adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...886L..36S (fulltext)
; Chiang Youdin 2010 ; Klahr et al. 2018 ). In Schneider et al. ( 2019 ), we studied the transition from test particle to collective behavior in a levitation experiment, analyzing the free-fall velocity of grains in a cloud. We empirically found that the sedimentation velocity depends on what we call sensitivity factor F S and the closeness C of the individual particle asThe closeness C of a particle is constructed from the interparticle distances between the grain and all other particles j as N is the total number of particles. The sensitivity factor F S in Equation ( 5 ) depends on the average solid-to-gas ratio of the system:The solid-to-gas ratio is defined as the ratio between the total dust mass and the average gas mass,where m p is the mass of a single particle, V is the total volume covered by particles, ρ g is the gas density in the chamber, ρ p is the bulk density of the individual grains, and s is the particle diameter. As seen in Equation ( 5 ), Schneider et al. ( 2019 ) also empirically found that particles are only influenced by the other particles in a cloud if the average solid-to-gas ratio is above a threshold value crit ; otherwise, F s = 0 and particles sediment with v 0 . The sensitivity α connecting the solid-to-gas ratio to the sensitivity factor in Equation ( 5 ) was just a constant in Schneider et al. ( 2019 ). This description is purely empirical and was deduced from a single experiment so far. Here, we present a systematic analysis, where we varied the gas pressure, particle size, and rotation frequency of the chamber and improved the setup and data acquisition. 2. Levitation Experiment 2.1. Setup The setup of the experiment (see Figure 1 ) follows the principle used in aggregation experiments by Poppe Blum ( 1997 ) and Blum et al. ( 1998 ), but is especially based on earlier experiments on dense clouds by Schneider et al. ( 2019 ). Figure 1. Experimental setup without auxiliary parts. The vacuum chamber is evacuated to a preset pressure. Two cameras observe the particles from the front. Illumination is provided by LED modules. Particles—in this study, hollow glass spheres of different sizes and densities—are dispersed once at the beginning of the experiment, within a rotating vacuum chamber with low ambient pressure. Particles are injected using a vibrating sieve in an extension of the vacuum chamber. The gas inside follows the rigid rotation of the chamber. The vacuum chamber has a diameter of 320 mm. Inside the chamber, a ring of LEDs is used to illuminate the particles. The scattered light of the particles is detected by two non-rotating cameras. These cameras observe the particles from the front at a distance of 40 cm on a 1′′, 5 megapixel sensor with a spatial resolution in the order of 10 μ m. The frame rate is 40 fps with an exposure time of 6 ms. The field of view is 19 × 15 cm. Imaging and synchronization of both cameras are controlled by a machine vision computer. Spatial calibration was realized with a
Paper: 2017MNRAS.470.3124K (fulltext)
1999 ). The cycle duration is believed to be controlled by the meridional flow. Fig. 7 shows also the ratio of the cycle period to advection time. The ratio – though not constant – is of the order one and varies moderately. Figure 7. Cycle period for marginal dynamos (a) and the period ratio to the advection time (b). V b is the meridional velocity at the inner boundary r i at the latitude of 45°. Fig. 8 shows estimations of the Rossby and dynamo numbers. The Rossby number is position-dependent. The estimations of Fig. 8 were done for the radius r one pressure scaleheight above the base of the convection zone, i.e. for the radius r where r − r i = H p . The Rossby number Ro ∼ 2 of Fig. 8 agrees with estimation of van Saders et al. ( 2016 ). The two numbers in Fig. 8 are often used to parametrize observations or theoretical computations of magnetic activity. The two parameters of this figure vary moderately but are not constant. This implies that stellar activity can be only crudely parametrized by a single parameter. Figure 8. (a) The Rossby number estimated for the rotation periods of equation ( 2 ). (b) The marginal dynamo number ( 4 ) estimated with the isotropic part η of the eddy diffusivity at middle depth in the convection zone. 5 CONCLUSIONS Asteroseismic measurements of stellar ages revealed a dramatic decrease in the rate of stellar spin-down and magnetic activity for ages beyond about a half of the main-sequence life (Metcalfe et al. 2016 ; van Saders et al. 2016 ). This paper suggests an explanation for the violation of gyrochronology by a switch-off of the global dynamo for a rotation period exceeding the critical value estimated by equation ( 2 ). The switch-off should not be abrupt. Dynamo theory explains the observed variations in amplitudes and periods of solar cycles by fluctuations in dynamo parameters (cf. Choudhuri 2011 ). The fluctuations will cause a star to wander between the parameter regions of (supercritical) dynamo of ‘normal’ magnetic activity and (subcritical) dynamo of fading activity if the rotation period is close to its critical value. The dynamo models with fluctuating parameters reproduce the statistics of grand solar minima (Usoskin, Solanki Kovaltsov 2007 ) only if the dynamo number is close to its marginal value (Moss et al. 2008 ; Olemskoy Kitchatinov 2013 ; Karak, Kitchatinov Brandenburg 2015 ). It may be appropriate to note that observations of total solar eclipses in the Maunder minimum did not find an extended solar corona (Eddy 1976 ) indicating a relatively small co-rotation radius and inefficient spin-down. Unfortunately, the concept of dynamo switch-off at the critical rotation period cannot be supported by dynamo modelling because of uncertainties in key dynamo parameters. The uncertainties can, however, be reduced using this concept. Equation ( 5 ) for α-parameter or similar equation for the dynamo number can be used to estimate the dynamo parameter values for a star of given rotation
Paper: 2021A&A...646A..78M (fulltext)
are associated in a binary system with a large mass ratio. This suggests the existence in the Kepler LEGACY sample of a sizeable fraction of binaries. This is supported by the fact that N17 detected two SB2s in their sample of ten stars from snapshot observations only. It would not be surprising considering the ubiquity of companions in solar-like stars (e.g., Duquennoy Mayor 1991 ). This aspect certainly deserves further investigation (i.e. a dedicated RV monitoring). As guidance for further ensemble seismic modelling of Kepler main-sequence stars, our comparison with the results of (semi-)automatic methods suggests that the study of Buchhave Latham (2015) is a valuable source of effective temperatures and metallicities. For the elemental abundances, the works of Bruntt et al. (2012) and Brewer et al. (2016) appear to be of similar quality for most elements. However, our results tend to be in closer agreement with those of the latter study. We confirm that the ASPCAP effective temperatures used for the APOKASC catalogue are systematically too cool for dwarfs. This is not completely unexpected given that the pipeline is naturally optimised for red giants ( García Pérez et al. 2016 ). The consequence is that a grid-based modelling that makes use of the SDSS photometric T eff scale may be more reliable for relatively unevolved Kepler targets, as already claimed by Serenelli et al. (2017) . However, one can see, in Fig. 12 , the existence of a systematic discrepancy between the SDSS T eff values and those of Brewer et al. (2016) . We find evidence in our sample that the APOKASC metallicities for dwarfs (solely based on ASPCAP) are typically underestimated by ∼0.1 dex. A similar conclusion is reached when considering the data of Brewer et al. (2016) . Correcting for such a bias would lead to a fractional improvement of a few percent in the stellar properties quoted in the APOKASC catalogue (e.g., ∼5% in age; Serenelli et al. 2017 ). As illustrated by our new seismic analysis of KIC 9965715 , even larger changes can be expected if the quality of the classical parameters is mediocre. 10.2. Stellar ages from chemical clocks beyond solar analogues We find that the quality of stellar ages empirically determined from abundance data is currently limited by the precision of the calibrations, which is typically 1.5–2 Gyr when venturing away from stars closely resembling our Sun. Constructing more complex relationships that take into account a dependency on one or two stellar parameters does improve the situation, if only slightly. For instance, incorporating T eff as an additional variable globally improves the agreement between the abundance- and seismic-based ages for the APOKASC stars studied by Brewer et al. (2016) . The calibrations presently available appear significantly less precise than what can be achieved for stars akin to the Sun (precision often below 1 Gyr), but some abundance ratios may hopefully prove more suitable than others outside
Paper: 2018ApJ...863..178M (fulltext)
fractional rms variability spectrum and the best-fit model in Figure 12 (right). The fractional variations in the normalization of the illuminating continuum and reflected emission are and , respectively. 7. Summary and Discussion We present the first results from our XMM-Newton observation of the NLS1 galaxy PG 1404+226. Here, we investigated the large-amplitude X-ray variability, the origin of the soft X-ray excess emission, and its connection with the intrinsic power-law emission through a detailed analysis of the time-averaged as well as time-resolved X-ray spectra, and frequency-averaged ( ν ∼ [1.4–100] × 10 −5 Hz) X-ray fractional rms spectrum. Below we summarize our results. 1. PG 1404+226 showed a short-term, large-amplitude variability event in which the X-ray (0.3–8 keV) count rate increased exponentially by a factor of ∼7 in about 10 ks and dropped sharply during the 2016 XMM-Newton observation. The hard X-ray (1–8 keV) Chandra /ACIS light curve also showed a rapid variability (a factor of ∼2 in about 5 ks) with an exponential rise and a sharp fall in 2000 (Dasgupta et al. 2005 ). The rapid X-ray variability had been observed in a few NLS1 galaxies (e.g., NGC 4051: Gierliński Done 2006 ; 1H 0707–495: Fabian et al. 2012 ; Mrk 335: Wilkins et al. 2015 ). However, the UV ( λ eff = 2910 Å) emission from PG 1404+226 is much less variable () than the X-ray (0.3–8 keV) variability (). 2. The source exhibited strong soft X-ray excess emission below ∼1 keV, which was fitted by both the intrinsic disk Comptonization and relativistic reflection models. The EPIC-PN spectral data revealed the presence of a highly ionized ( ξ ∼ 600 erg cm s −1 ) Ne x Ly α absorbing cloud along the line of sight with a column density of N H ∼ 5 × 10 22 cm −2 and a possible O viii Ly α emission line. However, we did not detect the presence of any outflow as found by Dasgupta et al. ( 2005 ). 3. The modeling of the RGS spectrum not only confirms the presence of the Ne x Ly α absorbing cloud and O viii Ly α emission line but also reveals an O viii Ly β emission line. 4. The time-resolved spectroscopy showed a significant variability in both the soft X-ray excess and primary power-law flux, although there were no noticeable variations in the soft X-ray excess temperature ( eV) and photon index of the primary power-law continuum. 5. In the disk Comptonization scenario, the rapid X-ray variability can be attributed to a variation in the source luminosity as indicated by the time-resolved spectroscopy. However, the modeling of the X-ray fractional rms spectrum using the “one-component disk Comptonization” model cannot reproduce the observed hard X-ray variability pattern and indicates a reflection origin for the soft X-ray excess emission (see Figure 12 , left). 6. In the relativistic reflection scenario, the observed large-amplitude X-ray variability was predominantly due to two components: an illuminating continuum and smeared reflected emission; both of them are
Paper: 2021ApJS..253...53W (fulltext)
can undergo extreme mass loss and develop dusty circumstellar shells, making them extend to fainter and extremely red optical colors. Below the AGB is the familiar RGB of evolved shell-hydrogen-burning low-mass stars, extending from F814W∼21 down through the AGB bump and red clump (RC) to the subgiants at F814W∼26. The AGB bump is comprised of early AGB (EAGB) stars, which are low-mass stars that undergo a pause in evolution when forming their double-shell structure (Gallart 1998 ). Below the AGB bump is the prominent RC of stable core-helium-burning low-mass stars. In addition, there is a small tail of stars departing from the RC toward the top left part of the CMD, which we tentatively identify as blends of RC plus MS stars, either because they are crowded together or are real binaries. Tails of reddened stars depart from the most prominent features, namely the RC and AGB bump, along the reddening vector, reflecting spreads in the internal extinction that often exceed half a magnitude in the V band. We compare our photometry to ground-based photometry of the same region from LGGS (Massey et al. 2006 ) and PAndAS (McConnachie et al. 2018 ) in Figure 19 . Compared to ground-based imaging, our catalog extends 3–5 magnitudes deeper, has three orders of magnitude more stars (22 million versus ∼70,000 in both LGGS and PAndAS), and has qualitatively more and tighter features. This comparison clearly demonstrates the improvement in available photometry and the power of HST to resolve stars in the M33 disk. Figure 19. Comparison of optical CMDs of M33 with photometry from LGGS (left, VI ), PAndAS (center, gi ), and this work (right, F475W/F814W GST). Although the filter systems are not identical, we use a common color and magnitude range on all axes to illustrate the difference in depth that can be achieved with HST. The LGGS and PAndAS catalogs have been culled to cover approximately the same area as the HST survey, but have not been culled on any photometric quality metrics. The median color and magnitude uncertainties in 1 mag bins are shown on the right side of each panel (black lines). 3.2. Star Clusters The PHATTER data are able to resolve individual stars within star clusters in M33, allowing detailed age and metallicity studies. We show a few examples of young stellar clusters of a range of masses in Figure 20 , all of which were identified by citizen scientists as part of the Local Group Cluster Search on Zooniverse (Johnson 2019 ; Wainer et al. 2020 ). 19 19 https://www.zooniverse.org/projects/lcjohnso/local-group-cluster-search The lower panels of this figure show zoomed-in F475W images of the clusters, and the upper panels show optical CMDs of the regions within the clusters’ radii. In all cases, the blue plume of main-sequence stars is visible, showing the young population of cluster stars. Moreover, the sequence on the CMD is more populated in the more massive, larger clusters, showing the quality of the resolved photometry. While
Paper: 2017ApJ...851..130H (fulltext)
from the spine. Previously published high-resolution images show that filaments consist of numerous very fine threads. On the other hand, panel (a), which is a cutout from the full-disk image, has the spatial resolution about 1′′–2′′. Therefore, the barbs seen here are understood to be groups of smaller threads below the spatial resolution. The directions of the polarization signals in panel (b) coincide with the H α spine and barb structure very well. The direction somewhat varies from part to part of the filament, but it is approximately parallel to the H α spine or barbs in any part. This result confirms previous deductions that the structure in filaments is confined to follow the magnetic field. Plasma motions in filaments have been observed to be along the fine threads (Lin et al. 2003 , 2005 , 2008 ), and also verify the field-aligned nature of the threads of filaments. Because the polarization directions of the filament in Figure 2 (b) are roughly aligned with either the spine or barbs, the average polarization direction for all of the filament structure without differentiating between the spine and barbs can be defined; it is shown by a dashed line in the inset at the lower-right of panel (b). The green line shows the direction of the axis of the filament, which represents the average direction of the polarity inversion line seen in panel (b), which is approximately along a straight line. In the case of the filament in Figure 2 , these two lines intersect at an angle of 180. In this case, the average polarization direction deviates from the filament axis by −180. The deviation angle is defined as an acute angle and a positive value corresponds to the counterclockwise deviation; in this case, the deviation is clockwise. This deviation angle is considered to be the deviation angle of the magnetic field vector with respect to the polarity inversion line. As mentioned in Section 1 , it is known that H α structures in filaments shows a chiral nature (see e.g., Zirker et al. 1997 ; Martin 1998 ). As in the case of the filament shown in Figure 2 , where the filament has barbs along the sides of the filament that bend downward to the right and make acute angles to the right side of the filament axis, the chirality is “right-bearing” (dextral). The alternative chirality is “left-bearing” (sinistral). The spine/barbs structure and the deviation by an acute angle of the average polarization direction with respect to the axis of the filament are schematically illustrated at the bottom of Figure 2 . It is known that right(left)-bearing filaments are dominant in the northern (southern) hemisphere, and actually the filament is located in the northern hemisphere as shown in Figure 1 . The magnetic field direction shown in Figure 2 (b) has the 180° ambiguity, and it cannot be solved only with our polarization measurements. However, Martin et al. ( 1994 ) revealed that the direction of the magnetic field can be interpreted as shown in the
Paper: 2021ApJ...912..117X (fulltext)
multiwavelength emission region. In fact, the X-ray and radio observations have also revealed that the morphology of the central PWN is shaped by reverse shock interactions inside the SNR (Temim et al. 2009 , 2015 ; Ma et al. 2016 ), as reverse shocks inside SNRs can accelerate electrons to high enough energies to produce gamma-ray emission. However, the pulsed emission of the central putative pulsar has not been detected from the radio to high-energy γ -ray bands so far. Compared with other composite SNRs, COMP G327.1-1.1 is an excellent source to explore the interaction between its host and inside PWN in combination with the multiwavelength observations from radio to γ -ray (Acero et al. 2013 ). In previous works (Acero et al. 2013 , 2016 ), the GeV γ -ray emission from the source had not been detected by the Fermi-LAT. In this paper, we search for the potential GeV emission from COMP G327.1-1.1 with Fermi-LAT to better understand its nature of emission in the high-energy band. The paper is organized as follows. The routines of data analysis are given in Section 2 , and the related results are presented in Section 3 . The likely origin of the GeV radiation is discussed in Section 4 , and we conclude in Section 5 . 2. Data Reduction The analysis was performed using the Fermi Science Tools version v11r5p3 . 3 3 http://fermi.gsfc.nasa.gov/ssc/data/analysis/software/ We follow the data analysis method as documented in Fermi Science Support Center. 4 4 http://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/ The Pass 8 data with “Source” event class (evtype = 3 and evclass = 128) and the instrumental response function “P8R3_SOURCE_V2” were adopted. To minimize the contamination from the Earth limb, we excluded events with the zenith angle > 90°. The time range of the photon events is from 2008 August 4 to 2020 October 24, and the energy range is from 800 MeV to 500 GeV considering a larger point-spread function (PSF) in the lower-energy band. The center of the region of interest (ROI) of 20° × 20° square is at (R.A. = 23863, decl. = −5506). 5 5 http://simbad.u-strasbg.fr/simbad/ We used the Fermi Large Telescope Fourth Source Catalog (4FGL; Abdollahi et al. 2020 ) and the script make4FGLxml.py 6 6 https://fermi.gsfc.nasa.gov/ssc/data/analysis/user/ to generate the source model file, which in total included 737 objects within 30° centered at the center of ROI, and then we added a point source with a power-law spectral model in the position of COMP G327.1+1.1. The fit was performed by using the tool gtlike in the Fermi Science Tools package. 7 7 https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/references.html Here, we select to free the normalizations and spectral parameters from all sources within 5°, and the normalizations of the two diffuse backgrounds including iso _ P8R3 _ SOURCE _ V2 _ v1.txt and gll _ iem _ v07.fits . 8 8 http://fermi.gsfc.nasa.gov/ssc/data/access/lat/BackgroundModels.html 3. Results The command gttsmap is used to
Paper: 2021ApJ...908..185C (fulltext)
from this distribution to infer a simulated distribution for the X-ray non-detected sources (see Figure 8 ). This result provides additional support for the need for a large population of CT AGNs to explain the black hole mass density while adhering to constraints imposed by the cosmic X-ray background (Comastri et al. 2015 ). It should be noted the distributions presented here are derived from modeled X-ray data with certain assumptions (e.g., source redshift, photon index, lack of intervening dust on larger scales), and that a more thorough treatment is required to understand the underlying distributions. A detailed analysis of such modeling is left for future work. 4.4. X-Ray Stacking Analysis As an additional test of our ability to select heavily obscured AGNs with no X-ray counterparts, we performed an X-ray stacking analysis using stackfast . 10 10 http://www.dartmouth.edu/stackfast/ For this analysis we chose to focus strictly on sources within the Chandra footprint, as Chandra yields the highest S/N of our three X-ray observatories. The stackfast software uses pre-reduced Chandra ACIS data products based on individual observations to match against a set of input sources. The pre-reduction stage allows stackfast to work much more efficiently than other stacking methods, as the data are only sorted and screened once, at initialization; the result is a set of stackable X-ray events containing position, energy, grade, and exposure times for each source within the master set of Chandra observations. For each of the sources in our sample, we extracted photon counts and exposure times in both soft (0.5–2 keV) and hard (2–7 keV) energies, and estimated X-ray fluxes and luminosities (see Table 6 ). Fluxes are computed using count-rate-to-flux ratios characteristic of Chandra ACIS-I responses in Cycle 12 (in approximately the middle time frame of the observations in our stacking analyses), and assuming a power-law spectrum with typical Galactic absorption ( = cm) and photon index Γ = 1.4. This spectral shape is broadly consistent with the observed ratios of hard and soft source counts in our stacking analyses as shown in Table 6 . The count-rate-to-flux ratios adopted are 1.11 × 10 erg cm count (0.5–2 keV count rate to 0.5–2 keV flux) and 3.04 × 10 erg cm count (2–7 keV count rate to 2–10 keV flux). We obtained uncertainties in the stacked flux by bootstrap resampling of the input sources. This yields uncertainties on the average flux for the sample, which reflect the distribution in input fluxes and are typically larger than Poisson photon counting uncertainties. Additional details regarding stackfast can be found in earlier implementations of the code (e.g., Hickox et al. 2007 ; Chen et al. 2013 ; Goulding et al. 2017 ). Table 6 X-Ray Stacking Data and Results Note. X-ray stacking results for energy ranges of 0.5–2 and 2–7 keV. For each energy range, we present exposure time, photon counts, and flux and luminosity estimates. The flux and