adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...859..134L (fulltext)
the evolution after 18.25 days but underpredicts the millimeter-band data and the observations above ≈10 GHz before 8.55 days. Figure 10. Same as Figure 8 , but now including energy injection between 0.15 and 0.26 days (Section 4.3 ), a standard RS contributing to the optical/NIR light curve before 2 × 10 −3 days and to the radio at 1.5 days (Section 5.1 ), and a refreshed RS contributing to the millimeter-band light curve (Section 5.3 ). Figure 11. Same as Figure 9 , but for the model (solid) shown in Figure 10 decomposed into FS (dashed) and RS (dotted) contributions. The two RS components are best distinguished at 1.5 days, where the lower-frequency component arises from an RS in the original shell producing the GRB, while the higher-frequency component arises from the collision of two shells at 0.15 days, which also produces the X-ray rebrightening. The red shaded regions represent the expected variability due to scintillation. To summarize, the continued high flux density of the X-ray detections after ≈1 day suggests that the X-ray excess at ≈0.26 days is not due to late-time central engine activity, but is caused by a rebrightening of the FS radiation. The multiband data are consistent with a wind-like circumburst medium, requiring a single episode of energy injection at ≈0.2 days. The resultant model fits the optical and X-ray evolution and matches the radio SED after 18 days. However, this model underpredicts the CARMA light curve, as well as the 5–83 GHz SED at 1.5 and 8.55 days. To account for these deficits, we next investigate the effect of including emission from additional components. We delineate the evidence for their presence and consider their possible physical origins. 5. Multicomponent Models 5.1. Reverse Shock In Section 3.2 , we discussed the apparent multicomponent structure of the radio SEDs between 1.5 and 18.2 days. In the previous section, we have shown that while a single FS model can reproduce the gross features of the radio light curves, such a model cannot explain the multiple peaks in the SED at 1.5 days, the ≈14 GHz peak at 4.5 days, and the SED at 8.55 days. Of these, the greatest discrepancy between data and model arises in the radio SEDs at 1.5 and 4.55 days. We now consider whether each spectral peak at 1.5 days can in turn be ascribed to radiation from an RS. An RS propagating into GRB ejecta is expected to produce synchrotron radiation with its own set of characteristic frequencies, ν a,r , , and ν c,r , and peak flux . These quantities are related to those of the FS at the deceleration time, ec , when the RS just crosses the ejecta, and the relation between the two sets of break frequencies and fluxes allows for a determination of the ejecta Lorentz factor and magnetization. After the RS crosses the ejecta, the flux above ν c,r declines rapidly, 16 16 The angular time delay effect prevents abrupt disappearance of flux above ν c,r ; instead, we expect (Kobayashi Zhang 2003 ). as no electron is
Paper: 2018MNRAS.478.3335Q (fulltext)
metallicity than QGs-A. The median stacked spectra, corrected for dust extinction, of QGs-A and QGs-B have a blue stellar continuum, suggesting a young mean stellar population. The analysis confirms a weak emission and that also other high ionization emission lines (such as []) are weak in both QGs stacked spectra, and the /H α ratios are consistent with a low ionization level. On the contrary, [], H α and H β (i.e. low ionization lines) are still strong and with an Hα flux slightly weaker () than the one of SF galaxies. We find that QGs have D n 4000 and EW(H α) values intermediate between SF and already quenched galaxies, confirming that they have just stopped their star formation and have a young/intermediate mean stellar population. In the colour-mass diagram, the bulk of the QGs-A and QGs-B resides in the blue cloud region, and only few of them (∼3 per cent) lie in the green valley region. The QGs have masses log( M /M ⊙ ) > 9.5, comparable with those of the SF population, being the QGs-B, on average, more massive. This suggests that, as expected in a downsizing scenario, star formation quenching has not started yet for low-mass galaxies, consistently with a lack of low-mass red galaxies. Their H α emission is compatible with a just quenched SFR of the order of 0.6–10 M ⊙ yr −1 , similar to that of the SF main sequence population, suggesting the presence of residual ionized gas in our QGs. The emission from the median stacked spectra is, however, weaker than that in the SF population, suggesting that the depletion of the gas has started. The morphologyand concentration index (C=R90/R50) of QGs are similar to those of the SF population, suggesting that no morphological transformation has occurred yet, in the early phase after the quenching of the star formation. However, some of them have a concentration index higher than the threshold that divides the early-type from the other galaxy types (i.e. C > 2.6) and they could have experienced morphological transformation during the interruption of the star formation. Compared to the parent SF population, we find an excess of QGs in high-density environments (∼42 per cent), in particular for QGs-B. QGs in high-density environments are preferentially satellites (from ∼60 to 80 per cent). Approximatively 5 per cent of QGs are in groups/clusters which have more than 30 members, but no one of them is the brightest galaxy in its environments. From the fraction of QG candidates ( of the SF population) we estimate the quenching time-scales for these populations to be between 10 and 18 Myr. These values are compatible with the sharp quenching models by C17 . If we assume that at most 50 per cent the entire SF-[]undet population (∼7.5 per cent) is in a low ionization state, as witnessed by the low but not extreme /H α ratio in their stacked spectrum, we obtain an upper limit to the quenching time-scale of ≃ 0.76 Gyr. In this case, the quenching time-scale is compatible with galaxies which are
Paper: 2018MNRAS.473..271V (fulltext)
et al. 2017 ; Vallini et al. 2017 ), which may affect the density field of GMCs, and ultimately increase the conversion factor. Finally, we compare our inferred conversion factor with the one resulting from the best-fitting formula, X CO /( cm 2 K km s − 1 ) = 6.3 × 10 19 α CO /(M ⊙ K km s − 1 pc 2 ), obtained by Narayanan et al. ( 2012 ). They simulate the hydrodynamic evolution of both isolated and merging disc galaxies finding that . If we substitute and Z = 0.5 Z ⊙ we obtain α CO = 2.9 M ⊙ /(K km s − 1 pc 2 ) in agreement within a factor ≈2 with our results. 4.5 ALMA observability In Section 4.3 we have shown that the peak of the Althæa CO SLED coincides with the CO(7–6) line. As CO transitions with J up ≥ 6 fall in the ALMA bands from z > 6, in what follows, we use the ALMA Sensitivity Calculator to compute the observing time required to detect the CO(7–6) line from (a galaxy similar to) Althæa. The total CO(7–6) luminosity of Althæa is L CO(7-6) = 10 7.1 L ⊙ , i.e. ≈1/16 of its [C] luminosity. If we assume the line width to be equal to that of [C] observations in LBGs at z ≈ 6 − 7 ( Maiolino et al. 2015 ; Knudsen et al. 2016 ; Pentericci et al. 2016 ; Bradač et al. 2017 ), this yields a peak flux density . 8 The ALMA full-array observing time required to detect (resolve over 1/3 × FWHM) the CO(7–6) line with a signal-to-noise ratio S/N = 5 is ≈13 h (≈38 h), i.e. it would be challenging but doable within the maximum observing time ( ≤ 50 h) allowed for ALMA regular programs. This is in line with what was found by Muñoz Furlanetto ( 2013 ), which pointed out that ALMA observations of high- J CO can be performed with reasonable integration times only in those z ≈ 6 galaxies which, like Althæa, are chemically evolved ( Z Althaea > 0.1 Z ⊙ ) and UV-bright ( M UV, Althaea ≈ −22). 5 CONCLUSIONS In this paper we have studied the CO emission properties of galaxies at the end of the Reionization Epoch. First, we have developed a semi-analytical model that, given the internal density field of a GMC and the impinging FUV flux ( G 0 ) at the cloud surface, computes the CO line emission. The density PDF of the GMC is set both by turbulence (parametrized by the Mach number ) and self-gravity (parametrized by t / t ff ). The radiative transfer is performed with cloudy and includes z = 6 CMB radiation. The model takes the mean gas density n 0 , , t / t ff , G 0 as inputs, and it returns the luminosity of the first nine CO rotational transitions. We validated the model with local observations, demonstrating its capability to reproduce: the M vir - relation; the observed CO excitation in local SB galaxies; and the CO SLED of the low-metallicity ( Z ≈ 0.5 Z ⊙ ) molecular cloud N159W in the LMC. We used our validated CO emission model to post-process a cosmological zoom-in simulation of a prototypical z ≈ 6 galaxy ( M ⋆ ∼ 10 10 M ⊙ , SFR ∼ 100 M ⊙ yr − 1 ), Althæa. We have made the following additional assumptions: (i) we adopt a constant CRIR (cfr.
Paper: 2017MNRAS.468..981M (fulltext)
Comptonization; case 2: partially absorbed pure reflection spectrum; case 3: partially absorbed Comptonization spectrum with reflection; case 4: partially absorbed Comptonization with reflection and variable local absorber. Parameters are: intrinsic column density associated with the direct (Compton) spectrum and relative covering fraction (pcf and pcf), intrinsic column density associated with the reflection spectrum and relative covering fraction (pcf and pcf), electron temperature, Compton parameter (see text), relative reflection factor, ionization parameter (see text), comp PS normalization, Gaussian line energy, Gaussian line equivalent width, power-law photon index (for the dust scattering halo), power-law normalization (for the dust scattering halo), JEM-X calibration constant and ISGRI calibration constant. All the quoted errors are 1 level ones. Notice that all models include an additional absorber to account for the interstellar absorption ( N H 8.310 21 cm 2 ). The FeK line flux has been expressed in terms of ergcm 2 s 1 instead of equivalent width in order to allow direct comparison with the lines flux from Model 2 (see Section 5 , for which measuring the equivalent width is problematic). 4 SPECTRAL MODELLING MODEL 1: PARTIALLY COVERED REFLECTED COMPTON SPECTRUM 4.1 Case 1: partially absorbed Comptonization We initially fitted our data using the Comptonization model comp PS in xspec (see Poutanen Svensson 1996 ) modified by the interstellar absorption ( tbnew in xspec ; Wilms, Allen McCray 2000 ) with a fixed column density of N H 8.310 21 cm 2 . We left the Thomson optical depth electron temperature kT e and the normalization in comp PS free to vary, while we fixed the seed photon temperature to kT bb 0.1keV (i.e. the seed photons are produced by a multicolour disc blackbody with an inner disc temperature of 0.1keV) and the inclination to 67 (see Khargharia etal. 2010 ). We also fixed to zero the ionization parameter since it only affects the reflection component, which is switched off in this case. We left all the remaining comp PS parameters fixed to their default values. Large residuals indicated that a more complex spectral model was required. Therefore, we added a neutral absorber partially covering the source ( tbnew_pcf ). The addition of a narrow Gaussian line to describe the ironK line (see King etal. 2015 ) was required to account for some residuals around 6.4keV. The line is unresolved in both the Swift /XRT and INTEGRAL /JEM-X spectra; therefore we fixed its width to 0.2keV. This model did not provide satisfactory fits, having 2 = 620.12 with 461d.o.f. and null hypothesis probability = 9.8210 8 . Large residuals especially above 10keV indicate that the spectrum likely shows a significant reflection component. 4.2 Case 2: partially absorbed pure reflection Since the emission from the dust scattering halo was at times significant in the sky region around the source, sometimes outshining the source itself (see
Paper: 2019ApJ...871...71S (fulltext)
K-S tests with respect to the radio properties, and found that the radio sizes of the absorbed and unabsorbed CSO samples could form two populations. In addition, the results of a two-dimensional two-sample K-S test confirmed the possible existence of such two populations. However, we caution that the two-dimensional two-sample K-S test requires a larger number of the absorbed sources (at least twice as many as in the current sample) to be valid. Our results could be interpreted in two ways. It could be that the CSOs with a high intrinsic X-ray absorbing column, N H > 10 23 cm −2 ; 0048+3157, OQ+208, 2021+614, 1511+518, 0108+388) have less evolved radio structures (smaller linear radio size) than X-ray unabsorbed CSOs with the same 5 GHz radio power. Thus, the environment in which a radio source expands might be able to confine the source growth already at the earliest stages of its evolution. This would provide the first observational evidence in the X-ray band that some compact jets may be frustrated by a dense environment and their kinematic ages may be underestimated. An alternative way of explaining the separation visible in Figure 4 is that the X-ray obscured sources have higher radio luminosity than the X-ray unobscured sources for a given radio size. The two factors that drive radio luminosity for a given size of a radio source are environmental density and jet power. Thus, one possible explanation is that the X-ray obscured sources are brighter for a given size because they were born in a denser environment. Another option is that they are brighter than the unobscured CSOs with the same size because they have higher jet power, which is related to central density, through e.g., the accretion rate (e.g., Allen et al. 2006 ; Tchekhovskoy et al. 2011 ; Ghisellini et al. 2014 ), or directly as in the model of Stawarz et al. ( 2008 ). Even though with the current data it is not possible to distinguish between these two explanations, we stress that in both cases the density of the CSO environment plays a key role. Moreover, our results raise a question about the origins of the X-ray emission and the location of the X-ray obscurer. If the CSO X-ray emission originates primarily from an active nucleus that becomes attenuated by obscuring material in the vicinity of the nucleus, then we should also see heavily obscured CSOs with larger radio structures. In contrast, the CSOs in our sample show low intrinsic X-ray absorption for the radio size exceeding a few tens of parsecs. If the CSO X-ray emission is dominated by inverse-Compton emission of the radio lobes (e.g., Stawarz et al. 2008 ; Ostorero et al. 2010 ), then the CSOs would become unobscured once their radio lobes expand beyond the region occupied by the dense material. Thus, it could be that the obscurer is located on scales smaller than a few tens of parsecs. Alternatively, the obscuring material could be destroyed or removed by the expanding radio source. Recently, Ostorero et al.
Paper: 2018A&A...616A.163V (fulltext)
the marked outliers are not reliable enough for searching for flare candidates alone (see Fig. 1 ): it sometimes also marks the beginning of the light-curve windows. Thus we only used the RANSAC estimate of the inlier points for statistics and calculated the standard deviation of the light curve (with the rotational modulation and most of the flare points removed). We considered those points as first-order flare candidates that were above a given detection level (above 3 σ by default). To achieve more robust results, we shifted the search window through the light curve, by default in steps of one-fourth of the light-curve window (see Fig. 2 , e.g.). In this way, every light-curve point was analyzed multiple times (with the exception of those that are at the beginning or at the end of the data set or large observational gaps), and if a light-curve point is considered as an outlier, that is, a flare point candidate, it received a “vote”. A light-curve section was considered a flare when it received enough votes (≥3, by default) in overlapping search windows and had at least a given number (2, by default) of consecutive points. We note that this last step was performed only after evaluating each light-curve segment. These criteria are basically the same as those defined by Eqs. (3a)– (3d) of Chang et al. (2015) and are consistent with adaptations made by Davenport (2016) . When running FLATW’RM from command line, the user can change the number of flare points needed for a flare, the detection level of the flares, and the full with at half maximum (FWHM) that is used for the analytic fit of the events. Optionally, the rotation period can be given as an input to run the code faster, or to fix it to a chosen value if the rotational modulation is too weak, and the polynomial degree can also be fixed. The number of votes needed for a flare candidate to be kept, the window size (compared to the rotation period), and the step length with which the search window is shifted cannot be changed from command line, but can be easily modified if the flare-finding function is imported to another code. Fig. 2. Demonstration of the voting algorithm on a light-curve section of KIC 1722506. The top plot shows the original light curve (in blue, near a normalized flux of 1), and each light-curve segment in individual windows tested by FLATW’RM (each a different color), and the flare candidates are marked for the given run with circles. In the bottom plot , the candidates with one (light gray), two (medium gray), and at least three (black) votes are plotted. In this setup, the flares plotted in black are kept as final flare candidates. 2.4. Summary of the selection process For clarity, we explicitly state the procedure that FLATW’RM steps through in locating the flares in the light curve. A period search is first performed on the input light curve. The input light curve is divided into windows of 1.5 × P that can be effectively fit with polynomials. Each light-curve
Paper: 2016MNRAS.460.2593W (fulltext)
they are not noticeably affected by the motions of individual stars within binary systems (because PMs provide a measure of the motions of stars integrated over a baseline and not an instantaneous measure of the velocity as RVs do). In the text that follows, we denote the velocity dispersions in RA and Dec. as σ α and σ δ , though technically the velocity dispersions of the PMs in RA and Dec. should be written as and . Fig. 5 shows the vector point diagram of PMs for the majority of Cyg OB2 members. The PMs of non-members of Cyg OB2 are not shown, but these have a similar distribution, implying that Cyg OB2 members cannot be kinematically separated from the field population. A number of outliers with very large relative PMs are evident in this diagram and are discussed in more detail in Section 3.5 . These outliers cause the velocity dispersion (Section 3.1 ) to come out very differently for a Gaussian dispersion compared to that calculated from an outlier-resistant method such as the interquartile range (IQR), and the use of a forward modelling approach (Section 3.2 ) reinforces this. Figure 5. Vector point diagram for members of Cyg OB2 with PMs (note that PMs are not placed on an absolute reference system but are only calculated relative to each other) with the spectroscopically known OB stars shown as red dots. The left-hand panel shows 869 of the 873 stars with PMs (four stars with very high PMs are not shown), highlighting the presence of a number of runaways or possible contaminants with high PMs. The right-hand panel (inset shown in the left-hand panel with a blue box) shows a close-up of the centre of the velocity space, with 822 of the 873 stars shown. 3.1 Calculating the velocity dispersions Fig. 6 shows the binned velocity distribution (over the range covered by the majority of stars), which has a broadly Gaussian distribution with low-amplitude, broad wings. The simplest estimate of the velocity dispersion is the standard deviation, which for our sample of 873 stars gives mas yr −1 and mas yr −1 (uncertainties calculated from a Monte Carlo simulation in which the individual PMs are varied by their estimated uncertainties). These velocity dispersions are considerably larger than the observed distribution because they are being enlarged by a number of outliers with large velocity offsets. There are 23 (17) stars with |μ| > 10 mas yr −1 in the RA (Dec.) dimension, and 1 (2) of these have |μ| > 100 mas yr −1 . These outliers could be removed by a process of ‘sigma clipping’, but this is a procedure fraught with difficulties of subjectivity and irreproducibility. Figure 6. PM velocity distributions along both axes (black histogram) showing the interquartile range velocity dispersions (green dashed lines) and the two-component Gaussian mixture model for the velocity dispersion (red dashed lines). The best-fitting velocity dispersions are listed in each case, which for the Gaussian mixture model is the dominant Cyg OB2 component.
Paper: 2018MNRAS.480..965K (fulltext)
optical copper lines are free of non-local thermodynamic equilibrium (NLTE) effects (e.g. Mishenina et al. 2002 ). The first indication that something could be wrong with this statement came from Shi et al. ( 2014 ), who noted that the important NLTE mechanism affecting the Cu i spectrum is the ultraviolet (UV) overionization. Later, Yan, Shi Zhao ( 2015 ) investigated copper abundance in 64 late-type intermediate metal-poor stars from Galactic disc and halo. They succeeded in showing that NLTE effects are important for this atom (the correction is about 0.17 dex for metallicity −1.5). The NLTE calculations enhanced the copper abundance obtained in the range of metal-poor stars, giving a flatter distribution of [Cu/H] versus [Fe/H] than was declared in previous works based on the local thermodynamic equilibrium (LTE) assumption. This conclusion was confirmed by Yan et al. ( 2016 ) who stated that NLTE effects are strong for copper in metal-poor stars. Until now, the most comprehensive study of the Galactic evolution of copper in the light of NLTE computation was performed by Andrievsky et al. ( 2018 ). They showed that for a sample of intermediate and extreme metal-poor stars ([Fe/H] from −4.2 to −1.4) the mean [Cu/Fe] value is about −0.22 dex. This is very different from the results of LTE analyses, which show significant underabundance at the early stages of Galactic evolution. Very recently, Roederer Barklem ( 2018 ) performed new work on copper abundance in late-type stars using UV Cu ii lines, which are supposedly free from the influence of the NLTE effect. The Hubble Space Telescope ( HST ) and ground-based spectra were used for this aim. They showed that the mean copper abundance in six warm metal-poor ([Fe/H] from −2.50 to −0.95) dwarfs derived from Cu i and Cu ii lines differs by 0.36 dex (Cu ii lines give higher abundance compared with Cu i lines). Generally, this difference agrees with derived NLTE corrections in metal-poor stars reported by Yan et al. ( 2015 ) and Andrievsky et al. ( 2018 ). In this paper, we examine NLTE copper abundance in a sample of the same six metal-poor stars derived from different Cu i lines (including the far-UV region) and Cu ii lines. We aim to obtain an independent estimate of the reliability of our NLTE corrections, as reported in Andrievsky et al. ( 2018 ). 2 SPECTROSCOPIC MATERIAL We employed UV stellar spectra that were secured with the help of the Space Telescope Imaging Spectrograph (STIS; Kimble et al. 1998 ; Woodgate et al. 1998 ) on board the HST . Observations were made using the E230H echelle grating, the 0.09 x 0.2 arcsec 2 slit and the NUV Multianode Microchannel Array detector.The resolved power was R = 114 000. The investigated Cu i and Cu ii lines are situated in the range 2024−2248 Å. We retrieved archive spectra from the Mikulski Archive for Space Telescopes (MAST). All spectra are centred at 2013 Å. The following lines fall in the spectral range: Cu i 2024 Å and Cu ii 2037, 2055,