 1 ). In order to semibrute force locate the global minimum, a list of several initial guesses for each of the means, standard deviations and amplitudes to be fit was made. A grid was then formed of all possible combinations of these parameters to use as initial guesses for the LevenbergMarquardt fitter, which found the local minimum for each initial guess. The residual between the data and model was used as an estimator of the goodness of fit for each grid position and used to identify the best overall fit. Where the fitting process failed, we manually inspected the emission-line spectrum. In most cases, low signal to noise or
Paper: 2016ApJ...831...67B (fulltext)
assumed distance of 5.3 kpc. Figure 19.129. Clump BYF 57 ICO Clump BYF 57 in 12 CO J = 1 → 0, mapped in I CO . Black I 12 CO contours in each frame are every 8 K km s −1 . The 7 ′′ Mopra beam is shown in the BR corner, corresponding to a scale of 0.95 pc at an assumed distance of 5.3 kpc. Figure 19.130. Clump BYF 57 Irms Clump BYF 57 in 12 CO J = 1 → 0, mapped in I rms . Black I 12 CO contours in each frame are every 8 K km s −1 . The 7 ′′ Mopra beam is shown in the BR corner, corresponding to a scale of 0.95 pc at an assumed distance of 5.3 kpc. Figure 19.131. Clump BYF 57 VLSR Clump BYF 57 in 12 CO J = 1 → 0, mapped in V LSR . Black I 12 CO contours in each frame are every 8 K km s −1 . The 7 ′′ Mopra beam is shown in the BR corner, corresponding to a scale of 0.95 pc at an assumed distance of 5.3 kpc. Figure 19.132. Clump BYF 57 Vrms Clump BYF 57 in 12 CO J = 1 → 0, mapped in V rms . Black I 12 CO contours in each frame are every 8 K km s −1 . The 7 ′′ Mopra beam is shown in the BR corner, corresponding to a scale of 0.95 pc at an assumed distance of 5.3 kpc. Figure 19.133. Clump BYF 57 σV Clump BYF 57 in 12 CO J = 1 → 0, mapped in σ V . Black I 12 CO contours in each frame are every 8 K km s −1 . The 7 ′′ Mopra beam is shown in the BR corner, corresponding to a scale of 0.95 pc at an assumed distance of 5.3 kpc. Figure 19.134. Clumps BYF 63-80 (Region 9) Tmb(Peak) Clumps BYF 63-80 (Region 9) in 12 CO J = 1 → 0, mapped in T mb (Peak). Black I 12 CO contours in each frame are every 20 K km s −1 . The 7 ′′ Mopra beam is shown in the TL corner, corresponding to a scale of 0.45 pc at an assumed distance of 2.5 kpc. Figure 19.135. Clumps BYF 63-80 (Region 9) Trms Clumps BYF 63-80 (Region 9) in 12 CO J = 1 → 0, mapped in T rms . Black I 12 CO contours in each frame are every 20 K km s −1 . The 7 ′′ Mopra beam is shown in the TL corner, corresponding to a scale of 0.45 pc at an assumed distance of 2.5 kpc. Figure 19.136. Clumps BYF 63-80 (Region 9) ICO Clumps BYF 63-80 (Region 9) in 12 CO J = 1 → 0, mapped in I CO . Black I 12 CO contours in each frame are every 20 K km s −1 . The 7 ′′ Mopra beam is shown in the TL corner, corresponding to a scale of 0.45 pc at an assumed distance of 2.5 kpc. Figure 19.137. Clumps BYF 63-80 (Region 9) Irms Clumps BYF 63-80 (Region 9) in 12 CO J = 1 → 0, mapped in I rms . Black I 12 CO contours in each frame are every 20 K km s −1 . The 7 ′′ Mopra beam is shown in the TL corner, corresponding to a scale of 0.45 pc at an assumed distance of 2.5 kpc. Figure 19.138. Clumps BYF 63-80 (Region 9) VLSR Clumps BYF 63-80 (Region 9) in 12 CO J = 1 → 0, mapped in V LSR . Black I 12 CO contours in each frame are every 20 K km s −1 . The 7 ′′ Mopra beam is shown in the TL corner, corresponding to a scale of 0.45 pc at an assumed distance of 2.5 kpc. Figure 19.139. Clumps BYF 63-80 (Region 9) Vrms Clumps BYF 63-80 (Region 9) in 12 CO J = 1 → 0, mapped in V rms . Black I 12 CO contours in each frame are every 20 K km s −1 . The 7