data. Section 3 shows an example of SIR and presents the identification criteria for SIRs and shocks. Section 4 presents the statistical distributions of the properties of SIRs and their associated shocks, and compares the results with those at 1 au. The last section concludes with a summary of the contributions. 2. <italic>MAVEN</italic> Data The MAVEN spacecraft carries nine sensors that measure the solar wind and the plasma and neutral environment at Mars. In this work, we use data from the following three instruments: Solar Wind Ion Analyzer (SWIA; Halekas et al. 2015 ), Magnetometer (MAG; Connerney et al. 2015 ), and Solar Wind Electron Analyzer (SWEA; Mitchell et al. 2016 ). SWIA is an electrostatic analyzer that measures 3D distributions of ions from 25 eV to 25 keV. When MAVEN is in the solar wind, SWIA provides the solar wind parameters with a cadence of 4 s. MAG measures the three components of magnetic field with 32 Hz cadence. We use 4 s resolution MAG data that are extracted from the full resolution measurements and are in one-to-one correspondence with SWIA data. SWEA provides the energy and angular distributions of 3–4600 eV electrons with 2 s time resolution. We utilize the MAVEN data in Mars Solar Orbital (MSO) coordinates, in which X points from Mars toward the Sun, Y points in the opposite direction of the Mars orbital velocity component perpendicular to X, and Z completes the right-handed system. 3. Observations of an SIR during 2016 August 2–3 Figure 1 shows solar wind plasma and magnetic field data from MAVEN during 2016 August 2–3 encompassing a typical SIR event. The SIR is clearly identified by the signatures including a compression of IMF ∣ B ∣, an increase of proton speed V p , an increase of proton number density N p , an enhancement of proton temperature T p , and a significant enhancement of total perpendicular pressure P t (e.g., Guo et al. 2015 , 2016 ; Wang et al. 2018 ). P t is defined as the sum of the magnetic pressure and plasma thermal pressure perpendicular to the magnetic field (Gosling et al. 1987 ). Note that SWIA does not have the capability to discriminate ions based on mass, and thus the plasma parameters are calculated assuming that all ions are protons. SWEA does not provide direct measurements of solar wind electron temperature and density. In the calculation of P t , the electron contributions are not included and the proton temperature is assumed to be isotropic. An SI in the SIR, characterized by a decrease in proton density and a simultaneous increase in proton temperature, can be discerned at about 13:25 UT on August 2. A pair of forward–reverse shocks bounding the SIR are observed at about 03:44 UT on August 2 and at about 07:37 UT on August 3 respectively. Figure 1. MAVEN observations of an SIR at 1.52 au during 2016 August 2–3. From top to bottom: the MAVEN spacecraft altitude, pitch angle distributions of electron (∼282 eV), electron energy spectra, ion energy spectra, proton speed ( V p

O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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-Tag O O B-Mission O O O O O O O B-CelestialObject O O O O O O O O B-CelestialObject O O O O O O O O O O O B-Instrument I-Instrument I-Instrument I-Instrument B-Instrument B-Citation I-Citation I-Citation I-Citation O B-Instrument B-Instrument B-Citation I-Citation I-Citation I-Citation O O B-Instrument I-Instrument I-Instrument I-Instrument B-Instrument B-Citation I-Citation I-Citation I-Citation O B-Instrument O O O O O O O O O O O O O O O O O B-Mission O O O B-CelestialObject O B-Instrument 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 B-Instrument O O O O O O O O O O O O O O O B-Instrument O B-Instrument O O O O O O O O O O O O O O O O O O B-Mission O O O O O O O O O O O O B-CelestialObject O O B-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 O O O O O O O O B-CelestialObject O O O O O O O B-Mission O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 B-Instrument 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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-Mission O O O O O O O O O O O O O O O O B-Mission O O O O O O O O O O O O O O O O O O O O


difference in realization may cause a bigger difference in the mass evolution (however, still very small) than using the same realization but with a different code. Figure 7. Comparison of models performed with different N -body codes. The blue and orange lines are the differences in the remaining bound masses between models 18 and 19 on one hand (performed with φ grape and ph4 , respectively) and 17 on the other (performed with nbody6++ ). The light green lines are similarly the differences between models 17A and 17B on one hand and 17 on the other that only differ in random seed. These models are in our weak tidal field eccentric orbit and have N = 10 5 stars. No gravitational softening was used. The small and seemingly random residuals indicate that there is no systematic difference in these models. Despite the differences between nbody6++ , φ grape , and ph4 , we find that the evolution of N = 10 5 clusters in weak tidal fields is nearly identical from one code to the next. 3.3 Softened models Gravitational softening is a technique to ease the computation of N -body simulation. In ‘Plummer’-type softening, the gravitational force between two bodies is modified from ∼ r −2 to ∼( r 2 + a 2 ) −1 , where a is called the softening length. For r ≫ a , the modified force approaches the true Newtonian force, but otherwise it approaches a constant. This hinders the formation of bound systems on scales ≤ a which otherwise have small time-steps and are difficult to integrate unless a regularization technique is used. Modifying the gravitational force in such a way has implications for hyperbolic encounters as well, the effect is decreasing the Coulomb logarithm and thus increasing of the two-body relaxation time. 3.3.1 Model with <italic>N</italic> = 105 Model 22 is the softened equivalent of model 17 presented in Section 3.1 . The two models evolve in a very similar way, and most quantities (total mass-loss, Lagrange radii) match when 17 is ‘slowed down’ by a factor of 1.18. Between 0.2 and 5 Gyr (roughly the time of the core collapse), model 17 experiences 461 kicks and 8593 sweeps, while model 22 experiences 129 kicks and 6620 sweeps. The softer potential causes a significant reduction in the number of kicks (as expected given the treatment of close encounters) and a moderate reduction in the number of sweeps. 3.3.2 Model with <italic>N</italic> = 106 Model 6 is the softened equivalent of model 1 presented in Section 3.1 . It behaves unexpectedly. While it is similar to model 1 , one would expect a somewhat lower mass-loss rate due to the gravitational softening, as seen when comparing models 22 and 17 . However, the result is the opposite. The difference in kick rate is especially stark: Between 0.2 and 5 Gyr, model 1 experiences 558 kicks and 14 530 sweeps, while model 6 experiences 5369 kicks and 16 607 sweeps. This ∼tenfold increase in the kick rate (as opposed to a decrease by a factor of 3.6 when softening is added in

O O O O O O O O O O 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 O B-Model I-Model I-Model I-Model O O O O O B-Software I-Software O B-Software O O O O O O O O O B-Software O O O O O O O O O O B-Model I-Model I-Model I-Model 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-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 B-Software O B-Software I-Software O O B-Software 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-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 O O O O O O O B-Formula I-Formula I-Formula 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 B-Tag O B-Tag B-Model I-Model 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 B-Model 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 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 B-Tag O B-Tag B-Model I-Model O O O O O B-Model I-Model 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 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


an sGRB that can be observed along its axis. This successful jet with its cocoon, which is sometimes called the “structured jet,” is scenario E in Figure 2 of Mooley et al. ( 2018b ), and it is similar to the one considered by Lazzati et al. ( 2018 ) and Margutti et al. ( 2018 ). In the second simulation the jet is launched for a short duration and is choked before breaking out of the ejecta. In both cases the cocoon does not affect the UV/optical emission on a timescale of half a day, but we show that the shock breakout of the successful jet can potentially generate the γ -ray signal (Figure 4 ). In both simulations there are afterglow parameters (ISM density and microphysics) for which the observed radio and X-ray emission is produced as shown in Figure 3 . Figure 4. Shock breakout γ -ray light curve (top panel) and spectrum (bottom panel) from the successful jet simulation. The blue thin line is the spectrum during the peak, while the red thick line is the spectrum during the tail. 4. Was There a Relativistic Jet in GW170817? A relativistic jet is necessary to produce an sGRB. Therefore, the question whether a relativistic jet can successfully break out of a neutron star merger ejecta and in particular whether a jet broke out in this event is interesting. In GW170817 we consider the option of a successful jet as possible but less likely. First, it does not explain the early optical light. Second, and more importantly, to explain the observed radio emission, either the jet should be much weaker than the cocoon, so that its contribution is unimportant, or it should be so powerful that its contribution begins only at very late time, at least months after the merger. Without fine-tuning, the jet is not expected to have much less energy than the cocoon (Nakar Piran 2017 ), while a very powerful jet produces a very luminous sGRB, and these are known to be extremely rare (Nakar 2007 ; Wanderman Piran 2015 ). However, this is only circumstantial evidence. Unfortunately, a weak successful jet does not contribute to the observed emission, and therefore we may never know whether it existed or not. A powerful jet, however, might be detectable. If it is powerful enough, it may be detectable while being still off-axis. In that case the radio light curve should rise sharply and then decline as a power law, after a short plateau. This unique prediction of the jet signature is the easiest to identify. A less powerful jet may contribute to the radio light curve, but its contribution may not be easily identified (see, e.g., Figure 3 and Lazzati et al. 2018 ; Margutti et al. 2018 ), since a continuous rise to a peak followed by a decay can arise either from a successful or from a choked jet (see Figure 3 and Nakar Piran 2018 ). Additional information, especially in the radio near the time that the afterglow flux peaks, may provide the key to resolve this question. First, the radio image of the source is expected to be resolved soon. Figure 5 shows the images

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


Apellániz 2013 ). In Section 2 , we present the characterization and definition of the GALANTE photometric system. In Section 3 , we analyse in detail the set of GALANTE standard stars. Transformation equations between the SDSS and the GALANTE photometric system are obtained in Section 4 , setting up the comparison between the synthetic magnitudes of 378 stars from the Next Generation Spectral Library (NGSL) plus 122 stars from a new catalogue from MAW in both photometric systems. Next, in Section 5 we estimate zero-points from the GALANTE photometry using the SDSS and RefCat2 . Finally, in Section 6 we summarize the results of this paper. 2 THE GALANTE PHOTOMETRIC SYSTEM 2.1 DESCRIPTION Deriving physical stellar (or galaxy) parameters is the main goal of every photometric system. In our case, we achieve this aim with a new filter set designed to plug some gaps found in previous surveys. The GALANTE photometric system has been defined using four J-PLUS filters (Cenarro et al. 2019 ) and three purpose-built filters. This composition of intermediate- and narrow-band filters covers the optical range from 3000 Å to 9000 Å. This filter set has been selected with a direct goal: deriving stellar effective temperatures for hot stars in an optimal way (Maíz Apellániz Sota 2008 ; Maíz Apellániz et al. 2014 ; Maíz Apellániz Barbá 2018 ). We will also be able to obtain the gravity and metallicity (the latter for stars cooler than 10 000 K), allowing us to discriminate between giants, supergiants, dwarfs, and between solar or SMC metallicities. In Figs 1 and 2 , we present the set-up of the filters. Figs 1(a) and (b) show the layout of the J-PLUS and GALANTE filters, respectively, leaning on the Vega spectrum. Note that in this case we only plot the transmission curves of the filters, allowing to the reader a visual location of each one. Focusing on GALANTE, Fig. 1(b) plots the configuration of the selected GALANTE filters, which enable us to measure the key zones in the spectra to derive physical parameters. In Fig. 2 , we plot the response functions of the J-PLUS and the GALANTE photometric system filters. In discontinuous grey, we represent the response curve of the CCD and mirror, while discontinuous green is the CCD, mirror and atmospheric transmission at 1.3 airmass curve. The SDSS response curves are represented in continuous black, and the other continuous curves are the GALANTE and J-PLUS filter response curves convolved with CCD, mirror and atmospheric transmission at 1.3 airmass. Using Fig. 3 , we can also explain the function of each GALANTE filter with greater accuracy. Filter F348M is a u -like filter dispose to measure the continuum from the left of the Balmer jump. We can combine this one with F420N and F450N to measure the Balmer jump on both sides and derive the T eff of stars. Both filters of our design, F420N and F450N, are the most original (compared to other large-scale recent photometric surveys) of the set-up. They have been

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(See Also Section 2.7 ) Note. The motivation behind each assumption is described in the corresponding sections. We briefly summarize (column 4) the effect produced by each assumption to help the reader distinguish the genuine trends from those induced by our prior hypotheses. The reference BHAR/SFR trends are taken from Mullaney et al. ( 2012 , M12), Rodighiero et al. ( 2015 , R15), and Aird et al. ( 2019 , A19). (∗∗) The relative normalization is chosen to fit the corresponding data points of Figure 4 . 2.3. Galaxy Stellar Mass Function The first step displayed in Figure 1 consists in setting the input galaxy M ⋆ function at different redshifts. The prescription is taken from Davidzon et al. ( 2017 ), who exploited the latest optical to infrared photometry collected in the COSMOS field over the UltraVISTA area (1.5 deg 2 , see Laigle et al. 2016 ). They provide the M ⋆ function separately between SF and passive galaxies, based on the [NUV − r ]/[ r − J ] colors (Ilbert et al. 2013 ). Throughout this work, we consider only the M ⋆ function relative to the SF galaxy population (i.e., MS and SB galaxies). Next, we split the M ⋆ function of SF galaxies among the MS and SB populations. Given that the relative fraction of the two populations has been shown not to vary with M ⋆ (e.g., Rodighiero et al. 2011 ; Sargent et al. 2012 ), we only consider how their relative fraction evolves with redshift by following the prescription of Béthermin et al. ( 2012 ). The fraction of SB galaxies ( f SB ) appears to evolve linearly, from f SB = 0.015 ( z = 0) to f SB = 0.03 ( z = 1), while it stays flat at higher redshifts. By simply scaling the SF galaxy M ⋆ function down by f SB (or 1 − f SB ), we end up with the M ⋆ function of MS and SB galaxies at each redshift (Figure 2 ). We note that the input M ⋆ function of Davidzon et al. ( 2017 ) is already corrected for the Eddington bias, which might have some impact at the high- M ⋆ end. After dissecting among MS and SB galaxies, we interpolate the corresponding M ⋆ function at redshift z = 0.5, z = 1, z = 2, and z = 3 across a M ⋆ range of 10 8 M ⋆ 10 12 M ⊙ . Figure 2. Galaxy M ⋆ function at various redshifts for SF galaxies, taken from Davidzon et al. ( 2017 ). Solid and dashed lines mark MS and SB galaxies, respectively. 2.4. The MS and the SB Loci We use the MS prescription presented in Schreiber et al. ( 2015 ), which incorporates a redshift evolution and a bending toward higher M ⋆ . Schreiber et al. ( 2015 ) studied a sample of Herschel -selected galaxies out to z ∼ 4, dissecting the observed distribution of MS offsets (=SFR/SFR MS , see their Equation (10) and Figure 19) among the MS and SB populations. By fitting that distribution via a double lognormal function, MS galaxies are centered at 0.87 × SFR MS , while SB galaxies are centered at 5.3 × SFR MS (Schreiber et al. 2015 ). Both relations were rescaled to a Chabrier ( 2003 ) IMF. The 1 σ dispersion for both the MS and SB loci is assumed to be 0.3

O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I-Citation B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation I-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 B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O B-Wavelength O B-Wavelength O O O O B-CelestialRegion I-CelestialRegion O O B-CelestialRegion I-CelestialRegion 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 B-Wavelength O B-Wavelength O B-Wavelength O B-Wavelength 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 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 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 B-Formula I-Formula I-Formula I-Formula O B-Formula I-Formula I-Formula O B-Formula I-Formula I-Formula I-Formula 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 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 B-Formula I-Formula I-Formula B-Formula I-Formula I-Formula B-Formula I-Formula I-Formula 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 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 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 B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O B-Telescope 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 B-Citation I-Citation I-Citation I-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


≤ 2 redshift range. (b) The same as (a) for 10 examples of each of our three stochastic SFH families. Except for the two very lowest metallicities, increases with redshift in a nearly linear fashion, with little overlap between the tracks for the three SFH families. 5. Summary We combined SSP SEDs from the Starburst99 and BC03 codes for young and old stellar ages, respectively, and generated arrays of CSP SEDs for a large variety of exponentially declining and stochastic SFHs by stacking SSP SEDs as a function of the adopted time-dependent SFRs. For our large suite of models with stochastic SFHs, we take the average metallicity evolution as a function of cosmic age into account. We calculated the intrinsic flux ratios between rest-frame visible–near-IR and rest-frame mid-IR 3.5 μ m on a grid of six metallicities and 16 ages of stellar populations for 13 different SFH families, for 29 visible–near-IR filters, and 5 mid-IR filters with central wavelengths near 3.5 μ m. We find that taking metallicity evolution into account results in significantly higher values at higher redshifts and for higher-metallicity stellar populations at those redshifts. We also provide the range of and for different Hubble types of nearby galaxies, and confirm that our models agree with the observed values. We demonstrated that the method can infer to a level of fidelity that is comparable to that of more established methods. We conclude that the method and its extension, the method presented here, are valid as a first-order dust-correction method, when using the morphology and size of a galaxy as broad a priori constraints to its SFH and redshift, respectively. The method will be applicable to large samples of galaxies for which resolved imagery is available in one rest-frame visible–near-IR filter and one rest-frame λ ∼ 3.5 μ m bandpass. We make our CSP synthesis models and all our results available via a dedicated website in the form of FITS and PNG maps, and ASCII data tables of values as a function of age and metallicity. Appendix A A Set of Distinguishable SSPs We construct our combined model SSP SEDs for a manageably small, yet comprehensive set of parameters that span the range of representative stellar population properties and dust extinction. We consider six metallicity and 16 age values of the stellar population, four extinction laws, and eight V -band extinction values (see Table 6 ). We adopt a grid of 16 stellar population ages between 8 8 In Planck 2015 cosmology (Planck Collaboration et al. 2016 ), the Hubble time t H ≃ 13.8 Gyr. Hence, t = 13.25 Gyr corresponds to 0.55 Gyr after the Big Bang or , consistent with the Planck Collaboration (Paper 2016) results of τ = 0.058 and , so this maximum age is a deliberate choice. 3 Myr and 13.25 Gyr in steps that roughly double for ages between 10 Myr and 10 Gyr. This allows us to follow the rapid evolution of massive stars, without reserving too many SEDs for the slow evolution of low-mass stars. Because the MW

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spectrum would be expected from diffusive shock accelerations, but harder spectra are possible when the particle energy is converted from the electromagnetic energy of the sources, for example, in pulsar magnetospheres (Arons 2003 ), or in reconnection processes (Guo et al. 2014 ). Besides, an interplay between the acceleration and dissipation processes at the acceleration site could also lead to a hard injection spectrum (Dermer et al. 2012 ; Murase et al. 2012 ). Murase et al. ( 2016 ) and Bechtol et al. ( 2015 ) demonstrated that a pp scenario with a spectrum normalized to the IceCube datapoint at ∼10 TeV would overproduce γ rays above the flux of the non-blazar component of the isotropic γ -ray background. In this work, we will instead consider the case of an injection spectrum index equal or smaller than two, but normalized to the IceCube datapoint at 30 TeV (which is about three times lower than that at 10 TeV). Specifically, in Section 3 , we focus on a benchmark case of an injection spectrum and maximum energy . We discuss the effects of different choices of dN / dE and in Section 4 . 2.2. Cosmic-Ray Diffusion Galaxy clusters are known as efficient containers of cosmic rays (Völk et al. 1996 ; Berezinsky et al. 1997 ). Depending on the particle’s Larmor radius r L and the field’s coherence length l c , the propagation of cosmic rays in the cluster turbulent magnetic field can be divided into semi-diffusive () and diffusive () regimes (Kotera Lemoine 2008 ). When a particle’s energy reaches , the particle enters the semi-diffusive regime with a diffusion coefficient ofHowever, most particles have energy much lower than , and thus stay in the diffusive regime. The diffusion coefficient in this regime can be written as (Brunetti Jones 2014 )where is the spectrum index for Kolmogorov diffusion, and are the typical amplitude and scale of magnetic field fluctuations in massive clusters (Brunetti Jones 2015 ). The diffusion time of cosmic rays in the cluster magnetic field can be estimated asCosmic rays with diffusion time longer than the Hubble time would be completely confined by the cluster magnetic field, defining the lower limit of the energy of cosmic rays we expect to observe from the cluster as Particles with would have a better chance to escape from the cluster magnetic field. To estimate this escape probability, we first write down the probability that a particle with diffusion coefficient D reaches radius r after time t :assuming that the particle starts from r = 0 and the field is homogenous. The probability that a particle can successfully escape from a cluster of size within the Hubble time is then (Kotera Lemoine 2008 ) Cosmic rays accelerated in the strong shocks in the accretion region are expected to have a spectrum index . The flux of high-energy cosmic rays from a cluster with mass at redshift z = 0.1 can be calculated aswhere d L is the luminosity distance, and f π is the pion production rate that will be introduced

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(denoted by the cyan diamond) where the spectra of Si iv , C ii , and Mg ii are fairly bright and show some blueshifts. Figure 1. Observation overview of the B1.6 flare event on 2016 December 6. Top left panel: an AIA 131 Å image during the rise phase of the flare. The green dotted line and the purple contours mark the IRIS slit position and the flaring footpoints, respectively, which are the same as in the SJI 1400 Å image (bottom left panel). Top right panel: light curves of GOES 1–8 Å (black), AIA 131 Å (blue, arbitrary scale), and SJI 1400 Å (red, arbitrary scale) from the flaring region. The vertical black line represents the peak time of the flare. The blue and red dashed lines indicate the times when the AIA 131 Å and SJI 1400 Å images were observed, respectively. The two red dotted lines mark the time range (10:37:00–10:39:00 UT) as shown in Figures 3 and 4 . Bottom right panel: spectra of Si iv , C ii , and Mg ii along the slit at 10:37:45 UT. The vertical dotted line represents the reference wavelength for each of the spectral lines. The cyan diamond denotes the footpoint location that is cut across by the slit, as also shown in the SJI 1400 Å image. The two horizontal lines mark the space range (10′′) that is plotted in Figure 3 . 3.1.2. Blueshifts in the Si <sc>iv</sc>, C <sc>ii</sc>, and Mg <sc>ii</sc> Lines Figure 2 shows the Si iv , C ii , and Mg ii line profiles at the footpoint location denoted by the diamond (see Figure 1 ) for two times. It is seen that all the three lines are blueshifted, especially at an earlier time (10:37:34 UT, top panels). The blueshift velocities are measured to be 20, 13, and 6.3 km s −1 for the Si iv , C ii , and Mg ii lines, respectively, namely decreasing as the line formation temperature decreases. A few seconds later (10:37:45 UT, bottom panels), all the velocities decrease nearly by half. Note that some of the Mg ii (and also C ii ) line profiles are almost singly peaked, i.e., without a central reversal, which is a common feature for flaring footpoints. Figure 2. Si iv , C ii , and Mg ii line profiles at the footpoint location denoted by the cyan diamond as shown in Figure 1 for two times. The dotted line in each panel represents the reference wavelength of the spectral line. For the Si iv line, we adopt a single Gaussian fitting (the red curve) to derive the Doppler velocity ( V g ). For the optically thick C ii and Mg ii lines, we make a moment analysis to obtain the Doppler velocity ( V m ). The spatio-temporal variations of the total intensity, Doppler velocity, and line width of the Si iv and Mg ii lines are plotted in Figure 3 . From the intensity map we can see that the bright footpoints (the contours) show some apparent motion toward the north. Interestingly, at the footpoints (e.g., the ones denoted by the diamond), both of the Si iv and Mg ii lines exhibit blueshifts particularly at an early time. Such a blueshift in cool lines has scarcely been reported before. A similar

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To estimate the effective area correction between the different detectors of Fermi and BAT, we adopted the following methodology (Page et al. 2009 ): (i) To find the best-fitting model: Each of the time-resolved spectra of all detectors (BAT, GBM: n6, n7, n9, and b1) was jointly analysed using different spectral functions such as power law, cutoff-power law, and Band, without including the constant factor correction. In this exercise, the time-averaged and time-resolved spectra were found to be better fitted by cutoff-power-law function. (ii) Finding the normalization-offsets: We then simultaneously fitted the five bright time-resolved spectra with the best-fitting model, cutoff-power law, by multiplying the model by a constant of normalization for each instrument. The constant of BAT data was frozen to unity, while those of the GBM detectors were kept free. In addition, these constants were also tied across the respective data sets of the different time-resolved intervals. The relative normalizations were found as, for n6 detector, for n7 detector, for n9 detector, and for b1 detector. These constant factors are consistent with the expected intercalibration uncertainty of up to and hence were used for the subsequent spectral analyses. 3.2 Parameters evolution For characterizing the spectra, we fit the brightest time intervals in both episodes with several phenomenological models including power law with an exponential cutoff (cutoff-power law), blackbody + cutoff-power law, and traditional Band functions. In Fig. 2 , the reduced χ 2 obtained for the different models in the different time intervals are shown. We note that in the brightest time interval (bin 5) of the first episode, blackbody + cutoff-power law model is statistically favoured (i.e reduced χ 2 ∼ 1). On the addition of blackbody component to the cutoff-power law, we also find an improvement in Akaike Information Criterion (AIC; Akaike 1974 ) and Bayesian Information Criterion (BIC; Schwarz et al. 1978 ) by 15 and 7, respectively (see Appendix B ). At the same time, in all other time intervals, this model is also found to give comparable statistics when compared with other models. The Monte Carlo (MC) simulations for estimating the statistical significance of the additional blackbody component with the cutoff-power-law model are performed for the brightest time interval. The difference of χ 2 -statistics between cutoff-power-law model and blackbody + cutoff-power-law model is found to be 19. Here, we assume cutoff-power-law model as the null hypothesis model and blackbody + cutoff-power-law model as the alternate model and simulations are run for realizations. We find that the probability of observing Δ χ 2 > 19 is 10 −2.52 , which corresponds to ∼3 σ significance level. Thus, by considering the above fit statistics and MC simulations obtained in the brightest time bin, we consider blackbody + cutoff-power-law model to be the best-fitting model for all the time-resolved intervals

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only 13 SNe are removed as a result. The latter is much more impactful, however, removing ∼700 spectra (mostly from the SED Machine; Blagorodnova et al. 2018 ) and nearly as many SNe. In fact, this amounts to the most severe cut on SNe and the second most severe cut on spectra (after the ‘overlap’ cut). With future improvements to deepSIP it may be possible to revisit and lessen the extremity of this cut, but this will have to wait until those improvements can be realized. Irrelevant photometric passbands: After the cuts described above, the 60 140 photometric points that remain are in one of 62 different passbands, some of which are irrelevant to our analysis and must therefore be removed. By far, the primary factor in rendering observations in a given passband ‘irrelevant’ is if that band is too broad. For example, 4923 Gaia G -band (Jordi et al. 2010 ), 2104 Pan-STARRS w -band (Chambers Pan-STARRS Team 2018 ), 612 ATLAS c -band or o -band (Tonry et al. 2018 ), and many other wide-band observations are removed, along with a further 5061 unfiltered photometric points. A second factor in determining the relevance of a passband is the wavelengths of light that it passes. As our SDM uses a WLR implementation that is trained at optical and near-infrared wavelengths, we must discard passbands that lie outside this domain, thereby removing some Swift observations (Brown et al. 2014 ) among others. All told, 15 624 photometric points are removed, and those that remain are homogenized down to 21 distinct passbands. Photometric uncertainties: Of the remaining 44 516 photometric points, 10 429 do not include a symmetric uncertainty in the reported magnitude. We are able to ‘fix’ 6120 of these by taking the larger of their (asymmetric) lower and upper magnitude uncertainty estimates, but 4393 remain which have no identifiable quantitative uncertainty indicator. We take the conservative approach of removing these data, leaving us with just under 1200 SNe covered by ∼2900 spectra and ∼ 40 000 photometric points. 2 + Distinct passbands: As the SDM requires, at a minimum, two photometric points in different passbands (and a spectrum), we remove SNe that fail this requirement. This reduces the sample to 747 SNe with ∼2400 spectra and ∼ 35 000 photometric points. Literature search for photometric systems: While we have taken care above to homogenize the passbands in our sample (in the sense of resolving the differing names given to identical filters), we must also understand the photometric systems to which they belong. To do so, we visit the primary source provided by the OSC for each remaining point in our photometry sample (amounting to 248 sources) and attempt to determine the photometric system. We find that 26 SNe (covered by 5719 photometric points) are either not an SN Ia or are not an SN Ia subclass that can be handled by our WLR. These are dropped, as are an additional 7220 photometric points whose photometric system is either not clear or not

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3. Enclosed gas mass profiles as a function of radius for the known potential Galactic YMCs precursors. The transition point from solid to lower opacity indicates the beam resolution of the data. The triangle markers correspond to the initial conditions used by Banerjee Kroupa ( 2013 ) to simulate the monolithic formation of the R136 YMC. The circular markers correspond to the initial conditions used by Banerjee Kroupa ( 2014 ) to simulate the monolithic formation of the NGC 3603 YMC. The star markers correspond to the initial conditions given in the fifth row of table 1 in Assmann et al. ( 2011 ) to simulate a monolithically forming star cluster. As these simulations are 3D models, and the data that we discuss in this paper are 2D projections, we must take care to ensure that we are making a fair comparison to these models. In Banerjee Kroupa ( 2013 , 2014 ), they present their initial conditions as initial cluster mass, initial gas mass and half-mass radius. Using their assumed ∼33 per cent SFE, we combine these masses to obtain the total gas mass prior to star formation. We also use the following relation to convert the half-mass radius to the Plummer radius (Heggie Hut 2003 ) – Assmann et al. ( 2011 ) already provide the Plummer radius. We then take the total 3D gas mass for all of the models and convert this to a projected mass (Heggie Hut 2003 ) – Overall Fig. 3 shows that the initial projected masses within a given radius for these various models generally too high, particularly at smaller radii. We note that the Banerjee Kroupa ( 2014 ) model for NGC 3603 appears consistent with observations at radii > 0.5 pc. However, it is overdense on smaller spatial scales. Whilst the models generally predict mass surface densities that are greater than the observed clouds, this is not unambiguously the case. The highly star-forming clouds such as Sagittarius B2, W49 and W51 all lie very close to these models. Though we reiterate that the mass estimates, particularly in the highly star-forming clouds, are upper limits. Accounting for temperature effects would create an even larger disparity between the observations and simulations. We also note that the simulated models assume that both the gas and stars are initially distributed according to a Plummer distribution, which is not true for the gas in the clouds in our sample (see Section 4 ). This is a critical difference between the true conditions in the ISM and those assumed in these models. The gravitational potential of the gas is dominant until a significant number of stars have formed within the gas, and it is clear that the initial potential does not arise from a Plummer-like gravitational potential. 4 DISCUSSION In Walker et al. ( 2015 ), we used a sample of YMCs and possible YMC precursor clouds at the Galactic Centre to attempt to distinguish between two commonly argued modes of YMC formation – popping or conveyor-belt modes. We did this simply by comparing their mass surface density

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


5 . Nevertheless, the resulting statistical errors of shear, as opposed to its bias, are larger by roughly the factor 1.7 with the uniform prior, which reflects the larger a-priori uncertainty on . This implies that a shear bias due to an incorrect prior is negligible in this experiment. 5. Discussion Unlike galaxy ellipticities defined in terms of unweighted moments, GLAM provide a practical definition of ellipticity with useful properties for any chosen adaptive weight : they (i) are identical with the ellipticity of isophotes of elliptical galaxy images; (ii) under the influence of shear they behave exactly like defined with unweighted moments; (iii) they are unbiased estimators of reduced shear; (i)(iii) assume ideal conditions without pixellation, PSF convolution, or pixel noise. These effects fundamentally limit our ability to determine the GLAM ellipticity (see below). Under ideal conditions, see SS97, it is known that for unweighted moments already obeys (iii). However, unweighted moments are formally infinite for realistic galaxy images because of pixel noise so that weighting is a necessity which is done adaptively for GLAM . For adaptive weights, we have shown (i) and (ii) in Sects. 2.2 and 2.3 . Their relevance as unbiased estimators for reduced shear, statement (iii), follows thereafter from (ii) and the conclusions in SS97 for unweighted moments. We emphasise that GLAM do not require “deweighting” in a lensing analysis ( Melchior et al. 2011 ): the ellipticity in term of the weighted moments is actually unbiased. In particular, we do not have to devise the same weight function for all galaxies as any weight function equally obeys (ii). If the minimum of the functional ( 3 ) uniquely exists, the moments of the best-fitting template will, for a constant scalar , be the adaptively weighted moments of the galaxy light profile for the weight (Appendix A ). This means that the radial profile of the weight at separation from the centroid is for a template profile . We exploited this relation between adaptive moments and moments of a best-fitting elliptical profile to analyse limits to adaptive moment-based methods under the loss of image information. Namely, for realistic images subject to pixellation and PSF effects, underfitting bias can be present for GLAM if estimated from the post-seeing image. It potentially arises because of a loss of information on the pre-seeing galaxy image, fundamentally due to pixellation. To explore the limitations, we assume noise-free images and express in Sect. 2.4 the mapping between a pre-seeing and post-seeing image by the linear operation . The pre-seeing adaptive moments are equivalent to a least-square fit with residual of an elliptical profile to the pre-seeing image . If estimated from the post-seeing image whilst ignoring residuals, the inferred ellipticity is biased for and a singular . The latter indicates a loss of information on the pre-seeing image. For noise-free images and the estimator

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


unaffected by the Milky Way potential. We see in the lower panel that even for V peak the relation is broadly flat with significant scatter. For example, without including pericentre information, the V peak error bars for CVenI and Draco overlap, suggesting that these two galaxies with similar stellar masses could have formed within haloes of the same size. The revised estimate suggest that Draco I likely formed within a halo that was more massive. Improving constraints on the dynamical mass of the dSphs at present combined with better constraints on their pericentres from Gaia can provide deeper understanding of the past evolution and the mass of the progenitor haloes of MW dwarf spheroidal galaxies. Among the galaxies we have considered, Draco and Ursa Minor are affected the most by including pericentre information. These, the densest dSph satellites, have relatively small pericentres ( r peri ≲ 50 kpc) and this pushes their inferred V max values lower than they would have been without pericentre information. We find that both Draco and Ursa Minor appear to inhabit subhaloes with V max ≃ 20–27 km s −1 (at one sigma) and are less dense than would be expected for the most massive subhaloes with small pericentres (see Fig. 1 ). This exacerbates the traditional TBTF problem (Boylan-Kolchin et al. 2011 , 2012 ). The inferred range of V peak values is also tightened for these galaxies. Both galaxies have inferred peak maximum circular velocities (prior to infall) that put them in range to have been significantly affected by reionization, V peak ≃ 25–37 km s −1 (e.g. Bullock, Kravtsov Weinberg 2000 ; Gnedin 2000 ; Benítez-Llambay et al. 2017 ; Fitts et al. 2017 ). It is worth considering whether our results are affected by numerical resolution. There is a fair amount of evidence to suggest that simulations such as ours do not accurately track halo survival after they have lost a significant () amount of mass, with ∼3000 particles appearing to be a critical limit below which haloes disrupt unphysically (e.g. Van den Bosch Ogiya 2018 ; Errani Navarro 2020 ). In terms of halo counts alone, this effect should not affect current cosmological zoom simulations significantly, since accreted halo mass functions rise steeply with increasing mass, d n /d M ∼ M −1.8 (e.g. Giocoli, Tormen van den Bosch 2008 ). This means that for every halo of mass M that was accreted long ago and has lost 99 per cent of its original mass (100 M ), there are as many as ∼4000 haloes of mass M that were accreted more recently and that can be accurately counted. As discussed in Kelley et al. ( 2019 ), convergence tests suggest that our halo counts are mostly complete down to V max > 4.5 km s −1 , or only ∼150 particles. Of particular relevance to this work is whether or not internal halo structures, e.g. V max / V peak distributions at fixed V max , are tracked appropriately. We have limited our analysis to haloes with V max > 10 km s −1 , or ≳ 3000 particles. According to the

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 B-CelestialObject 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 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 B-Telescope 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-CelestialObject O B-CelestialObject I-CelestialObject 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 O O O O O O O O O O O O O O O O B-CelestialObject O B-CelestialObject I-CelestialObject 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 B-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 B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation 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 O O O O O O O O O O O O O O O O B-TextGarbage 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 B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula 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 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 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 B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O


(Hindmarsh 1983 ). We regularly reconverge the PT structure with the radiative/convective code during the time integration until we reach a steady-state for both the chemistry and energy conservation/hydrostatic equilibrium. We then get a coherent atmospheric structure with out-of-equilibrium chemical abundances. As in Tremblin et al. ( 2016 ), we propose that a process similar to fingering convection is responsible for this turbulent mixing and leads to the out-of-equilibrium abundances. Fingering convection can be triggered in the atmospheres of brown dwarfs and exoplanets because of the gradient of mean molecular weight induced by the chemical transitions CO/CH 4 and N 2 /NH 3 at chemical equilibrium. Similar to convective motions that lead toadiabatic PT structure in the deep atmosphere, fingering convection could impact the PT structure of the atmosphere. As in Tremblin et al. ( 2016 ), we propose that fingering-convective motions can induce a temperature gradient reduction, leading to a reddening in the modeled spectrum. This temperature gradient reduction in the atmosphere is modeled using an artificially reduced adiabatic index γ in between two pressure levels on top of the convective zone. In Figure 1 , we show the influence of various model parameters at effective temperatures of 1700 and 1300 K, representative of the objects studied in this study. At 1700 K we can see a degeneracy between gravity and metallicity on the spectral shape of the H and K bands. Decreasing gravity or increasing metallicity tends to form a triangular H band and a strong and flatter K band. Decreasing the temperature gradient in the atmosphere by decreasing the effective adiabatic index γ increases the reddening in J − K . Increasing the surface gravity at an effective temperature of 1300 K has a strong effect on the out-of-equilibrium chemistry and the quenching of CO in the deep atmosphere. At higher gravities, the CO/CH 4 transition is deeper in the atmosphere, hence it is more difficult to prevent the formation of CH 4 (strongly appearing at log g = 4.5) for a given mixing coefficient K zz . Thus, varying K zz would approximately lead to the same effect as varying gravity for T eff = 1300 K. For the fitting of the observations we have probed a similar parameter space, log g in [3.5, 4.5], [M/H] in [0, 0.5], T eff in [1200, 1800], and γ in [1.01, 1.1]. Figure 1. Effects of varying different parameters on the spectral models obtained with the ATMO code. 2.1. DENIS J1425279-365023 DENIS J1425279-365023 was first identified as an L5 dwarf (Kendall et al. 2004 ) with a trigonometric distance of ∼11.57 ± 0.11 pc (Dieterich et al. 2014 ). A SpeX Prism obtained by Gagné et al. ( 2015a ) indicates that the object is a L4 INT-G object based on the index-based classification of Allers Liu ( 2013 ). Based on the galactic position and space velocity of the object, Gagné et al. ( 2015a , 2015b ) also concluded that DENIS J1425-36 is a bona fide member of the

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


constraints and verification of the TeV emission mechanism in 30 Dor C. Hadronic and leptonic mechanisms predict different flux for the (10 GeV–1 TeV) band, both currently below Fermi -LAT ( Atwood et al. 2009 ; Ackermann et al. 2011 ) sensitivity and between Fermi and H.E.S.S. covered energy ranges, but within the capabilities of C.T.A. An example of the prospects of C.T.A. in this regard is shown by Acero et al. (2017b ) as applied to the SNR RX J1713.7–3946. The recent H.E.S.S. paper on RX J1713-3946 ( H.E.S.S. Collaboration 2018 ) demonstrates the possibilities for C.T.A. observations of LMC objects. H.E.S.S. revealed the TeV shell of RX J1713.7–3946 in unprecedented detail, and was found to extend further than the X-ray shell. This allowed the probing of particle escape, while the GeV and TeV spectra were covered to a level of accuracy that allowed a very detailed comparison of leptonic and hadronic emission mechanisms, including a magnetic field map for the leptonic case. 5 Conclusion We have presented an analysis of new Chandra observations of 30 Dor C in the Large Magellanic Cloud, the first superbubble detected in TeV γ -rays. These observations provided the sharpest view of the synchrotron X-ray shell of 30 Dor C, allowing us to perform a detailed morphological study and estimate the B -field in the superbubble, a key discriminator in assessing the dominant TeV γ -ray emission mechanism with a low B -field ~ 15 μ G required for a purely leptonic origin, a B -field of ≳ 50 μ G for a completely hadronic-dominated origin, or B -field of ~ 20 μ G for a 50–50 contribution of the leptonic and hadronic mechanisms to the TeV γ -rays. Using the new Chandra data, MCELS2 H α , and 6 cm radio continuum images we found an anti-correlation between the synchrotron X-ray and H α /6 cm shells. In addition, we discussed how long-slit spectroscopy of various regions of the 30 Dor C shell has shown no evidence of the high velocities necessary to explain the synchrotron X-rays (≳ 3000 km s −1 ). Rather the H α expansion velocities are more typical of an expanding superbubble ( 100 km s −1 ). We suggested that the SNR responsible for the synchrotron X-rays has reached the 30 Dor C supershell and has stalled in some regions, but continues through gaps in the shell in others. This is similar to the observed anti-correlation seen in RCW 86 ( Vink et al. 2006 ; Helder et al. 2013 ), which is attributed to the SNR evolving into a wind-blown cavity and encountering density gradients, though the velocity differences between synchrotron X-ray and H α shells are not as extreme. This may be a result of more pronounced density gradients and/or the fact that the shock radius is so much larger in 30 Dor C, meaning the shock energy has been distributed over a large area, making it more sensitive to these density gradients. We estimated the downstream B -field from the synchrotron X-ray shell. This was achieved by fitting the observed radial profile in sectors around 30

O O O O O B-Wavelength O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O O O O O B-Wavelength I-Wavelength I-Wavelength O O O O B-Observatory B-Telescope O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O B-Observatory O B-Telescope O O O O O O O O B-Telescope O O O O O O B-Telescope O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O B-Telescope O O B-CelestialObject I-CelestialObject O B-Citation I-Citation I-Citation O O O O O B-Telescope O O B-CelestialObject O B-Telescope O O B-Wavelength O O B-CelestialObject I-CelestialObject 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 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 B-Telescope O O B-CelestialObject I-CelestialObject I-CelestialObject O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O B-Wavelength B-Wavelength I-Wavelength O O O O O O O O O B-Wavelength 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 B-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 O O O O O O O B-Wavelength B-Wavelength I-Wavelength O O O B-Telescope O B-Instrument B-Wavelength I-Wavelength O O B-Wavelength I-Wavelength B-Wavelength O O O O O O O O O B-Wavelength O B-Wavelength I-Wavelength B-Wavelength I-Wavelength O O O O O O B-ObservationalTechniques I-ObservationalTechniques 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 B-Wavelength 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 B-Wavelength 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 B-CelestialObject I-CelestialObject 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 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 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-Wavelength O O O O O O O O O O O O O B-CelestialObject


map in configuration space with a specific measurement error given. As possible noise sources, we take into account background fluctuations due to undetected clusters and the instrumental noise of ALMA itself. Furthermore, we include an estimate of the fluctuations introduced by primary CMB anisotropies. Since the different noise contributions are not correlated with each other, the total uncertainty is then given by Gaussian error propagation. 4.1 Background fluctuations due to undetected clusters Galaxy clusters unresolved by the telescope beam contribute a background noise level y bg that needs to be taken into account in all following calculations. Clusters are unresolved if they appear (much) smaller than the beam size. The background signal is thus dominated by low-mass clusters (Bartelmann 2001 ). Since an ideally homogeneous background could be removed from the data, we only have to consider the average background fluctuation level Δ y bg . The mean background level contributed by unresolved clusters with the differential mass function n ( M , z ) is where the mass- and redshift-integrations have to be carried out over that area in the mass–redshift plane where clusters are unresolved. The integrated Compton- y parameter Y ( M , z ) of a cluster with mass M at redshift z is where the angular diameter distance D d to the cluster appears and the total number of electrons N e . We choose the cluster mass function n ( M , z ) described by Sheth Tormen ( 1999 ), The number of background sources follows a continuous probability distribution. If one neglects any spatial cluster correlations, the rms due to background fluctuation is 4.2 Instrument noise Another relevant source of noise is the measurement noise of the telescope and the detector. We assume that the measurement error of the telescope has a Gaussian distribution with standard deviation σ Tel around zero, with σ Tel depending on the telescope and detector configuration and the setup of the specific observation. Since we need a resolution for our simulation that is comparable to other observations of massive galaxy clusters, we choose the characteristics of the Atacama Large Millimeter/submillimeter Array ( ALMA ) as an example telescope for achieving sufficiently precise thermal SZ observations. For all simulations presented in the remainder of this work, we use the following configuration of ALMA : All N ant = 32 antennas are assumed to be available with a baseline to be set. This configuration has then a given angular resolution in a frequency band chosen to be centred on ν = 116 GHz. Its bandwidth is assumed to be Δν = 7.5 GHz. According to the ALMA user manual (Lundgren 2012 ) the point-source sensitivity σ Tel of ALMA is where T sys is the temperature of the system, 1 η q the quantum efficiency (η q = 0.96), η c the correlator efficiency (η c = 0.88), A eff the effective area of an individual antenna, n p the number of polarization states ( n p = 2), and t int is the

O O O O O 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-Observatory 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-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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 B-Observatory I-Observatory I-Observatory I-Observatory O B-Observatory O O O O 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-Observatory 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 B-Formula I-Formula I-Formula I-Formula O O O O O O B-Formula I-Formula I-Formula I-Formula O O O B-Observatory O O B-Citation I-Citation O O O O O O O B-Observatory 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 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 B-Formula I-Formula I-Formula I-Formula O O O O O


et al. 2014 ). When the collision velocity is assumed to be the velocity difference between the stem and bowl region (∼1.5 km s −1 ), the kinetic energy released by the collision is estimated to be erg cm −3 , where the H 2 density of 10 3 cm −3 is arbitrarily assumed. This can be balanced by the cooling rate by Goldsmith Langer ( 1978 ), and the equilibrium temperature is roughly estimated to be 100 K. Here, we assume the cooling time of 10 4 yr according to Inoue Inutsuka ( 2012 ). This temperature is comparable with that observed in the interacting region. Thus, this rough estimate implies that the slight temperature enhancement is possible in the interacting region. Warm gas with a temperature of 74 K, heated by the filament–filament collision, is cooled down mainly by rotational line emission of CO for an H 2 density of 10 3 cm −3 (Inoue Inutsuka 2012 ). As mentioned above, the cooling time () is estimated to be around 10 4 yr according to Koyama Inutsuka ( 2000 ) with a cooling rate of 10 −26 erg cm −3 s −1 (Inoue Inutsuka 2012 ). Assuming that this warm gas expands with a constant velocity of 1.5 km s −1 , which corresponds to the typical velocity difference between the blueshifted and redshifted components of the Pipe nebula of 3.0 km s −1 and 4–5 km s −1 , respectively, the gas can extend in space by about 0.015 pc within the cooling time. This length corresponds to a typical spatial scale of a shocked warm region, and hence we hereafter denote it as (=0.015 pc). On the other hand, the extent of the cloud along the line of sight () can roughly be estimated from the H 2 density ( n (H 2 )), the OH column density ( N (OH)), and the OH fractional abundance ( X (OH)) relative to H 2 with the following equation:We employ an H 2 density of 10 3 cm −3 and an OH column density of 5.3 × 10 13 cm −2 , the latter of which is determined for the (0′, 32′) position (Table 5 ). Assuming an OH abundance of 10 −7 , which is a typical value in diffuse clouds (Wiesemeyer et al. 2012 ), is estimated to be ∼0.2 pc. If the OH fractional abundance is one order of magnitude higher ( X (OH) = 10 −6 ), which could be possible in a shocked gas according to Draine Katz ( 1986 ), could be 0.02 pc. The latter value ( ∼ 0.02 pc) for the shocked gas case ( X (OH) = 10 −6 ) is comparable to the of 0.015 pc. This result suggests the presence of shock-induced compression at the interface of the two filaments, and supports the filament–filament collisions picture in the Pipe nebula. 7. Summary We have observed the four hfs components of the OH 18 cm transitions toward the Pipe nebula along the two strip lines and determined the gas kinetic temperatures accurately. Along strip-1, the gas kinetic temperature is found to have no relation to the apparent distance to the nearby star, θ -Oph. This result suggests that the heating effect from θ -Oph does not significantly contribute to the heating of the Pipe nebula, although a possibility that θ -Oph illuminates the

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


on the impurity, physical structure, and the temperature of ice grains. In turn, we may be able to set constraints on the processes experienced by 2I and its formation conditions. There are three objectives of our study: (1) to search for diagnostic absorption features of water ice and other compounds (such as hydrated minerals or organic materials) in the coma or on its surface; (2) to compare water ice features in 2I to other Solar System icy objects (e.g., comets, Centaurs, and Kuiper Belt objects) and set constraints on its birth conditions; (3) to search for the ultra-red organic materials that may be present on the surface of 2I or in the coma as a result of long-term exposure to Galactic cosmic rays or UV bombardment. 2. Observations and data reduction We observed 2I on three separate occasions. The first two observations were made using director discretionary time allocations. The first observation was carried out in the morning twilight on 2019 September 19 UT using the SpeX spectrograph at the 3m IRTF telescope. The second observation was made on September 24 UT with the Gemini Near-InfraRed Spectrograph (GNIRS) spectrograph on the 8m GEMINI telescope. The last observation was made in service mode, again with the SpeX/IRTF, on October 9. The observing geometry is shown in Table 1 . Table 1. Journal of the 2I observations. Notes. (†) r h and Δ are the heliocentric and geocentric distances, respectively; (‡) α is the phase angle; (§) χ is the airmass. The SpeX observations were made with the high-throughput prism mode (0.8–2.5 μ m) and a 0.8′′ × 15′′ slit that provides a spectral resolving power of R ∼ 150 ( Rayner et al. 2003 ). The GNIRS observations were taken with the short camera (plate scale: 0.15′′ pix −1 ). The SXD cross-dispersed mode, 32 l mm −1 grating and 1.0′′ slit, provides an averaged resolving power of R ∼ 1700 over 0.9–2.5 μ m ( Elias et al. 2006 ). At least one nearby G-type star was observed together with the comet during each run, which was used both as the telluric correction standard star and the solar analog. The SpeX data were reduced using the reduction pipeline SpeXtool ( Cushing et al. 2004 ). The GNIRS data were reduced using the Image Reduction and Analysis Facility (IRAF) software and the Gemini IRAF package. Taking advantage of the similarity between the SpeX data and GNIRS data, we used the SpeXtool xtellcor routine for telluric correction and the xmergeorders routine for merging the GNIRS SXD spectra. 3. Results The NIR observations of 2I are presented in Fig. 1 . Although the three spectra were taken on different dates and with different instruments, they consistently appear featureless and show moderately red spectral slopes. We did not detect the diagnostic absorption bands of water ice at 1.5 and 2.0 μ m. Among the three NIR observations, the Gemini dataset has the highest signal-to-noise ratio (S/N). Thus, we use the Gemini spectra for further analysis. Fig. 1. Near-IR spectra of 2I/Borisov,

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B-EntityOfFutureInterest 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 B-CelestialObject 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 B-Grant I-Grant I-Grant I-Grant O O O O O O O O O O O O O O O O O B-Instrument O O O B-Telescope I-Telescope I-Telescope O O O O O O O O O O O B-Instrument I-Instrument I-Instrument B-Instrument O O O B-Telescope I-Telescope I-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 O O 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 B-Wavelength I-Wavelength I-Wavelength O O O O O O O O O O O O O B-Formula I-Formula I-Formula O B-Citation I-Citation I-Citation I-Citation O O B-Instrument O O O O O B-Instrument I-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 B-Formula I-Formula I-Formula O B-Wavelength I-Wavelength I-Wavelength 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 B-Instrument O O O O O O O B-Software O B-Citation I-Citation I-Citation I-Citation O O B-Instrument O O O O O B-Software I-Software I-Software I-Software I-Software B-Software O O O B-Software I-Software I-Software O O O O O O O B-Instrument O O B-Instrument O O O O B-Software B-Software O O O O O O B-Software O O O O B-Instrument 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 B-Wavelength O B-Wavelength I-Wavelength I-Wavelength O O O B-Wavelength O O B-Dataset I-Dataset O O O O O O O O O O B-Telescope O O O O O O B-Wavelength O O B-CelestialObject


of least massive black holes (shown as green points) in order to compare the properties of the descendants of the haloes with massive quasars with those of haloes whose black hole mass is much lower, around that of the seed black hole mass. While matching haloes in the BT hydrodynamic and BTMassTracer simulation, we note that the halo mass is different in the matched haloes due to baryonic effects and there can be differences, which are mostly within 20 per cent. Figure 4. Two-dimensional histogram of the logarithm of halo mass (log 10 ( M halo [M ⊙ ])) and the black hole mass (log 10 ( M BH [M ⊙ ])) in the BT Hydrodynamic simulation at z = 8. The 23 most massive black holes are shown by scatter points in purple, while the least massive blackholes are shown in green. We also show as cyan points, the blackholes in the progenitor haloes of the most massive haloes at z = 0. Note that, for the cyan points, the halo masses shown correspond to that of the progenitors at z = 8. In the left-hand panel of Fig. 5 , we plot the halo mass function of the dark matter haloes at z = 8, 5, 2.5, 1.2 and 0. We also plot as points on the halo mass function at z = 8 and 0 the hosts of the most massive black hole tracer particles (corresponding to the purple points in Fig. 4 ). We find that at redshift z = 0, the halo masses are distributed over a wide range, compared to that at z = 8. From the figure, we can therefore clearly see that the descendants of the most massive black holes at z = 8 are not all the most massive haloes at z = 0. However, the descendants are still relatively massive haloes with mass above M * = 5.0 × 10 12 h −1 M ⊙ at z = 0. The individual tracks for the halo masses of the four most massive black hole tracers are also shown in the figure. We can see that these objects approximately keep their rank order over all redshifts. Figure 5. Left-hand panel: halo Mass function at z = 8, 5, 2.5, 1.2 and 0, and the location of the tracked hosts of the four most massive BH's on the mass function. Right-hand panel: normalized histogram of the halo masses at z = 0 of the descendants of the most massive black holes at z = 8 compared with the histogram of most massive haloes at z = 0. To further understand the distribution of the halo masses, we plot the normalized histogram of the host halo masses of the descendants of the z = 8 massive black holes, compared with the 23 most massive haloes at z = 0 in the right-hand panel of Fig. 5 . While the most massive haloes are distributed above 3.2 × 10 14 h −1 M ⊙ with a peak in the distribution at ∼1.8 × 10 15 h −1 M ⊙ , the quasar descendants have a peak distribution in the mass at ∼2.24 × 10 14 h −1 M ⊙ . These 23 most massive haloes and 23 quasar descendants have a space density of 4 × 10 −7 h 3 Mpc −3 . The massive haloes are all Coma-like objects, whereas the quasar descendants have masses an order of magnitude smaller, comparable to galaxy groups with a space density ∼ 10 − 5 h 3 Mpc − 3 . We can

O O O O O O O O O O O O O O O O O O O O O O O O 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 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 B-Formula I-Formula I-Formula I-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 O O B-Model I-Model I-Model 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 B-Formula I-Formula I-Formula 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 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 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 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 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 B-Formula I-Formula I-Formula 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 I-Formula I-Formula I-Formula 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 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 B-Formula I-Formula I-Formula 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 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 B-Formula I-Formula I-Formula 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 O O O O O O O O O O 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-EntityOfFutureInterest O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


molecules can be due to their reaction to form NH 4 SH in the deposited ice. The most intense band of NH 4 SH and other ammonium salts that may be present is the transition located at 1470 cm −1 . It is Unfortunate that this band overlaps with the methanol band in the same region and complicates the estimation of the column density of . Figure 3. IR spectra of H 2 S–CH 3 OH–NH 3 –H 2 O (3:3:3:1) deposited on a cold substrate (50 K) before (blue) and after (red) electron irradiation with a 10 keV electron beam (fluence = 2 × 10 21 eV cm −2 ). Figure 4. Left: Gaussian deconvolution for the 2556 cm −1 absorption corresponding to H 2 S. Band A corresponds to CH 3 OH, bands B and C correspond to H 2 S (Jiménez-Escobar et al. 2014 ), and band D corresponds to H 2 O. Right: Gaussian deconvolution for the 3367 cm −1 absorption corresponding to NH 3 . The red curve in Figure 3 shows the post-irradiation spectrum of the “with H 2 S” mixture. We note a significant decrease in band intensity of the initial constituents, and particularly a total disappearance of the H 2 S band. This is interpreted as complete destruction of the H 2 S molecules by electron irradiation. Among products of irradiation we observe the same molecules produced after irradiation of the “without H 2 S” mixture. Additionally, in the “with H 2 S” mixture alone, after irradiation an intense band appears at 2040 cm −1 . This band is assigned to the C = O stretching mode of OCS, which is observed at 2019 cm −1 in pure OCS ice and shifted to 2046 cm −1 in OCS-H 2 O and OCS-CO 2 mixtures (Ferrante et al. 2008 ). Previous experimental studies reported the formation of OCS after irradiation of ices containing CO or CO 2 as the carbon source and H 2 S or SO 2 as sulfur source (Ferrante et al. 2008 ; Chen et al. 2015 ). Our assignment of the 2040 cm −1 band to OCS is supported by the absence of this band in the infrared spectrum of the irradiated “without H 2 S” mixture, where no sulfur was present. OCS can be a product of reactions between a CO (which is produced from the dissociation of methanol as explained above) and an S atom or HS radical, as illustrated here: The column density of OCS after irradiation at 50 K (before heating) is 1.7 × 10 16 molecules cm −2 . This corresponds to a ratio of N (OCS)/ N (H 2 S) = 8.5%. In Figure 3 it is noteworthy that the absorption band at 1477 cm −1 assigned to methanol is in the same position as the strong absorption band of () (Howett et al. 2007 ). This makes it difficult to detect any production of the ammonium salt during the irradiation and heating of the sample. Thus, to assess the chemistry, we have performed an irradiation experiment using 15 N labeled ammonia. Figure 5 compares the infrared spectra of labeled and unlabeled mixtures after irradiation. We observe a shift of 16 cm −1 of the OCN − band in agreement with the isotopic shift 17 cm −1 measured in the matrix-isolated molecule (Maki Decius 1959 ). This confirms our assignment of this

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


probability distributions are shown in the right-hand panel of Fig. 2 , and lead to a strong bias towards Solar and super-Solar metallicity systems as potential terrestrial planet hosts, while substantial tails of potentially habitable terrestrial planet systems remain down to low metallicities. Given the flat evolution in the mean gas phase metallicity of the Universe over cosmic time, this probability distribution has relatively little effect on the evolution of habitabilities at any lookback time 10 Gyr, but instead acts primarily as a scaling factor, truncating the maximum habitable fraction at any redshift at ∼50 per cent. It will, however, tend to suppress the habitable fraction in low-mass galaxies (log(M/M ⊙ ) 8.5), since these have lower typical metallicities as shown in Fig. 3 . We note that since Lineweaver ( 2001 ) defined this probability distribution, the number of exoplanetary systems known has grown exponentially (see e.g. Winn Fabrycky 2015 , for a recent review). None the less, the metallicity dependence of planet hosting remains a complex subject, with relatively poor observational constraints, particularly at the low end of the planetary and stellar-mass functions where observational selection effects are significant. None the less, the distribution above remains broadly consistent with current observations. Wang Fischer ( 2015 ) examined the distribution of planets selected by the Kepler telescope. They lacked the sample size to determine the detailed metallicity distribution of the planet hosts, but found that the median metallicity for terrestrial planet hosts is log( Z /Z ⊙ ) = 0.04 (with Z ⊙ = 0.018), which matches the peak of the Lineweaver ( 2001 ) distribution in P HE ( Z ). By contrast, other studies such as that of Adams Kane ( 2016 ) and Buchhave et al. ( 2012 ) have found no statistically significant difference in metallicity between stars hosting terrestrial planet candidates and the whole Kepler sample, even when only planets in their habitable zones are considered. Hobson et al. ( 2017 ) also suggest that, while M-dwarf stars hosting massive planets are biased towards high metallicities, no such bias is seen for the hosts of low-mass planets. At the lowest metallicities, Zackrisson et al. ( 2016 ) have studied the history of metallicity enhancement in the Universe and its effect on terrestrial planet formation. They apply a gradual iron enrichment cut-off between 10 per cent and 1 per cent of Solar metallicity, extending well below the Lineweaver ( 2001 ) cut-off we apply, although, as Fig. 2 demonstrates, the vast bulk of galaxies have a mean gas phase metallicity above 10 per cent Solar for the entire lookback interval being considered. It is likely that future exoplanet telescope missions such as TESS and PLATO , together with extensive ground-based follow-up, will be required to clarify this issue. However, as noted above, given the relatively slow evolution of mean metallicities in cosmic time, we

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