adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...859...49P (fulltext)
and the observation ID are given in each plot. The soft and hard LCs (left panel), HR plot (middle panel), and the DCF plot between the soft and hard bands (right panel) of 1ES 0347−121 are shown in Figure 3 . No significant spectral change is seen from the HR plot. We checked for variations quantitatively using a standard χ 2 test,where x i is the HR value, σ i is its corresponding error, and is the mean HR value. We considered a variation in the HR to be significant only if χ 2 > χ 2 99, ν , where ν is the number of degrees of freedom (DoF) and the significance level is 0.99. These results are given for all these blazars in Table 4 , where we see that for each source , so no significant spectral variations were detected. Figure 3. Soft (3–10 keV, denoted by red filled circles) and hard (10–79 keV, denoted by black filled circles) LCs (left panels), hardness ratios (middle panels), and the discrete correlation functions between soft and hard LCs (right panels) of the blazars 1ES 0347−121, 1ES 0414+009, RGB J0710+591,1ES 1101−232, 1ES 1218+304, and H 2356−309. The source names and observation IDs are given in the left panels. Table 4 Test of Temporal Variation of HR The DCF plot is flat, which, in the presence of significant variations, would indicate no correlation between the two energy bands, but since no variations are detectable this type of DCF is expected. 4.2. 1ES 0414+009 The TeV blazar 1ES 0414+009 ( α 2000 = 04 h 16 m 530; δ 2000 = +01°05′20′′) is an HBL at z = 0.287 (Halpern et al. 1991 ). It was first detected in X-rays with the High Energy Astronomy Observatory ( HEAO 1 A-1) (Ulmer et al. 1980 ) and was classified as a BL Lac object by Ulmer et al. ( 1983 ). It was observed above 200 GeV by VERITAS with source flux equal to (5.2 ± 1.1 stat ± 2.6 sys ) × 10 −12 photons cm −2 s −1 (Aliu et al. 2012 ). 1ES 0414+009 was observed with NuSTAR on 2015 November 25 with a good exposure time of 34.16 ks. The 3–79 keV LC of 1ES 0414+009 is shown in Figure 1 . The data are both noisy and sparse, resulting in no significant IDV detection, which is consistent with the value of fractional variance given in Table 2 . Consequently, the ACF plot, given in Figure 2 , does not show any hint of variability timescale. The soft and hard LCs of 1ES 0414+009 are plotted in the left panel of Figure 3 . The HR plot in the middle panel of that figure reveals no significant spectral variations (also see Table 4 ) and the DCF plot shown in the right panel of Figure 3 again is flat. 4.3. RGB J0710+591 The high-frequency-peaked BL Lacertae object RGB J0710+591 ( α 2000 = 07 h 10 m 264; δ 2000 = +59°09′00′′) is located at a redshift of z = 0.125 (Giommi et al. 1991 ) and also was first detected by HEAO A-1 (Wood et al. 1984 ). It was detected with VERITAS with the integral flux (above 300 GeV) recorded to be (3.9 ± 0.8) × 10 −12 cm −2 s −1 (Acciari et al. 2010 ). NuSTAR observed RGB J0710+591 on 2015 September 1 for 26.48 ks. As seen from the LC in Figure 1 the
Paper: 2016ApJ...827..123W (fulltext)
dynamo than precession. We must point out that this conclusion is valid only at this Ekman number. At a smaller Ekman number, the precessing flow is more complex, which favors the onset of the dynamo, and this conclusion may not hold any longer (we leave the large-scale simulations at smaller Ekman numbers for other researchers who will be interested in the result of this work). The comparison between Figures 1 (a) and 2 (a) suggests that not only the mean poloidal kinetic energy but also the fluctuation amplitude of the precessing flow are much higher than those of the convective flow. This implies that the precessional instabilities are more vigorous than the convective instabilities in the two successful dynamos. The dominant azimuthal mode in the convective flow is m = 3 (Table 1 ), indicating a shorter length scale than the dominant spin-over mode m = 1 in the precessing flow. Figure 2 (b) shows the time evolution of the magnetic energy. Compared to Figure 1 (b), the magnetic energy of the convection dynamo becomes noticeable at the time ≈5000, which is much later than the time ≈400 of the precession dynamo. It is not surprising that the magnetic energy of the convection dynamo is also much lower than that of the precession dynamo (Table 1 ) because of the lower Rm of the former. Figure 2. Convection dynamo at . Here, we show the time evolution of the poloidal kinetic energy (a) and the magnetic energy (b). After studying the dynamos driven by precession alone and by convection alone, we study the combined effect of precession and convection. In the above two dynamos, the dynamo driven by precession alone cannot be maintained at Po = −0.2 and the dynamo driven by convection alone cannot be maintained at . We test whether the preceission-convection dynamo can be maintained at Po = −0.2 and . This dynamo works! It indicates that the combined effect of precession and convection can indeed facilitate the onset of the dynamo. As we discussed, the conventional Ra differs by more than one million, which indicates that precession greatly helps the onset of the convection dynamo. Moreover, it is interesting that the Rm = 677 of the precession-convection dynamo is lower than the Rm = 694 of the failed precession dynamo, as shown in Table 1 . This suggests that the combination of precession and convection has some non-trivial effect and triggers the dynamo action at a lower Rm of the pure precession dynamo. This non-trivial effect probably arises from the resonance of precessional instability and convective instability. Figure 3 shows the time evolution of this precession-convection dynamo and its time-averaged values are listed in Table 1 . Although the of the precession-convection dynamo is lower than the of the convection dynamo, the Nu of the former is higher (Table 1 ). This is because the poloidal flow driven by precession contributes more to heat transfer than convection, i.e., of the precessing flow at Po = −0.2 is already much higher than
Paper: 2020A&A...642A..46H (fulltext)
reproduced with a two-component photometric model that consists of a PN-poor inner halo with a de Vaucouleurs profile and a PN-rich exponential outer component. Relying solely on the information from surface photometry, we were unable to constrain the scale radius of the exponential profile. We constrained the shape of the exponential profile using the information from resolved stellar populations. Harris et al. (2007a) and Lee Jang (2016) published number-density profiles of MR and MP RGB stellar populations in the halo of M 105. We combined the information from these studies to infer that the density distribution of the most MP stars ([M/H] −1.0) follows an exponential profile with a central surface brightness of μ 0 = 27.7 ± 0.1 mag arcsec −2 and a scale radius of r h = 258 ± 2′′ (Sect. 5 ). Using the information from the RGB populations, we constructed a two-component photometric model, where the two components were weighted by their α -parameter values. In the PN-poor inner halo, the light distribution is dominated by intermediate-metallicity ( − 1.0 ≤ [M/H] −0.5) and MR ([M/H] ≥ −0.5) stars, while in the PN-rich outer halo the MP stars dominate the light distribution. The α -parameter values of the two components are as follows: for the inner PN-poor component, and for the outer PN-rich component. We find a difference of a factor of seven in the α -parameter values of the inner versus outer components, implying that the luminosity-specific PN number of the stellar population that traces the old and MP halo at large radii is seven times higher. We interpret the high α -parameter value and the steep faint-end PNLF slope as tell-tale signs of an additional old and MP stellar population whose contribution towards the overall light distribution increases at large radii. This interpretation is corroborated by the results of Harris et al. (2007a) and Lee Jang (2016) based on resolved stellar populations. While in earlier works ( Longobardi et al. 2013 ; Hartke et al. 2017 ) this association was made mostly by inference, we are now able to directly establish the link between the high α -parameter value, the steep PNLF slope, and the increase in the old and MP stellar population in the outer regions of M 105 (identified by Harris et al. 2007a ). The data of Harris et al. (2007a) clearly ruled out a single chemical evolution sequence for the entire halo stellar population and instead suggested a multi-stage evolutionary model. For the outer MP halo, two different scenarios were proposed: Harris et al. (2007a) argued that the MP halo was built up from pristine gas and that star-formation therein was truncated early, at the time of cosmological reionisation. In contrast, Lee Jang (2016) proposed a formation dominated by dissipationless mergers and accretion. The scenario by Harris et al. (2007a) implies that the faint MP halo reached the current extension out to 40−50 kpc at high redshift, which is at odds with the observed slow growth of the
Paper: 2018A&A...618A..91G (fulltext)
data to analyse them together with our CO ( J = 3−2) observations. BD+30 3639 was the source with the best complementary CO ( J = 2−1) data, given the size of the source and the beam size of the CO ( J = 2−1) observations. A discussion of the use of these diagnostic plots is presented for different molecules in the Appendix of van der Tak et al. (2007) . Linear molecules, such as CO, are density probes at low densities, while at higher densities, the line ratios are more sensitive to temperature. However, with no other information than a single line ratio (such as our case), it is only possible to provide lower limits to density and temperature. Table 4 Size, distance, and morphology of PNe with CO emission. References. (1) Cahn et al. (1992) , (2) Robinson et al. (1992) , (3) Groenewegen et al. (2012) , (4) Frew (2008) , (5) Sahai et al. (2007) , (6) Acker et al. (1992) , (7) Tajitsu Tamura (1998) , and (8) Guzmán et al. (2006) . In Fig. 2 we show the diagnostic plot for the CO ( J = 3−2) to CO ( J = 2−1) intensity ratio, assuming 2.73 K as the background temperature and a line width of 50 km s −1 . The plot shows the results for a column density of 1 × 10 16 cm −2 , but we find that the CO line ratio is not very sensitive to column density within the range 3 × 10 15 cm −2 to ~4 × 10 16 cm −2 . The plotted ratios corresponded to the observed values for BD+30 3639 (1.6), NGC 3132 (1.3), IC 4406, and M2-9 (0.7, for the last two). However, for the latter three sources, the measured ratios are only lower limits because of different CO ( J = 2−1) and CO ( J = 3−2) beam sizes (the former larger than the latter) and the source size (larger than both beam sizes). The CO intensity ratio constrains a lower limit to the H 2 volume density, n H 2 > 10 3 –10 3.7 cm −3 , and kinetic temperature, T kin > 10–35 K. The highest lower limit values are given for IC 4406 and M2-9. 3.6 Correlation with H<sub>2</sub> We would like to probe the relationship between the detection of CO and H 2 . We compared our sample with the sample analysed by Kastner et al. (1996) , whose study was made by imaging the molecular hydrogen emission ( λ = 2.122 μ m) using the near-infrared cryogenic optical bench mounted at Kitt Peak National Observatory, from the National Optical Astronomy Observatories. They observed 60 PNe and detected H 2 in 23 of them. When compared with out sample, 9 objects are in common. Of these 9 objects, 4 were detected in H 2 , and all are also detected in CO as part of our survey. The objects are NGC 3132, IC 4406, PN M2-9, and BD+30 3639. The remaining objects (five) were not detected in H 2 and are they detected here in CO either. These PNe are PN M3-2, PN MyCn18, NGC 6778, NGC 6790, and HD 193538. Although the overlap between PANDORA and the H 2 survey of Kastner et al. (1996) is small, we find a clear correlation between the detection of CO and the detection of H 2 . Fig. 2 Results from the LVG analysis of the CO lines. The curves represent the
Paper: 2017MNRAS.466..286M (fulltext)
is in good agreement with the correlation coefficient measured from the 545 and 857GHz dust maps in the Planck Sky Model (dashed lines). Dust is more than 99percent correlated between these frequencies. In the Planck 2013 analysis of cosmological parameters, the following simple parameteric model for the dust power spectrum was adopted: The parameters , c and are fixed by matching to the mask-differenced dust power spectra. Given the universality of the dust power spectrum, neither the choice of frequency nor mask sizes are critical in fixing these parameters. Using the 857GHz mask 40mask 25 spectrum, we find 0.387, c 162.9 and 0.168, so that asymptotically . This dependence at high multipoles is compatible with previous high-resolution measurements of the dust power spectrum in high Galactic latitude cirrus clouds, C 2.7 0.1 (Miville-Deschnes, Lagache Puget 2002 ; Miville-Deschnes etal. 2010 ), although our mask-differenced results consistently give slopes at the shallower end of this range. For a given mask, the dust power in the spectrum is specified by the parameter . For the values of , c and given above, is the dust power at multipole 10. The solid black lines in Fig. 4 show the model of equation ( 4 ) compared to the mask 40mask 25 and mask 50mask 25 spectra for the 353, 545 and 857GHz spectra. The model provides a reasonably good fit but one can see evidence for an excess at 500. We have therefore fit the dust spectra to a slightly more complicated fitting function: where B , 2 , 2 , c 2 , 2 and are parameters that we fit to the 857857 mask 50mask 25 spectrum. The best-fitting parameters are B 0.74, 2 0.19, 0.58, c 2 543.1, 2 11.6 and 0.07. We normalize the template at 10, so that the dust amplitudes derived using the templates of equations ( 5 ) and ( 4 ) can be compared directly. The best-fitting template of equation( 5 ) is shown by the red lines in Fig. 4 . Equation ( 5 ) provides a better fit to both the mask 40mask 25 and mask 50mask 25 spectra than equation ( 4 ), capturing the small excess at 500. Unless otherwise stated, we use the template shape of equation ( 5 ) in our baseline analysis. In summary, using mask-differenced spectra to isolate the contribution from Galactic dust, we find that the dust power spectrum has a nearly universal shape independent of frequency and sky fraction up to mask 60. For larger sky fractions, we see relatively small departures from universality that depends on the point source mask and are most likely associated with the anisotropy of the point source masks at low Galactic latitudes. The shape of the dust power spectrum is modelled accurately by the fitting function of equation( 5 ), which provides a template shape used in our likelihood analysis. We have verified that the dust power spectra are almost perfectly correlated over the frequency range 353857GHz. We therefore solve for three dust amplitudes, , and in our likelihood analysis assuming that the dust power spectrum is completely
Paper: 2017MNRAS.468.3883P (fulltext)
It can be seen that it agrees well with the other methods tested. The three methods, whilst not in exact concurrence, are in good agreement. Thus, to separate spirals and ellipticals in our sample, we use the parameter, both because it gives the intermediate values for fraction of spirals and ellipticals, and because it is more reliable and less computationally expensive when computed for a very large number of galaxies. We also present numerical results from our tests using g r band luminosity as the divide between spiral and elliptical galaxies, with a cut defined by equation ( A1 ) (Figs A2 and A3 ). Figure A2. Similar to Fig. A1 , but here galaxies are split based on the cutoff in g r band luminosity from equation ( A1 ). Figure A3. Distribution of ETGs in the R plane, as in Fig. 3 , but using g r luminosity, rather than as the cutoff between spirals and ellipticals. We find a smaller number of ellipticals by the same selection criteria, roughly half those in the ATLAS 3D survey, and a similar distribution, with a slightly higher fraction of highly elongated or fast-spinning galaxies compared to that found with a cutoff. We also experimented with making a cutoff between star-forming and quiescent galaxies based on their magnitude in the g and r bands. Galaxies for which g r is large are redder with little star formation and these quiescent galaxies are often ellipticals, whilst smaller g r colours characterize bluer, star-forming galaxies, which in observational surveys correspond well with the population of spirals (Kormendy 2016 ). We define a cut between star-forming galaxies as those below the line and quiescent galaxies as those above, motivated by the distribution of the whole sample of galaxies in Illustris (Vogelsberger etal. 2014 ). The luminosities are found by summing the individual luminosity of all stellar particles in the galaxy. The fraction of quiescent to star-forming galaxies, by mass, is in line with observations (Muzzin etal. 2013 ) over the whole mass range examined (down to haloes with stellar mass of 10 10 M ). Fig. A3 is a comparison with the galaxy population found in the ATLAS 3D survey, as presented in Section 3.1 using a cutoff defined by . We find less galaxies classified as ellipticals in the same volume, roughly half the previous number, with a wider spread in the distribution. There are more, faster spinning galaxies included though still few as rapidly rotating as in the ATLAS 3D survey. The population of high-mass galaxies is essentially unchanged, still with particularly elongated high-mass SRs as mentioned in Section 3.1 . Fig. A2 shows the stellar mass function derived using a colour cut. Whilst the results are more in line with the observed stellar mass function at low masses, we believe that this is due to the dependence of both on galaxy mass, rather than any strong link between luminosity and kinematic behaviour. Bottrell etal. ( 2017 ) performed similar analysis on carefully constructed mock
Paper: 2016ApJ...818...68A (fulltext)
are identical to the selection in Adriani et al. ( 2013b ), and we refer to that paper for more details: 1. Event quality selections: We have selected events that do not produce secondary particles by requiring a single track fitted within the spectrometer fiducial acceptance and a maximum of one paddle hit in the two top scintillators of the ToF system. The analysis procedure was similar to previous work on high-energy proton and helium fluxes (Adriani et al. 2011 ). 2. Galactic particle selection: Galactic events were selected by imposing that the lower edge of the rigidity bin to which the event belongs exceeds the critical rigidity, , defined as 1.3 times the cutoff rigidity computed in the Störmer vertical approximation (Shea et al 1987 ) as , where L is the McIlwain L-shell parameter obtained by using the Resurs-DK1 orbital information and the IGRF magnetic field model (MacMillan Maus 2005 ). 3. Charge selection: The charge identification uses the ionization measurements provided by the silicon sensors of the magnetic spectrometer. Depending on the number of hit sensors, there can be up to 12 dE / dx measurements. The arithmetic mean of those measurements is shown in Figure 2 as a function of the rigidity. The actual selection of Z = 1 or Z = 2 particles is depicted by the solid lines. A similar figure has already been shown in Adriani et al. ( 2013b ) but was kept in this paper to help the reader. Figure 2. Ionization loss ( dE / dx in MIP, energy loss of minimum ionizing particles) in the silicon detectors of the tracking system as a function of reconstructed rigidity. The black lines represent the selection for Z = 1 or Z = 2 nuclei. 3.2. Isotope Separation in the <sans-serif><italic>PAMELA</italic></sans-serif> Instrument In each sample of Z = 1 and Z = 2 particles, an isotopic separation at fixed rigidity is possible since the mass of each particle follows the relationwhere R is the magnetic rigidity, is the particle charge, and γ stands for the Lorentz factor. The particle velocity β can be provided either directly from the timing measurement of the ToF system, or indirectly from the energy loss in the calorimeter, which follows β via the Bethe-Bloch formula: (neglecting logarithmic terms). 3.2.1. Isotope Separation Using ToF versus Rigidity For the ToF analysis, we can use directly the β provided by the timing measurement. In Figure 3 we show β versus the particle rigidity for Z = 1 and Z = 2 data. The black lines in the figure represent the expectations for each isotope. A similar figure has already been shown in Adriani et al. ( 2013b ) but was kept in this paper for an easier comparison with the alternative identification method using the calorimeter. Figure 3. β versus rigidity for Z = 1 (top) and Z = 2 (bottom) particles. The black lines were calculated for each isotope using Equation ( 1 ). Since the particle mass is calculated using Equation ( 1 ), misidentified 4 He wrongly reconstructed as Z = 1 particles could result
Paper: 2019ApJ...876...63L (fulltext)
also been recently reported in HH 212 in the inner 100 au with a detection of CH 3 CHO and deuterated water HDO (Codella et al. 2016 , 2018 ). Now at higher resolution, we find CH 3 CHO to reside in the disk atmosphere. Moreover, we also detect 8 other organic molecules (H 2 CCO, t-HCOOH, CH 3 OH, CH 2 DOH, 13 CH 3 OH, CH 2 DCN, HCOOCH 3 , and CH 3 CH 2 OH) in the disk atmosphere, with an excitation temperature of ∼150 ± 50 K. Adding 3 other organic molecules (D 2 CO, CH 3 SH and NH 2 CHO) from Lee et al. ( 2017c ), we have 12 organic molecules, with 9 of them being COMs, detected in the disk atmosphere. These results indicate that the hot corino in HH 212 is actually a warm atmosphere of the disk. 4.2. Formation of Organic Molecules We can study the formation of the organic molecules in the disk atmosphere by comparing their abundances to those estimated in the hot corinos around other Class 0 low-mass protostars. The abundance of CH 3 CHO, which is commonly observed in hot corinos, is estimated here to be 3.9 × 10 −9 in HH 212, similar to those found in other hot corinos, e.g., 5.8 × 10 −9 in IRAS 16293-2422B (Jørgensen et al. 2016 ), 2.4 × 10 −9 in B335 (Imai et al. 2016 ), and 4.2 × 10 −9 in NGC 1333 IRAS4A2 (López-Sepulcre et al. 2017 ). To facilitate comparison, we calculate and compare the abundances of the organic molecules relative to CH 3 CHO. Figure 4 shows the comparison of the relative abundances of seven organic molecules (excluding deuterated species and 13 C isotopologue) to those in IRAS 16293-2422B (Jørgensen et al. 2018 ), B335 (Imai et al. 2016 ), and NGC 1333 IRAS4A2 (López-Sepulcre et al. 2017 ). In this comparison, CH 3 SH is excluded because of no reliable measurement of this molecule in other hot corinos to compare with. Interestingly, the relative abundances of most molecules here in HH 212 are similar to those in other hot corinos to within a factor of few, suggesting that the formation of these molecules in the disk atmosphere could be similar to that in other hot corinos. Notice that the abundance of CH 3 SH here is also consistent with that predicted in the hot corino, which can be as high as 5 × 10 −8 (Majumdar et al. 2016 ). Moreover, the abundances here are also similar to those seen in the Class I corino SVS13-A (Bianchi et al. 2019 ) and to those seen in Comet Lovejoy, which shows similar abundances to IRAS 16293-2422B (Biver et al. 2015 ). Figure 4. Comparison of the abundances of organic molecules in the HH 212 disk atmosphere with those in the hot corinos around other low-mass protostars. As shown in Table 2 , our measurements have an uncertainty of about 50%. As discussed in Lee et al. ( 2017c ), the high degree of deuteration in methanol (with [CH 2 DOH]/[CH 3 OH] ∼0.12) and the detection of doubly deuterated formaldehyde suggest that the methanol and formaldehyde in the disk atmosphere are originally formed on icy grains and later desorbed (evaporated) into gas phase due to the heating possibly by