adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...587A..57Z (fulltext)
after the observing sequence because of a slight saturation of the off-axis PSF obtained before the observation. An example of the output of this technique is shown in Fig 2 . Fig. 2 38th IFS spectral channel (1.63 m) with planets HR 8799 de obtained with KLIP reduction. A second approach, where ADI and SDI were used together, was then implemented. The single exposures in each of the 32 spectral datacubes were averaged. The PSF library used for the PCA fitting taking all frames into account, both along the temporal and spectral dimensions, where the PSF has moved by more than a separation criteria . In this scheme, the library is constructed using the frames spatially rescaled according to their respective wavelength. Various tests showed that a value of FWHM gives good results in terms of S/N for the detection of the planets. To account for the flux losses induced by the speckle subtraction algorithm, forward modeling is performed via the off-axis PSF of the above-mentioned star as a model for the PSF of the planet. The model is projected on the same PCA modes as the science data, and the flux loss is measured as the attenuation of the flux for the model before and after projection on the PCA modes. The fluxes of the planets are measured in the final spectral channels with aperture photometry in an aperture of diameter 0.8 (4 pixels). The flux is finally corrected for the flux loss estimated using the forward modeling. The error bars on the photometry were measured in the same way as for the IRDIS data described in Sect. 3.2 , except that we did not consider the flux variation during the exposure, as one of the two PSFs is slightly saturated. With this approach, we obtained eight spectral extractions from four independent pipelines. For each planet, a final estimate of the flux ratio between the star and planet was obtained by computing the median of the results. Astrometric measurements for planets HR 8799 de measured from the IFS are shown in Table 5 .The error budget is presented in Table 6 . The error bars are compatible with the expected ones for these targets, following the simulations of Zurlo et al. (2014) . Fig. 3 Contrastand off-axis PSFs curves for the IRDIFS_EXT data set. The 5 contrast for the two instruments are plotted as a function of the angular separation. The off-axis PSF of the star is represented as a black continuous line. Planets around HR 8799 are also shown as photometric points, the error bars are inside the dimension of the dots. The dotted vertical line indicates the coronagraph IWA. Fig. 4 Mass limits deduced from the contrast curves for the IRDIS and IFS instruments. An age of 30 Myr is assumed.Two different models are represented: the “hot-start models” BT-SETTL models, and the “warm-start models” from Spiegel Burrows (2012) . The separations where a possible planet f is expected to be (from Goździewski Migaszewski 2014 ) are shown as dashed black lines. The 5 contrast curves of IRDIS and IFS for the IRDIFS_EXT
Paper: 2018MNRAS.476.5479P (fulltext)
plot the evolution of the median size of the distribution (our results in large green circles) and compare our values to those reported in the literature (in light green): square (Pirzkal et al. 2007 ), hexagon (Taniguchi et al. 2009 ), triangles (Bond et al. 2009 , 2011 ), circles (Malhotra et al. 2012 ), diamond (Kobayashi et al. 2016 ), and inverted triangle (Guaita et al. 2015 ). We show as blue squares the median size for a sample of HAEs selected at lower redshift using the same narrow-band technique (Paulino-Afonso et al. 2017 ). We complement this figure with results for UV-selected SFGs from the literature (in light blue): large diamond (van der Wel et al. 2014 ) and left-facing triangle (Ribeiro et al. 2016 ). Finally, we show the derived size evolution of LAEs (green solid line) and SFGs (blue dashed line). The inset plot shows the estimated size ratio between SFGs and LAEs. Estimates point to SFGs being ∼5 times larger at z ∼ 0 and of the same size as LAEs at z ∼ 5.5. We hypothesize that Lyα-selected galaxies are small/compact throughout cosmic time likely linked with the physical processes that drive Lyα escape. At higher and higher redshifts, typical SFGs start to have the typical sizes of Lyα emitters, which can be seen as an alternative explanation for the rise of the Lyα emitting fraction of SFGs/LBGs into z ∼ 6. 6.3 Relations between LAEs, HAEs, and UV-selected galaxies When compared to the typical sizes of SFGs (selected as Hα emitters, HAEs) that have been studied in a previous work (Paulino-Afonso et al. 2017 ), we immediately see that the two populations are not alike in terms of their extent. Despite having only one common period with observations of both populations (at z = 2.23), where HAEs are almost two times larger than LAEs, our prediction of LAE sizes at lower redshifts is consistently lower than what we report for the HAE population. This is even more contrasting if we include the sample at z ∼ 0, where almost no evolution is expected for the LAE population. This is potentially corroborated by the existence of green pea galaxies (Cardamone et al. 2009 ; Izotov, Guseva Thuan 2011 ) that are compact in nature and found to have Lyα detections and high Lyα escape fractions (e.g. Henry et al. 2015 ; Yang et al. 2016 ; Verhamme et al. 2017 ). We also use two recent and comprehensive studies on the evolution of UV-selected SFGs (van der Wel et al. 2014 ; Ribeiro et al. 2016 , see also Shibuya, Ouchi Harikane 2015 ) that overlap both HAEs and LAEs that we have studied to complement our observations. These confirm our findings that at z ∼ 2 the typical star-forming population is larger in size than the LAE population (by a factor of ∼3). However, we see that this difference fades away and, by z ∼ 5, the two populations are indistinguishable from one another, in what their median extent is concerned. These results are in agreement with previous findings where both populations are compared (e.g. Malhotra et al. 2012 ;
Paper: 2020MNRAS.492.5853L (acknowledgments)
ACKNOWLEDGEMENTS The authors thank the anonymous referee for the careful reading and useful feedback. These results made use of the DCT at Lowell Observatory. Lowell is a private, non-profit institution dedicated to astrophysical research and public appreciation of astronomy and operates the DCT in partnership with Boston University, the University of Maryland, the University of Toledo, Northern Arizona University, and Yale University. In addition, some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. We also acknowledge the UoC data base for providing us the rich data set to make this study possible. TL gratefully acknowledges support for this project from the University of Toledo Graduate Fellowship. JC acknowledges support from an NSERC Discovery Grant and a Western University SERB Accelerator Award. We thank Teznie Pugh, Jason Sanborn, and Heidi Larson at Lowell Observatory for technical support during our observation. Finally, we thank Prof. Aviv Amirav for helpful communications and useful suggestions.
Paper: 2019A&A...623A.169P (fulltext)
These results have important implicationsfor the delivery of water to the asteroid belt and on Earth, as also suggested by previous studies focusing on the in situ formation or a more limited migration of the giant planets ( Turrini et al. 2011 ; Turrini Svetsov 2014 ; Grazier et al. 2014 ). Raymond Izidoro (2017a ) simulated both an in situ growth and a large scale migration ofthe giant planets including gas drag. They found that a fraction of objects from beyond the current position of Jupiter are implanted in the main asteroid belt during the growth of the giant planets, generating a large-scale mixing of the small bodies. The source region of implanted asteroids was from 4 to 9 au in the in situ scenario, identified as the precursors of the C -type asteroids, and from 4 to 15 when migration was taken into account. In our migration model, we also found implanted bodies from 4 to 16 au in the asteroid belt, but most of them, 96.6%, come from 4 to 5 au region (that end up piled up in the 2:1 resonance with Jupiter at about 3 au as shown in Figs. 4 and 5 ) and just 3.4% of them come from 6 to 16 au. Differently from Raymond Izidoro (2017a ), we did not simulate the preexisting asteroid belt for computational time reasons, but we can analyse our results considering a preexisting “virtual” asteroid belt composed by S- and C- type asteroids, with the S-type dominating the inner belt and C-types more common in the outer belt. Taking into account the Minimum Mass Solar Nebula, the asteroid belt mass is about 1 M ⊕ . From the same assumption, a particle in our simulations carries 5 × 10 −5 M ⊕ and thus we can make a crude estimation of the mass that is injected in the asteroid belt. From the 5 Myr snapshots we counted the particles that ended up in the asteroid belt. The results are that in the nominal model, roughly 1 M ⊕ of outer solar system planetesimals are injected into the asteroid belt. This number is to be compared to the amount of mass in the primordial asteroid belt, that is roughly 1 M ⊕ . Hence, after the migration, about 50% of the belt is composed of outer solar system planetesimals. The nominal model is the most efficient in injecting bodies into the asteroid belt and the least efficient ones are the in situ model and the growth tracks 2.0 times faster than the nominal model, both inject only ~0.2 M ⊕ . It is important to notice that, in this model, 96.6% of the mass injected in the belt resides in the 2:1 resonance and a subsequent instability of the giant planets after the disc dispersal can affect dramatically this resonant region, depleting it almost completely. We will show this result in Sect. 5 . This will reduce drastically the contamination of the belt, leaving just 10 −2 M ⊕ of P and D type asteroids in the belt to be compared with the 1 M ⊕ preexisting belt. Even though the contamination is not so important in terms of mass, secular erosion of the mass of the asteroid belt only accounts for 50% of the its mass loss (
Paper: 2016MNRAS.459L..81B (acknowledgments)
We thank Oleg Kochukhov and Colin Folsom for useful discussions. We acknowledge support from the ANR (Agence Nationale de la Recherche) project Imagine. This research has made use of the SIMBAD data base operated at CDS, Strasbourg (France), and of NASA's Astrophysics Data System (ADS).
Paper: 2020ApJ...892...53A (fulltext)
to an injected ν τ flux with E 0 = 1 EeV. Normalized cumulative distributions from secondary τ -leptons are shown in Figure 4 for injected neutrinos at angles corresponding to the best-fit reconstructed direction of AAE-141220. Figure 4. Normalized cumulative distributions for Earth-emerging τ -leptons. Colors correspond to the incoming τ -neutrino energy, and the gray band is the 95% containment on the error of the reconstructed shower energy of AAE-141220. We next inject a flux of EeV tau neutrinos and find the spectral shape of the secondary ν τ flux that would be incident on IceCube. As we observed zero coincident events in the time window of 10 3 s around AAE-141220 in the prompt analysis, we calculate the maximum allowed flux normalization (at a 90% confidence level) on the primary flux that would evade this nonobservation. The results are displayed in Figure 5 . Figure 5. Upper limits (90% C.L.) placed by calculating the secondary neutrino flux (purple histogram) from an incident flux of EeV neutrinos assuming constant emission over 10 3 s and comparing to the nonobservation of IceCube events in the prompt analysis described in Section 3.1 for AAE-141220. The flux implied by the ANITA observations (black), represented in this figure as , using information about ANITA’s acceptance (Romero-Wolf et al. 2019 ) overshoots this upper limit (purple arrow) by many orders of magnitude. For comparison, upper limits on the time-integrated muon neutrino flux from the prompt analysis are shown in blue. All fluxes are per flavor . Figure 6. (Top two rows) Sky maps and TS distributions from all three analyses for AAC-150108. For AAE-061228, IceCube was not in a full detector configuration at the time of the event, and thus only the steady and rolling analyses were used to search for neutrino emission. Sky maps and TS distributions for these analyses are displayed in the bottom two rows. Although IceCube’s sensitivity is peaked many orders of magnitude below the reconstructed energies of the AAEs, the limits set on any potential neutrino source that created AAE-141220 are more constraining by several orders of magnitude than the implied flux by the ANITA observations. If one considers constant emission over the entire live time of the IceCube event selection, then the time-integrated flux limit set by the IceCube nonobservation of AAE-141220 becomes around 1 order of magnitude less constraining, as is apparent in the steady limits in Figure 2 . However, for the implied normalization placed by ANITA observations, this value would increase by approximately 2 orders of magnitude due to the limited live time of the ANITA flight. This has the overall effect of increasing the tension between these two normalizations by approximately 1 more order of magnitude than for the 10 3 s follow-up shown in Figure 5 . It is worth noting that the logic for scaling time-integrated limits also applies to AAE-061228, even though we cannot constrain the shorter
Paper: 2018A&A...616A.130L (fulltext)
data point density of a photometric LC (i.e. order of several thousands). However, in order to obtain a more realistic error value for q , the mass function is taken into account using the P orb and K 1 values and their errors: where M 1 , M 2 , K 1 , i , and P orb are explained in Table 1 and G is the gravitational constant. Following the error propagation method and assuming M 1 = 1.6 M ⊙ , then according to the definition of mass ratio (Table 1 ), the error of q is derived as 0.0031, which is adopted as the final value. The parameters of the set (c) were calculated using the software A BS P AR EB ( Liakos 2015 ) in mode 2 and they have the usual meaning. In Fig. 6 , we show the photometric model on one LC and the three-dimensional (3D) Roche geometry representation of the system, while in Fig. 7 (upper and middle panels) and Fig. 8 we illustrate the SC data and their residuals after the subtraction of the theoretical LCs for the two data sets. Fig. 6. Upper panel : theoretical (solid line) over observed (points) LC for KIC 8553788. Lower panel : 3D representation of the Roche geometry of the system at orbital phase 0.75. Fig. 7. Upper panel : short cadence LCs of Q5 for KIC 8553788. Middle panel : LC residuals after the subtraction of the binary models. Bottom panel : LC residuals after the subtraction of the pulsation model. Fig. 8. Upper panel : short cadence LCs of Q14 for KIC 8553788. Bottom panel : LC residuals after the subtraction of the binary models. During the modelling of the individual LCs, it was noticed that the spots migrate from one day to another; although this is a by-product result and a detailed study is not an aim of the present paper, their parameters are listed in Table B.1 . Moreover, in the same appendix, plots are given showing the location changes of the spots as well as their location on the surface of the secondary component on the first and the last days of each data set (Figs. B.1 – B.3 ). The reliability of the solution, especially for the extremely low value of the mass ratio, may be checked in the future using the RVs of the secondary component. However, it should to be noted that according to Kallrath Milone ( 1999 , their Sect. 4.1.1.1) the photometric mass ratio can be determined with great confidence for lobe-filling or overcontact systems when at least one light curve and one RV curve exist. Moreover, another useful and recent example regarding the reliability of the “ q -search” method concerns the semidetached systems KIC 10581918 and KIC 10619109. Liakos (2017) determined their mass ratio values based only on their Kepler LCs and without any use of RVs. These photometric values of q were later seen to be in very close agreement with the spectroscopically determined ones of Matson et al. (2017) . Concluding, KIC 8553788 was found to be in a semi-detached state, with the less massive and cooler component filling its Roche lobe, and to have a very low mass ratio of 0.043. Its primary component is a
Paper: 2021ApJ...913..107K (fulltext)
of Trappist-1f from a magma ocean phase to present assuming the modern surface volatile inventory exceeds 1 GPa (ocean depth >∼100 km). Figure 4. Trappist-1f’s coupled redox–thermal–climate evolution. The model is applied from magma ocean to present with initial water inventories ranging from 30–300 Earth oceans (∼1–10 GPa), and initial CO 2 inventories ranging from 20 to 6000 bar. The lines are median values and shaded regions denote 95% confidence intervals across all model runs. The coupled model includes (a) mantle and surface temperature evolution, (b) the solidification of the magma ocean from core to surface, (c) atmospheric evolution, (d) surface ocean condensation, (e) surface energy budget, (f) CO 2 outgassing and weathering fluxes, (g) crustal production, (h) mantle redox state, and (i) the surface redox budget. Despite the large pressure overburden from the surface ocean, crustal production may persist for billions of years due to mantle hydration suppressing the silicate solidus (g). Nonetheless, Trappist-1f does not experience magmatic outgassing due to the high solubility of volatiles in melt at GPa pressures (f), (i). According to this model, Trappist-1f ought to have experienced a ∼100 Myr magma ocean due to the extended pre-main sequence of Trappist-1 (Figure 4 (b)); absorbed shortwave radiation exceeds the runaway greenhouse limit for the first ∼100 Myr of Trappist-1f’s evolution (Figure 4 (e)). During this long-duration magma ocean phase, loss of hydrogen to space oxidized the mantle (Figure 4 (h)), consistent with previous models of magma ocean evolution on planets around M dwarfs (Schaefer et al. 2016 ; Wordsworth et al. 2018 ; Barth et al. 2020 ). Once absorbed shortwave radiation drops below the runaway greenhouse limit, the magma ocean freezes and a deep liquid water ocean condenses out of the steam atmosphere (Figure 4 (d)). Despite the large pressure overburden from this surface ocean, melt production may persist to the present (Figure 4 (g)). This is because crustal hydration reactions and subduction deliver water to the interior (Figure 4 (i)), suppressing the silicate solidus and hence allowing for partial melting. However, despite high crustal production rates, there is no magmatic outgassing throughout Trappist-1f’s evolution (Figures 4 (f), (i)) due to the high solubility of volatiles at GPa pressures. 4. Discussion To what extent is the simple melt-gas equilibrium partitioning approach described here a reasonable representation of waterworld degassing? Outgassing fluxes from magmatic sources are determined by the integrated loss of volatiles over the decompression path from the mantle to the surface as magma ascends and cools (Edmonds Woods 2018 ). The decompression path reflects changes in volatile solubilities as magma ascends from its mantle source to the surface. Solubilities typically decrease with decreasing pressure, which leads to degassing of volatiles along the decompression path (so-called “first
Paper: 2016A&A...586A..50G (fulltext)
the dendrogram structure, i.e. compact clumps, while the fainter symbols represent their parent structures. There is a hint of an increase in temperature with time, from an initial temperature K to K over a period Myr, as illustrated by the dashed line in panel b) ; note that the cool features from 0.7–0.9 Myr are at a significantly different velocity and therefore cannot be part of the same stream. The Brick is an outlier (it is hotter) whether or not there is a trend. The symbol sizes are proportional to the brightness and are meant to give an indication of how reliable the temperature measurements are. The gray vertical lines through the circles indicate the formal 1 errors on the temperature. Fig. 16 a) A temperature map derived from the NH 3 (1, 1)/(2, 2) line ratio from Ott et al. (2014) . The NH 3 thermometer has a more limited temperature range when using only these two lines, so the map is cut off at K. b) The -derived temperature map over the same region from Fig. 7 a. Figure 16 shows the Ott et al. (2014) temperature map in the same color scheme that we have used in Fig. 7 . There are some trends that are common between the two maps: the Sgr A complex and Sgr B2 complex are the warmest regions, the diffuse gas surrounding these is somewhat cooler, and the positive latitude component of the Molinari et al. (2011) “ring” is slightly warmer than the negative latitude component. The most significant difference occurs in the southwest portion of the Sgr B2 cloud. In this region, the NH 3 temperature ranges from the high ( K) temperatures of the Sgr B2 N hot ridge into a cooler background –40 K. By contrast, the temperatures stay high, indicating gas temperatures K extending to the southwest of Sgr B2 S. This difference probably indicates that there is a cool, diffuse component being detected in NH 3 , while is detecting a hotter, denser component. This region will provide a good comparison target for other thermometric observations. Further comparison of the NH 3 and thermometers is necessary, and will be possible with future ATCA and GBT surveys of higher ammonia transitions. These will be aided by observations of both higher and lower transitions ( Mangum Wootten 1993 ) to verify the presence of a cool, diffuse component. 5.6. Comparison to other measurements with p-H<inline-formula specific-use="simple-math"><sub>2</sub></inline-formula> CO The largest survey of temperatures prior to this work was by Ao et al. (2013) , who observed the inner 75 pc. They performed a similar analysis to ours but over a smaller area. They reported similar temperatures, with some small differences due to a different set of collision rates adopted (we used Wiesenfeld Faure 2013 , they used Green 1991 ). The Ao et al. (2013) survey found uniformly high temperatures within the bright Sgr A cloud complex. Our survey finds a greater variety in gas temperatures due to the larger area covered. There have also been observations of temperatures with