adsabs/FOCAL huggingface dataset:
Recent studies have used the spectral indices [OII], Hδ, and Dn4000 to probe the stellar population of galaxies at intermediate redshifts (0.5 ≲ z ≲ 1.2) because they are available in the observed optical frame. All these indicators, when combined, can be used to distinguish actively star-forming, (post-) starburst, and old or passive galaxies because they are expected to occupy different regions of the possible parameter space (e.g. Couch & Sharples 1987; Balogh et al. 1999; Poggianti et al. 1999, 2009; Fritz et al. 2014). The [OII]λ3737 emission traces on-going star formation (timescales of ∼10 Myr, e.g. Couch & Sharples 1987; Poggianti et al. 1999, 2006; Kennicutt 1998; Kewley et al. 2004), but it also depends on the metallicity and can be a poor tracer for dusty galaxies (e.g. Kewley et al. 2004; Yan et al. 2006; Kocevski et al. 2011). By measuring the [OII] equivalent width (EW), we can also crudely trace the specific SFR (sSFR), which is found to be anti-correlated with stellar mass (e.g. Bridge et al. 2015; Cava et al. 2015; Darvish et al. 2015a), with more massive star-forming galaxies having lower [OII] EWs. Additionally, higher density environments are found to depress [OII] emission (e.g. Balogh et al. 1999; Darvish et al. 2015a). The Hδ line (and other strong Balmer absorption lines) can be indicative of a post-starburst phase (≈100 − 1000 Myr after the burst, e.g. Couch & Sharples 1987; Balogh et al. 1999; Poggianti et al. 1999, 2009; Dressler et al. 2004; Vergani et al. 2010; Mansheim et al. 2017a) if a strong absorption (typical of A stars, where hydrogen absorption is strongest) is observed and no tracers of on-going star formation are found (Couch & Sharples 1987). Recently, Wu et al. (2018) found that the Hδ EW correlates with stellar mass, with more massive galaxies having weaker Hδ absorption lines, but they did not study the effect of the environment (see also e.g. Siudek et al. 2017, for a similar result on passive galaxies). Finally, a measure of the flux break at 4000 Å (D4000 and Dn4000, as defined by Bruzual 1983; Balogh et al. 1999, respectively) traces the age of the galaxy and also the stellar metallicity (especially for older systems) to a lesser degree. This break is produced by a combination of metal absorption on the atmosphere of old and cool stars and the lack of flux from young and hot OB stars (e.g. Poggianti & Barbaro 1997; Kauffmann et al. 2003), and so it is sensitive to the average age of the stellar population. The 4000 Å break is also found to be stronger for galaxies with higher stellar mass (e.g. Muzzin et al. 2012; Vergani et al. 2008; Hernán-Caballero et al. 2013; Siudek et al. 2017; Wu et al. 2018), which indicates that their stellar populations might be older, in an average sense. In terms of local density, Muzzin et al. (2012) found that galaxies in cluster environments have stronger breaks on average than their field counterparts at similar stellar masses, which the authors argued can be explained by the different fractions of star-forming and quiescent galaxies in different environments.
Citation: Kocevski et al. 2011
Citation function: Background; Background
To date, large surveys focused on detecting AGN systems have been conducted at a range of different wavelengths and, in particular, for the IR and X-ray domains (Alexander et al. 2005, 2008; Ivison et al. 2004; Lutz et al. 2005; Menéndez-Delmestre et al. 2007, 2009; Valiante et al. 2007; Pope et al. 2008; Bonzini et al. 2013; Smolčić et al. 2017; Wang et al. 2013; Stach et al. 2019). Due to the dust extinction in the near-IR, optical, and UV, as well as gas absorption in X-ray bands, these surveys are often incomplete. In the mid- and far-IR, incompleteness of AGN surveys may arise from the fact that not all AGN have significant IR emission from a dusty torus and therefore may not be detected. It is postulated that up to a third of AGN are undetected in these surveys (Mateos et al. 2017). Moreover, other studies have compared AGN selected from various wavebands and find that their host galaxies tend to have different properties in terms of colour (Hickox et al. 2009) and star-formation rates (SFR; Juneau et al. 2013; Ellison et al. 2016). In particular, Hickox et al. (2009) illustrated that there is only very little overlap between their 122 radio-selected AGN and those selected by X-ray or IR. Therefore, dust-free radio surveys are needed to provided a more complete census of the AGN population. Traditional radio surveys are only sensitive to radio-loud (RL) AGN, which only represent a tiny fraction (10 ∼ 20%) of the whole AGN population; however, modern radio surveys can achieve a flux depth where radio-quiet AGN can be detected (see Prandoni et al. 2018 for a review). Recent work has focused on the radio as it is sensitive to AGN and star formation concordantly, thus providing a method of surveying AGN and star-formation activity across cosmic time (e.g. Smolčić et al. 2017; Padovani et al. 2015). A lot of work has also been done to look for AGN-driven radio emission, which has been identified by an excess of radio emission compared to what is expected based on the radio-FIR correlation, holding for star-forming galaxies (e.g. Ivison et al. 2010; Condon et al. 2002; Thomson et al. 2014; Magnelli et al. 2015).
Citation: Juneau et al. 2013
Citation function: Motivation; Motivation
The main assumption is that the statistical properties of the interaction of the charged particles with the fields are dominated by Gaussian distributions. This is in correspondence with the Central Limit Theorem (CLT), which requires that all stochastic systems evolve asymptotically towards Gaussian statistics provided that (i) many interactions are involved, (ii) the change in state in individual interactions is always small, and (iii) subsequent changes of state are statistically independent of each other. Besides these necessary conditions for the applicability of the FP equation, also other simplifying assumption for a better tractability of the FP equation are being made, such as (1) the magnetic fluctuations are homogeneous in space, (2) the electromagnetic fields are quasi-static, (3) the interaction has a finite decorrelation time, etc. (see more details in the books Schlickeiser 2003; Zank 2014). Unfortunately, in astrophysical and laboratory plasmas, most of the above assumptions are not valid, yet the FP equation is used extensively, without a proof of its validity. This is especially true when the plasma particles are accelerated to high energies impulsively (e.g. in solar flares, coronal mass ejections, or the Earth’s magnetotail). The acceleration volume is finite and the expected fluctuating electromagnetic fields are strong ($\vert \delta \boldsymbol{B} \vert \ge \vert \boldsymbol{B}_0 \vert$). In solar active regions, the complex magnetic topologies host many null magnetic points which are randomly distributed inside the erupting or flaring volume (Aulanier et al. 2000; Pontin 2011). In these cases, the interaction of the particles with the strong em disturbances is transient and has no time to lead to Gaussian statistics or to become homogeneous in space (Isliker, Archontis & Vlahos 2019). Before analysing the interaction of the particles with the em fluctuations, it is important to understand the evolution of the em waves. With the use of resistive MHD codes one can show that a spectrum of high amplitude electromagnetic fluctuations evolves rapidly and leads to a fragmented current system, where reconnecting current sheets and large amplitude magnetic fluctuations are present (Arzner & Vlahos 2004; Dmitruk, Matthaeus & Seenu 2004; Vlahos, Isliker & Lepreti 2004; Isliker, Vlahos & Constantinescu 2017a), and where the various statistics clearly are non-Gaussian, following largely the paradigm of the stable Levy distributions (Isliker et al. 2017a; Isliker et al. 2019). In strongly turbulent plasmas, the magnetic fluctuations are non-collective modes and cannot be described with a simple dispersion relation $\omega =\omega (\boldsymbol{k}).$ The em environment generated from the evolution of large amplitude em fluctuations is well documented in the current literature and models much better many impulsive astrophysical and laboratory plasmas (see Dmitruk et al. 2004; Zhdankin et al. 2013; Isliker et al. 2017a and the reviews by Cargill et al. 2012; Karimabadi et al. 2014; Vlahos & Isliker 2019).
Citation: Schlickeiser 2003
Citation function: Background; Background
In the search for new exoplanets, M dwarfs are ideal targets due to their high abundance in the Galaxy (Bochanski et al. 2010). However, M-type stars are prone to high levels of stellar activity (Walkowicz et al. 2011; Loyd et al. 2016, 2018b) that can impact the radial velocity and/or transit signal of such systems through phenomena such as flaring (Tofflemire et al. 2012), star spots, plages and faculae (Boisse et al. 2011; Llama & Shkolnik 2015; Cauley et al. 2018; Roettenbacher et al. 2022; Bruno et al. 2022), and other activity-induced variability (Dumusque 2018; Rackham et al. 2019; Bellotti et al. 2022; Collier Cameron et al. 2021). Aside from the observational implications, the planet’s physical and chemical state can be altered by stellar activity as well due to, for example, coronal mass ejections (CMEs) and stellar particle events (SPEs) (Yamashiki et al. 2019; Atri 2017, 2020; Segura et al. 2010), winds (Vidotto et al. 2015; Vidotto & Cleary 2020; Chebly et al. 2022; Colombo et al. 2022), and stellar flares (Segura et al. 2010; Venot et al. 2016; Chadney et al. 2017; Tilley et al. 2019; Chen et al. 2021; Louca et al. 2022), the latter being sudden releases of radiative energy triggered by magnetic reconnection (Benz & Güdel 2010). Stellar flares result in a temporary increase in incident flux on the planet’s atmosphere, which in turn increases the photochemical reaction rates that can change the chemical composition. Photochemistry, and photolysis in particular, is a key driver of chemical disequilibrium in the atmospheres of close-orbiting, gaseous planets. Photolysis does not only deplete the upper layers from species such as CH4 and NH3, but it can enrich the middle regions with haze precursors such as HCN and C2H2 as well, particularly on cooler planets (Moses et al. 2011; Venot et al. 2012; Zahnle & Marley 2014; Agúndez et al. 2014; Moses 2014; Miguel & Kaltenegger 2014; Rimmer & Helling 2016; Drummond et al. 2016; Hobbs et al. 2019; Shulyak et al. 2020; Barth et al. 2021; Baeyens et al. 2022). Stellar flares thus have the potential to alter the chemical composition and, subsequently, alter the atmosphere’s signature in transmission spectra.
Citation: Yamashiki et al. 2019
Citation function: Background
Three semiannual galactic hydrogen spectra as a function of energy between 40 and 250 MeV have been obtained in three different consecutive time periods (from 2018 August 6 to 2020 January 5) very much inside the heliosphere (1 au); the energy profiles are shown as black circles in Figure 6. Each measured energy spectrum is compared to the theoretical prediction from the HelMod model (Boschini et al. 2019) in the same period (blue solid curve); the maximum and minimum uncertainties related to this prediction are also reported in the plots, as dashed and dotted lines, respectively. As a further comparison, data from the SOHO/EPHIN spacecraft (red square marker) between 40 MeV and 53 MeV are also presented (Müller-Mellin et al. 1995). The agreement appears to be good in all the three examined periods, considering both statistical and systematic uncertainties. Ratio between HEPD data and models (displayed in the narrower bottom panels of Figure 6) gradually worsens at lower energies, particularly below 65 MeV, where the spectrum calculated by HEPD is systematically higher. Possible explanations for this discrepancy include a contamination from high-energy protons that is not fully removed using the simulation, and a possible contamination derived from nuclei fragmentation or from very inclined sub-cutoff protons that can enter the FoV of the instrument, even after the rigidity cutoff selection. However, although systematic uncertainties are higher than 10% in the lowest portion of the energy spectra, these results could help constrain theoretical models of particle transport from the border of the heliosphere, down to 1 au. From a comparison between the first spectrum (2018 August 6–2019 January 15) and the last one (2019 June 29–2020 January 5) an overall increase of ∼9% is observed, in very good agreement with the variation observed in SOHO/EPHIN (∼8.5%). This behavior is expected, because, as the solar activity continues to wind down (from 2018 to 2020), the effect of the Sun magnetic field diminishes, resulting in higher proton fluxes. On the other hand, HEPD data do not show a clear energy dependence in the modulation over time (typically lower energies should be more modulated with respect to higher ones); unfortunately, for HEPD the overall errors (statistical and systematic) in the first and last energy bins do not allow such a precise evaluation. Table 1 contains explicit values for the galactic hydrogen spectra in the three time periods and for each of the 16 energy bins allowed by the instrument resolution; statistical and systematic uncertainties are also reported.
Citation: Müller-Mellin et al. 1995
Citation function: Uses; Similarities
For JW39 and JW100 the disks’ slopes are consistent with linearity, that is the trend expected from Equation (4). This may suggests that the smoothing scale (∼10 kpc) is similar to the CRe transport scale, and, hence, the slopes resemble the standard SFR calibrators. Therefore, the spatial correlation slopes observed in the disks of JW39 and JW100 are consistent with the idea that the CRe transport scale in these galaxies is larger than observed in spiral galaxy disks (∼1–5 kpc). We investigate this result by comparing the spatial scales, L, of the two principal CRe transport processes in these systems: diffusion and advection. In the case of CRe diffusion, the spatial scale is:
5



L=4Dτ≃1.1×D1028cm2s−1τ10Myrkpc,

where τ is the timescale and D is the diffusion coefficient, which depends on the CRe energy and the local magnetic field power spectrum, and can vary between 1027 and 1029 cm2 s−1 (Strong et al. 2007). For advection, the typical spatial scale is:
6



L=V·τ≃V100kms−1τ10Myrkpc,

where V is the CRe velocity. Equation (5) shows that to reach L = 10 kpc, for a typical D = 1 × 1028 cm2 s−1, the timescale is of the order of ∼900 Myr, which is longer than the typical CRe radiative time in galactic disks. Covering these spatial scales in ≤108 yr requires a diffusion coefficient of D ≥ 9 × 1028 cm2 s−1, which is slightly higher than observed in spiral galaxies (e.g., Strong et al. 2007; Tabatabaei et al. 2013; Heesen et al. 2019). On the other hand, these scales could be consistent with the CRe advection (Equation (6)), as a typical velocity of 100 km s−1 would be able to cover 10 kpc in ∼10 Myr, which is more reasonable for low-energy electrons emitting at 144 MHz. Therefore, our results might hint that CRe transport in the disks of these jellyfish galaxies, JW39 and JW100, is either dominated by advection, due to ram pressure which is stripping the nonthermal ISM (see Section 3.1), or that the CRe diffusion might be more efficient than in normal galaxies. The diffusion coefficient D depends on the local magnetic field configuration and turbulence spectrum (Strong et al. 2007), thus it may be possible that the RPS, by affecting the ISM’s microphysics, may induce higher values of D and, hence, more efficient CRe diffusion (Equation (5)) than observed in normal spiral galaxies. However, we note that, for the rest of the sample, the spatial correlations in their stellar disks are not consistent with linearity (Figure 5). We argue that this might be due to projection effects that mix the disk and the extraplanar emissions, which, on the basis of what we observe for JW39, JO60, JW100, and JO206, follows a flat, almost uniform, distribution. Thus this blend may result in a flattening of the disks’ slopes. Another possible explanation could be a discrepancy between the sampling resolution and the transport scale that was not solved by the smoothing.
Citation: Tabatabaei et al. 2013
Citation function: Differences
To judge the origin of a star in an SNR, its kinetics characteristics may provide very important informations. Generally, except for being stripped-off a part of its envelope, the companion may receive a velocity kick from the supernova ejecta, but the kick velocity is usually much smaller than the orbital velocity (Marietta et al. 2000; Meng et al. 2007; Pakmor et al. 2008; Liu et al. 2012; Pan et al. 2012). Then, the orbital velocity of the companion at the moment of supernova explosion may represent its final space velocity to a great extant, especially for the sdB companions here, which almost do not receive any kick velocity for their large value of $A/R_{\rm 2}^{\rm SN}$. If the spatial velocity of a star in an SNR is very different from the others in the SNR, the star is very possible to be the surviving companion in the remnant (Ruiz-Lapuente et al. 2004). In Fig. 7, we present the companion orbital velocity relative to binary centroid versus the companion radius at the moment of supernova explosion. From the figure, we can see that different companions may have very different orbital velocity. For MS companions, the orbital velocity is from 150 to 200 ${\rm km\, s^{\rm -1}}$, while the RG companions have an orbital velocity of 50–110 ${\rm km\, s^{\rm -1}}$. For the sdB companions, the orbital velocity covers a large range, from 50 to 190 ${\rm km\, s^{\rm -1}}$. Such a large range is mainly derived from the large initial orbital period range for the systems producing sdB companions, i.e. log (Pi/d) is from ∼0.4 to 1.2. In other words, although the mass transfer between a binary system must begin in HG for producing a sdB companion, it may occur at the stage very close to the MS end or at the end of the HG. The upper limit of the orbital velocity here is lower than that in Meng & Podsiadlowski (2017) by 60 ${\rm km\,s^{\rm -1}}$, which originates from the fact that after MWD = 1.378 M⊙, the binary orbital period increases with mass transfer for a reversed mass ratio (see fig. 2 in Meng & Podsiadlowski 2017). For the same reason, the lower limit of the orbital velocity is also lower than that in Meng & Podsiadlowski (2017).
Citation: Ruiz-Lapuente et al. 2004
Citation function: Uses
Galactic winds have been ubiquitously observed in galaxies at both low and high redshifts, and they are critical to galaxy formation and evolution. Simulations calibrated to match these observations predict that a large amount of galactic material is ejected as a wind before reaccreting to either form stars or be ejected once again (Oppenheimer et al. 2010; Anglés-Alcázar et al. 2017). Current cosmological hydrodynamic simulations of galaxy formation employ a variety of subgrid models (e.g. Springel & Hernquist 2003; Oppenheimer & Davé 2006; Stinson et al. 2006; Dalla Vecchia & Schaye 2008; Agertz et al. 2013; Schaye et al. 2015; Davé, Thompson & Hopkins 2016; Tremmel et al. 2017; Pillepich et al. 2018; Davé et al. 2019; Huang et al. 2020a) that artificially launch galactic winds, but the results are sensitive to numerical resolution and the exact subgrid model employed (Huang et al. 2019, 2020a). Simulations without these subgrid wind models (e.g. Hopkins et al. 2018; Kim & Ostriker 2015; Martizzi et al. 2016) allow winds to occur ‘naturally’, but these simulations may not resolve the scales necessary to resolve the important known physical processes (Scannapieco & Brüggen 2015; Brüggen & Scannapieco 2016; Schneider & Robertson 2017; McCourt et al. 2018; Huang et al. 2020b). Hence, modelling galactic winds accurately remains a theoretical challenge for even the most refined high-resolution simulations of galaxies (see Naab & Ostriker 2017, for a review). Even if one were able to accurately model the formation of galactic winds, the subsequent propagation in galactic haloes depends on a complicated interplay of many physical processes that occur on a wide range of physical scales that cannot be simultaneously resolved in a single simulation. For example, to robustly model the propagation and disintegration of moving clouds in various situations requires cloud-crushing simulations with at least sub-parsec scale resolution (Schneider & Robertson 2017; McCourt et al. 2018), which is orders of magnitudes below the resolution limits of cosmological simulations. Furthermore, most cosmological hydrodynamic simulations concentrate their resolution in the dense, star-forming regions of galaxies and thus have lower resolution in the circumgalactic medium (CGM, but see Hummels et al. 2019; Mandelker et al. 2019; Peeples et al. 2019; Suresh et al. 2019; van de Voort et al. 2019). To date, cosmological simulations do not include physically motivated subgrid models for galactic wind evolution, which are required to capture these small-scale physical processes.
Citation: Tremmel et al. 2017
Citation function: Background; Background
Citation: Huang et al. 2020a
Citation function: Background; Background
Among various DM candidates, the most popular candidate is the weakly interacting massive particles (WIMPs; like the neutralino), which have mass in the GeV range (Jungman et al. 1996; Bertone et al. 2005; Hooper & Profumo 2007; Feng 2010). The WIMPs are non-relativistic at the epoch of decoupling from the interacting particles and have negligible free-streaming velocities. Therefore, they are “cold,” called cold dark matter (CDM). In  the CDM scenario, “halos” formed in small clumps, and then merged together into larger and massive objects. Galaxies formed in these halos because of the cooling of atomic hydrogen (H; Tegmark et al. 1997) or molecular hydrogen (H2, Ciardi et al. 2000; Haiman et al. 2000). On large cosmological scales (from the range 





 down to 





), the CDM paradigm has had great success in explaining the observed universe and reproducing the luminous structures (Fixsen et al. 1996; Borgani & Guzzo 2001; Lange et al. 2001; Cole et al. 2005; Tegmark et al. 2006; Benson 2010; Hinshaw et al. 2013; Slosar et al. 2013; Wang 2013; Planck Collaboration et al. 2014; Wei et al. 2016). However, on small scales (





), there are still some discrepancies between the CDM paradigm and observations. (a) The core–cusp problem (Navarro et al. 1997; Subramanian et al. 2000). CDM simulations predict a cusp–core DM halo, whereas the observations find them cored (Salucci et al. 2012). (b) The too big to fail problem (Boylan-Kolchin et al. 2012). CDM simulations predict a central DM density significantly higher than observation allows. (c) The “missing satellite problem.” N-body simulations based on the CDM paradigm predict a number of subhalos larger than that of satellites found in our Galaxy (Klypin et al. 1999; Moore et al. 1999; Papastergis et al. 2011). Many methods have been proposed to solve these small-scale problems, such as modifying the nature of the DM from the CDM paradigm (Hu et al. 2000; Spergel & Steinhardt 2000; Su & Chen 2011; Menci et al. 2012), adding supernova feedback effect in simulation (Weinberg & Katz 2002; Mashchenko et al. 2006; Governato et al. 2010; Pontzen & Governato 2014), and considering the interplay between DM and baryons during the formation of the galaxy (El-Zant et al. 2001; Tonini et al. 2006; Pontzen & Governato 2014). However, these methods are insufficient to solve all of the above problems.
Citation: Haiman et al. 2000
Citation function: Background; Background