adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...594A..67O (fulltext)
al. (2011) . The percentages in Fig. 8 are therefore a minimum estimate only. Despite this, we can see in cases (ii) and (iii), a higher percentage of the flux remains at its original connection for the duration of the simulations. The change in connectivity of the field lines results in strong outflow velocities from the end of the current layers. White arrows showing the projected horizontal velocity components in the central region () in the mid-plane are displayed in Fig. 9 for case (i) at . These show large velocities from the narrow current layer along , where the maximum velocity is , where is the maximum Alfvén velocity in the mid-plane (at ), an indication of reconnection occurring. We note, that the magnetic field is 3D and reconnection is not confined to the mid-plane, but use the current contours and velocities in the mid-plane to illustrate the differences between the cases. Fig. 9 Contour of the magnitude of the current density in the mid-plane between at for case (i) with 2 sources. The arrows show the projected velocity in the plane. Fig. 10 Contours of the magnitude of the current density in the mid-plane between at a) and b) for case (ii) with 4 weak sources. The arrows show the projected velocity in the plane. Similarly, in case (ii), strong outflow velocities are seen from the current “wings” shown in Fig. 10 a at and very similar behaviour is observed for case (iii) (not shown). Later in cases (ii) and (iii), the current contours in Fig. 10 b show the “X” current formation to fragment and then (in addition to the outwards velocity towards the edge of the domain) there are inwards velocity outflows of reconnected field lines towards the centre of the mid-plane. This leads to further current being built up in the centre of the domain. The maximum velocity (in the mid-plane) for cases (ii) and (iii) are and , respectively, and occurs at , after this fragmentation of the “X” current formation. This is much smaller than in case (i) and suggests that the reconnection is weaker. The location of the sources in the case with two flux tubes appears highly idealised to produce the concentrated single current layer in the centre of the domain. In comparison, in the four-source case, as they are evenly distributed around the centre of rotation, a shear is first created between the neighbouring flux tubes, which due to the relative angles of the magnetic field lines will always be less than that induced in the two-source case and produces reduced current values and results in a smaller percentage of flux changing connectivity. 3.1.2. Energetics The energy evolution is shown by the volume integrated energy densities in Fig. 11 for the cases with two, four weak and four compact sources, displayed in pink, blue and black, respectively and can be described as follows: when the driving velocity acts on the field, the field lines are advected and the magnetic field becomes twisted, gradually increasing the magnetic energy in the domain (see
Paper: 2021ApJ...908..171N (fulltext)
which was managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The fits had an average rms residual of 0.15 km s −1 and an average maximum residual of 0.3 km s −1 . Two observations have larger uncertainties. The signal-to-noise ratio (S/N) for the 814 cm −1 setting from 2007 was too low to use telluric lines in the GV Tau N spectrum. Instead, the dispersion function was fit to lines in the telluric standard (rms 0.06 km s −1 ), and this solution was applied to GV Tau N. Three measurable telluric lines in the GV Tau N spectrum were used to assign a zero-point (wavelength shift) uncertainty of 0.5 km s −1 . The 932 cm −1 setting from 2007 only contains three telluric lines; hence, an independent dispersion solution was not possible. In this case, the pipeline wavelength scale was retained, with an assumed uncertainty of 1 km s −1 . Model telluric transmission spectra were utilized for a few of the settings. Because the S/N for the 780 cm −1 setting was degraded as a result of insufficient S/N in the telluric calibrator, corrections for atmospheric lines were made using the software package TERRASPEC (Bender 2010 ). TERRASPEC was also used to improve the correction for telluric water lines in the 767 and 807 cm −1 settings. For all of the observations, additional continuum normalization was carried out for each order of interest by fitting a low-order polynomial to the continuum outside the intervals of absorption from known molecular transitions in GV Tau N. 3. Analysis In the TEXES spectra we detect numerous absorption lines of NH 3 , HCN, and C 2 H 2 ; a few lines of H 2 O in absorption; and H 2 in emission (Table 2 ). The H 2 emission from GV Tau was previously reported in Bitner et al. ( 2008 ) and is not discussed further here. Most of the HCN and C 2 H 2 lines were measured in 2006, with additional lines measured in 2007. The unexpected detection in 2006 of numerous NH 3 absorption lines was followed up in 2007 with grating settings that probed lower-energy NH 3 transitions. Weak absorption lines of water were also detected in the 2007 spectra. The entire set of pipeline-reduced spectra for GV Tau N is shown in Figure A1 . The positions and identifications of the detected lines are indicated. Table 2 All Lines Notes. a *: detection; x: nondetection; ...: not covered; t: telluric line; e: order edge/roll-off; u: high uncertainty; d: blended with other line. b ν 4 + ν 5 – ν 4 band, i.e., absorption out of the ν 4 level. c The 813.85 cm −1 ortho- and 813.83 cm −1 para-H 2 O lines were treated as a single line with combined g l . While the H 2 O lines are pure rotational transitions within the ground vibrational state, the NH 3 , HCN, and C 2 H 2 absorption lines are predominantly rovibrational transitions out of the ground vibrational state to a higher vibrational state; the C 2 H 2 ν 4 + ν 5 – ν 4 line is an exception (see Section 3.1 ). The HCN ν 2
Paper: 2019A&A...625A..61M (fulltext)
of spectra for 10 K, 50 K, and 300 K, while series F[001]a lacks a 10 K spectrum. Figure 1 shows the measured transmission spectra ( T meas , solid and dashed lines) plotted over the wavenumber ( in cm −1 ); the corresponding wavelengths (30 μ m–500 μ m) can be inferred from the upper axes. Spectra for a-polarization are given in panel (a), b-polarization in (b), c-polarization in (c). The two incomplete series F[001]a and F[010]c are plotted as dashed lines. The former has been scaled by a factor of 1.8 in order to demonstrate the good agreement to series F[010]a after multiplicative scaling (see Sect. 3.2 ). The strong temperature dependence of the transmission of the forsterite crystal is easily recognizable in all three polarization directions. While measurable transmission is detected only below 50 cm −1 at room temperature, the crystal becomes transparent already below 250 cm −1 at 10 K. For each temperature, the transmission increases towards smaller wavenumbers, apart from absorption bands positioned at 202 cm −1 (particularly strong in a-polarization, but weaker also in c-polarization) and 145 cm −1 (in b-polarization, very weak in a- and c-polarization). These bands are the already mentioned 49 μ m and 69 μ m single-phonon bands of forsterite, respectively. They are excited exclusively by a- and b-polarized radiation, respectively, the weaker appearances in other polarization directions are due to the small error of the polarizer alignment and to the beam divergence of the FTIR spectrometer (±7.5 deg). Apart from these bands, a relatively weak minimum at ∼90 cm −1 also appears in several of the spectra for a-polarization. As there is no such long-wavelength infrared band predicted by theory ( Iishi 1978 ) and the band is not present at 10 K, we think that it is an artifact of the measurement setup. We note that the position of the band is one of the intensity minima created by multiple reflection in the inner cryostat windows. These minima occurring with a period of 18 cm −1 are sometimes not well compensated by the reference measurements and lead either to periodic structures in the spectrum (e.g., in the 10 K and 50 K spectra in Fig. 1 b), or coincide for unknown reasons with erratic peaks (mainly maxima) in the enstatite spectra (see Sect. 3.3 ). Fig. 1. Temperature-dependent transmission spectra of the forsterite crystal for orientation of the electric field parallel to the a-axis ( panel a ), b-axis ( panel b ), and c-axis ( panel c ). Complete temperature series (F[010]a, F[001]b, F[100]c) are plotted as solid lines, the two incomplete series as dashed lines. The F[001]a curves have been scaled by a factor of 1.8; they approximately match the F[010]a curves for the same temperatures. Dotted lines: calculated maximum transmission curves taking into account the intensity reflected by the crystal surfaces. 3.2. Maximum transmission curves and measurement errors The dotted lines in Fig. 1 indicate the theoretical maximum
Paper: 2019MNRAS.489L..63V (fulltext)
limits were calculated from the misfit function when all solutions with a fit at least of 99percent of the best fit were accepted. Figure 2. Inversion for optimum cosmological parameters of (a) the CDM model and (b) the opaque universe model. The colour shows the mean of the absolute distance modulus residua between the predicted model and the SN Ia data as a function of (a) the Hubble constant H 0 and the dark energy , and (b) the Hubble constant H 0 and the B -band intergalactic opacity B . Only the most accurate 552 SNe Ia with z 1.4 and an error less than 0.50 mag are used. The optimum solutions marked by the white plus signs are defined by (a) and ( 0.122 and 2.466) and (b) and ( 0.088 and 1.920). The residual Hubble plots are shown in Fig. 3 . The optimum solutions have roughly comparable misfits for both models: 0.16 for the CDM model (Fig. 3a ) and 0.17 for the model of the opaque universe (Fig. 3b ). However, as illustrated in Figs 3(c) and (d), the CDM model performs slightly better than the opaque universe model, because of a better fit of the binned residua for redshifts less than 0.6. Nevertheless, the model of the opaque universe also accounts for the observed dimming of the SNe Ia within the confidence level of 95percent (Fig. 3d ). Figure 3. Residual Hubble plots for (a, b) the individual SN Ia data and (c, d) the binned SN Ia data. (a, c) The flat CDM model with and . (b, d) The opaque universe model with and . The error bars in (c, d) show the 95percent confidence intervals. Data are taken from Suzuki etal. ( 2012 ). Importantly, the optimum solutions are sensitive to the redshift interval of the SNe Ia used in the inversion. If the optimum model of the opaque universe is searched using the SNe Ia with redshifts z 0.6 only, and the SNe Ia are grouped in the following redshift bins: z [0, 0.025, 0.05, 0.1, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.5, 0.6], we get and (Fig. 4 ). We see in Fig. 4(b) that the model fits the SN Ia measurements at z 0.6 much better than in Fig. 3(d) , but a significant discrepancy appears at higher redshifts. A similar result was obtained by Goobar etal. ( 2002 ) and Riess etal. ( 2004 ), who concluded that the discrepancy at high redshifts excludes the SN Ia dimming to be produced by intergalactic dust. Figure 4. Residual Hubble plots for (a) the individual and (b) binned SN Ia data for the opaque universe model with and . The vertical dashed line in (b) denotes the upper redshift limit of the SN Ia data considered in the inversion. The error bars in (b) show the 95percent confidence intervals. Data are taken from Suzuki etal. ( 2012 ). Note that the best estimate of H 0 for the CDM model obtained by Riess etal. ( 2016 ) using the SN Ia data is . The precision of the distance scale was further improved by a factor of 2.5 by Riess etal. ( 2018 ). This value is within the uncertainty of the estimate of H 0 for the CDM model obtained in this paper and shown in Fig. 2(a) . The accuracy of the
Paper: 2019MNRAS.487.1689P (fulltext)
larger intensity variations in the range and a patchy distribution peaked at the location of recent star formation events. The patchy ionization structure yields an escape fraction of , a value consistent with observations of lower redshift galaxies with similar brightness (e.g. Bouwens et al. 2016 ; Grazian et al. 2017 ), and averaged values of simulated galaxies with similar mass (Xu et al. 2016 ). We note, however, that large fluctuations are expected between different evolutionary stages (Trebitsch et al. 2017 ). The [C ii ] line luminosity of Freesia is . The emission extends on a few kpc scales, with peaks around Freesia-A, Freesia-B, and dense non-star-forming clumps found near the galaxy. The emission mostly comes from gas with and illuminated by an ISRF intensity . Instead, [O iii ] emission is concentrated around Freesia-A, the star-bursting component of the system, and its emission is dominated by gas with a wider range of properties, i.e. and U ≃ (6 ± 2) × 10 −3 . At the Freesia-A location, the oxygen line is very bright, . However, the smaller extent of the [O iii ] emitting region implies a lower galaxy-integrated emission, i.e. . With respect to [C ii ], the [O iii ] show both a spatial offset () and a spectral shift of (), reminiscent of similar evidence found in some systems at high z (Carniani et al. 2017 ). Freesia lies below the local [C ii ]–SFR relation (De Looze et al. 2014 , within ≃ 2σ), as it is in a starburst phase; that is, it sits above the KS relation (Schmidt 1959 ; Kennicutt 1998 , within ≃ 2σ). Spatial analysis reveals that patches of the galaxy that are above the resolved local – De Looze et al. ( 2014 ) relation are located below KS, and vice versa. In particular, due to the recent starburst, the dense centre () of Freesia lies above the KS () relation. At such high star formation surface densities the increases with less rapidly than expected from the local relation De Looze et al. ( 2014 ), which however only covers the low end of the range. Thus, the observed [C ii ]–SFR deficit can be primarily ascribed to negative stellar feedback during starburst phases, disrupting MCs around star formation sites. This interpretation was originally proposed in Vallini et al. ( 2015 ); our results fully endorse it. Metallicity effects have a weaker impact on the origin of [C ii ]–SFR deviations. Gas with extremely low ( Z ≲ 10 −2 Z ⊙ ) metallicities fails below both the local – and the KS relations, because of lack of C + ions and inefficient H 2 formation, respectively. However, gas with Z ≲ 10 −2 Z ⊙ is scarce in Freesia and its combined contribution is sub-dominant when galaxy integrated relations are considered. This situation is common to most of the galaxies in our sample. A metallicity effect might be relevant for galaxies that cannot retain their metal production (Xu et al. 2016 ; Jeon Bromm 2019 ), which are expected to be smaller ( M ⋆ ≲ 5 × 10 6 M ⊙ ), and with a lower star formation (), for which however
Paper: 2021MNRAS.502.3582Y (fulltext)
bias models that distinguish between centrals and satellites in upcoming work. 2.5 Incompleteness factor <italic>f</italic><sub>ic</sub> The final parameter in our extended HOD model is the incompleteness factor (e.g. Leauthaud et al. 2016 ; Rodríguez-Torres et al. 2016 ; Guo, Yang Lu 2018 ). The inclusion of incompleteness is partially motivated by detection of incompleteness in the BOSS CMASS and LOWZ galaxy samples compared with theoretical stellar mass functions, but it is also needed to marginalize over uncertainties in the galaxy number densities since we do not model its full redshift dependence. We define f ic as a modification to in equation ( 1 ): where 0 f ic ≤ 1. Our implementation simply uniformly downsamples the mock galaxies by f ic to produce the desired galaxy number density. To summarize, our extended HOD model contains the five baseline parameters: M cut , M 1 , σ, α, and κ; six extended parameters: s, s p , s v , α c , A , and A e ; and one incompleteness factor f ic , for a total of 12 parameters. 3 DATA AND SIMULATIONS 3.1 BOSS CMASS galaxy sample The Baryon Oscillation Spectroscopic Survey (BOSS; Bolton et al. 2012 ; Dawson et al. 2013 ) is part of the SDSS-III programme (Eisenstein et al. 2011 ). BOSS Data Release 12 (DR12) provides redshifts for 1.5 million galaxies in an effective area of 9329 deg 2 divided into two samples: LOWZ and CMASS. The LOWZ galaxies are selected to be the brightest and reddest of the low-redshift galaxy population at z 0.4, whereas the CMASS sample is designed to isolate galaxies of approximately constant mass at higher redshift ( z > 0.4), most of them being also luminous red galaxies (LRGs; Reid et al. 2016 ; Rodríguez-Torres et al. 2016 ). The survey footprint is divided into chunks that are covered in overlapping plates of radius . Each plate can house up to 1000 fibres, but due to the finite size of the fibre housing, no two fibres can be placed closer than 62 arcsec, referred to as the fibre collision scale (Guo, Zehavi Zheng 2012 ). For this paper, we limit our measurements to the galaxy sample in the redshift range 0.46 z 0.6 in DR12. We choose this moderate redshift range for completeness and to minimize the systematics due to redshift evolution. Applying this redshift range to both the North and South Galactic Caps gives a total of approximately 600 000 galaxies in our sample. We showcase the number density variation over redshift in Fig. 1 . The average galaxy number density is given by n data = (3.01 ± 0.03) × 10 −4 h 3 Mpc −3 . Figure 1. The CMASS DR12 galaxy comoving number density distribution across our redshift range. The red dashed line corresponds to the North Galactic Cap, whereas the blue dotted line corresponds to the South Galactic Cap. The green solid line shows the combined number density. The two vertical lines mark z = 0.46 and z = 0.6, respectively. Fig. 2 shows the redshift-space 2PCF of the same BOSS sample and its corresponding correlation matrix,
Paper: 2018A&A...616A..55P (fulltext)
since 2003. WIRCam is mounted at the prime focus of the 3.6 m CFH telescope. It is equipped with an image stabilization unit, which consists of a tip-tilt glass plate in front of the camera activated by the signal read-out from small 14 × 14 pixel regions centred on bright stars ( Puget et al. 2004 ). The camera cryostat includes the eight-lens field corrector and an entrance window. The detector focal plane consists of a mosaic of four 2 × 2 k HAWAII2-RG detectors separated by 7 mm. The sampling on sky is 0.′′3 per 18 μm pixel, providing a total field of view of 20′ × 20′ with a 2′ wide central cross between the four detectors. The transmission curve of the Y -band filter used in this work is shown on Fig. 1 , together with the MegaCam filter transmission curves. Fig. 1 Instrument and telescope total efficiency (optics and detector) in the MegaCam u *, g ’, r ’, i ’, z ’ filters and WIRCam Y -band. The survey observations consisted of four dithered 75 s Y -band images of the same field. They were split into two visits, with dithering within a visit and between visits of 2′′. The two visits were separated by at least 20 days in order to discard slow moving objects and strongly variable objects. The time difference between visits of the same field varied strongly between the four CFHTLS fields. For W1 and W4, the observations were performed very rapidly at the beginning of the survey, while for W2 and W3 (winter and spring fields), they suffered from poorer weather conditions and were executed over much longer periods of time. The interval between visits of the same fields were in the range of 20–40 days for 50% (W1) and 90% (W4) of the observations, whereas for the W2 and W3 fields, most of the visits were separated by approximately 1 yr. The mapping of the fields was organized so as to match the CFHTLS one-square-degree tiles, with nine WIRCam pointings per CFHTLS tile. The observations were defined so as to prevent the execution of the second-visit observations in the same sequence as for the first visits, which could have generated similar persistence patterns in the data. A total of approximately 150 h of observations were conducted during four CFHT semesters, 2010B, 2011B, 2012A, and 2012B. The observations were carefully checked by our team in the days following the observations using the Elixir-IR pre-processing package (see next section), and observations that were executed under high background conditions and/or poor seeing conditions were not validated and were subsequently re-executed. 3 Data processing 3.1 Needfor a custom pipeline: Elixir-IR The CFHT offers to all its WIRCam users a data detrending and calibration service, the “I”iwi pipeline ( Thanjavur et al. 2011 ), which aims to remove the instrumental signature and delivers an absolute photometric calibration within 5%. This pipeline relies on generic recipes, such as acquiring flat fields over a whole observing run in order to create master detrending frames that are applied
Paper: 2017MNRAS.467.2834B (fulltext)
(), by using equation ( 36 ) for a clear atmosphere The red curve in Fig. 4 depicts our approximation, which follows the solution of Lecavelier des Etangs etal. ( 2008 ) when s , and is zero otherwise. Fig. 5 shows the amount by which our approximation underestimates the effective atmospheric thickness. It is at most E 1 (e ) 0.492 when s 0.561. We can apply the same line of reasoning to quickly determine which CIA features rise above the surface continuum. We use instead of equation ( 43 ), where k is the modified mean collision-induced cross-section with its minimum set at the surface collision-induced cross-section ( k s ), given by We then determine the spectral modulation between any two spectral regions with equation ( 34 ). The surface decreases the spectral modulation only if s or k s k in one of the two spectral regions. Figure 5. Amount by which the effective thickness of the atmosphere (in units of scaleheight) is underestimated for q 1 (see equations 11 and 12 ) with the approximation outlined in Section 3.4 as a function of the surface slant optical depth. Can we use this approximation to quickly determine the location of a surface from observations? If the observed spectral modulation is due to a single species and is less than that expected in a clear atmosphere, we can relate the observed spectral modulation, ( h / H ) obs , with respect to a local maximum in its cross-section ( i ) by Unfortunately, the surface location cannot be unambiguously determined because the mole fraction ( f i ) of the species is unknown. Indeed, equation ( 46 ) shows that the spectral modulation of a species increases with abundance in the presence of a surface, as demonstrated numerically by Btrmieux ( 2016 ) for a refractive boundary. The only way to break this degeneracy are two-fold: either the spectral signature is that of a major species (i.e. f i 1) and we can determine the location of the surface, or we can predict the location of the surface and then determine the abundance of the minor species. Given that refractive boundaries are dependent on the bulk properties of the exoplanetary atmosphere as well as the planetstar geometry, quantities that are retrieved during the analysis of transit data, location of refractive boundaries can be predicted in principle and used to place a lower limit on the abundance of detected minor species (Btrmieux 2016 ). 3.5 Transmission spectrum versus formalism To illustrate the impact of the choice of formalism on a computed transmission spectrum, we apply the following formalisms on simulations by Btrmieux ( 2016 ): the formalism of Lecavelier des Etangs etal. ( 2008 ), the exact solution embodied by equation ( 35 ) with q 1, and the approximation discussed in Section 3.4 . The chosen atmospheric model is that of a Jovian-like planet with an H 2 He atmosphere identical to Jupiters, a 600K isothermal profile, and a constant H 2 O abundance with altitude with a mole fraction of 10 8 . We choose the