adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...636A.116K (fulltext)
to the study would be to ensure that observations of a given object on all telescope configurations are all carried out well within the expected Keplerian orbital time at the inner rim. This would allow a possible smoothing out of the asymmetric emission to be lifted. A second improvement would be to push for a higher angular resolution at VLTI using the longest baseline (≈ 200 m). This implementation is currently under study at the European Southern Observatory (ESO). Finally we note that some objects are sufficiently to the north so that they could be observed with VLTI and the Center for High Angular Resolution Astronomy (CHARA), which would provide a remarkable ( u , v ) coverage and angular resolution. Table 5 Sizes of the stellar environments: theoretical sublimation radius ( θ sub ) and the measured half-flux radius ( θ 1∕2 ). 5.5 Comparing the images to the theoretical dust sublimation radius We can measure the sizes of circumstellar emission from the images by taking the half-light radius corrected for the disk orientation (see Appendix D ). We assessed the accuracy of the size measurement against the true sizes from radiative transfer models in Appendix F by reconstructing images on the synthetic data generated from those models. The recovered sizes match the true sizes of the models with a sharp inner rim as long as the inner rim is resolved enough (i.e., θ 1∕2 ∕ θ res ≥ 1.2). However, we find a bias for sizes from extended inner rim models (see Appendix F ). We tend to recover sizes that are smaller than the true sizes for very resolved targets (i.e., θ 1∕2 ∕ θ res ≥ 2.5). Most of the targets (12/15) are relatively well resolved (i.e., θ 1∕2 ∕ θ res ≥ 1.3). In Fig. 8 , we plotted the sizes of the targets against their luminosity for both this work and L17. For the imaged object, we see good agreement between both size determinations, that is, using parametric models and image reconstruction. For some targets (HD 145718 and R CrA), the difference is significant (ratio larger than 1.5 between the two sizes) and this is likely due to model bias (as the parameteric model for R CrA was an ellipsoid instead of a ring and the stellar-to-total flux ratios are different for HD 145718). We note that the size difference is in the opposite direction of the size bias from image reconstruction that we identified in Appendix F . Fig. 8 Size-luminosity relation. The green circles are the sizes measured from images, red crosses are the sizes from parametric models as determined in L17, and cyan triangles are the physical spatial resolution reached by our observations. The two size estimations and the spatial resolution are linked by a vertical dotted line. The dashed gray line represents the theoretical sublimation radius. In order to have a reference to interpret the sizes of the circumstellar emission from the reconstructed images, we computed the theoretical dust sublimation radii using: (see, e.g., Monnier Millan-Gabet 2002 ; Kama et al.
Paper: 2019ApJ...884..153P (fulltext)
of the log N –log S distribution in this region were found to be in very good agreement with that predicted for CV populations (Fornasini et al. 2014 ). The NuSTAR survey of this region measured a log N –log S distribution that is consistent with Chandra sources if the average source spectrum is thermal with kT ≈ 15 keV (Fornasini et al. 2017 ). This is significantly harder than the kT ≈ 8 keV spectrum derived by previous soft X-ray measurements of the Galactic center (Muno et al. 2004 ; Heard Warwick 2013 ) but softer than NuSTAR measurements of the Galactic center, thus possibly indicating a lower fraction of IPs relative to other types of CVs in this region. 3. <italic>NuSTAR</italic> Data Preparation 3.1. Introduction to Zero- and Two-bounce Photons To obtain the wide solid-angle aperture necessary to study the diffuse X-ray emission from the Galactic bulge, we exploit NuSTAR ’s ability to measure unfocused, or “zero-bounce,” X-rays from an ∼1°–3° radius field of view (FOV). The details of the NuSTAR instrument relevant for this analysis are summarized here. NuSTAR has two identical telescopes, each consisting of an independent optic and focal-plane detector, referred to as FPMA and FPMB. The optics use a conical approximation to the grazing incidence Wolter I design, in which X-rays are focused by reflection from an upper parabolic mirror section and then a lower hyperbolic mirror section. The FOV for these focused, or “two-bounce,” photons is ∼13′ × 13′. To block unfocused X-rays from reaching the NuSTAR detectors, a series of aperture stops are attached to each focal-plane bench. However, this shielding is not complete, and there remains an ∼35 radius aperture, partially blocked by the optics bench, from which totally unfocused, or zero-bounce, photons can reach the detectors. The solid-angle aperture for zero-bounce photons is over 2 orders of magnitude larger than the FOV for focused photons, even after accounting for blocking by the optics and vignetting effects due to the aperture stop. This large solid angle for zero-bounce photons allows us to study the diffuse emission of the Galactic bulge. In this analysis, we construct a spectrum using photons from the full detector plane, which are a combination of photons from the zero-bounce aperture, two-bounce FOV, and instrument background (similar to the technique used in Ng et al. 2019 ). Via this technique, we lose the imaging capabilities of the NuSTAR optics but gain spectral information from a large region of the sky. Although the aperture for zero-bounce photons is much larger, the two-bounce FOV is not entirely negligible due to the increasing intensity of the Galactic diffuse flux in the two-bounce FOV of our observations. Using the mass model of the nuclear stellar disk and bulge, as described in Section 4.3 , we estimate that the two-bounce count rate from the Galactic center in our spectrum is ∼20%–25% as high as the zero-bounce count rate from the bulge. We thus need to
Paper: 2019MNRAS.484.1270S (fulltext)
versus dust-corrected O 32 values (a proxy for ionization parameter) for our galaxies (blue circles), colour-coded by absolute i ′-band magnitude. We plot as black points the XMPs uncovered uniformly from the SDSS spectroscopic sample by Sánchez Almeida et al. ( 2016 ). The uncertainties (1σ) are plotted for our galaxies, and are hidden by the markers in several cases. The vast majority of SDSS XMPs are clustered at Hβ EWs 50 Å and O 32 3. Relatively few previously identified XMPs extend into the high-sSFR region of this diagram at Hβ EWs >50 where our objects reside. The gas in the XMPs presented here is also more highly ionized than in most XMPs from the SDSS spectroscopic sample, consistent with substantially different stellar populations and resulting ionizing radiation fields. The O 32 ratio is commonly used as a proxy for the ionization parameter, or the density of ionizing radiation relative to the gas density. Fig. 9 reveals that indeed, the XMPs presented here host more highly ionized gas than most XMPs from the SDSS spectroscopic sample. Our XMPs have O 32 ratios ranging as high as 10, with median O 32 = 4.1 (and an even higher median O 32 = 6.4 for the subset with Hβ equivalent widths above 100 Å). This is substantially higher than the majority of SDSS XMPs shown, which have median O 32 = 1.2. This indicates that the gas in the galaxies we have uncovered is more highly ionized than in most previously known XMPs, consistent with these systems hosting harder ionizing radiation fields. The offset towards higher Hβ equivalent widths and O 32 ratios in our XMPs suggests that these systems host significantly more prominent young massive stellar populations than typical previously known XMPs. Fig. 9 indicates that the average XMP from the SDSS spectroscopic sample has undergone a more extended star formation history, resulting in an optical spectrum with a substantial contribution from stars formed >10 Myr ago and less highly ionized gas. The EUV and X-ray radiation powered by a typical nearby XMP with a Hβ equivalent width of 30 Å (sSFR ≃ 1 under constant star formation) is potentially very different from that of a reionization-era galaxy which has formed essentially all of its stars in the last 100 Myr (sSFR ≃ 5–10 ). Future studies targeting these high-sSFR XMPs will provide a new window on to the massive stars and X-ray populations that likely dominate the early universe. As a first step towards constraining their EUV ionizing spectra, we examine high-ionization nebular He ii emission in our galaxies. The He ii λ4686 nebular emission line is a recombination line powered by extremely energetic photons >54.4 eV. This line is commonly observed in low-metallicity star-forming dwarf galaxies (e.g. Garnett et al. 1991 ; Brinchmann, Kunth Durret 2008 ; Shirazi Brinchmann 2012 ; Senchyna et al. 2017 ), but the origin of the necessary >54.4 eV photons is still a matter of debate. The commonly cited candidate sources of narrow He ii are very
Paper: 2019ApJS..241...15L (fulltext)
for further testing and investigation. However, large active regions often last a long time and erupt repeatedly, so there are good reasons to take this hypothesis seriously. For such an active region, the scenario that we have found in the 2012 July case could come up again without much difficulty: the earlier eruptions from the active region can precondition the preexisting ambient solar wind, and the later successive ones from the same active region, while moving through the preconditioned wind, may interact en route to 1 au. This situation could also happen for active regions that are eruptive and close to each other. Historical records of some extreme events seem to support our hypothesis. Around the 1859 Carrington event, which also occurred in a weak solar cycle, long-duration brilliant aurorae were observed from August 28 through September 3. Green Boardsen ( 2006 ) suggest that the active region erupted successively and that two closely spaced CMEs interacted in interplanetary space, leading to the extreme nature of the Carrington event. Similarly, long-duration low-latitude auroral displays were seen for almost nine nights during 1770 September. Hayakawa et al. ( 2017 ) indicate that multiple consecutive eruptions from the same active region, which may have resulted in the “perfect storm” situation as we propose in Paper I , must have occurred for such a long duration of auroral observations. A more recent case, the ultrafast CME in 1972 August, reached the Earth in only 14.6 hr, which is the shortest transit time ever observed. Knipp et al. ( 2018 ) suggest that two earlier eruptions from the same active region likely cleared the path for the subsequent ultrafast event, which is exactly our “preconditioning” idea discussed in Paper I and here in the current work. We speculate that some of the 2003 Halloween storms may also be “perfect storms,” including the preconditioning effect and/or CME–CME interactions, because several large active regions continuously erupted for a long time (e.g., Skoug et al. 2004 ; Gopalswamy et al. 2005 ). We think that the hypothesis is valuable in that it points out the reality of extreme events from today's Sun. They generally are not simple and can involve many reinforcing factors. While the paper appears to stress the importance of interplanetary evolution of CMEs, the “perfect storm” concept also includes solar aspects, such as the persistent eruptive nature of the active region and the simultaneous presence of several big active regions on the disk. A particular instance is that widespread magnetic connectivity between concurrent active regions leads to sympathetic eruptions (e.g., Schrijver Title 2011 ; Jin et al. 2016 ; Wang et al. 2016 ). A nearly “global storm” could be made under this solar-side “perfect storm” scenario. This may have occurred during the 2003 Halloween period, which had a few large active regions, including one that emerged quickly and became connected to the
Paper: 2021ApJ...913...12D (fulltext)
Analogously to generative adversarial networks (Goodfellow et al. 2014 ), the conditional autoencoder is adversarially trained to generate samples that hinder the critic network’s ability to do its job. The joint distribution p ( x , u , z ) can be expressed using Bayes’ rule asThis can be rewritten asif and only if u is statistically independent from z . If the joint distribution p ( u , z ) is not factorizable, the distributions q ( x , u , z ) and p ( x , u , z ) will be different. It follows from this that u and z are statistically independent if and only if the samples ( x , u , z ) drawn according to p ( x , u , z ) are indistinguishable from those sampled from q ( x , u , z ). How can we generate samples from these two distributions? If we consider our autoencoder to be an idealistic autoencoder capable of perfectly reconstructing its inputs, then the encoder and decoder can be viewed as respectively approximately parameterizing p ( z ∣ u , x ) and p ( x ∣ u , z ), which are both deterministic functions. We can thus draw samples from p ( x , u , z ) = p ( z ∣ u , x ) p ( u , x ) by first randomly sampling from the data set to obtain ( u , x ) and then using the encoder to obtain the associated z . We can similarly draw samples from q ( x , u , z ) = p ( x ∣ u , z ) p ( u ) p ( z ) through reusing our samples drawn from p ( x , u , z ). By scrambling the ( u , z ) pairs within a batch, we can effectively remove any joint information between u and z (Belghazi et al. 2018 ), which results in samples drawn from the marginal distribution ( u , z ) ∼ p ( u ) p ( z ). We can then use the decoder that approximates p ( x ∣ u , z ) to obtain approximate samples q ( x , u , z ) drawn from p ( x ∣ u , z ) p ( u ) p ( z ). As stated above, enforcing statistical independence is the same as finding a latent representation, z , such that samples drawn according to these two procedures are indistinguishable. This bears a strong similarity to the training objective of generative adversarial networks that attempt to train a generator such that generated samples are indistinguishable from samples drawn from a data set. As such, we can take inspiration from existing generative adversarial network architectures to solve our disentanglement objective. In generative adversarial networks (Goodfellow et al. 2014 ), a critic network is trained to distinguish between samples drawn from a data set and samples created by a generator network fed samples from a well-understood probability distribution. The generator and critic network are jointly optimized in a minimax game. That is, the critic attempts to maximally distinguish between the two data streams, and the generator attempts to minimize the critic network’s ability at doing so. The global optimum of this two-player game occurs when both the generator and critic network can no longer improve—when the two data streams are identical. For our disentanglement neural network architecture, we take heavy inspiration
Paper: 2016ApJ...821L..11S (fulltext)
reproduces the observed interstellar extinction curve from UV to near-IR (Fitzpatrick 1999 ; Draine 2011 ). The optical results prefer the strong forward scattering with , comparable to the present near-IR values. In the near-IR, the present results can be several times larger than the WD01 prediction. Simply, the discrepancy can be explained if the larger dust grains are more dominant compared to the WD01 model. The WD01 value decreases toward longer wavelengths because the isotropic scattering by the Rayleigh scattering becomes more dominant. In the WD01 model, the typical grain size is (Draine 2011 ), which is much smaller than the near-IR wavelengths. To explain the derived large g -value, a lot of large grains may be required to cancel out the isotropic scattering by the smaller grains. However, the existence of a lot of large grains is expected to conflict with various observed properties of interstellar dust, such as interstellar depletion and wavelength dependence of interstellar extinction and polarization. This may indicate that the actual near-IR g -value is smaller than that obtained in the present study. Figure 2. Optical to near-IR scattering asymmetry factor . The present results at 1.25 and are indicated by filled red circles. The triangle, diamonds, and squares denote the results derived from Lillie Witt ( 1976 ), Witt et al. ( 1990 ), and Mattila ( 1970 ), respectively. The solid curve represents the value expected from the WD01 model with R V = 3.1 Milky Way dust (Draine 2003 ). When interstellar dust grains are exposed to UV photons, continuous near-IR emission is created as a result of the stochastic heating of the small grains and/or fluorescence by large molecules such as PAH. In fact, the UV-excited dust emission component has been observed in reflection nebulae at 1.25– wavelengths (Sellgren et al. 1992 , 1996 ). If such an emission component also exists in the general diffuse interstellar medium, the UV-excited dust emission is expected to be higher toward low Galactic latitudes because, from the Galaxy Evolution Explorer ( GALEX ) data, the near-UV to far-UV ratio in low Galactic latitudes is reported to be smaller than that in high latitudes by more than a factor of two (Figure 10 of Murthy 2014 ). The possible Galactic latitude dependence of the UV-excited dust emission would also contribute to the derived latitude dependence and may reduce the very large asymmetry factor g to the reasonable value. In this sense, the derived latitude dependence in the present study may indicate the first detection of UV-excited dust emission in the diffuse interstellar medium at 1.25 and . Similarly, to explain the very high albedo derived in the galaxy NGC 4826 in the -band, Witt et al. ( 1994 ) suggested the contribution from the near-IR dust emission in addition to the scattered light. To determine the relative contribution of the scattered light and UV-excited dust emission from the derived latitude dependence, detailed
Paper: 2021ApJ...908L..35E (fulltext)
Sérsic fit, finding good agreement within 2%–3%. Therefore, we did not adjust our stellar mass for missing light. 3. Analysis 3.1. Velocity Dispersion We measure the observed stellar line-of-sight velocity dispersions (LOSVD) of our galaxy sample using pPXF (Cappellari 2017 ), which utilizes a maximum penalized likelihood approach to fitting spectra to a library of stellar or stellar population templates and determines galaxy kinematics. For our analysis, we use the Indo–US stellar library templates (Valdes et al. 2004 ) with R ∼4200, which are smoothed to the binned resolution of the reduced spectra of R ∼3000. We mask out portions of the spectra where telluric atmospheric absorption bands are significant and also where the spectra are potentially impacted by [O ii ] or [O iii ] emission lines. Good fits to the continuum are obtained for all galaxies, as shown in Figure 1 . We do not introduce additional freedom (e.g., additive/multiplicative polynomial) to attempt better match to the continuum. The results are not sensitive to this choice. As our 1D spectra are optimally extracted with a Gaussian kernel based on the PSF, i.e., seeing-limited, we correct our velocity dispersions within the aperture to velocity dispersions within r e as per van de Sande et al. ( 2013 ), resulting in a mean correction of 0.02 dex. The measured velocity dispersions including aperture corrections are shown in Table 2 . We find large velocity dispersions for all galaxies ranging from 200 − 300 km s −1 . We derive LOSVD uncertainties using Monte Carlo simulations. We take the best-fit spectrum to the observed data from pPXF and perturb its flux 1000 times by a random value drawn from a Gaussian with a standard deviation consistent with the uncertainties of the observed spectrum (determined as outlined in Section 2.2 ). The LOSVD for each iteration is determined and the standard deviation of all 1000 Monte Carlo simulations is used as the error for the LOSVD. These errors are larger than the formal pPXF uncertainties by a factor of 1.1–2.2. To test potential systematic errors of our measurements, we ran pPXF for multiple scenarios. We exclude the H β line and find consistent results, suggesting that emission line-infilling is not significant for these galaxies. To explore dependence on stellar library, we use the MILES stellar library (Sanchez-Blazquez et al. 2006 ; Falcón-Barroso et al. 2011 ) and find that the results agree within the uncertainties. To investigate stability and consistency, we take the best-fit spectrum for each galaxy and smooth it to simulate different velocity dispersions, perturbing the flux by observational errors as per the Monte Carlo simulation above. We are able to recover velocity dispersions for the range 150–500 km s −1 within the pPXF uncertainties, which shows that we should be able to recover dispersions and uncertainties from data of this quality. The median residuals for the recovered velocity dispersions are small (≲17 km s −1
Paper: 2019MNRAS.483.2615K (fulltext)
so rapidly that it becomes useless. This means that simultaneous operations of LISA and radio instruments such as SKA are beneficial for making full use of our multimessenger strategy. Following consideration of the dispersion measure, we determine the steps of trial values for the amplitude and phase of the modulation by requiring that the pulse arrival time changes by the sampling time. The number of required trials for the modulation amplitude is given by and that for the modulation phase is given by Additionally, the uncertainty in the eccentricity of Δ e ≈ 0.003 could change the arrival time by t mod Δ e , and this will require trials of for our fiducial parameters. We expect that extension to a slightly eccentric orbit will be straightforward. We should remark on the spin precession. Its period will be for our fiducial parameters (Barker O’Connell 1975 ), and we expect its effect to be marginal. We leave the careful assessment for future work, and information from LISA will prove useful even when the precession must be corrected for. 3.3 Comparison with an all-sky survey To elucidate usefulness of the multimessenger observation with LISA , we compare its performance with an all-sky survey of radio instruments. To keep the sensitivity fixed, we assume that the integration time per pointing is the same for both observation strategies. We also fix the sampling time. A comparison with an all-sky survey based on the phase-modulation search, with which the sensitivity is not maximal, is briefly made in the end of this section. First, the all-sky survey trivially requires a large number of pointings, and thus the integration time must actually be shortened. If we do not restrict the search to a limited portion of the sky, e.g. the Galactic plane, equation ( 17 ) suggests that O (10 4 ) pointings in terms of a single-dish beam are required to find a limited number of targets. Even if instruments with a large field-of-view are available (Smits et al. 2009 ), the number of pointings may not be reduced below O (10 2 ). If we evaluate the number of pointings in terms of tied-array beams, the all-sky survey requires O (10 8 ) pointings, which is larger by O (10 6 ) compared to the multimessenger observation with LISA , equation ( 20 ). However, results of the all-sky survey can be used for various applications including standard pulsar searches, and thus this comparison is valid only when we focus on detections of pulsars in the shortest period class of binary neutron stars. Next, the coherent integration becomes significantly costly because of brute-force corrections for the orbital modulation. We have to search over a large parameter space of not only the modulation amplitude and phase but also the orbital frequency. By requiring that the phase error caused by the frequency mismatch accumulated during the integration time does not induce a timing error larger than the sampling time, particularly for the harmonic summing to be successful, the step in