Search

[Abridged]Ultra-faint dwarfs (UFDs) are expected to be the relics of the earliest galaxies forming in the Universe. Observations show the presence of a stellar halo around them, which can give precious insights into the evolution of UFDs. This work investigates how merger properties impact the formation of stellar halos around UFDs, focusing on Tucana II, the most promising UFD assembled through mergers. We develop N-body simulations of isolated mergers between two UFDs with $1M_\odot$ stellar resolution. We build a suite of simulations by varying: i) the merger mass ratio, $M_1/M_2$, the specific ii) kinetic energy, $k$, and iii) angular momentum, $l$, iv) the dark-to-stellar mass ratio, $M_{DM}/M_*$, of the progenitors and iv) their stellar size, $R_{1/2}$. We use a neural network to explore the parameter space, emulating the properties of the "post-merger" UFD by quantifying the half-mass radius ($R_*$) and the fraction of stars at radii $>5R_*$ ($f_5$). Our principal component analysis clearly shows that $f_5$ ($R_*$) is primarily determined by $M_1/M_2$ ($R_{1/2}$), with $R_{1/2}$ ($M_1/M_2$) playing a secondary role. Both $f_5$ and $R_*$ show almost no dependence on $k$, $l$, and $M_{DM}/M_*$ in the explored range. Using our emulator, we find that to form the stellar halo observed in Tucana II, i.e. $f_5=10\pm5\%$ and $R_*=120\pm30$pc, we need $M_1/M_2=8_{-3}^{+4}$ and $R_{1/2}=97^{+25}_{-18}$pc. Such findings are corroborated by the consistency ($\chi^2=0.5-2$) between the stellar density profile observed and those of simulations having $M_1/M_2$ and $R_{1/2}$ close to the emulator's predictions. Ongoing and planned spectroscopic surveys will greatly increase the statistics of observed stars and thus stellar halos in UFDs. By interpreting such observations with our model, we will provide new insights into the assembly history of UFDs and thus on the early galaxy formation process., Comment: 12 pages, 11 figures, submitted to A&A

Galaxy formation and evolution critically depend on understanding the complex photo-chemical processes that govern the evolution and thermodynamics of the InterStellar Medium (ISM). Computationally, solving chemistry is among the most heavy tasks in cosmological and astrophysical simulations. The evolution of such non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim at substituting such procedural solvers with fast, pre-trained, emulators based on neural operators. We emulate a non-equilibrium chemical network up to H$_2$ formation (9 species, 52 reactions) by adopting the DeepONet formalism, i.e. by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks. We use $\texttt{KROME}$ to generate a training set spanning $-2\leq \log(n/\mathrm{cm}^{-3}) \leq 3.5$, $\log(20) \leq\log(T/\mathrm{K}) \leq 5.5$, $-6 \leq \log(n_i/n) < 0$, and by adopting an incident radiation field $\textbf{F}$ sampled in 10 energy bins with a continuity prior. We separately train the solver for $T$ and each $n_i$ for $\simeq 4.34\,\rm GPUhrs$. Compared with the reference solutions obtained by $\texttt{KROME}$ for single zone models, the typical precision obtained is of order $10^{-2}$, i.e. the $10 \times$ better with a training that is $40 \times$ less costly with respect to previous emulators which however considered only a fixed $\mathbf{F}$. The present model achieves a speed-up of a factor of $128 \times$ with respect to stiff ODE solvers. Our neural emulator represents a significant leap forward in the modeling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency., Comment: 13 pages, 5 figures, Accepted for publication in A&A

Non-equilibrium chemistry is a key process in the study of the InterStellar Medium (ISM), in particular the formation of molecular clouds and thus stars. However, computationally it is among the most difficult tasks to include in astrophysical simulations, because of the typically high (>40) number of reactions, the short evolutionary timescales (about $10^4$ times less than the ISM dynamical time) and the characteristic non-linearity and stiffness of the associated Ordinary Differential Equations system (ODEs). In this proof of concept work, we show that Physics Informed Neural Networks (PINN) are a viable alternative to traditional ODE time integrators for stiff thermo-chemical systems, i.e. up to molecular hydrogen formation (9 species and 46 reactions). Testing different chemical networks in a wide range of densities ($-2< \log n/{\rm cm}^{-3}< 3$) and temperatures ($1 < \log T/{\rm K}< 5$), we find that a basic architecture can give a comfortable convergence only for simplified chemical systems: to properly capture the sudden chemical and thermal variations a Deep Galerkin Method is needed. Once trained ($\sim 10^3$ GPUhr), the PINN well reproduces the strong non-linear nature of the solutions (errors $\lesssim 10\%$) and can give speed-ups up to a factor of $\sim 200$ with respect to traditional ODE solvers. Further, the latter have completion times that vary by about $\sim 30\%$ for different initial $n$ and $T$, while the PINN method gives negligible variations. Both the speed-up and the potential improvement in load balancing imply that PINN-powered simulations are a very palatable way to solve complex chemical calculation in astrophysical and cosmological problems., Comment: 16 pages, 12 figures, accepted for publication on MNRAS

Neutrino-matter interactions play a key role in binary neutron star mergers. Thermodynamics conditions at the surfaces where neutrinos decouple from matter influence neutrino spectra, ultimately affecting the evolution of the remnant and the properties of the ejecta. In this work, we post-process results of general relativistic merger simulations employing microphysical equations of state and approximate neutrino transport to investigate the thermodynamics conditions at which weak and thermal equilibrium freezes out (equilibrium surfaces), as well as conditions at which the transition between diffusion and free-streaming regime occurs (diffusion surfaces). We find that the rest mass density and the neutrino energy are the most relevant quantities in determining the location of the decoupling surfaces. For mean energy neutrinos ($\langle E_{{\nu}_e} \rangle \approx 9~{\rm MeV}$, $\langle E_{{\bar{\nu}}_e} \rangle \approx 15~{\rm MeV}$, $\langle E_{{\nu}_{\mu,\tau}} \rangle \approx 25~{\rm MeV}$), diffusion surfaces are located around $10^{11}{\rm g~cm^{-3}}$ for all neutrino species, while equilibrium surfaces for heavy flavor neutrinos are significantly deeper (several $10^{12}{\rm g~cm^{-3}}$) than the ones of $\bar{\nu}_e$ and $\nu_e$ ($\gtrsim 10^{11}{\rm g~cm^{-3}}$). The resulting decoupling temperatures are in good agreement with the average neutrino energies ($\langle E_{\nu} \rangle \sim 3.15~T$), with the softer equation of state characterized by systematically larger decoupling temperatures ($\Delta T \lesssim 1~{\rm MeV}$). Neutrinos streaming at infinity with different energies come from very different regions of the remnant. The presence of a massive NS or of a BH in the remnant influences the neutrino thermalization process., Comment: 20 pages, 13 figures