adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.493....1H (fulltext)
At = 0, cold accretion and cold wind recycling each contributes roughly half of the total stars formed, while hot mode accretion contributes of star formation. Cold accretion dominates the supply of star-forming gas in all three simulations until = 2.0, after which cold wind recycling and hot mode accretion start to be important. Compared to the other simulations, the haloes in Refσ3 form many more stars from both cold accretion and wind recycling, not because of more inflow but because they have less outflow as a result of the smaller mass-loading factors. The slower wind speed in the RefSlow model increases cold wind recycling by a large amount compared to the fiducial simulation. As a result, the galaxies at = 0 are slightly more massive as wind recycling gains importance after = 2, but their stellar masses are still consistent with the observations. Intermediate-mass haloes : The = 0 stellar mass from all three simulations is consistent with the observations within a small factor. The evolution of stellar content in these haloes is qualitatively similar to the small-mass haloes but with several major differences. First, stars from all accretion channels formed earlier in these more massive haloes, as is expected from the hierarchical assembly of galaxies. Secondly, both hot accretion and hot wind recycling, though still subdominant over most of the time, become more important at low redshifts, and together contribute of the total stars formed at = 0. Thirdly, cold accretion still dominates star formation at > 2 but nearly stops after = 2 when the shock-heated gas starts to develop a hot corona in these haloes. In the end, cold accretion only accounts for of the total stars formed. Cold wind recycling still plays a critical role in determining the final stellar mass of the galaxies, and largely accounts for the differences among the three simulations. Its contribution is more prominent in the RefSlow simulation. Massive haloes : At the massive end, galaxies from the Ref and Refσ3 simulations evolve very similarly and overproduce stars by a factor of 3 at = 0 (Fig. 11 ). The Ref simulation has larger η in small haloes, but the differences in η decrease with σ and become the same when . Therefore, the larger mass-loading factor in the Ref simulation only affects the progenitor galaxies during the earliest stages of their assembly when they were still small. Since these haloes assembled fast at high redshifts, the different scalings of η and σ in the low-mass haloes have little effect on the massive galaxies in our simulations. Compared to the intermediate-mass haloes, they have even earlier star formation and a higher fraction of hot accretion and hot wind recycling. Except for the RefSlow simulation, where cold wind recycling is clearly more important for stellar growth than the other channels, all four accretion channels contribute comparable amounts in the other simulations, with cold accretion + cold wind recycling and hot accretion + hot
Paper: 2021ApJ...909...93R (fulltext)
survive and maintain high levels of molecular gas fraction. To support this explanation, there is a noticeable difference in the number of YSOs detected in the Southern Pillars (>800) and in the western region where the Northern Cloud is (∼100), suggesting a lower level of triggered star formation toward the direction of the denser gas (Smith et al. 2010b ). 4.1.3. 8 <italic>μ</italic>m Figure 10 shows a comparison between the radio continuum emission and the 8 μ m image from Spitzer. In general, we detect some anticorrelation between the two tracers, with the radio continuum emission filling the cavities observed in the 8 μ m map. A Spearman’s rank correlation between the two images gives a value of 0.42, which indicates a poor level of correlation. As 8 μ m traces the polycyclic aromatic hydrocarbon (PAH) emission, it will be bright on the surfaces of the clouds being radiated by the nearby massive stars. Now, with the radio continuum map in hand, we are able to witness the complex distribution of the different gas phases in Carina, which deviates strongly from the classical view of a spherical region expanding symmetrically into the surrounding ISM. Because of the exquisite detail of our radio continuum image, we can see that the hot gas can indeed permeate through the molecular cloud and shape the material into features such as pillars, small shells, and arc-like structures. Figure 10. Color-composite image of the CNC-Gum 31 complex. Green shows the Spitzer IRAC 8 μ m map, while red shows the ATCA continuum image. A clear spatial anticorrelation between the two tracers is observed, with the radio continuum emission filling the cavities observed in the 8 μ m map. The 8 μ m map traces the PAH emission, so it will be bright on the cloud’s surfaces. Orange arrows show the apparent direction of the hot gas traced by the radio continuum map as it permeates through the denser gas, illuminating the cloud surfaces as traced by PAH emission. As the ionization fronts advance through the cloud, they can penetrate deep toward low-density regions and ultimately escape. Figure 4 shows structures resembling “plumes” at the edges of the H ii region. These features are likely to be tracing hot gas that had eroded the material in the nebula, extending to outside the molecular cloud, and now diffusing with the global component of the hot gas in the Galactic plane at distances >100 pc. There is clear alignment between the morphology of the illuminated gas traced by PAH emission and the plume-shaped structures traced by the radio continuum map. The direction of the elongated structures identified in the northeast section of the 8 μ m map (such as pillars and walls) is roughly the same as the direction of the plumes detected in the radio continuum (Figure 4 ). This correlation is more evident toward the Southern Pillars region, where the radio continuum emission is distributed along the cavities between the pillars. 4.1.4. 70 <italic>μ</italic>m In Figure 11 we
Paper: 2017A&A...608A.116C (acknowledgments)
The authors gratefully acknowledge financial support by CONICET through PIP 112-201501-00525. The authors are grateful to the anonymous referee for numerous suggestions and corrections on this paper.
Paper: 2017ApJ...851....4R (acknowledgments)
The authors would like to thank Nick Crump for assistance in the data processing, Harry Warren for comments on an early version of this paper, and the anonymous referee for many comments that have broadened and improved the scope of this work. This research was performed while J.W.R. held an NRC Research Associateship award at the U.S. Naval Research Laboratory with the support of NASA. This work was supported by JSPS KAKENHI Grant Numbers JP16K17671, JP15H05814. CHIANTI is a collaborative project involving George Mason University, the University of Michigan (USA), and the University of Cambridge (UK). This research has made use of NASA’s Astrophysics Data System.
Paper: 2019A&A...627A.135B (fulltext)
the estimates of physical parameters based on the spectral energy distribution (SED) fitting tool. The final results of this work are summarized in Sect. 6 . 2. Observations, data reduction, and photometric and astrometric calibrations The observations were carried out using the 0.81 m (32′′) Tenagra telescope, which uses a science camera with 2048 × 2048 pixels having an effective plate scale of ∼0.98′′ per pixel and a field of view of 16.8 × 16.8 arcmin 2 . The images in the V R I filters were acquired between May 2012 and June 2013 over 90 nights, often three times each night but within one hour, while the B -band images were taken only over two nights. There are only 5 frames in B and around 250 frames in V R I , taken with exposures varying from 420s in B to 90s in I . Calibration images (bias and flats) were obtained nightly and the pre-processing of images (bias subtraction, flat-fielding, etc.) was done in IRAF 1 . Finally, 760 scientifically useful images were used to perform photometry. Figure 1 shows the color-composite image of the selected star-forming region towards IC 5070 obtained using the V R I images taken on the first night. Fig. 1. Color composite image of the young star-forming region towards Pelican Nebula obtained using the V R I -band images. The cyan circles represent the location of the variable stars. The time-series photometry of the processed images was performed using DAOPHOT/ALLSTAR ( Stetson 1987 ) and DAOMATCH/DAOMASTER ( Stetson 1993 ) routines. In this process all sources above the 4 σ threshold in each image are selected and the aperture photometry is obtained with a radius of 4 pixels. The point spread function (PSF) is determined from 20 bright and isolated stars in each image, which is used to perform the PSF photometry using ALLSTAR on all images. The frames taken on the first night are selected as the master image in each filter and the PSF photometry are used as input to DAOMATCH to derive accurate frame-to-frame coordinate transformations. These transformations are used to obtain the corrected magnitudes of the stars relative to their magnitudes in the master image for all frames using DAOMASTER. The photometry includes 1307 sources with more than 50 observations in the target field, and over 1000 stars with both V - and I -band measurements. Within our target region, optical counterparts of 77 of the ∼135 YSOs from Rebull et al. (2011) were found within 3′′ matching radius in our photometry. The photometric calibration to Landolt filters ( Landolt 2009 ) was performed using the standard star photometry carried out on the same night as the master frame. The standard transformation equations are: where, b , v , r , i are instrumental magnitudes; B , V , R , I are standard magnitudes; and X b , X v , X r , X i are the airmass in the B V R I filters. The maximum uncertainty of these coefficients and the dispersion in these relations are on the order of 0.02 magnitude. If the star is observed in one
Paper: 2019ApJ...885...51F (acknowledgments)
We would like to acknowledge insightful discussion with Ofer Cohen as we were getting this project underway. This work was supported by an INSPIRE grant from the NASA Science Foundation (AST-1461918). Ms. Mei Maruo’s participation was supported by the Tomodachi STEM @ Rice University Program funded by the US–Japan Council.
Paper: 2018ApJ...857..142M (fulltext)
(Makarov 2012 ). This result holds for a wider range of realistic models, such as the Newtonian creep model developed by Ferraz-Mello ( 2015 ). The orbital eccentricity and average viscosity are the two crucial parameters defining these probabilities. The issue of dynamical stability and long-term survival of a tightly packed close-in system such as TRAPPIST-1 has already been discussed in the discovery paper by Gillon et al. ( 2017 ). A system with the observed set of parameters breaks up within a relatively short time in numerical N -body integrations. Fine-tuning of initial orbital parameters is required to find a long-term stable configuration within the observational uncertainties. This indicates that the multidimensional parameter space is shredded, with small islands of stable solutions separated by areas of instability. How can a real physical system of high complexity converge on a small island of stability among an infinite number of possible unstable configurations? The stability islands should be attractors, i.e., there should exist a regulatory force driving the system to one of such islands and damping small perturbations. Gillon et al. ( 2017 ) suggested that considerable tidal effects can provide the required stabilization. However, tidal forces act much too slowly even for such close planets, while the break-up of an unstable system happens well within 1 Myr. Even though tidal effects are important on the lifetime scale, we still have to explain how the system originally found a stable configuration. Tamayo et al. ( 2017 ) proposed that a convergent migration in a protoplanetary disk can be such a regulatory mechanism. Subject to dynamic friction in the disk, young planets migrate generally inward and toward smaller eccentricities. Still, something would halt this process when a planet reaches a stability niche, even before the disk dispersal. This may be possible when the stability niche is also associated with a certain mean-motion resonance (MMR), or better still, a system of MMRs with other planets, because an MMR is known to be an attractor. The surviving protoplanets end up in intricate patterns of near-commensurability (Terquem Papaloizou 2007 ), a class of N -body systems radically different from the solar system. Luger et al. ( 2017 ) find that the TRAPPIST-1 system represents a chain of MMRs, where each triplet of adjacent planets is locked in a three-body resonance. In this paper, we begin with testing a long-term stable configuration suggested by Quarles et al. ( 2017 ). We also test the system for chaos, using the siblings method in numerical integrations. The resulting orbital eccentricities for the seven planets, termed for brevity as TR1-1, TR1-2, ..., TR1-7 (instead of the traditional notation TRAPPIST-1 b , c , ..., h ), are used to estimate the likely spin–orbit end states of the planets, assuming an Earth-like rheology described by the Maxwell model. We show that not all of the planets are expected to
Paper: 2020ApJ...897...68A (fulltext)
the quick knowledge of effectiveness and the computational methods (like extrapolations and flux-rope fitting) for further determining the magnitude of the CME impact. Bothmer Schwenn ( 1998 ) compared the helicity of a set of quiescent filaments using the hemispheric helicity preference (Rust Kumar 1994 ) with their ICME counterparts. They found a significant agreement in quiescent filaments to the rule (89%; a total of 9 filaments). Savani et al. ( 2015 ) also used the hemispheric helicity rule for obtaining the axis orientation for eight events but they warn that extra care must be taken while determining axis orientation of filaments that are between multiple active regions. Several statistical surveys on the hemispheric preference of helicity show varying degrees of compliance (for example, see HaiQing et al. 2009 ). In some of these studies (e.g., Martin et al. 1994 ), quiet regions show a higher degree of preference compared to the active regions. However, none of the studies show a 100% compliance with the hemispheric helicity rule indicating that independently obtaining the field direction at each source region is important. In order to avoid errors arising from exceptions in the hemispheric helicity rule and/or issues in disambiguation techniques, we have relied solely on filament observations and LOS magnetograms for obtaining the required parameters. We believe our method has performed well and has given reliable results. The human eye performs well in obtaining the chirality with images of reasonable resolution, but it is impractical to have human eyes analyze each and every filament. The advent of computer vision in solar physics is promising: automating filament chirality determination using machine-learning techniques (Martens et al. 2012 ; Aggarwal et al. 2018 ) will help in preparing computationally efficient and effective measures against severe space weather effects. Knowing whether there will be a geomagnetic effect or not is of primary importance to start taking precautionary measures. However, precautionary measures during a false alarm and the resulting expenditures can be avoided with the method used in this paper. Furthermore, our method of determining the axial field direction is more quick and straightforward than computationally expensive methods such as force free extrapolations and determining helicity (Bothmer Schwenn 1998 ; Yurchyshyn et al. 2001 etc.). In the current study we consider only the north–south component of the axial magnetic field of the filament. This is deemed useful as a first step in omitting ICMEs that will not be geoeffective. However, it is necessary to know the exact, quantitative direction and strength to determine where an ICME might reconnect with the magnetosphere and its severity. Utilizing vector magnetograms to determine the exact orientation and further study the field in the corresponding flux ropes at L1 will be useful in determining their orientations. Active region filaments
Paper: 2017MNRAS.472.4382R (fulltext)
concentration and asymmetry derived with pyca ; and (11) [O iii ] luminosity in units of 10 40 erg s −1 . Table 2. Control sample parameters. (1) identification of the AGN host associated with the control galaxy; (2)–(12) same as (1)–(11) of Table 1 . Twelve control sample objects have been paired to more than one AGN hosts and appear more than once in the table. In order to assess quantitatively the morphological variety of the AGN and control samples, we have derived the concentration index C and asymmetry index A of the sample galaxies using the publicly available code pyca (Menanteau et al. 2005 ). This calculation was made using the r -band images in order to maximize the galaxy signal-to-noise ratio. We have also obtained for all galaxies of the sample the morphological classifications from GZ1 (Lintott et al. 2011 ). From the GZ1 classification probabilities, we have separated the galaxies in four large classes: ellipticals (E), spirals (S), merging (M) and intermediate elliptical/spiral (E/S); this last class corresponds to galaxies whose GZ1 elliptical and spiral probabilities are rigorously the same and exceed 50 per cent when combined. Most of the 62 AGN in our sample have luminosities erg s −1 , with only 17 having larger luminosities. We use this threshold – postulated by Kauffmann et al. ( 2003 ) – to characterize these 17 AGNs as ‘strong AGN’. SDSS-III ugriz negative images (Fukugita et al. 1996 ) of the four most luminous of these strong AGNs are shown in Fig. 1 ; the remainder are available online. Figure 1. SDSS-III ugriz negative images of the four strongest AGN from our sample (left-hand columns) and their control sample equivalents (centre and right-hand columns). The mangaID of each host is indicated above its respective panel. Each chart is 51 arcsec on a side; north is up, east to the left. Inspection of the SDSS-III spectra showed that most of the strong AGN in our sample present coronal lines: at least one of the lines [NeV] λ3425 Å, [FeV ii ] λ3760 Å or [FeX] λ6374 Å are detectable above 3σ. A small number of objects among the most [O iii ]-luminous ‘weak AGN’ also present some of these lines. Coronal lines are thought to be produced by photoionization from the AGN (Mazzalay, Rodríguez-Ardila Komossa 2010 ; Rodríguez-Ardila et al. 2011 ) and to be associated with outflows (Müller-Sánchez et al. 2011 ). Being insensitive to star formation as opposed to the [O iii ] λ5007 Å, the presence of coronal lines reinforces the AGN nature of the 17 most [O iii ]-luminous objects in our sample, and suggests a transition between high- and low-ionization AGN at erg s −1 , in concordance with Kauffmann et al. ( 2003 ). We note that our AGN selection is based on single-fibre SDSS-III observations, which limits us to sources with AGN-photoionization signatures within 3 arcsec of the galactic centre. Wylezalek et al. (in preparation) has recently developed an AGN selection algorithm taking full advantage of the spatial dimension of
Paper: 2019ApJ...876...91H (fulltext)
is comparable to our results. There are many possible drivers for high excitation conditions. Daddi et al. ( 2015 ) found that the ratio of CO(5–4) to lower J CO emission is well correlated with the average intensity of the radiation field and with Σ SFR among local spirals, local ULIRGs, and z ∼ 1.5 MS galaxies. This is consistent with the model of Narayanan Krumholz ( 2014 ). They combined hydrodynamic simulations of disk galaxies and galaxy mergers with molecular line radiative transfer calculations, motivated to examine the origin of a broad range of observed CO SLEDs in local and high-redshift galaxies. They found that CO SLEDs are well correlated with the galaxy-wide SFR surface density, which is attributed to the increase of gas density and temperature. As shown in Figure 8 , we find that their model for galaxies with Σ SFR ∼ 5 M ⊙ yr −1 kpc −2 agrees with our results. Figure 8. CO SLEDs normalized to the CO(1–0) line for the GRB 080207 host galaxy and other types of galaxies. The values for the Milky Way, M82, SMGs, and QSOs/radio galaxies (RGs) are taken from the compiled data in Carilli Walter ( 2013 ), (U)LIRGs are from Papadopoulos et al. ( 2012 ), and z ∼ 1.5 MS galaxies are from Daddi et al. ( 2015 ) and Bolatto et al. ( 2015 ). We plot the (U)LIRGs, SMGs, and QSOs/RGs with CO detection up to J ≥ 4. The average values are shown in bold dashed lines. The gray solid line represents the constant brightness temperature ( J 2 ). The gray dotted–dashed line represents the model of Narayanan Krumholz ( 2014 ) with Σ SFR = 5 M ⊙ yr −1 kpc −2 . Table 4 CO Brightness Temperature Line Ratio Note. The error is 1 σ . It might also be possible that the high CO excitation condition is attributed to a strong radiation from an active galactic nucleus (AGN). While no strong evidence for an AGN has been reported, the optical emission-line diagnostic with the Baldwin–Phillips–Terlevich (BPT) diagram (Baldwin et al. 1981 ) studied in Krühler et al. ( 2015 ) shows that the host galaxy is located in a starburst–AGN composite region. The significantly high radio-derived SFR (850 M ⊙ yr −1 ) derived in Perley Perley ( 2013 ) compared to other methods could be explained by the contribution of an AGN to the observed radio flux. Since peaks in CO SLED for AGNs are J ≳ 8–10 (e.g., Meijerink et al. 2006 ), where high interstellar medium (ISM) excitation occurs in X-ray dissociation regions, observations of higher J CO lines are important in diagnosing the presence of an AGN. 4.6. Implication for GRBs as a Tracer of SFR History There is ongoing debate as to whether GRBs occur in normal star-forming galaxies or occur under particular circumstances, and whether they can be used as an unbiased tool to trace star formation in the universe. It is suggested that GRBs prefer low-metallicity environments with a threshold of 0.3–0.7 Z ⊙ (e.g., Woosley Heger 2006 ; Graham Fruchter 2013 ; Vergani et al. 2015 , 2017 ; Perley et al. 2016b ). The metallicity effect on the
Paper: 2019A&A...626A..72G (acknowledgments)
The authors would like to thank D. Aubert for making his SPL code available to us. We acknowledge fruitful discussions with K. Benabed, S. Colombi, M. Kilbinger, and S. Prunet in early phases of the project. We also thank G. Lavaux, Y. Rasera, and M-A Breton for stimulating interactions around this project. We are also grateful to A. Leauthaud for constructive comments about the comparison with her GGL lensing results. CL is supported by a Beecroft Fellowship. This work was supported by the Agence Nationale de la Recherche (ANR) as part of the SPIN(E) ANR-13-BS05-0005 ( http://cosmicorigin.org ) ERC grant 670193, and AMALGAM ANR-12-JS05-0006 projects, and by the Centre National des Etudes Spatiales (CNES). NEC is supported by a RAS research fellowship. This research is also funded by the European Research Council (ERC) under the Horizon 2020 research and innovation programme grant agreement of the European Union: ERC-2015-AdG 695561 (ByoPiC, https://byopic.eu ). This work has made use of the Horizon Cluster hosted by the Institut d’Astrophysique de Paris. We thank S. Rouberol for running the cluster smoothly for us.