adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019A&A...627A.103V (fulltext)
be expected to be smaller than both N and N̄ p . Indeed, a maximum likelihood fit υ ML ( z max ) of the PDF given by Eq. ( 13 ) to the set of simulated { z max } with N p as free parameter provides N̄ p = 2210. The right panel of Fig. 3 shows that something similar holds for the GDM since the theoretical value N * = 17 684 is greater than the number Ñ ∗ = 15330 corresponding to the maximum likelihood fit g ML ( z max ). Here, it is worth stressing that υ Th ( z max ) and g Th ( z max ), and hence the corresponding CDFs Υ Th ( z max ) and G Th ( z max ) shown in Fig. 4 are almost identical. The same holds for υ ML ( z max ) and g ML ( z max ) and the corresponding CDFs Υ ML ( z max ) and G ML ( z max ) again shown in Fig. 4 . As expected the two approaches are practically equivalent, given that both methods are essentially based on the same assumptions. Moreover, in Fig. 4 all these CDFs appear close to the sample CDF. This indicates that the value of the parameters N p and N * is not of critical importance. Fig. 3. Left panel : histogram H ( z max ) vs. the theoretical PDF υ Th ( z max ), mean PDF υ Mean ( z max ) and the maximum likelihood PDF υ ML ( z max ) of the value z max of the highest peak of a zero-mean unit-variance GRF with autocorrelation given by a circular Gaussian with dispersion set to three pixels. The numerical experiment is based on the simulation of 5 × 10 3 GRF’s of size 500 × 500 pixels. υ Th ( z max ) has been computed using Eqs. ( 18 ) and ( 8 ) with N p obtained as explained in Vio17, whereas υ mean ( z max ) has been computed with N p given by the mean number of peaks in the simulated GRF’s. In the case of υ ML ( z max ) the number N p comes out from a maximum likelihood method (see text). Right panel : corresponding g Th ( z max ), computed by means of Eq. ( 27 ) with N * given by the ratio of the total area of the GRF to the window area πl 2 with (see text). Also the PDF g ML ( z max ) has been computed with N * resulting from a maximum likelihood method. To notice that υ Th ( z max ) and g Th ( z max ) are almost identical. The same holds for υ ML ( z max ) and g ML ( z max ). Fig. 4. Cumulative distribution functions corresponding to the PDFs in Fig. 3 . A further comparison of the two methods concerns the computational burden. In this respect the GDM is superior in situations where the autocorrelation function has circular symmetry (i.e., it is characterized by only a scale parameter), since the more expensive step consists in the computation of the quantities σ j , j = 0, 1, 2, which requires the numerical computation of three one-dimensional integrals. On the other hand, PAM requires the numerical computation of a number of integrals equal to the number of peaks selected for the detection. In general the difference in computational time is on the order of a few seconds even in the case of large images. The computational superiority of GDM vanishes in the presence of autocorrelation functions when they are not
Paper: 2019A&A...622A.103B (fulltext)
properties. This means that the stellar mass (or the dust luminosity when there is no stellar population included in the model) is not a free parameter even though it is technically possible to treat it as such. This would greatly expand the size of the parameter space by adding an extra dimension, while slowing the computation of the grid and the analysis by a similar amount, and degrade the accuracy of the estimation of the physical properties. Optionally, CIGALE can also handle fluxes for which only upper limits have been determined. We adopt the method presented in Appendix A2 of Sawicki (2012) . This affects the aforementioned computing steps in several ways. First, the computation of the χ 2 is divided between regular quantities (first three terms corresponding to Eq. ( 14 )) and those with only an upper limit (last three terms): with “erf” being the error function, f ul the flux upper limit, and the indices i , j , and k indicating respectively the fluxes, extensive properties, and intensive properties. See Eq. (A6)–(A10) of Sawicki (2012) for a full derivation 6 . The main difficulty is to determine α . For this we have to numerically solve ∂ χ 2 /∂ α = 0 for every model, which is equivalent to solving Eq. (A11) from Sawicki (2012) : We do so using the scipy.optimize.root root-finding method. Once the χ 2 are computed, the subsequent steps no longer depend on the presence or absence of upper limits. We have to note that objects in a catalogue do not all need to be fitted with the same set of data. CIGALE will automatically only consider the available data for a given object. The lack of certain data for some targets has a direct impact on some of the aforementioned computation steps. For steps 2 and 3, we simply do not include the data that have been marked as invalid in the input file (value lower than 0 or set to “nan”, by convention) in the computation of α and χ 2 . Another feature of this module is the possibility to assess whether or not physical properties can actually be estimated in a reliable way through the analysis of a mock catalogue. The idea is to compare the physical properties of the mock catalogue, which are known exactly, to the estimates from the analysis of the likelihood distribution. To build the mock catalogue we consider the best fit for each object. We then modify each quantity by adding a value taken from a Gaussian distribution with the same standard deviation as the uncertainty on the observation. This mock catalogue is then analysed in the exact same way as the original observations. Physical properties for which the exact and estimated values are similar can be estimated reliably. Applications of the pdf_analysis module to a sample of representative galaxies is presented in Sect. 5.3 . 5. Examples of <monospace>CIGALE</monospace> use cases As seen above, CIGALE has been designed to be flexible and versatile so that it may have various applications: estimation of the physical properties of an object
Paper: 2018A&A...619A..62S (acknowledgments)
I thank Fabian Emmerich for help with type-setting and Hauke Wörpel for useful hints regarding Gaia distances. I thank an anonymous referee whose comments helped to improve the quality and clarity of the paper. This work has made use of data from the European Space Agency (ESA) Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
Paper: 2020ApJ...902...24D (fulltext)
HD 269902 (bottom). In HD 269902, most of the power in the WWZ is contained in the lower frequencies where the stochastic low-frequency background dominates—unsurprisingly given that the star has the second largest value of α out of the five FYPS (0.024 ± 0.001). Only by altering the scaling of the image of the WWZ are we able to see the ridge associated with the lowest-frequency peak, and it is only detected with low S/N. In both stars, the lowest-frequency peak in the RPS is associated with a broad band of power in the WWZ, similar to the WWZ of HD 269953, with a secondary band appearing at day −1 in HD 269840, at the approximate location of the highest peak in the RPS. 4. Discussion 4.1. Asteroseismic Modeling The discovery of fast pulsations in YSGs has very interesting implications for the study of the interiors of evolved massive stars. The 25 M ⊙ solar metallicity Geneva model (Ekström et al. 2012 ) has a main-sequence lifetime of approximately 7 Myr. It then crosses the H-R diagram in under 1 Myr, and half a Myr later, has evolved bluewards once more to become a Wolf–Rayet star (Massey et al. 2017 ). Due to the incredibly short lifetime of YSGs on both crossings of the H-R diagram, theoretical uncertainty has long stymied our understanding of massive star evolution (Kippenhahn Weigert 1990 ). Pulsation frequencies extracted from long-baseline lightcurves assembled from space-based observations are perhaps the most precise measurements we can make; typical values of for frequencies listed in Tables A1 – A5 are . In better-studied stars, such precision allows for the diagnosis of incredibly complicated physics and is truly the benchmark of testing stellar evolution theory (Aerts 2019 ). No asteroseismic models for YSGs that reliably converge exist (Jeffery Saio 2016 ), so the era of precision YSG asteroseismology is not yet upon us. However, we can compare the frequencies observed in the FYPS with the characteristic Lamb and Brunt–Väisälä angular frequencies in a model YSG: where l is the harmonic degree, c s is the sound speed, r is the radius of the local shell, g is the gravity, and the remaining variables are defined as in Aerts et al. ( 2010 ). Modes with angular frequencies and are mostly restored by pressure ( p -modes), and modes with and are mostly restored by buoyancy ( g -modes). While rudimentary, such a comparison would illustrate the approximate regions of the stellar structure the observed pulsations will allow us to probe. To do this, we use version 12778 of Modules for Experiments in Stellar Astrophysics (MESA, Paxton et al. 2011 , 2013 , 2015 , 2018 , 2019 ) to evolve a grid of nonrotating stellar models with initial masses between 15 and 30 M ⊙ , with a spacing of 1 M ⊙ , initial metallicity set to Z = 0.006, and initial helium mass fraction set to . We first evolve the models through the entire pre-main-sequence stage until the entirety of the star’s luminosity comes from nuclear burning and then introduce more
Paper: 2018A&A...611A..45R (fulltext)
by the interaction of the supernova (SN) ejecta with the circumstellar medium (e.g. Ofek et al. 2007 ; Quimby et al. 2011c ; Chevalier Irwin 2011 ), (ii) pair-instability driven ( Gal-Yam et al. 2009 ; Gal-Yam Leonard 2009 ), or (iii) neutron-star driven ( Kasen Bildsten 2010 ; Dessart et al. 2012 ; Kotera et al. 2013 ; Metzger et al. 2014 ; Murase et al. 2015 ; Suzuki Maeda 2017 ). Given the variety of observed spectra, it is plausible that different processes are at play in different objects (e.g. Gal-Yam 2012a ; Nicholl et al. 2014 ). Interestingly, scenarios (i) and (iii) predict bright associated γ -ray emission in the GeV to TeV range ( Murase et al. 2011 , 2014 , 2015 ; Katz et al. 2012 ; Kotera et al. 2013 ). The search for such γ -ray emission with the Fermi Large Area Telescope (LAT) data is the scope of this paper. In the most conventional model (scenario i) SLSNe are powered by the interaction between the SN ejecta and a massive, optically thick circumstellar medium ( Smith McCray 2007 ; Smith et al. 2008 ; Miller et al. 2009 ; Benetti et al. 2014 ). SN2003ma and SN2006gy for example seem to be explained well by this phenomenology ( Smith McCray 2007 ; Ofek et al. 2007 ; Smith et al. 2010 ). Several authors ( Murase et al. 2011 , 2014 ; Katz et al. 2012 ) have demonstrated that a collisionless shock could then be formed and would host efficient cosmic-ray acceleration leading to non-thermal emission from radio-submillimeter to γ -rays. In the GeV range, this emission can escape from the system without severe attenuation, for specific shock velocities (about 4500–5000 km s −1 ) ( Murase et al. 2015 ), and at late times after the shock breakout. Ackermann et al. (2015) searched for this specific radiation with the LAT at the location of core-collapse SNe (Types IIn and Ib), with standard luminosity, spanning typical time windows of a few months to a year starting from the optical luminosity peak. No detection was reported and model-independent flux upper limits were derived. A fast-rotating central neutron star releasing its rotational energy into the SN ejecta could also drive SLSNe (model iii; e.g. Kasen Bildsten 2010 ). The rotation period has to be close to milliseconds to transfer significant energy to the ejecta. The strength of the initial dipole magnetic field of the star sets the timescale over which the energy is injected (a stronger field leads to faster decline). Magnetars ( B ~ 10 15 G) have thus been proposed as central engines for SLSNe ( Kasen Bildsten 2010 ; Dessart et al. 2012 ). Pulsars with millisecond periods at birth and milder dipole magnetic fields B ~ 10 13 G would also lead to bright peaks as well as a high-luminosity plateau lasting for several months to years ( Kotera et al. 2013 ; Murase et al. 2015 ). These authors further calculated that the young neutron-star wind nebula would present a bright X-ray and γ -ray peak, respectively, through synchrotron radiation and inverse Compton (IC) scattering,
Paper: 2018MNRAS.475.3502R (acknowledgments)
Acknowledgements H.R. thanks a CAPES fellowship and the CAPES PDSE programme (88881.132145/2016-01). J.M. thanks support by FAPESP (2012/24392-2 and 2014/18100-4) and CNPq (Productivity fellowship). We also thank Dr. Akito Tajitsu for his important support and initial data reduction during the observations with the Subaru Telescope. This study is based (in part) on data collected at Subaru Telescope, which is operated by the National Astronomical Observatory of Japan.
Paper: 2020MNRAS.499.2416A (fulltext)
radius from the particle data of the N -body simulation. We also determine the circular frequency, Ω, by averaging the radial accelerations of particles in an annulus with a width of 50 pc at each radius. In Fig. 8 , we show Ω + κ/ m ( m =2, 3, 4, and 5) and Ω as functions of R . The horizontal line in Fig. 8 indicates the pattern speed of the bar. The radii at which the line intersects with the frequency curves correspond to the guiding-centre radii for the 2:1, 3:1, 4:1, 5:1, and the corotation resonances. In our model of the MW, the Sun, at a distance of ∼8 kpc from the Galactic Centre, is close to the 3:1 OLR. It is then not surprising that this resonance is dominant in the local stellar population. Figure 8. The orbital frequencies as functions of the galactic radius, R . Ω was determined from the velocity curve and Ω + κ/ m ( m =2, 3, 4, and 5) as expected within the epicycle approximation are plotted. The vertical axis is normalized by Ω 8kpc . The horizontal line indicates the bar’s pattern speed and the vertical lines represent the guiding radii for the respective resonant orbits. 4.2 The MW’s bar pattern speed When projecting the Gaia DR2 data into the R – v φ plane, a series of ridges appear over a wide range of R (Antoja et al. 2018 ; Kawata et al. 2018 ; Ramos et al. 2018 ). Although the origin of these structures is still in debate, previous studies (e.g. Monari et al. 2017b , 2019a ; Fragkoudi et al. 2019 ; Barros et al. 2020 ) have discussed that these ridges may originate from resonances, while other studies have suggested perturbations by a satellite galaxy such as the Sagittarius dwarf (Khanna et al. 2019 ; Laporte et al. 2019 ) or by winding transient spiral structure (Hunt et al. 2018 , 2019 ). In this section, we study the relation between the observed ridges in Gaia DR2 and resonances in our simulated MW model: by projecting our simulated and classified stars into the R – v φ plane, we can directly compare to the same projection of the Gaia DR2 data. If the stars classified by resonance type align with the observed ridges, then this provides compelling evidence that these ridges are indeed caused by resonantly trapped stars. In Fig. 9 , we show the distributions of stars in the R – v φ plane as it arises in our simulated MW model (left-hand column) and in the Gaia DR2 (right-hand column). The top panels show 2D histograms of particles within 2 kpc from the Sun [, in our model]. This panel reveals a selection effect caused by Gaia ’s measurement uncertainties: due to us having selected stars with relative parallax error below 10 per cent the right-hand panel displays systematically fewer stars in approximately concentric rings around the Sun’s position at Visually, this causes the smaller extend of the red area in the right-hand panel. We expected, however, that our analysis is robust with respect to this selection effect: it implies that fewer stars make it into our DR2 projection than into the projection of our
Paper: 2016A&A...586A.108H (acknowledgments)
J.H. greatly appreciates the CNES post-doctoral fellowship program. J.H. and M.D. were supported by the project under the contract 11-BS56-008 (SHOCKS) of the French Agence National de la Recherche (ANR), JD by grant GACR 15-04816S of the Czech Science Foundation, DO by the grant NCN 2012/S/ST9/00022 of Polish National Science Center, and A. Marciniak by grant 2014/13/D/ST9/01818 of Polish National Science Center. We thank the referee, Mikko Kaasalainen, for his thorough review of our manuscript and his constructive comments and suggestions that led to a significant improvement of the text. The computations have been carried out on the “Mesocentre” computers, hosted by the Observatoire de la Côte d’Azur, and on the computational cluster Tiger at the Astronomical Institute of Charles University in Prague ( http://sirrah.troja.mff.cuni.cz/tiger ). Data from Pic du Midi Observatory were partly obtained with the 0.6 m telescope, a facility operated by observatoire Midi-Pyrénées and Association T60, an amateur association. The Joan Oró Telescope (TJO) of the Montsec Astronomical Observatory (OAdM) is owned by the Catalan Government and operated by the Institute for Space Studies of Catalonia (IEEC). We thank Franck Pino (INO-AZ) and Lech Mankiewicz (EU-HOU/Comenius) for the remote access to Ironwood North.
Paper: 2017MNRAS.466..677G (fulltext)
supported. The key features of each multiphase flow are summarized as follows. As rotation dominates over turbulent motions (Ta t v rot / v 1) in the massive galaxy, the condensation occurs along a conical helix, spinning up the gas which finally settles on to the equatorial region in a multiphase disc (Section 3 ). The disc is cylindrically stratified: the ionized (UV) gas envelops the neutral (optical/IR) layer which englobes the very thin molecular (radio) structure, dominating 95percent of the condensed mass. There is no major fragmentation observed, with the warm phase experiencing mild perturbations via pressure torques induced by condensation. The BHAR is suppressed by almost 2 dex compared with the cooling rate as most gas has found circularization. Accretion proceeds along a narrow polar funnel (10) or after reaching the inner equator through gas which condensed out of the plasma with low angular momentum. After 50Myr the disc has reached 1kpc extension. Extended molecular and warm discs with similar surface density have been observed in 1/3 of observed massive, typically quiescent, galaxies (Caon etal. 2000 ; Ocaa Flaquer etal. 2010 ; Young etal. 2011 ; Werner etal. 2014 ; Hamer etal. 2016 ). When rotation and turbulence are both inhibited, the hot plasma condenses radially, forming a compact multiphase cloud within the inner 100pc (Section 4 ). The inner multiphase density profiles are steep, following a free-fall slope ( r 3/2 within the SMBH influence region). The BHAR is almost identical to the cooling rate. Adiabatic, compressional heating inhibits the formation of the third molecular phase. The warm 8000K (optical/IR) gas dominates the condensed medium, albeit highly concentrated, with a very thin ionized UV layer. Similarly, a significant number of quiescent massive galaxies display a spherically symmetric warm H core (Caon etal. 2000 ; Werner etal. 2012 , 2014 ) and tend to lack cold gas, in particular when no AGN feedback or other major perturber are detected. In the more frequent, intermediate state the hot halo experiences both rotation and subsonic turbulent motions (160kms 1 ) leading to CCA (Section 5 ). The multiphase condensation occurs in the high density peaks via local runaway cooling. AGN heating prevents a global cooling inflow, facilitating the growth of multiphase structures. Initially, warm filaments condense out of the low-entropy plasma. The denser, perturbed regions further condense into molecular clouds, following a top-down rainfall. The CCA angular momentum transport is driven by pressure torques on 1kpc scale and dominated by chaotic inelastic collisions in the core (ram-pressure drag is secondary). The accretion rate can be modelled via quasi-spherical viscous accretion (Section 5.2 ), with an effective clump collisional viscosity c v c ; the mean free path is c 100pc with large scatter. The core cloud volume filling is a few per cent, rapidly declining beyond 1kpc. As ensemble, the multiphase clouds
Paper: 2018MNRAS.473.4795K (acknowledgments)
ACKNOWLEDGEMENTS This research has been supported by Arizona State University. We would like to thank Soma De, Tanmay Vachaspati, Daniel C. Jacobs, Adam Beardsley and Samuel Gordon for their insightful conversations. Some of the results in this paper have been derived using the healpix (Górski et al. 2005 ) package. This research made use of astropy , a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013 ).
Paper: 2017ApJ...838....4Y (fulltext)
z LAEs with high-resolution HST narrow-band imaging (Bond et al. 2010 ; Finkelstein et al. 2011 ). Bond et al. ( 2010 ) suggested that Ly α sizes are compact and similar to UV emission, but Finkelstein et al. ( 2011 ) posited that the half light radius of Ly α appears ∼1.6 times larger than the half light radius of UV continuum. These narrow-band HST images of high- z LAEs are very hard to obtain and have low S/Ns. Thus, the Ly α and UV sizes of low- z LAEs are valuable. Because Green Peas are analogs of high- z LAEs, our results suggest that most high- z LAEs likely have larger Ly α than UV sizes. The extended Ly α emission probably indicates gas outflows around galaxies illuminated by Ly α light. One interesting question regards the redshift evolution of Ly α sizes of LAEs. Recently, Wisotzki et al. ( 2016 ) measured Ly α radial profiles of a sample of LAEs at z = 3–6 from VLT/MUSE data. They found that, in 12 LAEs with both Ly α and UV continuum sizes, the Ly α light is considerably more extended than the UV continuum light. Here, we compare the sizes of Green Peas and the MUSE LAEs. Using the Ly α radial profiles in Wisotzki et al. ( 2016 ), we measured the deconvolved Ly α scale radius r e , assuming intrinsic exponential profiles, so that the methods are same when measuring the r e of MUSE LAES and Green Peas. As shown in Figure 5 , the Ly α -to-UV size ratios of Green Peas and MUSE LAEs are very similar. Notice that some MUSE LAEs have extended Ly α halos far beyond the scale radius. For Green Peas, however, we do not have robust data to characterize the Ly α emission beyond a few Kpcs. Figure 5. Left: Comparison of the Ly α- to-UV scale length of Green Peas (green squares) and MUSE z = 3–6 LAEs sample (red dots) (Wisotzki et al. 2016 ). The Ly α scale lengths of MUSE LAEs are measured from the radial profile in Wisotzki et al. ( 2016 ) using the same method as Green Peas. Right: Comparison of the Ly α -to-UV Petrosian 20% (P20) radius of LARS ∼ 0 galaxies and MUSE LAEs. Notice that the Petrosian 20% radius of MUSE sample is measured from the best-fit model of radial profile. The dashed cyan lines show constant ratios of 1:1, 2:1, 5:1, and 10:1. In the right panel of Figure 5 , we compare the Petrosian 20% radius ( R P20 ) of MUSE LAEs (Table 2 in Wisotzki et al. ( 2016 )) to that of the LARS sample (Table 1 in Hayes et al. 2014 ). Compared to the five strong Ly α emitters in LARS sample (marked by stars, LARS01, 02, 05, 07, and 14), the MUSE LAEs only have about two times larger ratios of R P20 (Ly α ) to R P20 (UV). One caveat of the comparison is that the Petrosian radius of MUSE sample is measured from the best-fit model of radial profile, instead of the observed data, which is different from the method used in the LARS sample. This might be the reason that the R P20 (UV) of MUSE LAEs are about 2–4 kpc, approximately a factor of three larger than the R P20 (UV) of the five LARS Ly α emitters. Based on our rough comparison of Green