adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...882...70M (fulltext)
in such a faint galaxy during our survey (Section 3 ) and we argue that no host galaxy exists at the SN location even if we take this redshift possibility. The nearest galaxy on the sky (galaxy A in Figure 1 ) could be at z ∼ 0.25 (Figure 7 ). With the 3.86 kpc arcsec −1 at z = 0.2455, the 07 separation corresponds only to the distance of 2.7 kpc from the center of galaxy A. An interesting coincidence is that the extended galaxy at the north of HSC16aayt (galaxy B in Figure 1 ) is at z =0.25053 ± 0.00005, which is spectroscopically measured by the Sloan Digital Sky Survey 20 20 https://www.sdss.org/ (SDSS). Therefore, this galaxy B can also be the host galaxy of HSC16aayt if it is at z = 0.2455, although the galaxy center at (R.A., decl.)=(10:02:05.46, +02:58:10.62) has a separation of 124 from HSC16aayt. The angular scale at z = 0.2505 is 3.91 kpc arcsec −1 and the physical distance between the galaxy center to the SN location is 48.5 kpc at z = 0.2505. Again, assuming a typical massive star lifetime of 10 Myr, the progenitor needs to have the tangential velocity of to travel 48.5 kpc before the explosion. Another intriguing fact is the redshift difference between the host galaxy ( z = 0.2505) and HSC16aayt ( z = 0.2455). Assuming that HSC16aayt is actually at z = 0.2505, the redshift difference indicates that HSC16aayt is moving away from the host toward us with the velocity of . Combined with the tangential velocity of , the progenitor of HSC16aayt was moving away from the host galaxy with a velocity of if galaxy B is the host galaxy. The SDSS spectrum of galaxy B shows that it is an active galactic nucleus. Thus, we can estimate its central BH mass from its spectrum in this case. By using the H α luminosity (3.6 × 10 41 erg s −1 ) and the FWHM of the H α emission from the host galaxy (46.8 Å in the rest frame) obtained by the SDSS spectrum, the central BH mass is estimated to be (Greene Ho 2007 ). The velocity that can be gained by the BH interaction through the Hills mechanism iswhere a is the binary separation, M total is the total mass of the binary system, M BH is the BH mass, and f R is a factor of unity (Brown 2015 ). The extreme velocities of the order of estimated above can be achieved by the BH at the center of galaxy B. 6.2.2. <italic>z</italic> = 1.92 This redshift possibility appears if we assume the strongest line is Mg ii at 2800 Å in the rest frame. It is one of the strongest lines in ultraviolet in SNe IIn (e.g., Fransson et al. 2002 , 2014 ). However, no other possible emission lines can be identified in this redshift. If HSC16aayt is at z = 1.92, the peak SN magnitudes in the rest-frame ultraviolet are around −22 mag and HSC16aayt is a long-lasting SLSN IIn like SN 2006gy (Smith et al. 2010 ). If we assume z = 1.92, a host galaxy fainter than −17.7 mag can exist at the SN location and we cannot exclude the possibility that there is a non-detected host galaxy at the SN location. Galaxy A is, again, not likely to be a host
Paper: 2017ApJ...834..154P (fulltext)
examined in this study. The positions and colors are exactly as in Figure 2 . 5. DISCUSSION AND CONCLUSIONS Examining the most extreme deviations of the posterior area distributions, we find that there is only modest improvement in our ability to measure the intrinsic parameters when using the posterior area as our metric. Neither one nor two dimensional confidence region size were significantly affected by fixing the various orientation and distance parameters. There may be some additional precision obtained in the primary spin (most notably the primary spin magnitude), however, this requires knowledge of both the sky location and either the distance or relative inclination of the system. This is likely due to a strong correlation between and the depth of the waveform amplitude modulation observed from a precessing system which is, in turn, dependent on both the spin magnitude and tilt. Qualitatively, IMRPhenomPv2 and SpinTaylorT4 exhibit similar cumulative distributions for the same event set. It is notable, but not definitive, that the improvements in region size occur at absolute region sizes. For instance, the primary tilt angle is measured better with SpinTaylorT4 , but only when the region is already relatively well contained within less than a quarter of the prior area. Conversely, IMRPhenomPv2 performs better over a wider set of regions. The deviations, while marginal, do occur for slightly different combinations of fixed parameters between the two families which may indicate that information from the merger and ringdown have some effect on our ability to measure certain parameters (e.g., spin orientation and magnitude). This result reinforces the generally known result that the intrinsic source physical parameters and the source orientation parameters are mostly decoupled. Even in the case of precessing binaries where the spins and inclination are correlated because of the oscillation of the binary plane, we find here that having information about the inclination of the binary does not translate into noticeably better estimation of the spin parameters. Moreover, this result represents the best possible scenario in regards to incorporating priors from external astronomical information—it effectively introduces a prior which is a delta function. In practice, the prior information will have a finite width and many gains made here will likely be lost. Finally, we do note that, while not explored in detail here, the overall run times of the codes are reduced when fixing parameters. The effective decrease in dimensionality will improve convergence time in the MCMC used for measurement of the posteriors. Empirically, IMRPhenomPv2 was reduced by a factor of two between the unpinned case and the case fixing only sky location. Other configurations converged only slightly faster than the sky location case. The SpinTaylorT4 family configurations were affected in a similar way, but we observed a little less than a factor of two. For most compact
Paper: 2017A&A...606A..80Y (fulltext)
Schoonenberg Ormel 2017 ). Fig. 9 Revised critical solid abundance as a function of the dimensionless stopping time from Carrera et al. (2015) . The filled and open circles show our models with and without significant radial concentration of solids, respectively. The solid black line for and the dashed blue line for are the unmodified critical curve from Carrera et al. (2015) . The black solid line for shows the revised part of the critical curve, where the black dashed line is the extrapolation of it. For solid abundances above the black line (green region), spontaneous concentration of solid particles by the streaming instability can occur. Some discussion on our simplifying assumption of particles of the same size is in order here. It has been suggested by Bai Stone (2010a) that the particle-gas dynamics for a population of particles of various sizes is predominantly driven by the largest ones. They found a critical solid abundance of for particles of seven species ranged from to , and conjectured that it is the total abundance of the particles with that forms the criterion for triggering strong concentration of particles; that is, . However, if the dust coagulation is limited by the bouncing barrier, the largest particles in the distribution may only have ( Zsom et al. 2010 ; Dra ̧żkowska Dullemond 2014 ). In this case, the conjecture by Bai Stone (2010a) breaks down and it is not clear which size range of the particles would dictate the particle-gas dynamics in the system. On the other hand, when the dust coagulation is limited by the bouncing barrier, the distribution of the particle size tends to concentrate within about one order of magnitude near the barrier ( Zsom et al. 2010 ). Therefore, our assumption of particles of the same size might still assimilate this scenario. Finally, several open questions remain in concentrating small particles by the streaming instability. We have observed further sedimentation in dense filaments in our models, indicating that the velocity dispersion of the particles decreases in the dense regions. It remains to be seen if further dust growth could proceed inside such an environment, given the reduced relative velocities, and if the dust growth in turn could drive more spontaneous concentration of solid materials via the streaming instability. Moreover, the presence of the magnetic fields could render rich structure in the gaseous protoplanetary disks, and the disk mid-plane can be turbulent with various strengths at different locations ( Turner et al. 2014 ), which significantly affects the ability of solid materials to sediment onto the mid-plane (e.g., Okuzumi Hirose 2011 ; Zhu et al. 2015 ). This has nontrivial effects on the dust coagulation and the radial concentration of solids for planetesimal formation to occur ( Johansen et al. 2014 ; Testi et al. 2014 ). 6. Summary In this work, we revisit the condition for the spontaneous concentration of solid particles driven by the streaming instability
Paper: 2019A&A...624A.106A (fulltext)
peak shows up in the sum of the correlation at 900 nHz Instruments: no confirmed detection in other instruments (GONG, BiSON) measuring solar radial velocities. Although the detection claimed in F17 is not always reproducible, we must point out that it is quite remarkable to have obtained 2 peaks above the 10% threshold in Fig. 1 , which was not obtained in F17. It clearly shows that the levels of these 2 peaks are very sensitive to the details of the procedure. 3. Reproducing the analysis of FS18 3.1. g-mode asymptotic model In FS18, the analysis done in F17 was pushed further by correlating an asymptotic model of g -mode periods with the power spectrum of the time series of round-trip travel time. The asymptotic model of g -mode period P n , l can be found in Provost Berthomieu (1986) as: where n is the order of the mode, l is the degree of the mode, and P 0 , V 1 , V 2 and θ are all related to the solar model. Equation ( 2 ) can be solved for P n , l and can then be rewritten as: where is a quantity of order . Assuming that n ≫ 1, we can then rewrite P n , l as: where is the asymptotic periodicity of the modes of degree l , n 0 is an arbitrary value of g -mode order and α is given by: Finally, we can rewrite Eq. ( 3 ) as: This equation is the same as Eq. (14) in F17 except that α in our formula is directly related to P l while in F17 it is taken as a free parameter α l . In F17, the model of the g -mode frequency is then given by: where ν l is the rotational mode splitting for modes of degree l assumed to be independent of n and m . In addition, each mode of tesseral order m ≠ 0 of a given l is assumed to be observed with a weight w l , m less than 1. In summary, for l = 1 there are five independent parameters defining the model: P 1 , P min, 1 , α 1 , ν 1 and w 1, 1 ; while for l = 2, there are six independent parameters defining the model: P 2 , P min, 2 , α 2 , ν 2 , w 2, 1 and w 2, 2 . Here we note that F17 assumed that the parameters so defined are independent of each other, while as shown by Eq. ( 5 ) the P l and α depend upon P 0 . Here in order to reproduce, the findings of FS18 we followed the prescription of F17 for the independence of the parameters. 3.2. Optimisation of the correlation The model given by Eqs. ( 6 ) and ( 7 ) is then correlated with the smoothed power spectrum of time series of round-trip travel time. Here we note that the power spectrum is smoothed over 2 bins in FS18 rather than the 6 bins of F17. The computed correlation is made symmetrical by adding a flipped version of the correlation to itself. Then in FS18, the correlation value at 0 and at the splitting lag are optimised or fine tuned by adjusting all 5 parameters for l = 1 and all 6 parameters for l = 2. The optimisation procedure in FS18 is not detailed but done by hand (Fossat, priv. comm., 2018). In the course of the investigation, it appeared that the finding of a maximum could not be made possible by any simple optimisation routine mainly because
Paper: 2020A&A...635A.112L (fulltext)
such as the Pareto family and the generalized student T-distributions, are often associated with long-range dependence or memory processes ( Pisarenko 2010 ; Beran et al. 2013 ). The main question in this statistical study can be formulated as: Is the CME process “purely” stochastic? Or does the CME process have a long-range dependence or “memory” in such a way that an event is influenced by the occurrence of past events? To address this question, Wheatland (2003) analyzed a set of CMEs observed by LASCO during the ascending phase and part of the maximum of cycle 23 (1996–2001). The author fit the tail of the WT distribution (WT > 10 h) by a power law function (with index γ ≈ −2.36). The author also applied a Bayesian statistical analysis and concluded that the observed WT distribution corresponds to a time-dependent Poisson process. Performing a similar analysis but using LASCO observations in a restricted time range around the maximum of cycle 23 (1999–2001), Moon et al. (2003) reached the same conclusion that the WT distribution of 3187 CMEs may be represented by a nonstationary Poisson process. A power law distribution, using only 113 CMEs observed by the Solar Maximum Mission, during the minimum phase of the cycle (1984–1989), was also found by Méndez Berhondo et al. (2006) , with γ = 1.41. In this case, the authors associated the WT with a characteristic length Λ, (between two consecutive CMEs) as Λ ∝ V A WT, where V A is the Alfvén speed (this implies causality), and conclude that the WT power law distribution is consistent with their picture of characteristic length. It is clear that a power law may be used to fit the tail of the WT distribution, but as mentioned earlier, 83% of the total events have a WT under 10 h. Therefore, the tail after this WT time is not necessarily representative of the whole process (only the extreme events fall in the extended tail of the distribution). In fact, a power law distribution is heavy-tailed and may represent a scaling system, which, in turn, may have self-organized criticality or related to processes with long-range dependence or “memory” ( Pisarenko 2010 ; Beran et al. 2013 ). For instance, Takahashi et al. (2016) found a scaling relationship between CMEs and proton events. Also, Telloni et al. (2014) found that the WT distribution may be fit by a Weibull distribution (a heavy-tailed distribution), implying some degree of correlation between CMEs in opposition to a purely stochastic process. Our findings are in agreement with Jiggens Gabriel (2009 ; who found that SEP events appear to have some memory indicating that events are not completely random) and with the scaling relationship between CMEs and SEP found by Takahashi et al. (2016) . The possible correlation between consecutive CMEs is supported by the remarkable peak at short PA diff , which shows that the source region of a large number of CMEs is located within close distance (Fig. 5 ). This result confirms the finding of Moon et
Paper: 2018A&A...612A..72J (fulltext)
The gas line emission from the J = 3 – 2 transitions of three CO isotopologues, 13 CO (330.588 GHz, Klapper et al. 2000 ), C 18 O (329.331 GHz, Klapper et al. 2001 ) and C 17 O (337.061 GHz, Klapper et al. 2003 ) were extracted from the dataset, using the entire pixel map, after the lines were identified. The source A LSR velocity of 3.1 km s −1 ( Jørgensen et al. 2011 ) was used for all lines. Channels within ±5 km s −1 of each line were combined into an integrated intensity (moment zero) map for each isotopologue, using the image analysis software MIRIAD. Using this approach, the vast majority of the observed line emission was extracted, while avoiding major line-blending from other species. Figure 2 shows the integrated intensity maps of the three isotopologues. C 17 O appears to trace the dust distribution fairly well, as does the C 18 O emission. 13 CO, however, shows a markedly different emission structure, which is likely attributable to optical depth effects, where only the outer layer of the 13 CO structure is probed. Absorption toward source B is evident in all lines but to a smaller extent in C 17 O, revealing that C 17 O is the line with the lowest optical depth, as expected since it has the lowest abundance ( Wilson Rood 1994 ). An interesting feature is the broad emission arc between the sources, which is also present in the dust continuum emission, suggesting a common origin of both the dust and gas emission in this region. We focused on the overall structure of the emission and left a more detailed analysis of the gas kinematics to a future paper (Van der Wiel et al., in prep.). 3 Dust radiative transfer modeling This section describes the setup of the radiative transfer code RADMC-3D, including a description of the dust opacity, model grid and the envelope dust density model used. At the end of the Section, we present our investigation of the temperature structure difference between a single- and a binary protostar, where the envelope dust density model was used. We constructed 3D dust and gas line radiative transfer models consisting of a large envelope with a radius of 8 × 10 3 AU based on Schöier et al. (2002) , two disks around the protostars, as well as a dust filament between the two protostars. The disk’s dust densities were modeled with both the model protoplanetary disk (PP-disk) structure known from Class II sources (e.g., Andrews et al. 2012 ) and the density solution from the angular momentum conservation of an infalling, rotating collapse ( Terebey et al. 1984 ). This infalling, rotating collapse density structure is hereafter referred to as a rotating toroid model, based on the toroidal shape appearing at the centrifugal radius ( Terebey et al. 1984 ), which is the radius where the infalling material with the largest angular momentum impacts the midplane. In terms of luminosity, all models, whether they used one or two radiation sources, were limited to a total luminosity of 21 L ⊙ . This luminosity is based
Paper: 2018MNRAS.473.3854W (fulltext)
rest of the sky. In that case, we take into account to first order the area on the sky that is lost by excluding 15 deg below and above the Galactic plane. This amounts to roughly 10 000 deg 2 , leaving 31 000 deg 2 of the sky that can be searched. In our work, we find four to eight transient candidates in 23.5 deg 2 , and taking into account that Gaia scans ∼1200 deg 2 day −1 , we arrive at a rate of four to nine fast Gaia transients per day. This order of magnitude estimate does not take into account the area on the sky that is eliminated around bright stars and is likely an overestimate. 4.1 Future improvements The implementation of a random forest classifier, based on the low resolution spectra that are measured quasi-simultaneously with the photometry, can help to classify the newly obtained transients into broad classes (Blagorodnova et al. 2014 ). Gaia has the unique feature that each transit has an associated Bp and Rp spectrum, taken during or very close to the transient event. This is especially useful for short duration transient events, as they are likely to be back in quiescence by the time of dissemination (typically the data downlinking and processing takes 48 h). These spectra may thus contain vital spectral information in addition to the colour information they provide regarding the nature of the transient events. The calibration of these spectra is envisaged to be performed in the near future, and may help to both reject spurious events as well as aid the (preliminary) classification of newly discovered fast transients. In addition, improvements in the astrometry will be implemented in the Gaia Science Alerts data base in the near future. Currently, the source positions are based on a first pass of the on-ground attitude determination (OGA1) during IDT. In addition, a second iteration is performed during the later stages of data processing, leading to improved source positions (OGA2). The accuracy of the OGA2 source positions is one to two orders of magnitude better than OGA1. Although for a newly identified transient only the OGA1 position will be available shortly after detection, the implementation of the OGA2 positions for the historic transits will help to reject brightness variations that are inconsistent with the quiescent source position and thus improve the rejection of spurious events. Finally, the reconstruction of the sky positions of sources on the secondary FoV will increase the confidence in detections of sources similar to FGTs 5 through 8, where it is currently unclear whether they are real or could be due to objects in the second FoV. Using the reconstructed second FoV will allow us to reject or confirm the astrophysical nature of these transient events and therefore will aid in the automatisation of the detection process. 5 SUMMARY In this work, we have explored the feasibility of using the Gaia CCD data to search for short time-scale transient events. Gaia provides sparsely sampled, high time resolution
Paper: 2020A&A...643A..43R (fulltext)
in Appendix D , TVL2 seems to be the best method, closer to the noiseless image reported in the last column of all the figures, followed by starlet, b-UWT(7/9), and b-UWT(7/9)+Wiener, which also produce good results. The PM, TV Chambolle, and BM3D are often very similar from a qualitative point of view. Most of the time, the flux of the objects detected by TVL2 is highly concentrated (see, e.g., Figs. D.2 and D.5 ), but in some cases the objects flux is distributed on a larger area, as visible for example, in Fig. D.6 . Starlet produces, in some cases, bright isotropic objects, which could lead to misleading results in terms of their morphological information (see, e.g., Figs. D.14 and D.16 ). In looking at Fig. D.10 , PSF seems to wrongly detect two objects, when other denoising methods detect them correctly. This qualitative analysis is confirmed by the corresponding quantitative analysis made, as visible in Fig. 8 . When more than one object is present in the figures, sometimes the different schemes are not capable of recognizing all the objects (see, e.g., Figs. D.3 , D.6 , D.9 , D.11 and D.13 ). Moreover, fluctuations of the background around the objects are not completely removed and the smoothing can create artifacts (see, e.g., Fig. D.13 , where an elliptical galaxy seems to appear as a spiral galaxy after denoising). b-UWT(7/9), and b-UWT(7/9)+Wiener are susceptible to background fluctuations and end up creating visual artifacts (see Figs. D.17 and D.18 ). When inspecting the snapshots from real images, a qualitative analysis is inconclusive, so we have to stick to the quantitative analysis in Fig. 12 . In fact, looking at the crops reported in Appendix E , the PM and bilateral are often so similar that it is difficult to distinguish them, although, based on Fig. 12 , the differences between them are clear (the PM performs worse than the bilateral). An analogous remark can be made for BM3D, which produces images similar to the PM and bilateral, as visible, for example, in Fig. E.12 . Here, the TVL2 is, again, the most promising method, which is visually close to the HUDF09 also thanks to the automatic optimization of its internal parameters, and this is confirmed by the results reported in Fig. 12 . Images from starlet, b-UWT(7/9), and b-UWT(7/9)+Wiener are quite similar to each other, even though those from starlet present fewer visual artifacts (see Figs. E.10 , E.12 – E.14 ). Differently from the other methods, NL-means produces nearly squared patches of uniform flux, which effectively reduce noise fluctuations (see Figs. E.8 and E.9 ), but also could lead to artifacts (see, e.g., Fig. E.5 , where the galaxy in the bottom-right corner assumes a nearly boxy-shape). Even if artifacts are generated and the morphology of the objects detected could be altered, we tested the efficiency of the algorithms in preserving the FWHM and the ellipticity of the objects in Sect. 5.6 , proving that on the average the shapes are preserved. A