adsabs/FOCAL huggingface dataset:
According to general relativity, in the limit in which the wavelength of gravitational waves is small compared to the radius of curvature of the background spacetime, the waves propagate with the velocity of light, i.e., c (see Will 1998, and references therein). In other theories, the speed 





 could differ from c. Let us define the parameter
4





If the gravitational wave velocity is subluminal (i.e., 





), then cosmic rays lose their energy via gravitational Cerenkov radiation significantly. The detection of ultrahigh-energy cosmic rays thus imposes a stringent constraint 





 (i.e., the “subluminal constraint”), depending on the Galactic or extragalactic origin of such particles (Caves 1980; Moore & Nelson 2001). However, there is no theoretical argument (or pathology) against GWs propagating faster than light (see Nishizawa & Nakamura 2014; Blas et al. 2016, and references therein) and the weak bounds from radiation damping in binary systems are 





 (Yagi et al. 2014). The time lag of arrival times between the GW and the simultaneously radiated photons is
5





where 





 is the differential distance the photons have traveled. Note that in this work we take the flat cosmological model (i.e., 





), 





, and 





 is Hubble’s constant (Riess et al. 2011; Ade et al. 2014). In general, ς may be a function of the GW frequency (f) and especially when graviton mass is non-zero (i.e., 





), which gives 





, where h is Planck’s constant. For simplicity we assume a constant ς and focus on the association of GWs with electromagnetic counterparts at redshifts 





. Hence Equation (5) yields (see also Will 1998; Nishizawa & Nakamura 2014)
6





In reality the photons and the coalescence are not usually simultaneous and we have 





, where 





 and 





 are the differences in arrival time and emission time, respectively, of the GW and the photons. In most cases, it is rather hard to get an a priori value for 





. Assuming 





 (i.e., the GW and the electromagnetic counterparts were emitted simultaneously; see Nishizawa (2016) for a more general discussion), we constrain the absolute amplitude of ς as
7





We call the above process the “canonical approach” to measuring the GW velocity directly, in which the graviton and photon are assumed to make the same journey (i.e., EEP is guaranteed). The advantage is that as long as a GW/GRB association is established one can constrain 





 directly. In Section 4.2 we outline an approach to measure the GW velocity with a simultaneous test of EEP.
Citation: Nishizawa & Nakamura 2014; Nishizawa & Nakamura 2014
Citation function: Uses; Uses
If the separation between the period of the planet and all the other periodic signals is large enough, and the RV signal has a similar or larger semi-amplitude, it is possible to determine the RV semi-amplitude for a USP planet without any assumptions about the number of planets in the system or the activity of the host star. Under such conditions, during a single night, the influence of any other signal is much smaller than the measurement error and, thus, it can be neglected. If two or more observations are gathered during the same night and they span a large fraction of the orbital phase, the RV semi-amplitude of the USP planet can be precisely measured by just applying nightly offsets to remove all the other signals (e.g. Hatzes et al. 2010; Howard et al. 2013; Pepe et al. 2013; Frustagli et al. 2020 for a recent example). Such an approach, also known as floating chunk offset method (FCO; Hatzes 2014), has proven extremely reliable even in the presence of complex activity signals, as shown by Malavolta et al. (2018). In our case, the shortest, next periodic signal (i. e. TOI-561 c at 10.78 d) is ≃24 times the period of TOI-561 b (i. e. the USP planet at 0.45 d), with similar predicted RV semi-amplitude, making this target suitable for the FCO approach. Thanks to our observational strategy (see Section 2.2), we could use 10 different nights for this analysis. Most notably, during two nights, we managed to gather six observations spanning nearly 5 h, i. e. more than 40 per cent of the orbital period of TOI-561 b, at opposite orbital phases, thus, providing a good coverage in phase of the RV curve. We did not include RV measurements with an associated error greater than 2.5 m s−1 (see Appendix B1). We performed the analysis with PyORBIT as specified in Section 5, assuming a circular orbit for the USP planet and including a RV jitter as a free parameter to take into account possible short-term stellar variability and any underestimation of the error bars. From our analysis, we obtained a RV semi-amplitude of Kp = 1.80 ± 0.38 m s−1, corresponding to a mass of Mp = 1.83 ± 0.39 M⊕. The resulting RV jitter is j  0.9 m s−1(84.13th percentile of the posterior). We show the phase folded RVs of the USP planet in Fig. 9. Since the greater reliability of this method over a full fit of the RV data set is counter-balanced by the smaller number of RVs, we decided not privilege one over the other. Therefore, we assumed as final semi-amplitude and mass of TOI-561 b the weighted mean of the values obtained from the two methods (FCO approach and joint photometric and RV fit), i. e. Kb = 1.56 ± 0.35 m s−1, corresponding to a mass of Mb = 1.59 ± 0.36 M⊕. Table 5 lists the above-mentioned values for TOI-561 b.
Citation: Hatzes 2014
Citation function: Uses; Compare/Contrast
Further diagnostics are clearly needed to form firm conclusions. There are likely to be three routes through which this can come. The first is via spectroscopy of the bumps in any further examples. High quality spectroscopy, allied to detailed modeling can yield diagnostics even in the case of relatively weak or featureless spectra, as recently demonstrated in the case of the ultra-long and luminous supernovae pairing GRB 111209A/SN2011kl (Greiner et al. 2015). The unique identification of features expected in luminous SNe (e.g., turn-off due to line blanketing, absorption lines seen in SLSNe) or TDFs (e.g., blueshifted narrow lines from streams (Strubbe & Quataert 2011)) would then provide a clinching argument as to the origin of the bumps in the longest high-energy transients. A second route arises through studying the locations of the transients within their hosts. Swift J1644+57 clearly arises very close to the galactic nucleus, and Swift J2058+0516 is also consistent with the nucleus of a much fainter galaxy (Pasham et al. 2015). In the case of GRBs, approximately 1/6 of examples are consistent with a galaxy nucleus (Fruchter et al. 2006; Svensson et al. 2010); this number may be lower for SLSNe (Lunnan et al. 2015) although the origin of SLSNe in the nuclei of galaxies may be ambiguous (e.g., Dong et al. 2016). Further examples, all in the nuclei of their hosts, would rapidly remove any SNe model from consideration. Finally, we can also consider the host galaxy more globally. TDFs can be observed in quiescent, non-star-forming galaxies while SLSNe are thought to arise from massive star collapse (Gal-Yam 2012) and in principle should occur only in star forming galaxies. A prime model for SLSNe is that they arise from supernovae in which the shock wave is re-energized by the spin-down energy of a recently formed magnetar (Kasen & Bildsten 2010). While magnetars similar to those suggested to power SLSNe can be formed via accretion induced collapse of two merging white dwarfs (Usov 1992; Levan et al. 2006), and may provide a similar energy input, in the case of a white dwarf merger there would be minimal remnant to re-energize, and hence no luminous SNe. This means that the presence of an extremely long event within an quiescent elliptical galaxy would rule out SNe models, and strongly favor an origin as a relativistic tidal flare. Since a reasonable fraction (∼50%) of candidate tidal disruptions arise from passive systems, (i.e., those with little sign of star formation) (e.g., Arcavi et al. 2014) such a test should be possible with only a handful of additional examples since we would expect to observe an example in a system without star formation in the near future.
Citation: Svensson et al. 2010
Citation function: Compare/Contrast
The first identification of a possible meteor shower with a radiant point in the constellation of Ursa minor was by Denning (1912), occurring around the winter solstice. However, very few observations were subsequently made. Hoffmeister recorded its activity in 1914, but its existence was not confirmed until 1945 (Becvr 1946). There are two reasons for the lack of records of the Ursids. First, the Ursids have very low activity levels in most years, its Zenith hourly rate (the number of meteors that would be observed under good observing conditions in one hour if the radiant was at the zenith) is usually ZHR 10 (Jenniskens 1994). Secondly, the weather can be bad in mid-December and many observers choose to only observe the more predictable Geminids. Ceplecha (1951) showed that the annual Ursid shower was related to comet 8P/Tuttle, a Jupiter family comet with a period of 13.6yr (JPL-HORIZONS, ssd.jpl.nasa.gov) and a perihelion distance slightly greater than 1au. This means that the comet is usually brightest (close to perihelion) and close to the Earth at roughly the same time. Hence, it has been observed at all perihelion passages since discovery apart from 1953, when observing conditions were poor throughout. These perihelion passages were in 1858, 1872, 1885, 1899, 1913, 1926, 1940, 1953, 1967, 1980, 1994 and 2008. As it can take a considerable time after being ejected from the nucleus for meteoroids to disperse away from the nucleus locality, an enhancement is generally to be expected in stream activity at the time when the comet is close to perihelion and new meteoroids are injected (see Williams, Johnson  Fox 1986 for an early discussion and mathematical formulation of this). An enhancement at some such times has also been observed in the Ursids, where the ZHR reaches around three times the normal rate (e.g. 1900, 1914, 1953, 1981 and 1996, data taken from www.meteorshowersonline.com). However, in the case of the Ursids, several perihelion passage years do not show any significant enhancement and so this is unlikely to be the explanation. Instead, Jenniskens etal. (2007) suggested that they are caused by cometary material released at very old comet perihelion passages between ad300ad1400 and appear as a wide and stretched stream (which we will call and refer to as a filament).
Citation: Ceplecha (1951)
Citation function: Background
The lack of data at complementary wavelengths also makes resolved multiwavelength analyses applied to low-redshift galaxies, such as the Schmidt–Kennicutt relation (the correlation between galaxies’ SFR and gas mass surface densities; e.g., Schmidt 1959; Kennicutt 1989, 1998), significantly less common at high redshift. High-resolution CO observations are critical for evaluating where high-redshift galaxies fall on the true surface density version of the Schmidt–Kennicutt relation, where ΣSFR and Σgas can be compared on a pixel-by-pixel basis within individual galaxies (as done for local galaxies; e.g., Kennicutt et al. 2007; Bigiel et al. 2008, 2011; Wei et al. 2010; Leroy et al. 2013). Many high-redshift analyses use star formation and gas properties averaged over the entire galaxy (e.g., Buat et al. 1989; Kennicutt 1989, 1998; Daddi et al. 2010b; Genzel et al. 2010; Tacconi et al. 2013) or avoid the additional uncertainties in source size and scaling factors by using the total luminosities of the star formation and gas tracers (e.g., Young et al. 1986; Solomon & Sage 1988; Gao & Solomon 2004). These different methods for determining SFRs and gas masses make it difficult to compare studies that focus on different galaxy populations, leading to significant uncertainties in the power-law index of the Schmidt–Kennicutt relation and the relative placement of different galaxy types in the ΣSFR–Σgas plane. Accurately characterizing the Schmidt–Kennicutt relation is important, since offsets imply a difference in star formation efficiency (SFE), and the power-law index probes the underlying physical processes of star formation (for example, a linear correlation would imply supply-limited star formation, whereas superlinear correlations occur if star formation depends on cloud–cloud collisions or total gas freefall collapse times; e.g., Tan 2000; Krumholz & McKee 2005; Ostriker & Shetty 2011). Systematic differences in the Schmidt–Kennicutt relation between different galaxy populations would imply important differences in their star formation processes.
Citation: Wei et al. 2010
Citation function: Motivation
Within a star-forming cloud core, the protostar is the main source of luminosity and heat due to the release of gravitational energy from contraction and material accretion. The amount of protostellar heating dictates the temperature structure of the cloud core. In an idealized spherical scenario, the temperature alone would dictate the snowline locations within the cloud core. However, star-forming cloud cores are not spherically symmetric. The outflow cavity, flattened structures around the protostar (e.g., pseudo-disks and rotationally supported disks; hereafter referred to as disks for simplicity), and variations within the envelope density can all impact how heat is distributed within the cloud core. Studies have shown that heating mainly escapes through the outflow cavity in deeply embedded sources (van Kempen et al. 2009; Yıldız et al. 2015; Murillo et al. 2018a). Thus the extent of chemical richness in the outflow cavity provides insight into the luminosity of the protostar and the physical conditions of the envelope (e.g., Drozdovskaya et al. 2015; Murillo et al. 2018b; Tychoniec et al. 2019, 2020). Observations of embedded protostars (so-called Class 0 and I systems) have shown the presence of disks, both as flattened dust continuum structures (e.g., J∅rgensen et al. 2009; Enoch et al. 2011; Persson et al. 2016; Segura-Cox et al. 2018; Tobin et al. 2020) and rotationally supported disks traced in molecular gas (e.g., Murillo et al. 2013; Harsono et al. 2014; Yen et al. 2015, 2017; Maret et al. 2020). These disks show a wide range of geometries and radii ranging from a few 10 AU up to ~200 AU. Additional studies have shown that the presence of a disk can alter the temperature profile along the equator (disk mid-plane) of the cloud core (e.g., Murillo et al. 2015, 2018b; van’t Hoff et al. 2018b; Hsieh et al. 2019a). Multiplicity, that is two or more protostars within a single cloud core, can produce further asymmetries due to differences in luminosity from the multiple components and their locations with respect to each other (e.g., Chen et al. 2009; Koumpia et al. 2016; Murillo et al. 2016, 2018b). At an early evolutionary stage, protostellar luminosity is dominated by accretion, that is accretion luminosity (Hartmann & Kenyon 1996). Variability in protostellar luminosity has been detected toward several targets (V1647 Ori: Ábrahám et al. 2004; Andrews et al. 2004; Acosta-Pulido et al. 2007; Fedele et al. 2007; Aspin & Reipurth 2009; OO Serpentis: Kóspál et al. 2007; CTF93 216-2 Caratti o Garatti et al. 2011; VSX J205126.1: Covey et al. 2011; Kóspál et al. 2011; HOPS383 Safron et al. 2015; S255IR-SMA1 Safron et al. 2015; Liu et al. 2018; EC53 Herczeg et al. 2017; Yoo et al. 2017). Such variability is considered to be a product of the nonuniform accretion of material with a variable amount and frequency onto the protostar, that is to say episodic accretion (Audard et al. 2014).
Citation: van Kempen et al. 2009
Citation function: Background
In this work, we follow the method for measuring the IGM thermal state based on Voigt profile decomposition of the Ly α forest (Schaye et al. 1999; Ricotti et al. 2000; McDonald et al. 2001). In this approach, a transmission spectrum is treated as a superposition of multiple discrete Voigt profiles, with each line described by three parameters: redshift zabs, Doppler broadening b, and neutral hydrogen column density $N_{\rm H\, {\small I}}$. By studying the statistical properties of these parameters, i.e. the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution, one can recover the thermal information encoded in the absorption profiles. The majority of past applications of this method constrained the IGM thermal state by fitting the low-b-$N_{\rm H\, {\small I}}$ cutoff of the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution (Schaye et al. 1999, 2000; Ricotti et al. 2000; McDonald et al. 2001; Rudie, Steidel & Pettini 2012; Boera et al. 2014; Bolton et al. 2014; Garzilli, Theuns & Schaye 2015, 2020; Hiss et al. 2018; Rorai et al. 2018). The motivation for this approach is that the Ly α lines are broadened by both thermal motion and non-thermal broadening resulting from combinations of Hubble flow, peculiar velocities, and turbulence. By isolating the narrow lines in the Ly α forest that constitutes the lower cutoff in $b-N_{{{{\rm H\, {\small I}}}}{}}$ distributions, of which the line-of-sight component of non-thermal broadening is expected to be zero, the broadening should be purely thermal, thus allowing one to constrain the IGM thermal state. However, this method has three crucial drawbacks. First, the IGM thermal state actually impacts all the lines besides just the narrowest lines. Therefore, by restricting attention to data in the distribution outskirts, this approach throws away information and reduces the sensitivity to the IGM thermal state significantly (Rorai et al. 2018; Hiss et al. 2019). Secondly, in practice, determining the location of the cutoff is vulnerable to systematic effects, such as contamination from the narrow metal lines (Hiss et al. 2018; Rorai et al. 2018). Lastly, the results from this approach critically depend on the choice of low-b cutoff fitting techniques, where different techniques might result in inconsistent T0 and γ measurements (Hiss et al. 2018; Rorai et al. 2018).
Citation: Rorai et al. 2018
Citation function: Background
Citation: Rorai et al. 2018
Citation function: Background
Citation: Rorai et al. 2018
Citation function: Background
Citation: Rorai et al. 2018
Citation function: Background
Meanwhile, the quiescence of the stellar disks (albeit possibly not entirely passive), is the first indication of the presence of an apparent “outside-in” quenching mode in high-redshift massive galaxy populations, in contradiction to the classic “inside-out” configuration (Lang et al. 2014; Tacchella et al. 2015; Breda & Papaderos 2018). We consider various possible modes of quenching. Since we observe the galaxies to be undergoing an outside-in quenching, this could not have primarily occurred due to feedback from active galactic nuclei (AGN; Alatalo et al. 2015), known to quench galaxies inside-out (Tacchella et al. 2018). We also find no evidence of AGN activity from X-ray observations (Daddi et al. 2021), although a radio excess in Galaxy-C is indicative of weak past AGN activity in that galaxy. The process of morphological quenching (Martig et al. 2009), where the formation of a stellar bulge stabilizes the disk against further star-formation, is also improbable. The galaxies are far from being bulge-dominated, essential for this mode of quenching to be applicable, based on their stellar mass distributions. Also unlikely is cosmological starvation (Feldmann & Mayer 2015) since the availability of gas has already been established in RO-1001. Finally, ram-pressure stripping (RPS; Gunn & Gott 1972) could be regarded as a possible contributor as it is known to remove gas from external regions of galaxies (Bravo-Alfaro et al. 2000; Vollmer et al. 2001; Fumagalli et al. 2009; Boselli et al. 2014; Loni et al. 2021), therefore resulting in an apparent outside-in quenching (for a review, Boselli et al. 2022). It could also lead to compression of the gas in the galaxy which could result in the lopsidedness (although the stellar morphology would not be affected). However, as shown in Appendix B, a conservative lower-limit of the radius up to which RPS can remove gas from the sample in RO-1001 would be 10–15 kpc. This is more than an order of magnitude higher than the kpc-scale cores beyond which the galaxies have suppressed star-formation. Hence we do not expect RPS to be playing a major role.
Citation: Martig et al. 2009
Citation function: Compare/Contrast; Compare/Contrast
The abundances for cold and hot tracers with different Ye treatments are shown in Figure 12 for model s15_F120_R006. In general, the abundances for cold and hot tracers show a clear iron peak. The cold component is characterized by the odd–even distribution from carbon to calcium, following the progenitor composition. The hot component reaches heavier elements than the cold one with a slight dependency of the abundances on the exact electron fraction. The different assumptions for the initial Ye lead to some variability for the abundances beyond iron covering all expected possibilities for yields from neutrino-driven supernovae. The production of elements in the region of Sr, Y, and Zr is more efficient for slightly neutron-rich conditions, corresponding to RLn = 1.35 (Arcones & Montes 2011; Arcones & Bliss 2014). We observe a very similar behavior and dependency of abundances on the Ye distribution for all exploding models of the 15 M⊙ progenitor. As a summary, we show in Figure 13 the final abundances only for the narrow Ye distribution with RLn = 1 for all models. We do not find any clear correlation between the abundances and the final explosion energy, although we see slight dependency on the explosion energy at ∼1 s after bounce. The lack of correlation is probably due to our simple treatment of the neutrinos and the correction of the electron fraction, as well as to the fact that we are calculating abundances only for the 15 M⊙ progenitor. Simulations with detailed neutrino transport and for several progenitors are necessary to narrow the uncertainties in the abundances and to link those to other astrophysical conditions. Even if our results are not completely conclusive for abundances beyond iron, they indicate that there is not a strong variability for iron group elements. This may be important to estimate uncertainties in the production of 56Ni and 44Ti as shown in Figure 14 where we present an overview of all models including cold and hot components and variations of Ye.
Citation: Arcones & Montes 2011
Citation function: Uses; Similarities