alternatively imply incorrect assumptions about the underlying pulsar population properties. Figure 4. Eccentricities of DNSs identified within the LISA band with S/N > 7. Blue and green distributions are designed to match the low- and high-eccentricity DNSs in the MW, respectively. The two black distributions (different line styles) demonstrate the eccentricities expected from two ad hoc models for putative fast-merging DNSs. 4. Discussion and Conclusions Using the MW DNS merger rate of 210 Myr −1 derived from LIGO (Abbott et al. 2019 ), we find that a 4 yr (8 yr) LISA mission will detect on average 240 (330) DNSs. Approximately 25% of those will be “chirping,” allowing for their characterization as DNSs through their chirp mass. The remaining ≈75% will likely be indistinguishable from lower-mass double white dwarfs. Using a more pessimistic rate of 42 Myr −1 based on the Galactic population of DNSs, we find that LISA will detect 46 (65) DNSs for a 4 yr (8 yr) mission. However, the census of MW DNSs is incomplete as Doppler shifting of the radio waves that pulsars emit makes detecting DNSs with orbital periods ≲1 hr extremely challenging. If these systems exist in significant numbers, the nature of GW orbital decay implies that they were formed with similarly short orbital periods. The presence of such fast-merging DNSs can help resolve any difference between the DNS merger rates as determined by LIGO and the galactic population of DNSs. Because LISA bridges the orbital period gap separating DNSs detected with radio waves and by LIGO, it can detect DNSs that form with short orbital periods. As pointed out by Kyutoku et al. ( 2019 ), the sky position of a DNS as measured by LISA may allow for characterization by radio follow-up observations, provided the DNSs contain at least one radio bright pulsar. If, on the other hand, these systems are radio-quiet, the ability to measure orbital eccentricities as small as 10 −2 by LISA affords, perhaps, the only opportunity to measure and characterize the orbits of such systems, discerning between the different formation scenarios. The existence of such a fast-merging channel would have profound implications on the presence of r -process elements in the Universe. Recent studies have shown that, if r -process enrichment seen in the ultra-faint dwarf galaxies Reticulum II and Tucana III is formed from the merger of DNSs, then a fast-merging channel is required (Beniamini Piran 2016 ; Safarzadeh et al. 2019a ). Similar arguments are required to explain the existence of r -process enrichment in globular clusters (Zevin et al. 2019 ). These studies, which use binary population synthesis, suggest that such fast-merging DNSs could be formed through unstable Case BB mass transfer (Delgado Thomas 1981 ), in which a NS accretor enters a second common envelope when its stripped helium star companion evolves into a giant star. Dynamical formation provides an alternative evolutionary scenario that can explain the

O O O O O O O O O O O O O O O O O O B-Instrument O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O B-Instrument B-Citation I-Citation I-Citation I-Citation O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O B-Instrument O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O O B-CelestialObject O O O O B-Instrument O O O O O O O O O B-Wavelength O O O B-Instrument O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O B-Instrument O O O O O B-Wavelength O O O O O O O O O B-Wavelength O O O O O O O O O O B-Wavelength O O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


toward values of the order of τ c / τ IC ≈ 10 −3 –10 −2 for the relevant electron energies (see e.g., Pereira et al. 2016 ; del Palacio et al. 2018 for a modeling of AE Aur and other bow-shock systems). In particular, Pereira et al. ( 2016 ) claim that for the AE Aur system the energy density of the ambient radiation is largely dominated by dust emission, and found a value for the ratio τ c / τ IC roughly equal to 10 −2 . Finally, values of E max of the order of ≲1 TeV have been estimated in Pereira et al. ( 2016 ), implying that the logarithm in Equation ( 4 ) should be roughly of the order of ≈10. Given these fiducial values we can now estimate the IC flux in the X-ray band aswhich is indeed an upper limit because it is based on the most optimistic value for η e . Our estimate of the IC X-ray emission is consistent with the upper limit reported in Section 3.3 , but is in disagreement with previous and more optimistic estimates reported in the literature (e.g., López-Santiago et al. 2012 ; Pereira et al. 2016 ; del Palacio et al. 2018 ). The main reason for that is the different choice of the parameter η e , which in these previous works was assumed to be equal to η e ≈ 0.1, which implicitly implies that wind termination shocks were assumed to be much more effective than SNR shocks in accelerating electrons. In this respect, our estimate is thus more conservative: we assumed that wind termination shocks behave as SNR ones. To conclude, we stress that a similar reasoning can be adopted (referring to AE Aur) to interpret the radio upper limits. For the fiducial value of the magnetic field of ∼30 μ G, electrons of energy ≳1 GeV emit synchrotron radiation in the GHz domain. The characteristic synchrotron cooling time τ s for such electrons is of the order of several megayears. An estimate of the expected radio emission from the bow shock can then be obtained by substituting τ IC with τ s in Equation ( 5 ), and by noticing that , which is about two orders of magnitude smaller than τ c / τ IC . However, this is almost exactly compensated by the fact that the radio upper limits are much more stringent than those derived from X-ray observations. Therefore, we can conclude that also the nondetection of the bow shock of AE Aur in radio waves is consistent with the assumption that such shocks behave as SNR ones. Within this framework, the detection of radio synchrotron emission from the bow shock of the runaway star BD+43°3654 might be tentatively explained by the very large kinetic energy characterizing its stellar wind. For this system, estimates of the stellar mass-loss rate range from yr −1 (Peri et al. 2012 ) up to 1.6 × 10 −4 M ⊙ yr −1 (Kobulnicky et al. 2010 ), i.e., a factor of ≈30...800 larger than that estimated for AE Aur. Also, the estimated velocity of the wind of BD+43°3654 (2300 km s −1 ) is roughly a factor of two larger than that estimated fro AE Aur (Benaglia et al. 2010 ). Therefore, the wind kinetic energy is ≈10 2 ... 3 × 10 3

O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O B-CelestialObject I-CelestialObject O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O B-Wavelength O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O B-Wavelength O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O


(red crosses for the flux, yellow shadows for the flux error) is also plotted. We first subtract the stellar light in the SED by assuming a Rayleigh–Jeans tail scaled by the WISE W1 band data point and then fit the data points at 8–1000 μ m with the modified blackbody + MIR power-law model (namely, CMC fits; green lines). The final model SED is shown as dark gray lines. The T , α , and β parameters derived from the CMC fits are indicated in the top right corner of each panel. In a similar fashion as Rémy-Ruyer et al. ( 2013 ), a single modified blackbody is also fit to the Herschel data points for comparison (light gray line). Table 7 Low-metallicity Galaxies Used to Derive the Templates Note. Column (1): the galaxy name. Column (2): the metallicity 12 + log(O/H). Column (3): the dust temperature derived from the CMC fit (see text). Column (4): the FIR emission index. Column (5): the MIR power-law index. Column (6): the existence of the MIR Spitzer spectrum: good —the galaxy has a full range 5–35 μ m MIR spectrum; limited —the galaxy has an MIR spectrum with limited coverage; poor —IC 10 is quite extended, as judged from its MIPS 24 μ m image, making the Spitzer spectrum a poor reflection of the overall MIR continuum. Figure 10 shows the full IR SEDs (after adding the Spitzer spectra) for the 19 DGS galaxies. The MIR continua derived from the photometry fitting and those directly obtained from the MIR spectra are consistent. The MIR regions of these low-metallicity objects present substantial forbidden-line emissions and weak or no polycyclic aromatic hydrocarbon emission (e.g., Wu et al. 2006 , 2007 ), which contribute little to the continuum. For the final model SEDs of the 19 DGS galaxies, we discard the MIR portion of the fit SED continua based on WISE and Herschel photometry and replace it with scaled Spitzer spectra. Figure 10. Full IR SEDs of the 19 DGS galaxies that have Spitzer /IRS low-resolution spectra with high enough S/N, sequenced by their metallicity with the lowest at the bottom. The Spitzer spectra are scaled to match the continuum SEDs. A.3. Features of Low-metallicity Galaxy Templates Figure 11 compares the 19 low-metallicity templates with the normal SF galaxy templates in Rieke et al. ( 2009 ). The SEDs of these dwarf galaxies show a lot of variation. Compared with the solar-metallicity galaxies, the low-metallicity IR SEDs derived in this work tend to have the following features: 1. Higher FIR dust temperature. For the low-metallicity galaxies, the typical dust temperature is T dust = 34 ± 7.7. Our value is similar to the result by Rémy-Ruyer et al. ( 2013 ) ( T dust ∼ 32 K). Compared with the Herschel KINGFISH sample ( T dust ∼ 23 K; Rémy-Ruyer et al. 2013 ), which contains more metal-rich environments, the dust in these low-metallicity systems is generally warmer. 2. Steeply rising MIR continua. For low-metallicity galaxies in this work, the MIR continua slope α = 3.8 ± 0.8, which is much larger than typical

O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O B-Telescope B-Wavelength I-Wavelength O O O O O O O O O B-Wavelength I-Wavelength I-Wavelength O O O B-Model I-Model I-Model I-Model I-Model O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O B-Wavelength O O O O O B-Wavelength O O O O O O O O B-Wavelength B-Telescope O O O O O O O O B-Wavelength I-Wavelength I-Wavelength B-Wavelength O O O O O O B-Wavelength O O O O O B-CelestialObject I-CelestialObject O O O O O O O B-Instrument B-Wavelength I-Wavelength I-Wavelength O O O B-Telescope O O O O O O O B-Wavelength O O O O O O B-Wavelength B-Model O O O B-Telescope O O O O B-Survey O O B-Wavelength O O O O O O O O O O O O B-Wavelength O O O O B-Wavelength O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation O O O O O O O O O O O B-Model O O O B-Survey O O O O B-Wavelength O O O O B-Model O O O B-Telescope O B-Telescope O O O O O O B-Telescope O O O O B-Wavelength B-Model O O O B-Survey O O O B-Telescope B-Instrument O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O B-Model O O O O O O O O O O O O O O O O B-Wavelength B-Model O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O B-Formula I-Formula I-Formula I-Formula I-Formula O O O B-Telescope B-Survey O O B-Formula I-Formula I-Formula I-Formula I-Formula B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O B-Wavelength O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O


in such a faint galaxy during our survey (Section 3 ) and we argue that no host galaxy exists at the SN location even if we take this redshift possibility. The nearest galaxy on the sky (galaxy A in Figure 1 ) could be at z ∼ 0.25 (Figure 7 ). With the 3.86 kpc arcsec −1 at z = 0.2455, the 07 separation corresponds only to the distance of 2.7 kpc from the center of galaxy A. An interesting coincidence is that the extended galaxy at the north of HSC16aayt (galaxy B in Figure 1 ) is at z =0.25053 ± 0.00005, which is spectroscopically measured by the Sloan Digital Sky Survey 20 20 https://www.sdss.org/ (SDSS). Therefore, this galaxy B can also be the host galaxy of HSC16aayt if it is at z = 0.2455, although the galaxy center at (R.A., decl.)=(10:02:05.46, +02:58:10.62) has a separation of 124 from HSC16aayt. The angular scale at z = 0.2505 is 3.91 kpc arcsec −1 and the physical distance between the galaxy center to the SN location is 48.5 kpc at z = 0.2505. Again, assuming a typical massive star lifetime of 10 Myr, the progenitor needs to have the tangential velocity of to travel 48.5 kpc before the explosion. Another intriguing fact is the redshift difference between the host galaxy ( z = 0.2505) and HSC16aayt ( z = 0.2455). Assuming that HSC16aayt is actually at z = 0.2505, the redshift difference indicates that HSC16aayt is moving away from the host toward us with the velocity of . Combined with the tangential velocity of , the progenitor of HSC16aayt was moving away from the host galaxy with a velocity of if galaxy B is the host galaxy. The SDSS spectrum of galaxy B shows that it is an active galactic nucleus. Thus, we can estimate its central BH mass from its spectrum in this case. By using the H α luminosity (3.6 × 10 41 erg s −1 ) and the FWHM of the H α emission from the host galaxy (46.8 Å in the rest frame) obtained by the SDSS spectrum, the central BH mass is estimated to be (Greene Ho 2007 ). The velocity that can be gained by the BH interaction through the Hills mechanism iswhere a is the binary separation, M total is the total mass of the binary system, M BH is the BH mass, and f R is a factor of unity (Brown 2015 ). The extreme velocities of the order of estimated above can be achieved by the BH at the center of galaxy B. 6.2.2. <italic>z</italic> = 1.92 This redshift possibility appears if we assume the strongest line is Mg ii at 2800 Å in the rest frame. It is one of the strongest lines in ultraviolet in SNe IIn (e.g., Fransson et al. 2002 , 2014 ). However, no other possible emission lines can be identified in this redshift. If HSC16aayt is at z = 1.92, the peak SN magnitudes in the rest-frame ultraviolet are around −22 mag and HSC16aayt is a long-lasting SLSN IIn like SN 2006gy (Smith et al. 2010 ). If we assume z = 1.92, a host galaxy fainter than −17.7 mag can exist at the SN location and we cannot exclude the possibility that there is a non-detected host galaxy at the SN location. Galaxy A is, again, not likely to be a host

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examined in this study. The positions and colors are exactly as in Figure 2 . 5. DISCUSSION AND CONCLUSIONS Examining the most extreme deviations of the posterior area distributions, we find that there is only modest improvement in our ability to measure the intrinsic parameters when using the posterior area as our metric. Neither one nor two dimensional confidence region size were significantly affected by fixing the various orientation and distance parameters. There may be some additional precision obtained in the primary spin (most notably the primary spin magnitude), however, this requires knowledge of both the sky location and either the distance or relative inclination of the system. This is likely due to a strong correlation between and the depth of the waveform amplitude modulation observed from a precessing system which is, in turn, dependent on both the spin magnitude and tilt. Qualitatively, IMRPhenomPv2 and SpinTaylorT4 exhibit similar cumulative distributions for the same event set. It is notable, but not definitive, that the improvements in region size occur at absolute region sizes. For instance, the primary tilt angle is measured better with SpinTaylorT4 , but only when the region is already relatively well contained within less than a quarter of the prior area. Conversely, IMRPhenomPv2 performs better over a wider set of regions. The deviations, while marginal, do occur for slightly different combinations of fixed parameters between the two families which may indicate that information from the merger and ringdown have some effect on our ability to measure certain parameters (e.g., spin orientation and magnitude). This result reinforces the generally known result that the intrinsic source physical parameters and the source orientation parameters are mostly decoupled. Even in the case of precessing binaries where the spins and inclination are correlated because of the oscillation of the binary plane, we find here that having information about the inclination of the binary does not translate into noticeably better estimation of the spin parameters. Moreover, this result represents the best possible scenario in regards to incorporating priors from external astronomical information—it effectively introduces a prior which is a delta function. In practice, the prior information will have a finite width and many gains made here will likely be lost. Finally, we do note that, while not explored in detail here, the overall run times of the codes are reduced when fixing parameters. The effective decrease in dimensionality will improve convergence time in the MCMC used for measurement of the posteriors. Empirically, IMRPhenomPv2 was reduced by a factor of two between the unpinned case and the case fixing only sky location. Other configurations converged only slightly faster than the sky location case. The SpinTaylorT4 family configurations were affected in a similar way, but we observed a little less than a factor of two. For most compact

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O


Schoonenberg Ormel 2017 ). Fig. 9 Revised critical solid abundance as a function of the dimensionless stopping time from Carrera et al. (2015) . The filled and open circles show our models with and without significant radial concentration of solids, respectively. The solid black line for and the dashed blue line for are the unmodified critical curve from Carrera et al. (2015) . The black solid line for shows the revised part of the critical curve, where the black dashed line is the extrapolation of it. For solid abundances above the black line (green region), spontaneous concentration of solid particles by the streaming instability can occur. Some discussion on our simplifying assumption of particles of the same size is in order here. It has been suggested by Bai Stone (2010a) that the particle-gas dynamics for a population of particles of various sizes is predominantly driven by the largest ones. They found a critical solid abundance of for particles of seven species ranged from to , and conjectured that it is the total abundance of the particles with that forms the criterion for triggering strong concentration of particles; that is, . However, if the dust coagulation is limited by the bouncing barrier, the largest particles in the distribution may only have ( Zsom et al. 2010 ; Dra ̧żkowska Dullemond 2014 ). In this case, the conjecture by Bai Stone (2010a) breaks down and it is not clear which size range of the particles would dictate the particle-gas dynamics in the system. On the other hand, when the dust coagulation is limited by the bouncing barrier, the distribution of the particle size tends to concentrate within about one order of magnitude near the barrier ( Zsom et al. 2010 ). Therefore, our assumption of particles of the same size might still assimilate this scenario. Finally, several open questions remain in concentrating small particles by the streaming instability. We have observed further sedimentation in dense filaments in our models, indicating that the velocity dispersion of the particles decreases in the dense regions. It remains to be seen if further dust growth could proceed inside such an environment, given the reduced relative velocities, and if the dust growth in turn could drive more spontaneous concentration of solid materials via the streaming instability. Moreover, the presence of the magnetic fields could render rich structure in the gaseous protoplanetary disks, and the disk mid-plane can be turbulent with various strengths at different locations ( Turner et al. 2014 ), which significantly affects the ability of solid materials to sediment onto the mid-plane (e.g., Okuzumi Hirose 2011 ; Zhu et al. 2015 ). This has nontrivial effects on the dust coagulation and the radial concentration of solids for planetesimal formation to occur ( Johansen et al. 2014 ; Testi et al. 2014 ). 6. Summary In this work, we revisit the condition for the spontaneous concentration of solid particles driven by the streaming instability

B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O


peak shows up in the sum of the correlation at 900 nHz Instruments: no confirmed detection in other instruments (GONG, BiSON) measuring solar radial velocities. Although the detection claimed in F17 is not always reproducible, we must point out that it is quite remarkable to have obtained 2 peaks above the 10% threshold in Fig. 1 , which was not obtained in F17. It clearly shows that the levels of these 2 peaks are very sensitive to the details of the procedure. 3. Reproducing the analysis of FS18 3.1. g-mode asymptotic model In FS18, the analysis done in F17 was pushed further by correlating an asymptotic model of g -mode periods with the power spectrum of the time series of round-trip travel time. The asymptotic model of g -mode period P n , l can be found in Provost Berthomieu (1986) as: where n is the order of the mode, l is the degree of the mode, and P 0 , V 1 , V 2 and θ are all related to the solar model. Equation ( 2 ) can be solved for P n , l and can then be rewritten as: where is a quantity of order . Assuming that n ≫ 1, we can then rewrite P n , l as: where is the asymptotic periodicity of the modes of degree l , n 0 is an arbitrary value of g -mode order and α is given by: Finally, we can rewrite Eq. ( 3 ) as: This equation is the same as Eq. (14) in F17 except that α in our formula is directly related to P l while in F17 it is taken as a free parameter α l . In F17, the model of the g -mode frequency is then given by: where ν l is the rotational mode splitting for modes of degree l assumed to be independent of n and m . In addition, each mode of tesseral order m ≠ 0 of a given l is assumed to be observed with a weight w l , m less than 1. In summary, for l = 1 there are five independent parameters defining the model: P 1 , P min, 1 , α 1 , ν 1 and w 1, 1 ; while for l = 2, there are six independent parameters defining the model: P 2 , P min, 2 , α 2 , ν 2 , w 2, 1 and w 2, 2 . Here we note that F17 assumed that the parameters so defined are independent of each other, while as shown by Eq. ( 5 ) the P l and α depend upon P 0 . Here in order to reproduce, the findings of FS18 we followed the prescription of F17 for the independence of the parameters. 3.2. Optimisation of the correlation The model given by Eqs. ( 6 ) and ( 7 ) is then correlated with the smoothed power spectrum of time series of round-trip travel time. Here we note that the power spectrum is smoothed over 2 bins in FS18 rather than the 6 bins of F17. The computed correlation is made symmetrical by adding a flipped version of the correlation to itself. Then in FS18, the correlation value at 0 and at the splitting lag are optimised or fine tuned by adjusting all 5 parameters for l = 1 and all 6 parameters for l = 2. The optimisation procedure in FS18 is not detailed but done by hand (Fossat, priv. comm., 2018). In the course of the investigation, it appeared that the finding of a maximum could not be made possible by any simple optimisation routine mainly because

O O O O O O O O O O O O O O O O O O O B-Instrument B-Instrument O B-CelestialObject O O O O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O B-Citation O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O B-Citation O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O O B-Citation O O O O O O O O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O B-Formula I-Formula I-Formula O O O O B-Citation O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O


such as the Pareto family and the generalized student T-distributions, are often associated with long-range dependence or memory processes ( Pisarenko 2010 ; Beran et al. 2013 ). The main question in this statistical study can be formulated as: Is the CME process “purely” stochastic? Or does the CME process have a long-range dependence or “memory” in such a way that an event is influenced by the occurrence of past events? To address this question, Wheatland (2003) analyzed a set of CMEs observed by LASCO during the ascending phase and part of the maximum of cycle 23 (1996–2001). The author fit the tail of the WT distribution (WT > 10 h) by a power law function (with index γ ≈ −2.36). The author also applied a Bayesian statistical analysis and concluded that the observed WT distribution corresponds to a time-dependent Poisson process. Performing a similar analysis but using LASCO observations in a restricted time range around the maximum of cycle 23 (1999–2001), Moon et al. (2003) reached the same conclusion that the WT distribution of 3187 CMEs may be represented by a nonstationary Poisson process. A power law distribution, using only 113 CMEs observed by the Solar Maximum Mission, during the minimum phase of the cycle (1984–1989), was also found by Méndez Berhondo et al. (2006) , with γ = 1.41. In this case, the authors associated the WT with a characteristic length Λ, (between two consecutive CMEs) as Λ ∝ V A WT, where V A is the Alfvén speed (this implies causality), and conclude that the WT power law distribution is consistent with their picture of characteristic length. It is clear that a power law may be used to fit the tail of the WT distribution, but as mentioned earlier, 83% of the total events have a WT under 10 h. Therefore, the tail after this WT time is not necessarily representative of the whole process (only the extreme events fall in the extended tail of the distribution). In fact, a power law distribution is heavy-tailed and may represent a scaling system, which, in turn, may have self-organized criticality or related to processes with long-range dependence or “memory” ( Pisarenko 2010 ; Beran et al. 2013 ). For instance, Takahashi et al. (2016) found a scaling relationship between CMEs and proton events. Also, Telloni et al. (2014) found that the WT distribution may be fit by a Weibull distribution (a heavy-tailed distribution), implying some degree of correlation between CMEs in opposition to a purely stochastic process. Our findings are in agreement with Jiggens Gabriel (2009 ; who found that SEP events appear to have some memory indicating that events are not completely random) and with the scaling relationship between CMEs and SEP found by Takahashi et al. (2016) . The possible correlation between consecutive CMEs is supported by the remarkable peak at short PA diff , which shows that the source region of a large number of CMEs is located within close distance (Fig. 5 ). This result confirms the finding of Moon et

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The gas line emission from the J = 3 – 2 transitions of three CO isotopologues, 13 CO (330.588 GHz, Klapper et al. 2000 ), C 18 O (329.331 GHz, Klapper et al. 2001 ) and C 17 O (337.061 GHz, Klapper et al. 2003 ) were extracted from the dataset, using the entire pixel map, after the lines were identified. The source A LSR velocity of 3.1 km s −1 ( Jørgensen et al. 2011 ) was used for all lines. Channels within ±5 km s −1 of each line were combined into an integrated intensity (moment zero) map for each isotopologue, using the image analysis software MIRIAD. Using this approach, the vast majority of the observed line emission was extracted, while avoiding major line-blending from other species. Figure 2 shows the integrated intensity maps of the three isotopologues. C 17 O appears to trace the dust distribution fairly well, as does the C 18 O emission. 13 CO, however, shows a markedly different emission structure, which is likely attributable to optical depth effects, where only the outer layer of the 13 CO structure is probed. Absorption toward source B is evident in all lines but to a smaller extent in C 17 O, revealing that C 17 O is the line with the lowest optical depth, as expected since it has the lowest abundance ( Wilson Rood 1994 ). An interesting feature is the broad emission arc between the sources, which is also present in the dust continuum emission, suggesting a common origin of both the dust and gas emission in this region. We focused on the overall structure of the emission and left a more detailed analysis of the gas kinematics to a future paper (Van der Wiel et al., in prep.). 3 Dust radiative transfer modeling This section describes the setup of the radiative transfer code RADMC-3D, including a description of the dust opacity, model grid and the envelope dust density model used. At the end of the Section, we present our investigation of the temperature structure difference between a single- and a binary protostar, where the envelope dust density model was used. We constructed 3D dust and gas line radiative transfer models consisting of a large envelope with a radius of 8 × 10 3 AU based on Schöier et al. (2002) , two disks around the protostars, as well as a dust filament between the two protostars. The disk’s dust densities were modeled with both the model protoplanetary disk (PP-disk) structure known from Class II sources (e.g., Andrews et al. 2012 ) and the density solution from the angular momentum conservation of an infalling, rotating collapse ( Terebey et al. 1984 ). This infalling, rotating collapse density structure is hereafter referred to as a rotating toroid model, based on the toroidal shape appearing at the centrifugal radius ( Terebey et al. 1984 ), which is the radius where the infalling material with the largest angular momentum impacts the midplane. In terms of luminosity, all models, whether they used one or two radiation sources, were limited to a total luminosity of 21 L ⊙ . This luminosity is based

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rest of the sky. In that case, we take into account to first order the area on the sky that is lost by excluding 15 deg below and above the Galactic plane. This amounts to roughly 10 000 deg 2 , leaving 31 000 deg 2 of the sky that can be searched. In our work, we find four to eight transient candidates in 23.5 deg 2 , and taking into account that Gaia scans ∼1200 deg 2 day −1 , we arrive at a rate of four to nine fast Gaia transients per day. This order of magnitude estimate does not take into account the area on the sky that is eliminated around bright stars and is likely an overestimate. 4.1 Future improvements The implementation of a random forest classifier, based on the low resolution spectra that are measured quasi-simultaneously with the photometry, can help to classify the newly obtained transients into broad classes (Blagorodnova et al. 2014 ). Gaia has the unique feature that each transit has an associated Bp and Rp spectrum, taken during or very close to the transient event. This is especially useful for short duration transient events, as they are likely to be back in quiescence by the time of dissemination (typically the data downlinking and processing takes 48 h). These spectra may thus contain vital spectral information in addition to the colour information they provide regarding the nature of the transient events. The calibration of these spectra is envisaged to be performed in the near future, and may help to both reject spurious events as well as aid the (preliminary) classification of newly discovered fast transients. In addition, improvements in the astrometry will be implemented in the Gaia Science Alerts data base in the near future. Currently, the source positions are based on a first pass of the on-ground attitude determination (OGA1) during IDT. In addition, a second iteration is performed during the later stages of data processing, leading to improved source positions (OGA2). The accuracy of the OGA2 source positions is one to two orders of magnitude better than OGA1. Although for a newly identified transient only the OGA1 position will be available shortly after detection, the implementation of the OGA2 positions for the historic transits will help to reject brightness variations that are inconsistent with the quiescent source position and thus improve the rejection of spurious events. Finally, the reconstruction of the sky positions of sources on the secondary FoV will increase the confidence in detections of sources similar to FGTs 5 through 8, where it is currently unclear whether they are real or could be due to objects in the second FoV. Using the reconstructed second FoV will allow us to reject or confirm the astrophysical nature of these transient events and therefore will aid in the automatisation of the detection process. 5 SUMMARY In this work, we have explored the feasibility of using the Gaia CCD data to search for short time-scale transient events. Gaia provides sparsely sampled, high time resolution

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObjectRegion I-CelestialObjectRegion O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Telescope O O O O O O O O O O B-Wavelength O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Database I-Database I-Database I-Database I-Database O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset O O O O B-Dataset I-Dataset I-Dataset O O O O O O O O O B-Dataset O O O O O O O O B-Dataset O O O O O O O O O O O B-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope B-Instrument O O O O O O O O B-Telescope O O O O O O


in Appendix D , TVL2 seems to be the best method, closer to the noiseless image reported in the last column of all the figures, followed by starlet, b-UWT(7/9), and b-UWT(7/9)+Wiener, which also produce good results. The PM, TV Chambolle, and BM3D are often very similar from a qualitative point of view. Most of the time, the flux of the objects detected by TVL2 is highly concentrated (see, e.g., Figs. D.2 and D.5 ), but in some cases the objects flux is distributed on a larger area, as visible for example, in Fig. D.6 . Starlet produces, in some cases, bright isotropic objects, which could lead to misleading results in terms of their morphological information (see, e.g., Figs. D.14 and D.16 ). In looking at Fig. D.10 , PSF seems to wrongly detect two objects, when other denoising methods detect them correctly. This qualitative analysis is confirmed by the corresponding quantitative analysis made, as visible in Fig. 8 . When more than one object is present in the figures, sometimes the different schemes are not capable of recognizing all the objects (see, e.g., Figs. D.3 , D.6 , D.9 , D.11 and D.13 ). Moreover, fluctuations of the background around the objects are not completely removed and the smoothing can create artifacts (see, e.g., Fig. D.13 , where an elliptical galaxy seems to appear as a spiral galaxy after denoising). b-UWT(7/9), and b-UWT(7/9)+Wiener are susceptible to background fluctuations and end up creating visual artifacts (see Figs. D.17 and D.18 ). When inspecting the snapshots from real images, a qualitative analysis is inconclusive, so we have to stick to the quantitative analysis in Fig. 12 . In fact, looking at the crops reported in Appendix E , the PM and bilateral are often so similar that it is difficult to distinguish them, although, based on Fig. 12 , the differences between them are clear (the PM performs worse than the bilateral). An analogous remark can be made for BM3D, which produces images similar to the PM and bilateral, as visible, for example, in Fig. E.12 . Here, the TVL2 is, again, the most promising method, which is visually close to the HUDF09 also thanks to the automatic optimization of its internal parameters, and this is confirmed by the results reported in Fig. 12 . Images from starlet, b-UWT(7/9), and b-UWT(7/9)+Wiener are quite similar to each other, even though those from starlet present fewer visual artifacts (see Figs. E.10 , E.12 – E.14 ). Differently from the other methods, NL-means produces nearly squared patches of uniform flux, which effectively reduce noise fluctuations (see Figs. E.8 and E.9 ), but also could lead to artifacts (see, e.g., Fig. E.5 , where the galaxy in the bottom-right corner assumes a nearly boxy-shape). Even if artifacts are generated and the morphology of the objects detected could be altered, we tested the efficiency of the algorithms in preserving the FWHM and the ellipticity of the objects in Sect. 5.6 , proving that on the average the shapes are preserved. A

O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O B-Software B-Software O B-Software O O O O O O B-Software B-Software I-Software O B-Software O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O B-Software O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O B-Software O O O O O O O B-Software O O O O O O B-Software O B-Software O O O O O O O O O O B-Software O O O O O O O O O O O O B-Dataset O O O O O O O O O O O O O O O O O O O O O O O O B-Software B-Software O B-Software O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


part of the GAMA survey. This provides the substantial advantage of deep and complete spectroscopic coverage to select targets and define environment. The GAMA regions also contain excellent photometric data across 21 bands from ultraviolet to far-infrared (Driver et al. 2016 ). Bryant et al. ( 2015 ) describe the selection of targets in the GAMA regions in detail, but for completeness we outline the selection here. The SAMI Galaxy Survey targets in the GAMA regions were selected within three 4 × 12° regions along the equator (Dec. ≃0°), centred at approximate right RA of 9, 12, and 15 h (see Table 1 ). Galaxies were targeted based on cuts in the redshift-stellar mass plane, with a stellar mass proxy that used i -band magnitude and g − i colour (Taylor et al. 2011 ; Bryant et al. 2015 ). Galaxies were selected within a series of four stepped volumes, with higher stellar mass limits at higher redshift (see fig. 4 of Bryant et al. 2015 ). The primary sample is limited to redshift z 0.095. Observations aimed to have high and uniform completeness for this primary sample. A secondary sample was also defined that included high mass galaxies to higher redshift ( z 0.115) and used fainter stellar mass limits. As the secondary targets were observed at lower priority, these are less complete (see Table 1 ). Fig. 1 shows the distribution of primary (observed: red, unobserved: blue) and secondary targets (observed: magenta, unobserved: cyan) in redshift and RA. A set of filler galaxies, at lower priority that the secondary targets, was also defined. These are discussed in Section 2.1.3 . Figure 1. The distribution of SAMI galaxies within the three GAMA regions. We show the original GAMA redshift survey (small grey points), observed (red), and unobserved (blue) primary SAMI galaxies, observed (magenta) and unobserved (cyan) secondary SAMI galaxies. Table 1. The coordinates and number of objects in the GAMA regions. For each region, we list the RA and declination (Dec.). For both the primary and secondary samples, we list the number of observed galaxies N obs , the number of good targets N good (i.e. those not flagged as bad for photometric reasons such as a bright star in the field of view) and the number of all targets N all (including objects with bad flags). The listed completeness for the primary sample is N obs / N good . Each potential target was visually checked to identify problems, such as bright nearby stars or sources being a sub-component of a larger galaxy. These were flagged within the input catalogue and their priority set so that they were not observed. The column named BAD_CLASS in the input catalogue (named InputCatGAMADR3) contains flags for different types of problem sources. A full description of the flags is given by Bryant et al. ( 2015 ). A small subset of the sources required adjustment of the coordinates for targeting (0.6 per cent of the input catalogue). This was typically in order to place an asymmetric galaxy or a close pair

O O O B-Survey O O O O O O O O O O O O O O O O O O O B-CelestialRegion I-CelestialRegion O O O O O O O O O B-Wavelength O B-Wavelength B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O B-CelestialRegion I-CelestialRegion O O O O O O O O O O O B-Survey I-Survey I-Survey O O O B-CelestialRegion I-CelestialRegion O O O O O O O O O O O B-CelestialRegion I-CelestialRegion O O O O B-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O B-Wavelength O B-Wavelength O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O B-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O B-CelestialRegion I-CelestialRegion I-CelestialRegion O O O O B-Survey O O O O O O O O O O O B-Survey O O O O O O O B-Survey O O O O O O O O O O O B-CelestialRegion I-CelestialRegion O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


the best-fit model yielding erg s −1 . Figure 1. Top: deconvolved spectrum (crosses) and best-fit model (solid line) observed with XMM-Newton. PN, MOS1, and MOS2 cameras are plotted in black, red, and blue, respectively. Bottom: residuals are shown. The spectrum is rebinned for visual clarity. Table 1 CQ 4479 X-Ray Fitting Parameters Note. X-ray spectrum fit parameter results from an absorbed power-law fit corrected for galactic absorption. When both parameters are left free, or one of each parameter is fixed using previous fit results, we consistently find an upper limit solution to characteristic of an unobscured quasar. 2.3. Far-ultraviolet to Far-infrared Photometry Far-ultraviolet (FUV) to far-infrared (FIR) observations are obtained from the Stripe 82X catalog (Ananna et al. 2017 ). The target has FUV and NUV coverage from GALEX (Martin et al. 2005 ), and optical coverage from the coadded SDSS catalogs (Jiang et al. 2014 ; Fliri Trujillo 2015 ). In the infrared, Stripe 82X was observed with the Spitzer Space Telescope at 3.6 and 4.5 μ m (Papovich et al. 2016 ; Timlin et al. 2016 ) as well as the WISE 3.4, 4.6, 12, and 22 μ m bands (Wright et al. 2010 ). Herschel/SPIRE observations using 250, 350, and 500 μ m bands are also available for the Stripe82X field from the Herschel Stripe82 Survey (HerS; Viero et al. 2014 ). For more information on the Stripe 82X catalog and its counterpart identification protocol, please see LaMassa et al. ( 2016 ) and Ananna et al. ( 2017 ). 2.4. SOFIA HAWC+ Observations Due to the large gap in spectral coverage from 22 to 250 μ m, crucial for measuring the dust-obscured star formation rate (SFR) and the AGN contribution to the mid-IR, new observations using SOFIA/HAWC+ (Dowell et al. 2013 ; Smith et al. 2014 ) were taken using band C at 89 μ m (PI: A. Kirkpatrick, PID: 07-0096). CQ 4479 was observed for ∼7223 s on 2019 September 7 during SOFIA Cycle 7 using the HAWC+ total intensity OTFMAP mode with a Lissajous pattern in nominal water vapor conditions. At this redshift, each 155 pixel subtends a linear width of 8.4 kpc. The total field of view of HAWC+ in this setup is 42 by 27, with an FWHM of 78. Following data acquisition, the observations were reduced using the HAWCDPR PIPELINE (Harper et al. 2018 ). Flux calibration was performed using an average reference calibration factor across the detector. Source extraction was performed using the CRUSH V. 2.50-2 detect routine (Kovács 2006 , 2008 ) in the faint object mode, yielding a flux measurement of 75.42 ± 14.2 mJy in a 78 FWHM beam (SNR = 5.31). The SOFIA HAWC+ band C image is shown in Figure 2 . Figure 2. Left: SOFIA HAWC+ Band C observation of CQ 4479 centered at the target coordinates of R.A. J2000 of 1 h 40 m 4071 and Decl. J2000 of 0°17′ 5817. For reference, the SOFIA HAWC+ Band C (89 μ m) PSF FWHM is the circle in the lower left (78). At z = 0.405, the spatial scale is 5.413 kpc/′′ (Wright 2006 ). Each pixel is 155, subtending a linear width of 8.4

O O O O O O O O O O O O O O O O O O O O O B-Telescope B-Instrument B-Instrument O B-Instrument O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O B-Wavelength O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O B-Wavelength O B-Wavelength B-Wavelength O B-Wavelength B-Wavelength O O O O O B-Dataset I-Dataset I-Dataset B-Citation I-Citation I-Citation I-Citation O O O O B-Wavelength O B-Wavelength O O B-Telescope B-Citation I-Citation I-Citation I-Citation O O B-Wavelength O O O O B-Survey O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O B-Wavelength B-CelestialRegion I-CelestialRegion O O O O B-Telescope I-Telescope I-Telescope O B-Wavelength O B-Wavelength I-Wavelength I-Wavelength B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O B-Telescope B-Wavelength B-Wavelength B-Wavelength O B-Wavelength I-Wavelength I-Wavelength O B-Citation I-Citation I-Citation I-Citation O B-Telescope O O B-Wavelength B-Wavelength O B-Wavelength I-Wavelength I-Wavelength I-Wavelength O O O O O B-CelestialRegion I-CelestialRegion O O B-Survey I-Survey I-Survey B-Survey B-Citation I-Citation I-Citation I-Citation O O O O O O B-Dataset I-Dataset I-Dataset O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O B-Telescope B-Instrument O O O O O O O O O O B-Wavelength O B-Wavelength I-Wavelength I-Wavelength O O O O O O O O O O O O O O O B-Wavelength O O O B-Telescope B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O B-Wavelength I-Wavelength O B-Wavelength I-Wavelength I-Wavelength O B-Person I-Person B-Proposal I-Proposal B-CelestialObject I-CelestialObject O O O O O O O O O O B-Telescope O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O B-Software I-Software B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O B-Software I-Software I-Software I-Software I-Software B-Citation I-Citation O B-Citation O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O B-Telescope B-Instrument B-Wavelength I-Wavelength O O O O O O O O O O B-Telescope B-Instrument B-Wavelength I-Wavelength O O B-CelestialObject I-CelestialObject O O O O O O B-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion O O O B-Telescope B-Instrument B-Wavelength I-Wavelength B-Wavelength I-Wavelength I-Wavelength O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O B-Citation I-Citation O O O O O O O O O O O


the DTW template matching scheme and a traditional peak detection algorithm. Curves are generated by varying the respective threshold parameter, DTW distance, and the peak prominence. 4 RESULTS Some of the detection scenarios involving true detection of long GRBs along with typical false-positive candidates are shown in Fig. 6 . The upper panels show the correctly identified events, while the lower panels depicted certain instances when the events picked up were due to noise artefacts. After the testing and validation steps mentioned in the previous section, we carried out a blind search targeting long GRB events on the CZTI data collected from 2015 October 8 to 2018 November 7. The pipeline detected 223 probable candidates, out of which 170 were already known to be GRBs. Detailed analysis of the rest led to the discovery of 35 long GRBs candidate events along with 18 false positives. Table 1 lists these newly discovered events along with their trigger time in UTC, T 90 , 2 peak count rate, total count rate, mean background counts, and detection significance with their uncertainties. Of these, GCN alerts were issued for two such events, namely GRB180526A (Sharma, Vibhute Bhattacharya 2018a ) and GRB180603A (Sharma, Vibhute Bhattacharya 2018b ). The observed number of false triggers are higher than the expected as shown in Fig. 5 . While the origin of many of these is not well understood, some of them seem to be related to the scenario shown in Fig. 6 (lower right), where we see multiple short-duration transients all occurring within tens of seconds. Figure 6. Few detection scenarios encountered while searching for long GRBs with one-second binning. The upper panel shows the correctly identified events, while the lower panel highlights instances of contamination from non-GRB transients. The horizontal red line determines the threshold caused by the noise background level, and significant points above this are clustered using the DBSCAN algorithm. Each cluster is uniquely coloured, with the vertical line providing the cluster centre. Table 1. GRBs candidate events detected with the machine learning algorithm described in this paper. GCN circulars have been issued for the highlighted events. Certain minor modifications were necessary to make the pipeline sensitive to GRBs occurring at shorter time-scales. As these events showed a higher sensitivity to the time bins’ size, it was necessary to vary the bin size across time-scales ranging from 32 to 512 ms for the entire data chunk and select the value that maximizes the peak count in at least three of the four quadrants. The time bin’s optimal value was determined using the differential evolution (Storn 1996 ) global optimizer, which performed a gradient-free direct search across this continuous parameter space. Besides, the 3σ detection threshold used for longer GRBs had to be increased to 4.5σ to minimize contamination from the background noise. In Fig. 7 we report the triggers seen for

O B-Model I-Model I-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject B-Citation I-Citation I-Citation I-Citation O O B-CelestialObject B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software I-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


value, as many of our fitting regions do not have enough counts to constrain either or abundance. 3. TEMPERATURE MAPS Temperature maps shown in Figure 2 were derived following the method described in Markevitch et al. ( 2000 ). We extracted six narrow-band flux images between 0.8 and 9 keV, excluding the 1.7–1.9 keV edge and 7.3–7.6 keV (possibly affected by poor subtraction of the instrumental lines). Both flux and error images were smoothed prior to deriving the temperature map. A single-temperature MEKAL model was then fitted to a set of six flux values from each pixel of the image, resulting in a smoothed temperature map. The absorption column and metal abundance were fixed to the cluster best-fit values. Two smoothing methods were used, as described below. Figure 2. (a) Variable smoothing temperature map with X-ray contours. The holes in the map are masked point sources. (b) Wavelet temperature map. The fitted images were reconstructed from component wavelet scales of 6.6, 13, 26, 53, 105, and 210 kpc. 3.1. Smoothing with the Variable-width Gaussian Kernel For this approach, the narrow-band images were smoothed using a Gaussian kernel for which the width at each image pixel is determined by surface brightness in the 0.8–4 keV band, with the goal of preserving detail in bright regions. As a reference for this smoothing method, but also as a high-quality X-ray image that shows the cluster structure on all scales and omits point sources, we used wavelet reconstruction of the 0.8–4 keV image, as described in Vikhlinin et al. ( 1994 , 1998 ). We extracted wavelet components (with the atrous kernel and scales increasing in geometric progression) on scales of 53, 105, 210, and 420 kpc (or 157, 315, 630, and 126′′). Point sources are contained in wavelet components on smaller scales than the first scale above, thus not included. These image components were then co-added with the residual image smoothed by the 840 kpc scale wavelet. This procedure retains most of the statistically significant extended structures on various angular scales. The resulting image is shown in Figure 1 (b) next to the original unsmoothed image; we will use it as a reference when discussing various features in this cluster. Based on this reference image, the narrow-band images and their corresponding error images (with point sources excised) were identically smoothed by a variable-width Gaussian. By inspecting the error in the derived temperature map, we determined that using the Gaussian smoothing radius ∝ flux, and smoothing radius between 13 and 200 kpc, achieved a balance between revealing the temperature variations and suppressing noise. The resulting temperature map is shown in Figure 2 (a). To check the validity of values shown in the map, we extracted the proper spectra in a few hot spots and a cooler spot (regions T1–T4 and T5 in Figure 2 (a), respectively) and fitted their projected temperatures in XSPEC. For T1, we obtain keV; for T2, keV; for T3, we

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O B-Wavelength O B-Wavelength I-Wavelength O O B-Wavelength I-Wavelength O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model O O O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O


low-order radial modes may be simultaneously excited (their fig.3) andbeat pulsation is, thus, possible. The linear pulsation models disprove this scenario as well. We have computed a grid of models with masses in the 0.20.4M range, with luminosities in the 2070L range and metallicity range the same as considered in Fig. 10 (other model parameters are the same as in Smolec etal. 2016 ). We note that little is known about the expected physical parameters of BEPs, as only one object has been observed so far (see, however, Karczmarek etal. 2017 ). The range of the physical parameters given above is motivated by the model study by Smolec etal. ( 2013 ) and assumes that model periods are not far from those observed in RR Lyr stars. The models assume an homogeneous envelope with chemical composition the same as in RR Lyr-type models. For the 0.2M and 0.3M models, the periods of the unstable radial modes are significantly longer than those observed for our stars. The period ratios for the 0.4M models are plotted in Fig. 11 . The models have longer periods and higher period ratios, and are far from the region in which double-periodic stars reside. Clearly, the discussed stars are not fundamental and first-overtone double-mode radial pulsators, at least, not if we assume their physical parameters are close to those expected for RR Lyr stars or BEPs (which mimic RR Lyr stars). Other combinations of radial modes also do not fit the discussed group. Figure 11. As Fig. 10 , but for BEP models of 0.4M . The different colours correspond to different metallicities, as indicated in the top panel of Fig. 10 . We note that simple triangular light curve shape is characteristic for fundamental-mode HADS stars. The Fourier parameters of these stars are similar as well (see fig.8 and figs47 in Poretti 2001 , note that the cosine convention is adopted in that paper). The periods of these stars are typically below 0.2d, however, while for our stars they are longer than 0.28d. The period ratios in the radial multi-mode HADS stars are also vastly different, see e.g. fig.10 in Pietrukowicz etal. ( 2013 ). Assuming that the dominant variability does correspond to the radial fundamental mode (the large amplitude and non-linear light curve shape make this hypothesis most plausible), the additional variability may correspond to the non-radial pulsation mode. Low-degree modes may be excited in RR Lyr models (Van Hoolst, Dziembowski Kawaler 1998 ; Dziembowski Cassisi 1999 ), preferentially in the vicinity of the radial modes. Although the frequency spectrum of such modes is very dense, their growth rates do display a local maximum close to the position of the radial modes. In particular, for 1 modes, the growth rates approach that of the radial first-overtone mode and their frequency is higher than the radial first-overtone frequency [see, e.g. fig.1 in Van Hoolst etal. ( 1998 ) or fig.1 in Dziembowski ( 2016 )]. This would lead to period ratios a bit lower than observed for

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O


near T 0 . Figure 1. Results of the AGILE -GRID standard analysis for the three Sources A (upper panel), B (middle panel) and C (lower panel) of Table 1 . Left panels: gamma-ray lightcurves above 100 MeV around the IceCube T 0 . Right panels: maps in equatorial coordinates (J2000) of gamma-ray intensity above 100 MeV in (ph cm −2 s −1 sr −1 ) corresponding to the gamma-ray detection before T 0 shown on the left. The AGILE 95% confidence level (c.l.) location contours are shown in white; the IceCube error boxes or circles are shown in yellow. A systematic error of 01 should be added to each AGILE source determination. The positions of the classified AGNs from the BZCAT Catalog (Massaro et al. 2015 ) and the FERMI -LAT 3FGL gamma-ray sources (Acero et al. 2015 ) are shown in cyan and red, respectively. The standard AGILE data analysis indicates that in all cases the peak gamma-ray emission is similarly observed within 1–2 days from T 0 . For Sources B (IC-170321) and C (IC-170922), weak gamma-ray emission is also observed over longer integration timescales that include T 0 . In particular, for the new Source B an integration of 15 days, starting from 2017 March 15 (12:00 UT), shows a detection above 4 σ with a flux . The AGILE centroid has Galactic coordinates ( l , b ) = (224.59, −10.53) ± 0.42 (deg) (95% stat. c.l.) ± 0.1 (deg) (syst.) (R.A., decl. (J2000) = (98.58, −15.08) (deg)), and is fully compatible with the IceCube centroid (see Figure 2 ). Figure 2. Left panel: AGILE -GRID intensity maps, in (ph cm −2 s −1 sr −1 ) and equatorial coordinates (J2000), centered at the position of the IceCube event IC-170321, over a long integration time of 15 days around T 0 (( T 0 − 6; T 0 + 11) days). The AGILE 95% c.l. location contour obtained with the AGILE standard analysis is shown in white, the IceCube error box in yellow. Right panel: AGILE -GRID 7 day time bin gamma-ray lightcurve ( E > 100 MeV) around T 0 , obtained from the AGILE standard analysis performed at the IC-170321 position. 2.1. Post-trial False Alarm Probability To evaluate the probability that each of these three gamma-ray sources is associated with the neutrino events by chance, we first evaluated the FAR for an AGILE QL detection per unit time δt and per unit solid angle δ Ω. As the unit time δt , we assumed the standard integration time of the QL maps ( δt = 2 days). For δ Ω, we assumed the solid angle subtended by a cone with half-aperture matching the standard circular radius of 15 used in the database search ( δ Ω ≃ 2.15 × 10 −3 sr). 22 22 See Appendix A for details of the FAR computation. We then estimated the post-trial false alarm probability P i of a random occurrence in space and time of a neutrino and a gamma-ray transient event separated by an interval Δ t and by the solid angle ΔΩ (corresponding to the angular distance Δ θ ) as (Connaughton et al. 2016 )where N i is the number of trials for a symmetric time window, is the FAR per 2 day map and per unit solid angle for

O O O O O O O O O B-Telescope B-Instrument O O O O O B-CelestialObject I-CelestialObject O O B-CelestialObject O O O B-CelestialObject O O O O O O O O B-Wavelength O O B-Wavelength I-Wavelength O O B-Instrument O O O O O O O O O O O B-Wavelength O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O B-Dataset I-Dataset B-Citation I-Citation I-Citation I-Citation O O O B-Dataset I-Dataset I-Dataset B-Wavelength O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O B-Telescope O O O O O O O O O B-Wavelength O O O O O O O O O O O O B-CelestialObject I-CelestialObject B-Identifier O B-CelestialObject B-Identifier O B-Wavelength O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O B-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion O O O O O O O B-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion I-CelestialRegion O O O O O O B-Instrument O O O O O O O O O B-Telescope B-Instrument O O O O O O O O O O O O O O O O O O O O O B-Instrument O B-Identifier O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O B-Telescope O O O O O O O B-Instrument O O O O O O B-Telescope B-Instrument O O O O B-Wavelength O O B-Formula I-Formula I-Formula I-Formula O O O O O O O B-Telescope O O O O O B-Identifier O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O


90072 two more transit windows of HD 10180 b, this time at 4.5 m. No transit structure was clearly detected in the resulting light curves (Fig. 4 , bottom), and a short MCMC analysis of the data led us to conclude that these new data do not increase the significance of the 2010 tentative detection. We thus conclude that the low-amplitude structure in our Cycle 6 Spitzer light curve is probably just correlated noise of instrumental origin that is not perfectly represented by our baseline model and that can easily be modeled by a shallow enough transit profile. For Spitzer , the average amplitude of these correlated noise structures is of a few dozen of ppm (see Sect. 6), making the firm detection of a unique transit shallower than 100 ppm impossible – similarly to the one expected for HD 10180 b (see discussion in Sect. 6 and our estimated detection thresholds in Table 1 ). This so-called red noise limit can be surpassed, but only by gathering more observations of the transit window, as we did here (see also the case of HD 40307 b in Gillon et al. 2010 ). Fig. 3 Same as Fig. 1 for the light curve of 2010 Jan. 16 for HD 10180 c. Fig. 4 Top : same as Fig. 3 , except that a possible transit of HD 10180 b was included in the global model. Highest posterior probability transit model for HD 10180 b is shown in purple. The posterior PDF for the transit timing of HD 10180 b derived from an RV analysis assuming 7 planets is shown above the figure. Bottom : 2013 Warm Spitzer photometry for HD 10180 binned per 30 min, corrected for the instrumental effects, normalized, and folded on the transit ephemeris for HD 10180 b based on our tentative detection of a transit and on the results of an RV analysis assuming 7 planets (d = time from most likely mid-transit time). 5.4. HD 13808 In 2012, we analyzed the HARPS dataset for the early K-dwarf HD 13808, confirming the existence of the two planets around it with periods of 14.2 d and 53.7 d announced by Mayor et al. ( 2011 , hereafter M11). Furthermore, our analysis revealed the existence of (1) a low-frequency signal that was well-modeled with a quadratic trend, whose origins is the magnetic cycle of the star (Queloz et al., in prep.). and (2) a low-amplitude Doppler signal with a period of 1.091 d which corresponds to a planet of with an interestingly high transit probability of 20%. The false-alarm probability (FAP) derived by Systemic for this short-period planet candidate was close to 1%. Based on this small FAP and the scientific importance of this putative planet if transiting, we decided to monitor two of its transit windows with Spitzer . Unfortunately, the resulting light curves did not show any convincing transit-like structure (Fig. 5 ). Our Spitzer data were not acquired during a transit window of the planet at 14.2 d, thus keeping its transiting nature unconstrained. In 2014, we analyzed the updated HARPS dataset. This analysis confirmed the existence of the low-frequency signal of magnetic cycle

O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O B-Mission I-Mission O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O B-Instrument O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O


than 25 (i.e., approximately 5 σ for a χ 2 with 2 degrees of freedom). We use the same criterion to compare the Band+BB and Band+BB+PL models, or the Band+CPL and Band+CPL+PL models. The spectral parameters obtained from the best models along with the C-stat values are presented in Table 1 . Often the brighter GRBs require more complex models such as SBPL + BB, Band + BB, Band + CPL + PL; etc., for fitting their spectra. Figure 2 shows the νF ν energy spectrum using the best model of spectral fit for each GRB in Table 1 . The top left panel shows the spectra fitted with Band or Band + BB and the top right panel shows the spectra fitted with SBPL or SBPL + BB. The bottom left panel shows GRB 160625B spectrum fitted with Band + BB + PL and GRB 160509A spectrum fitted with Band + CPL + PL. The bottom right panel shows the spectra of GRB 130427A, GRB 090926A, GRB 090902B and GRB 090510, all fitted with Band + PL. The shaded regions correspond to the 1 σ confidence intervals of the models. Figure 2. νF ν spectra of Fermi -LAT/GBM GRBs with known redshift resulting from the time-integrated spectral analysis over the T 90 duration. The top left panel shows the Band or Band+BB model fits, while the top right panel shows SBPL or SBPL+BB model fits. The bottom panels show more complex models along with their components. The 1 σ confidence regions of the models are shown with shades of the same color as the model lines. 3.2. Isotropic Energy Calculation We computed the isotropic radiated energy E iso in the source rest frame at a luminosity distance d L aswhereis the bolometric fluence integrated over the minimum photon energy E min = 1 keV, and the maximum photon energy E max = 10 4 or 10 5 keV. Here, N i ( E ) represents the best-fit spectral model discussed previously. The luminosity distance strongly depends on the cosmological model. Assuming a flat ΛCDM cosmology with , the d L can be expressed as We have used Monte Carlo simulations to estimate the errors on S bolo and E iso . In particular, we assume the parameters of the spectral models follow a multivariate Gaussian function. Using the covariance matrix obtained from a spectral fit, we generate 10 4 sets of random values for the parameters to calculate S bolo in Equation ( 2 ). We select 68.27% confidence intervals from the resulting distribution of S bolo values to evaluate its error. The results of S bolo and E iso are reported in Table 2 . The values of (F10) and (F10) correspond to the energy interval 1–10 4 keV, while the values of (F100) and (F100) correspond to the energy interval 1–10 5 keV. The intrinsic peak energies of the νF ν spectra are reported as in Table 2 . In all cases, . Figure 3 shows the distributions of bolometric fluence, isotropic radiated energy and peak energy of the νF ν spectra of Fermi GRBs and those in the W2016 sample. There are small differences between the bolometric fluence (F10) and (F100) for the Fermi sample (the green and red histograms, respectively,

O O O O O O O O O O O O O O O O O O O O O O O B-Model O B-Model I-Model O O B-Model O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O B-Model I-Model I-Model B-Model I-Model I-Model B-Model I-Model I-Model I-Model I-Model O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O B-Model O B-Model I-Model I-Model O O O O O O O O O O B-Model I-Model I-Model I-Model I-Model O O O O O B-CelestialObject I-CelestialObject O O O B-Model I-Model I-Model I-Model I-Model O B-CelestialObject I-CelestialObject O O O B-Model I-Model I-Model I-Model I-Model O O O O O O O O B-CelestialObject I-CelestialObject B-CelestialObject I-CelestialObject B-CelestialObject I-CelestialObject O B-CelestialObject I-CelestialObject O O O B-Model I-Model I-Model O O O O O O O O O O O O O O O O O O O B-Observatory B-Telescope O O O O O O O O O O O O O O O O O O O O O B-Model O B-Model I-Model O O O O O O O B-Model O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model O O O O O O O O O O O O B-Model I-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength I-Wavelength O O O O O O O O O O O O B-Wavelength I-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset O O O O B-Dataset I-Dataset O O O O O O O O O O O O O B-Dataset I-Dataset O O O O O O


] sample, we notice that although the measurements are consistent with each other at a given stellar mass for all redshifts, the slope of the trend decreases such that by z ≳ 2.2, the trend is flat. Interestingly, these measurements occur in the same mass range (9.2 log 10 M stellar /M ⊙ 11.0) where for all redshifts, the trend is flat and then increases for higher stellar masses. A similar flat, redshift-independent trend is seen for [O ii ] emitters within the stellar mass range of10 9.75–11.0 M ⊙ . Figure 6. The minimum halo mass dependency with stellar mass. We find a strong relationship at all redshift slices for our H β + [O iii ] samples and for the z = 1.47 [O ii ] sample. The z = 2.25 [O ii ] sample also shows an increasing trend, but is limited only to two stellar mass bins. The other [O ii ] samples are limited due to sample size and could not be separated in stellar mass bins. We find no redshift evolution in the relationship. Interestingly, at stellar masses >10 9.75 M ⊙ , the halo mass is found to be constant at ∼10 12.3 M ⊙ for H β + [O iii ] emitters and ∼10 12 M ⊙ for [O ii ] emitters. Fig. 7 shows the SHM ratio as a function of stellar mass, where we find it to be redshift-independent for all stellar masses. We find the SHM ratio for z = 0.84 and 1.42 H β + [O iii ] emitters to be constant between 8.5 log 10 M stellar /M ⊙ 9.75 and increasing for all redshift slices at M stellar > 10 9.75 M ⊙ . The [O ii ] emitters show a continuous increase in the SHM ratio from the lowest masses to the highest masses probed. Figure 7. The SHM ratio as a function of stellar mass. We find our [O ii ] measurements show a continuous, redshift-independent increase in the SHM ratio for the full stellar mass range. The H β + [O iii ] measurements show a constant ratio up to ∼10 9.75 M ⊙ followed by a continuous, redshift-independent increase in the ratio. We compare our measurements with the abundance matching measurements of Behroozi, Wechsler Conroy ( 2013b ) overlaid in grey. We find that our [O ii ] and H β + [O iii ] measurements are in agreement within 1σ except for our H β + [O iii ] 10 9.75–10.00 M ⊙ measurements. Overlaid in Fig. 7 are the measurements of Behroozi et al. ( 2013b ), which used abundance matching along with constraints from observational measurements of global stellar mass functions to calculate the SHM ratio up to z ∼ 8. Behroozi et al. ( 2013b ) found that the ratio is redshift-independent and we therefore only highlight in Fig. 7 the 1σ confidence region of their measurements that correspond to the redshifts of our sample. We find that all four redshift slices for the [O ii ] samples are in agreement with the Behroozi et al. ( 2013b ) measurements. Our H β + [O iii ] measurements are also in agreement for M stellar 10 9.5 M ⊙ and >10 10 M ⊙ . Note that the Behroozi et al. ( 2013b ) measurements are based on ‘global’ (passive+active galaxy) stellar mass functions, while our samples are comprised of ‘active’ galaxies

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O B-Formula I-Formula I-Formula O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O B-Wavelength I-Wavelength O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey I-Survey I-Survey I-Survey I-Survey I-Survey O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O B-Wavelength I-Wavelength O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O


tests Fig. 11 Reduced distribution for 30 GHz, 44 GHz, 70 GHz, for white noise ( left ), ideal, and realistic simulation ( right ), for 100 realizations. Also shown is the distribution corresponding to an ideal result. The numbers within the plot give the mean value of the distribution. Fig. 12 Reduced distribution for 70 GHz subsets. From top to bottom : full (4 yr) 70 GHz data, years 14 (full frequency), horn pairs 18/23, 19/22, 20/21 (4 yr). From left to right : white noise only, independent destriping ideal/realistic, full destriping ideal/realistic. In the case of full mission, independent and full destriping options are identical. Fig. 13 Effect of various approximations on the result for the worst LFI18/23 case. We add to the ideal simulation one non-ideality at a time: destriping mask, horn-uniform weighting, finite baseline length, and destriping resolution. The result for the ideal simulation case is shown in the background. We validate the noise covariance matrices through tests. We generated 100 realizations of harmonic vectors , as described above. For each vector and corresponding matrix we compute the value Here is the covariance matrix, and is the number of degrees of freedom. Multipole contributes degrees of freedom, except for the monopole and dipole, which are identically zero for and . This yields in total = 3(+1) degrees of freedom, which for = 50 gives = 7795. For a matrix that perfectly describes the noise properties, the results obey the Chi distribution, For large values of the distribution becomes Gaussian, with expectation value 1 and standard deviation of , which for = 7795 is 0.016. We compared the white noise covariance matrix of Eq. ( 11 ) against white noise simulations, and the full noise matrix of Eq. ( 29 ) against ideal and realistic simulations. Results from tests at the three Planck LFI frequencies, 30, 44, and 70 GHz are shown in Fig. 11 . We show a histogram of the values for 100 realizations, and quote their mean. The white noise matrix models the white noise residual nearly perfectly, mean ranging from 0.9992 to 1.0004, Comparison between the full matrix and ideal simulation gives slightly larger values, from 1.0002 at 70 GHz to 1.0037 at 44 GHz, while the expected one-sigma deviation for 100 realizations is 0.0016. Deviations from one are larger in the case of realistic simulation, as expected, given the approximations involved. At 30 GHz we find a mean of 1.0196, at 70 GHz 1.0037. Results for 70 GHz subsets of are shown in Fig. 12 . In the first column we compare the white noise matrix against a pure white noise simulation. Second and third column show results for the full matrix. Results are shown for two destriping options: full 70 GHz destriping, and independent destriping. In all cases, we find very good agreement with the white noise matrix and corresponding simulations. The same can be said about the full matrix and the ideal simulations. The mean

O O O O O O B-Wavelength I-Wavelength B-Wavelength I-Wavelength B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope B-Instrument O B-Wavelength B-Wavelength O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O B-Wavelength I-Wavelength O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O