adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJ...906..130O (fulltext)
of the complexity of modeling subtle variations. For example, improved modeling of spectral energy distributions of stellar populations could be accomplished with cycle-starnet . As another example, relating N -body simulations of dark matter distributions to large-scale galaxy data is an ideal candidate. For fields outside astronomy, medical imaging provides interesting opportunities for cycle-starnet because the data are highly dependent on the particular medical device that is used for data acquisition. These instrumental effects could be corrected either by combining simulations and clinical data or by learning the translation of data from one system to another. Last but not least, climate modeling is an area where efficiency could be improved by relating low-resolution models to high-resolution data, a domain-transfer problem where the difference between the domains is resolution. 5.2. Limitations and Future Implementations While cycle-starnet has many attractive properties, the training of cycle-starnet can be delicate and may still require some fine-tuning. The difficulty mainly arises from the adversarial training setup. Specifically, adversarial training provides competition between the autoencoder networks and the critic networks, making convergence not as straightforward as other machine-learning tasks such as supervised regression. We note that the other proposed methods, including The Payne (Ting et al. 2019 ), The Cannon (Ness et al. 2015 ), AstroNN (Leung Bovy 2019 ), and StarNet (S. Bialek et al. 2020, in preparation), are examples of supervised regression, and therefore are technically much easier to train. To train cycle-starnet , we have extensively explored numerous architectural choices and training details before deciding upon the final model. For instance, removing the adversarial losses leads to a smoother convergence, but without it, the latent representations are no longer shared, causing cross-domain translations to fail. Nevertheless, adversarial training is an active field of research in machine learning, with many new ideas proposed (Deshpande et al. 2018 ; Miyato et al. 2018 ; Donahue Simonyan 2019 ) since the original UNIT paper was published. Consequently, we expect that some of these advancements would benefit and stabilize the training of future versions of cycle-starnet . Besides the difficulty in training in an adversarial setting, we have made a few simplifying assumptions regarding the training spectra data sets. First, we assumed that the uncertainties provided by APOGEE are uncorrelated and accurately determined. How a mischaracterization of the noise may skew the training is yet to be studied. Additionally, the labels from both domains in this study span the same ranges. Early experiments suggest that having the two domains span a similar label space is an essential aspect of cycle-starnet . For a new survey, we might not know beforehand the range of the stellar labels. Knowing how to resolve this
Paper: 2018A&A...616A..58D (acknowledgments)
Acknowledgements We thank the anonymous referee for the comments improving the paper. This research was supported by the project Enabling Green E-science for the SKA Research Infrastructure (ENGAGE SKA), reference POCI-01-0145-FEDER-022217, funded by COMPETE 2020 and FCT, Portugal. D.B. acknowledges support from the DeutscheForschungsgemeinschaft , DFG project ISM–SPP 1573. The calculations were carried out at the ISM – Xeon Phi cluster of the Computational Astrophysics Group, University of Évora, acquired under project “Hybrid computing using accelerators coprocessors-modelling nature with a novell approach” (PI: M.A.), InAlentejo program, CCDRA, Portugal.
Paper: 2016A&A...588A..18M (acknowledgments)
V.S. thanks DFG for financial support (grant WE 1312/48-1) and D.S. thanks RFBR for support (grant 15-02-01407) and Eberhard Karls Universität, Tübingen for its hospitality. Criticisms from the referee led to significant improvement of the paper.
Paper: 2018MNRAS.474.3720W (acknowledgments)
Acknowledgements ESW acknowledges generous support from the Swedish National Space Board. SBC was supported by NASA’s Origins of Solar Systems and Emerging Worlds Programs.
Paper: 2016MNRAS.463L..64A (fulltext)
the Milky Way GRB scenario, we take Band parameter values: E 0 = 187.5 keV, α = −0.8, β = −2.3, spread over 1 keV–10 MeV range (Thomas et al. 2005 ). We obtain 3.96 × 10 18 eV cm −2 deposited in the top 1 g cm −2 of the atmosphere. The spectral information for the interstellar cloud scenario was obtained from Cummings, Stone Steenberg ( 2002 ) following the method in Pavlov et al. ( 2005 , 2011 ) giving the total fluence of 8.8 × 10 11 eV cm −2 s −1 . For modelling extreme solar events, estimating the spectrum is difficult because most of the events occurred before the satellite age. We selected the spectrum of the 2005 January 20 event here, because even though the event was not large by any standards, it was very well studied by a number of groups using both satellite and ground-based measurements. We rescaled its total energy to the Carrington event (2 × 10 33 erg) and took the Martian distance into account. The energy deposition rate was found to be 3 × 10 11 eV cm −2 s −1 assuming the event was spread over 24 h. The ion pair generation energy w of CO 2 is 33 eV ion pair −1 . The primary mechanism at play is photochemical escape, for example: CO 2 → CO + e − CO + O → O + CO O + + CO 2 → O + CO etc. A complete description of all the reactions and associated energetics can be found in standard textbooks such as Schunk Nagy ( 2009 ). We divide the deposited energy obtained from simulations by the ion pair generation energy to calculate ion pairs for each scenario. Our results are spread over a wide range of ion production rates, 1.2 × 10 16 ion pairs cm −2 s −1 from a Galactic GRB to 2.8 × 10 7 ion pairs cm −2 s −1 from gamma rays of an SN at 100 pc. Fig. 3 shows the ion production rates of different events along with their durations. Particles can escape the Martian atmosphere with just 0.125 eV amu −1 energy. With charge exchange, the fast ion becomes neutral and escapes, and the slower ion gets trapped in the magnetic field. Fast ions can directly knock the atoms out of the atmosphere, this process is known as sputtering. Ions also escape by gaining energy from ambipolar diffusion and by thermal escape according to Jeans equation. A Galactic GRB would directly deposit energy, produce ions (1.2 × 10 16 ion pairs cm −2 s −1 ) and transfer energy to kinetic energy of ions. The event is short lived but the magnitude of ionization and energy transfer is large. Thermal expansion due to a large energy deposition is possible in this scenario. In the nearby SN case, the gamma-ray and cosmic ray enhancements are ∼1 and ∼100 yr long, respectively. In addition to ion escape by large energy transfer, a fraction of the ions will also be picked up by solar wind and ICMEs during the period. Encounter with dense interstellar clouds would result in a ∼1-Myr-long enhancement in ionization rates (∼10 10 ion pairs cm −2 s −1 ). Extreme solar events are short-lived but as seen in Fig. 3 , can do significant damage and are relatively more frequent. Figure 1.
Paper: 2019MNRAS.483.2563R (fulltext)
shock front with high velocity, overtakes the older bullets and decelerates. In the last panel, the newly ejected bullet E is in a comparable position as former bullet D in the first frame. The protostar launches outflow bullets labelled A through E corresponding to the left and right outflow lobe. In the remainder of the paper, we will only refer to the left lobe and therefore we drop the subscript L henceforth. The outflow bullets form an S-shaped chain of Herbig–Haro objects. The S-shape is due to the varying orientation of the angular momentum, , caused by anisotropic accretion onto the IAD from core (Ybarra et al. 2006 ; Wu, Takakuwa Lim 2009 ; Zhang et al. 2013 ; Frank et al. 2014 ). The first two bullets interact with the collapsing dense core material and are thus not visible in Fig. 8 . The timespan from 115 kyr to 122 kyr is chosen to capture the end of a cycle of an outflow bullet, here bullet D, when it hits the leading shock front. This cycle happens in a similar fashion for all other bullets, except the first bullet A. This is the first bullet that breaks out of the core and survives as a coherent structure. Bullets B and C have higher velocity and have overtaken A. Bullet D starts off faster still, and by the final frame it has overtaken all the others and hit the leading shock, where it is slowed down. Bullet E is even faster, but by the final frame it has not yet caught up with D and is in a similar position as former bullet D in the first frame. A sixth bullet has been launched, but is still inside the core, and therefore can not be seen on Fig. 8 . Fig. 9 shows the accretion rates onto the sink, and onto the protostar, , between (when the protostar forms) and . is relatively constant, between ∼2 × 10 −5 and . The accretion rate tends to decrease over time. It drops briefly, following an outburst, because the outburst heats the accretion disc (cf. Lomax et al. 2014 , 2015 ). The accretion rate of the protostar is low in the quiescent phase, , but approaches during an outburst. Note that the time resolution in Fig. 9 is too coarse to resolve the very short outbursts. The downtime between outbursts is and the duration of an outburst is (Stamatellos et al. 2007 , 2011b ). Figure 9. Evolution of the accretion rates onto the sink particle, (green), and the protostar, (blue). The labels, A–F, correspond to the outflow bullets formed in the individual outbursts (see Figs. 8 and 10 ). Note that the time resolution in this figure is too coarse to fully resolve the very short outbursts. 4.1 Position–velocity diagrams In order to determine whether the simulated outflow reproduces the ‘Hubble-Law’ relation, and individual ‘Hubble Wedges’, as observed, for example, by Bachiller et al. ( 1990 ); Arce Goodman ( 2001 ); Tafalla et al. ( 2004 ); Santiago-García et al. ( 2009 ); Wang et al. ( 2014 ), we construct PV-diagrams, i.e. false-colour plots of on the plane. Here, r is radial distance, and is radial velocity, both measured from the
Paper: 2016MNRAS.455.2042W (acknowledgments)
Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CONICYT (Chile), the Australian Research Council (Australia), Ministério da Ciência, Tecnologia e Inovação (Brazil) and Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina). We are extremely grateful for the support of the NIFS teams at the Australian National University, Auspace, and Gemini Observatory for their tireless efforts during the instrument integration, commissioning and system verification: Jan Van Harmleen, Peter Young, Mark Jarnyk (deceased), Nick Porecki, Richard Gronke, Robert Boss, Brian Walls, Gelys Trancho, Inseok Song, Chris Carter, Peter Groskowski, Tatiana Paz, John White, and James Patao. Thanks must also go to Tracy Beck for her support during the commissioning and observations. We thank the anonymous referee for their valuable comments on the manuscript. MW acknowledges the generous travel support from Academia Sinica to attend the conference Star Formation Through Spectroimaging at High Angular Resolution in July 2011, which provided useful information for this study. This work was supported by the Australian Research Council through Discovery Project Grant DP120101792 (R. Salmeron).
Paper: 2020A&A...633L...9J (fulltext)
reality, some of them may change during a stellar lifetime. Inner processes in stellar evolution such diffusion ( Dotter et al. 2017 ) or planet formation or engulfment ( Meléndez et al. 2009 ; Tucci Maia et al. 2019 ) alter the chemical composition in stars. Other inner mixing processes produced during dredge-up or induced by rotation can alter abundances such as [C/N] or Li ( Masseron et al. 2017 ; Aguilera-Gómez et al. 2018 ). We have to assume today that a selection of chemical abundances remains approximately intact in FGK main-sequence stars and can be used to trace chemical evolution. A few years ago, da Silva et al. (2012) studied several traits for chemical evolution studies of solar-type stars. In particular, [Y/Mg], [Sr/Mg], [Y/Zn], and [Sr/Zn] showed a significant trend as a function of age for their entire sample. When they performed a linear fit of [Y/Mg] as a function of stellar age for a sample of nearby solar twins, Nissen (2015) obtained slope of ∼0.04 dex Gyr −1 and interpreted this relation using nucleosynthesis arguments: massive stars produced Mg and expelled it into the interstellar medium on a different timescale than intermediate-mass stars reaching the AGB and producing Y. This has motivated discussions about whether abundance ratios can be used as “chemical clocks”, in particular if ages are difficult to measure. The exact dependence of [Y/Mg] or [Ba/Mg] on age is still debated, because it seems to further depend on stellar properties such as metallicity ( Feltzing et al. 2017 ; Skúladóttir et al. 2019 ; Delgado Mena et al. 2019 ), but not evolutionary state, at least before the AGB phase ( Slumstrup et al. 2017 ). In addition to [Y/Mg] and [Ba/Mg], [Al/Y] has also shown similar dependences with age ( Nissen 2016 ; Spina et al. 2016 ), at least for solar twins. Several other works that benefit from accurate age estimates have studied trends of abundance ratios as a function of age ( Bedell et al. 2018 ; Spina et al. 2018 ; Delgado Mena et al. 2019 ; Feuillet et al. 2018 , to mention recent works). Most of these analyses have been restricted to using abundance ratios in their classical form, as a function of iron (see Delgado Mena et al. 2019 for exception). Motivated by the fact that the chemical clocks discussed above do not include iron, we searched for other combinations in a sample of 30 elemental abundances. We found 55 relations that are as significant as those mentioned above. This new set of ratios can be used for the same applications as [Y/Mg]. In addition, the ratios can be applied to models of Galaxy evolution and to build stellar phylogenies ( Jofré et al. 2017 ; Jackson et al., in prep.). We report these findings in this Letter. 2. Data and methods We considered the sample of 80 solar twins (including the Sun) that was analysed by Bedell et al. (2018 , and references therein) and Spina et al. (2018) . This is the largest sample of solar twins that has been analysed using the differential technique for
Paper: 2019ApJ...877...42S (acknowledgments)
We are grateful to Todd Hoeksema and Thierry Appourchaux for constructive discussion and Eric Fossat for providing the 80 s data he used. We also thank the SOHO GOLF and VIRGO/LOI teams for making their data available as per the SOHO data access policy, and the Birmingham Solar Oscillations Network group for access to the BiSON data. This work utilizes data obtained by the Global Oscillation Network Group (GONG) program, managed by the National Solar Observatory, which is operated by AURA Inc. under a cooperative agreement with the National Science Foundation. The data were acquired by instruments operated by the Big Bear Solar Observatory, High Altitude Observatory, Learmonth Solar Observatory, Udaipur Solar Observatory, Instituto de Astrofísica de Canarias, and Cerro Tololo Inter-American Observatory. We acknowledge support by HMI NASA contract NAS5-02139.
Paper: 2018ApJ...858...57E (fulltext)
on all rows as vertical dotted lines. Variation in GBT spectra among rows two, three, and four demonstrates some of the variations in OH content and distribution over the region that increases the uncertainty in T ex determined with the expected profile method. Also worth noting is the unusual case in the fourth row in which the 1665 MHz line appears to be brighter than the 1667 MHz line and the secondary component appears to dominate. This deviation is within the uncertainty so it could be attributed to noise, or perhaps it is evidence of a localized anomaly in the secondary component. Figure 5. VLA absorption spectra from Source 2 and GBT emission spectra from its vicinity for the expected profile. Gaussian fits are performed on the VLA spectra and shown superimposed with a dashed line, and the centroid velocity of the Gaussian fit is displayed as a vertical dotted line on each spectrum. VLA observations of Source 1 as well as three of the four nearby GBT observations for the expected emission profile are shown in Figure 4 . The fourth GBT observation was not used because T C at that position was too high. The VLA observations at Source 1 reveal the presence of two OH components: a primary component centered near −39 km s −1 (called Component A), and a secondary component centered near −37.5 km s −1 (called component B). The expected profiles made by averaging the GBT spectra from observations surrounding Sources 1 and 2, respectively, also display these two components, but in the case of the individual GBT spectra averaged, both components are not always detected together. As a result, it makes sense to treat the two components as separate OH features, fit the VLA absorption data as a sum of two Gaussian components, and compare the optical depth and line temperature values for the corresponding components when using Equation ( 2 ) to estimate the excitation temperatures. These T ex values can then be averaged together after their separate determinations, but if the components of the OH features are not treated separately, the analysis will be faulty. The results of this exercise are given in Table 1 . The uncertainties reported in this Table are calculated only from the error in the profile integral and the absorption optical depth measurements. Table 1 Excitation Temperatures for the Components of Source 1 Note. Errors from profile integrals and optical depth measurements. In order to estimate the error resulting from variation in OH, we look at some of the variation in OH profile integrals between adjacent pointings within our survey. We choose an example with background T C = 4.0 K in order to best approximate the regions surrounding Sources 1 and 2, which have similar background T C values, because we wish to avoid making assumptions about T ex and how that would affect emission profile integral strengths. Between coordinates l = 138625, b = 15, and l = 13875, b = 15, the profile integral at W5 radial velocities varies between a
Paper: 2017MNRAS.468.4579W (fulltext)
of observing a region as empty as the Local Void (Section 4.2.2). The simulated Local Volumes have lower number density by up to a factor of 2 at the faint end of the luminosity function in the WDM model (Fig. 8 ). The probability of a region as empty as the observed Local Void is as high as 34 per cent in the WDM model, five times higher than that in the CDM model (Fig. 9 ). A more complete Local Volume galaxy census (e.g. the Dark Energy Spectroscopic Instruments Bright Galaxy Survey; DESI Collaboration 2016 ) should therefore provide a way to constrain the nature of dark matter. APPENDIX A: HALO MASS FUNCTIONS IN SIMULATIONS Fig. A1 shows the FOF halo mass functions at different redshifts in the COLOR and COLOR-WARM simulations as described in Section 2.1 . WDM halo mass functions flatten towards the low-mass end compared to those in CDM. As first indicated by Wang White ( 2007 ), spurious haloes form due to a numerical discreetness effect in warm and hot dark matter simulations. The mass limit of dark matter haloes below which spurious haloes form depends on the N -body particle mass of the simulation and the WDM particle mass assumed. In COLOR-WARM, the N -body mass resolution is m dm 6.2010 6 h 1 M , and the WDM particle mass is m x 1.5keV. This corresponds to a mass limit of M lim 2.4210 9 h 1 M and M min 0.5 M lim given by equations derived by Wang White ( 2007 ) and Lovell etal. ( 2014 ), respectively. These limits are indicated by dashed and dotted vertical lines in Fig. A1 . Below these mass limits, the halo mass functions steepen significantly due to the emergence of spurious haloes. Figure A1. FOF group mass (total particle mass) function at three redshifts in the simulations. Solid lines denote the COLOR simulation and dashed lines COLOR-WARM. The vertical dashed and dotted lines are the limiting masses below which spurious haloes form in COLOR-WARM, given by Wang White ( 2007 ) and Lovell etal. ( 2014 ). APPENDIX B: BOX AND RESOLUTION EFFECTS In this study, the simulation boxes we use are much smaller than the MS-W7 simulation that the GP14 model was calibrated with. In addition, the mass resolution of our simulations is about 100times higher than that of MS-W7. Fig. B1 shows the galaxy SMFs at z 0 of six COLOR-sized regions extracted from the MR-W7 simulation. We find that variations of up to 0.3 dex in galaxy abundance are expected due to cosmic variance. Figure B1. Left-hand panel: six random COLOR-sized regions (small coloured boxes) are extracted from MR-W7 (black dots denote the whole box). Right-hand panel: the galaxy stellar mass functions of the selected COLOR-sized regions (line colour corresponds to the box colour in the left-hand panel) combined with GP14 model. The black line is the stellar mass function of MR-W7 combined with the GP14 model. Fig. B2 shows the SMFs of the GP14 model combined with MR-W7 and COLOR, while the dotted line is the SMF when COLOR trees have all the branches below the resolution of