adsabs/FOCAL huggingface dataset:
LoTSS is currently mapping all of the northern sky to a high sensitivity and resolution (S150MHz ~ 0.1 mJy beam−1 and FWHM ~ 6′′) at the relatively unexplored 120–168 MHz frequencies. In parallel with this, LOFAR is also undertaking deep observations of best studied multi-wavelength, degree scale fields in the northern sky, as part of the deep tier of LoTSS: the LoTSS Deep Fields (Tasse et al. 2021 and Sabater et al. 2021; hereafter Paper I and Paper II). The first three LoTSS Deep Fields are the European Large-Area ISO Survey-North 1 (ELAIS-N1; Oliver et al. 2000), Lockman Hole, and Boötes (Jannuzi & Dey 1999); these were chosen to have extensive multi-wavelength coverage from past and ongoing deep, wide-area surveys sampling the X-ray (e.g. Brandt et al. 2001; Hasinger et al. 2001; Manners et al. 2003; Murray et al. 2005), ultra-violet (UV; e.g. Martin et al. 2005; Morrissey et al. 2007) to optical (e.g. Jannuzi & Dey 1999; Cool 2007; Muzzin et al. 2009; Wilson et al. 2009; Chambers et al. 2016; Huber et al. 2017; Aihara et al. 2018) and to infrared (IR; e.g. Lonsdale et al. 2003; Lawrence et al. 2007; Ashby et al. 2009; Whitaker et al. 2011; Mauduit et al. 2012; Oliver et al. 2012) wavelengths; this is ideal for a wide range of our scientific objectives. These fields also benefit from additional radio observations at higher frequencies from the Giant Metrewave Radio Telescope (GMRT; e.g. Garn et al. 2008a,b; Sirothia et al. 2009; Intema et al. 2011; Ocran et al. 2019; Ishwara-Chandra et al. 2020) and the VLA (e.g. Ciliegi et al. 1999; Ibar et al. 2009). The current LoTSS Deep Fields dataset, covering ~ 26 deg2 (including multi-wavelength coverage) and reaching an unprecedented depth of S150MHz ~20 μJy beam−1, is comparable in depth to the deepest existing radio continuum surveys (e.g. VLA-COSMOS) but with more than an order of magnitude larger sky-area coverage. With this combination of deep, high-quality radio and multi-wavelength data over tens of square degrees, and along multiple sight-lines, the LoTSS Deep Fields are now able to probe a cosmological volume large enough to sample all galaxy environments to beyond z ~ 1, minimise the effects of cosmic variance (to an estimated level of ~4% for 0.5  z  1.0; Driver & Robotham 2010), and build statistical radio-selected samples of AGN and star-forming galaxies, even when simultaneously split by various physical parameters.
Citation: Lonsdale et al. 2003
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The scenario presented above has been formulated in various flavors to individually explain several of the properties reported here. The main addition of this work, namely the excitation of CO in distant main-sequence and starburst galaxies, fits in the general picture that we sketched. The ensemble of properties and correlations that we reported here can be also used to revisit the definition of what a starburst is. A standard operational classification is based on the distance from the observed empirical M⋆-SFR correlation, the main sequence. This proved to be a useful distinction and an excellent predictor of several trends (e.g., Sargent et al. 2014), but recent results, including our present and previous analysis (Puglisi et al. 2019), show that the demarcation between starburst and main-sequence galaxies is more blurred that we previously considered. We do detect starburst-like behaviors in galaxies on the main sequence (Elbaz et al. 2018), likely linked to the existence of transitional objects (Popping et al. 2017; Barro et al. 2017b; Gómez-Guijarro et al. 2019; Puglisi et al. 2019, and in prep. to limit the references to recent works based on submillimeter observations). Such transition might well imply an imminent increase of the SFR, driving the object in the realm of starbursts (e.g., Barro et al. 2017b), or its cessation, bringing the system back onto or even below the main sequence (Gómez-Guijarro et al. 2019; Puglisi et al. 2019), with the CO properties potentially able to distinguish between these two scenarios. Regardless of these transitional objects, a definition of starburst based on ΣSFR, rather than ΔMS, would naturally better account for the observed molecular gas excitation properties, dust temperatures and opacities, or SFE (see also Elbaz et al. 2011; Rujopakarn et al. 2011; Jiménez-Andrade et al. 2018; Tacconi et al. 2020). As an example, in Fig. 8 we show the mean SLED of the subsample of galaxies with both CO (2 − 1) and CO (5 − 4) coverage, split at its median ΣSFR. While only tentative at this stage, this suggests a trend of increasing CO excitation with ΣSFR, consistently with Fig. 7 and what mentioned above.
Citation: Puglisi et al. 2019
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Citation: Puglisi et al. 2019
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Citation: Gómez-Guijarro et al. 2019
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Citation: Gómez-Guijarro et al. 2019
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Citation: Puglisi et al. 2019
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In the Local Universe, most of our current knowledge of chemical distributions in disc galaxies comes from studies of the ISM via observations of HII regions or young stellar populations. Current observations are consistent with the metallicity profiles of the ISM having negative metallicity gradients, on average. Metallicity gradients in units of dex kpc-1 determine tight correlations with the global properties of galaxies such as the stellar mass or the size, which are erased when normalised by a characteristics radius such as the half-mass radius (e.g. Zaritsky et al. 1994; Sánchez et al. 2013). There are few indirect estimations of the evolution of the stellar metallicity gradients using planetary nebulae (PN; e.g. Henry et al. 2010; Stanghellini et al. 2010). Maciel et al. (2003) calculated the gradients of SPs with different ages in the Milky Way disc, finding a signal of increasingly negative metallicity gradients for older stars. Recently Magrini et al. (2016) analysed the metallicity gradients determined by PN for four nearby galaxies, finding them to be flatter than those detected using HII regions. Surveys such as CALIFA provide detailed information on the properties of the ISM and the SPs, including their chemical abundances and age distributions on a variety of galaxies (Sánchez-Blázquez et al. 2014; González Delgado et al. 2015). The SDDS-IV MaNGA survey (Bundy et al. 2015) also investigate spatially resolved SPs and radial age and metallicity gradients for nearby galaxies (Li et al. 2015; Wilkinson et al. 2015) and will provide a large statistical sample to confront with models. The available observations of HII regions for high-z galaxies do not allow the formation of a robust conclusion on the evolution of the metallicity gradients (e.g. Yuan et al. 2011; Queyrel et al. 2012; Stott et al. 2014; Jones et al. 2015). In fact, high-redshift observations show a complex situation with gas-phase components showing a variety of metallicity gradients that could respond to the action of different physical processes (e.g. Cresci et al. 2010).
Citation: Zaritsky et al. 1994
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Some of the accretion-powered X-ray pulsars showed additional features in emission between the 10 and 20 keV energy bands and more rarely in absorption between 8 and 10 keV in their respective residuals when fitted with a variety of continuum models (Coburn et al. 2002). Coburn et al. (2002) argued that such features may be caused by inadequacies of the continuum model rather than cyclotron resonance features. For example, it was observed that a single emission line at around 14 keV can fit two features around 10 and 20 keV for Vela X-1 (Kreykenbohm et al. 2002), Her X-1 (Coburn 2001), and Cep X-4 (McBride et al. 2007), and an absorption line between 8 and 10 keV can fit the features for 4U 1907+09, 4U 1538–52, and 4U 0352+309 as in Figure 6 of Coburn et al. (2002). In the case of GRO J2058+42, an introduction of a single Gaussian emission line in this range of energies could not appropriately fit the spectrum. The results described in Section 3.3 using ratios of spectral counts with respect to the Crab spectrum derived for four different phases of the pulsar (Figure 6) clearly indicate the presence of prominent depressions around 10 keV and 20 keV for phase 1 in particular, and its presence in other phases as well. Therefore, it confirms the presence of such absorption features in the spectral data associated with a physical origin and not due to any discrepancies of the continuum model as discussed above. Additionally, it also excludes the possibility of any uncertainty in the response matrix of the detector as the response matrix was not used to deconvolve the spectrum to calculate these ratios. The relative significance of these absorption features that was subsequently estimated after modeling the data and results is shown in Table 2 for the phase-averaged case and in Table 3 for the four different pulse phases. These observations strongly favor the presence and detection of these absorption features in their respective pulsar spectra.
Citation: Coburn 2001
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Numerical simulations using the N-body method are the primary instrument used to probe the non-linear regime of structure formation in cosmology and provide the basis for all theoretical predictions for the distribution of dark matter at the corresponding physical scales. Over the last few decades, such simulations have gained in refinement and complexity and have allowed the exploration of an ever larger range of scales (for a review see e.g. Bertschinger 1998; Springel et al. 2005; Dehnen & Reed 2011). Nevertheless, the understanding of their precision and their convergence towards the continuum limit remains, at very least, incomplete, in particular for smaller scales (see e.g. Splinter et al. 1998; Knebe et al. 2000; Romeo et al. 2008; Joyce et al. 2009; Power et al. 2016). In this context ‘scale-free’ cosmological models, in which both the expansion law and the power spectrum characterizing the initial fluctuations are simple power laws, have the advantage of relative simplicity, and they have for this reason been studied quite extensively in the literature (see e.g. Efstathiou et al. 1988; Colombi et al. 1996; Bertschinger 1998; Jain & Bertschinger 1998; Smith et al. 2003; Knollmann et al. 2008; Widrow et al. 2009; Orban 2013; Diemer & Kravtsov 2015). More specifically these models provide a testing ground for the numerical method through the predicted ‘self-similarity’ of the clustering: the temporal evolution of the clustering statistics must be equivalent to a rescaling of the distances. This follows from the fact that there is only one characteristic length scale (derived from the amplitude of the fluctuations) and one characteristic time-scale in the model. Further the exact rescaling function can be determined from the evolution in the linear regime of arbitrarily small fluctuations. However, discreteness and numerical effects typically introduce additional characteristic scales (e.g. force regularization at small scales, particle density, finite box size, etc.) which lead directly to a breaking of such self-similarity. Thus the self-similarity of clustering provides a potentially powerful tool to separate the scales affected by such non-physical effects from the physical results representing the continuum limit. The focus of this study is to exploit self-similar models to better understand the resolution at small scales of N-body simulations. In particular, we will use simulations with a very small force smoothing which allow us to follow carefully the propagation of self-similarity to small scales in the course of a simulation.
Citation: Smith et al. 2003
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The intensity of a geomagnetic storm is the result of the sustained interaction between the solar wind and the magnetosphere during the main phase of a storm. The most outstanding advantage of empirical formulae is their simplicity for space weather forecast: By inputting the solar wind parameters responsible for the main phase of a geomagnetic storm into an empirical formula, one can quickly get the estimated intensity of this geomagnetic storm. Of all empirical formulae, the Burton equation and OM equation have been used mostly to predict extreme geomagnetic storms. For example, the Burton equation was used to estimate the intensity of the storm that occurred on 1859 September 1–2 by Tsurutani et al. (2003), and Liu et al. (2014) used the OM equation to estimate the intensity of the storm on 2014 July 24. Extreme geomagnetic storms can cause widespread interference and damage to technological systems (Love 2021 and references therein) and then lead to significant economic loss (e.g., Council 2008; Schulte in den Bäumen et al. 2014; Eastwood et al. 2017; Riley & Love 2017). Hence, it is very important to ensure the accuracy of extreme geomagnetic storm forecasts. Now the question is whether the Burton equation or the OM equation can estimate the intensities of very large geomagnetic storms correctly. To answer this question, we compare the performance of the three models mentioned above using 15 great geomagnetic storms (ΔSYM-H ≤ −200 nT) that occurred during solar cycle 23. The solar wind parameters responsible for the main phases of the 15 storms are inputted into the three models to get the estimated intensities of the great geomagnetic storms (GGSs), which are compared with the observed intensities. To avoid any possible confusion, we use ΔSYM-H
b
, ΔSYM-Hom, and ΔSYM-H
w
 to indicate the intensities of a geomagnetic storm estimated by the Burton equation, the OM equation, and the WCL equation, respectively, while we use ΔSYM-Hob to represent the observed intensity, namely the real variation of the SYM-H index during the main phase of a geomagnetic storm. The rest of this article is organized as follows. The data source and method are presented in Section 2, the results are presented in Section 3, and the discussion and summary are presented in Section 4.
Citation: Eastwood et al. 2017
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Finally, we compared the extinction-corrected upper limit on the optical flux of the PSR J0633+0632 PWN (Section 2.1) with its unabsorbed 0.3–10 keV X-ray flux. This is $F_{\rm X}^{{\rm pwn}} = 2.92^{+0.79}_{-0.81} \times 10^{-13}$ erg cm−2 s−1 (Abdo et al. 2013), computed by fitting the PWN area with an ellipse of semimajor and semiminor axis of 0.58 and 0.54 arcmin, respectively, oriented 130° due east (Marelli 2012). We subtracted the flux contribution of the point-like X-ray source south-west of the pulsar position (Fig. 2), which is only spatially coincident with the PWN. The extinction-corrected optical flux of the PWN in the g′ band is $F_{\rm opt}^{{\rm pwn}} \lesssim 9.8 \times 10^{-13}$ erg cm−2 s−1, integrated over the same area as used to compute the PWN X-ray flux. As done for the pulsars, we assumed the most conservative value of the interstellar extinction. This yields an optical-to-X-ray flux ratio of Fopt/Fx ≲ 4.6. PWNe have been detected both in the optical and X-rays around the Crab pulsar, PSR J0205+6449, PSR B0540−69, and PSR J1124−5916. Our upper limit on the Fopt/Fx for the PSR J0633+0632 PWN is above the values obtained for the other PWNe, which are typically ∼ 0.02–0.04, apart from the Crab PWN which has an Fopt/Fx ∼ 2 (Zharikov et al. 2008). This means that, owing to the faintness of the PSR J0633+0632 PWN in the X-rays, much deeper optical observations are needed to set similar constraints on its optical emission. We also compared the extinction-corrected optical spectral flux upper limit on the PWN in the g′ and r′-bands with the extrapolation of its X-ray spectrum in the optical domain. Like in Abdo et al. (2013), we used the best-fitting spectral index of the PWN, $\Gamma _{\rm X}^{{\rm pwn}} = 1.19^{+0.59}_{-0.22}$. The PWN SED is shown in Fig. 5. As seen, we cannot rule the presence of a spectral break between the optical and the X-ray energy range. A break in the optical/X-ray SED has been observed in other PWNe. For instance, the PWN around PSR B0540−69 features a clear break, with the optical fluxes being fainter than expected from the extrapolation of the X-ray PWN spectrum (Mignani et al. 2012). This is also the case for the PSR J1124−5916 PWN (Zharikov et al. 2008). A break in the opposite direction is observed in the SED of the PSR J1833−1034 PWN (Zajczyk et al. 2012), where the infrared fluxes (the PWN is not yet detected in the optical) are about two orders of magnitude above the extrapolation of the PWN X-ray spectrum. Only in the case of the Crab and PSR J0205+6449 PWNe, the PWN spectrum is compatible with a single PL, extending from the X-rays to the optical (Hester 2008; Shibanov et al. 2008). Optical detections of more PWNe through dedicated observing campaigns can allow one to relate the differences in the SEDs to the characteristics of the PWN.
Citation: Zharikov et al. 2008; Zharikov et al. 2008
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The IGIMF theory (Kroupa et al. 2013) predicts a coupling between some properties of a galaxy (the SFR and the metallicity) and the IMF. Since the IMF in turn strongly affects the dynamical evolution of the galaxy, the feedback between galaxy evolution and IMF is difficult and the fully complexity of a variable IMF has not been yet included in hydrodynamical simulations (but see Bekki 2013; Ploeckinger et al. 2014; Recchi 2014). Even the here treated approach based on the so-called simple model of chemical evolution leads to implicit integral equations that must be solved iteratively. We note in passing that the dependence of the IMF on the metallicity is well established theoretically, as low-metallicity gas cools less efficiently and self-gravitating clumps are more resilient to fragmentation. This leads to the formation of dense cores of higher mass. For this reason, the IMF is supposed to be extremely biased towards massive stars if the metallicity is smaller than ∼10−4 Z⊙ (Schneider et al. 2002). Also the dependence of the IMF on the SFR is nowadays observationally well established (Hoversten & Glazebrook 2008; Meurer et al. 2009; Gunawardhana et al. 2011; Kroupa 2014), and the IGIMF theory is the only existing computable access accounting for these observations (Weidner et al. 2013). Thus, in spite of the inherent complexity, detailed simulations of galaxy evolution based on variable IMFs need to be performed. With this paper, we aimed at showing in a simple setting how to take into account IMF variations in models of the chemical evolution of galaxies (see also Martinelli & Matteucci 2000; Calura et al. 2010). The method outlined here can be used in order to extend analytical studies of the evolution of galaxies based on simple models of chemical evolution (e.g. Spitoni et al. 2010; Lilly et al. 2013; Pipino et al. 2014). We described how the solution of the simple model differs from the standard, textbook analytical solutions of the simple model. We also showed (building on previous results of Köppen et al. 2007) that the IGIMF theory naturally leads to a MZ relation. In fact, low-mass galaxies are characterized on average by smaller SFRs. According to the IGIMF theory, a low SFR leads to a steep, top-light IMF, in which the production of heavy elements by massive stars is extremely limited. More massive galaxies instead produce many more massive stars because of the higher level of SFR, hence the attained present-day metallicity is larger.
Citation: Kroupa 2014
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Since the manifold spirals arise in a system of reference which corotates with the bar, the manifold theory in its basic form predicts that the spiral arms should have the same pattern speed as the bar. This remark seems to come in conflict with observations both in our Galaxy (as reviewed e.g. in Bland-Hawthorn & Gerhard 2016; see also Antoja et al. 2014; Junqueira et al. 2015 and references therein) and in other galaxies (e.g. Vera-Villamizar et al. 2001; Boonyasait, Patsis & Gottesman 2005; Patsis, Kaufmann & Gottesman 2009; Meidt, Rand & Merrifield 2009; Speights & Westpfahl 2012; Speights & Rooke 2016). Considering again, galactic disc simulations, the leading paradigm over the years refers to simulations showing the coexistence of multiple pattern speeds (Sellwood & Sparke 1988; Little & Carlberg 1991; Rautiainen & Salo 1999; Quillen 2003; Minchev & Quillen 2006; Dubinski, Berentzen & Shlosman 2009; Quillen et al. 2011; Minchev et al. 2012; Baba, Saitoh & Wada 2013; Roca-Fabrega et al. 2013; Font et al. 2014; Baba 2015; but see also a noticeable exception in Roca-Fabrega et al. 2013), possibly connected also to the phenomenon of nonlinear coupling of multiple disc modes (Tagger et al. 1987; Tagger & Athanassoula 1991; Sellwood & Wilkinson 1993; Masset & Tagger 1997). On the other hand, it is well known that even isolated barred galaxies undergo substantial secular evolution (see Athanassoula 2013; Binney 2013; Kormendy 2013 in the tutorial volume Falcon-Barroso & Knapen 2013). The tendency to transfer angular momentum outwards (e.g. towards the halo or across the disc, Tremaine & Weinberg 1984; Debattista & Sellwood 1998; Debattista & Sellwood 2000; Athanassoula 2002; Athanassoula & Misiriotis 2002; Athanassoula 2003; OŃeill & Dubinski 2003; Holley-Bockelmann, Weinberg & Katz 2005; Berentzen, Shlosman & Jogee 2006; Martinez-Valpuesta, Shlosman & Heller 2006) leads the bar to slow down and grow in size at a rate which produces non-negligible change in dynamics at time-scales comparable even to a few bar periods. This process becomes complex, and even partially reversed due to the growth of ‘pseudo-bulges’ or peanuts (Kormendy & Kennicutt 2004), caused by dynamical instabilities such as chaos or the ‘buckling instability’ (Combes & Sanders 1981; Combes et al. 1990; Pfenniger & Friedli 1991; Raha et al. 1991; Bureau & Athanassoula 1999; Martinez-Valpuesta & Shlosman 2004; Bureau & Athanassoula 2005; Debattista et al. 2006). The reduction in size of the bar by the transfer of angular momentum under constant pattern speed is discussed in Weinberg & Katz (2007). Spiral activity acts as an additional factor of outwards transfer of angular momentum (Lynden-Bell & Kalnajs 1972), while a radial re-distribution of matter can take place even under a nearly preserved distribution of angular momentum (Hohl 1971; Sellwood & Binney 2002; Avila-Reese et al. 2005). Radial migration is enhanced by the amplification of chaos due to the overlapping of resonances among the various patterns (Quillen 2003; Minchev & Quillen 2006; Quillen et al. 2011).
Citation: Roca-Fabrega et al. 2013
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