arXiv subjects

Nina Filippova

Publications and source records attributed to Nina Filippova.

4 recordsLinked to original sources

arXiv2026

Non-ideal MHD and protostellar feedback effects on disc formation and evolution in numerical simulations of star cluster formation

While recent surveys have resolved hundreds of nearby protostellar discs, numerical simulations assuming ideal magnetohydrodynamics (MHD) have historically struggled to achieve disc formation due to efficient angular momentum removal by magnetic torques. Non-ideal MHD effects, relevant at the low ionization fractions typical of molecular clouds, have been shown to reduce the effectiveness of magnetic braking and promote disc formation. In this work, we present the results from a suite of calculations following the gravitational collapse of 50 $M_{\odot}$ turbulent molecular cloud cores down to the formation and evolution of stellar systems and protostellar discs. We use the radiation-MHD code GIZMO including non-ideal MHD (Ohmic resistivity, ambipolar diffusion, and the Hall effect) and the STARFORGE numerical framework for modeling star formation and stellar feedback. We compare the effects of assuming ideal vs. non-ideal MHD and including sub-grid protostellar jet feedback on disc formation and evolution. Discs form in all of our models but are least massive in the model with ideal MHD and sub-grid jet feedback. Apart from the ideal MHD$+$jets model, we do not observe any significant differences in disc properties between the ideal and non-ideal MHD models; however, ideal MHD discs are embedded in smaller rotating envelopes. Disc sizes are in general agreement with those of observed discs. Jet feedback increases core fragmentation and reduces final stellar masses. Our results suggest that magnetic braking does not efficiently suppress disc formation, regardless of whether ideal or non-ideal MHD is assumed, under the dynamical conditions in which multiple stellar systems form.

astro-ph.SR↗

arXiv2026

The Evolution of Star-Forming Gas in STARFORGE: From Clouds, to Cores, to Stars

Star formation occurs within dense regions of giant molecular clouds (GMCs), however, exactly how gas collects and evolves to form individual stars and what role dense cores play remains unclear. We use the Lagrangian cell information in the STARFORGE simulation suite to track star-forming gas in three GMCs with varying magnetic field strengths. We find that, once a protostar forms, the lifetime of the unaccreted gas correlates with the final stellar mass, where low-mass stars ($M_*$ < 0.5 M$_\odot$) accrete for 0.5-0.6 Myr from a relatively local reservoir of gas, and high-mass stars ($M_*$ > 2 M$_\odot$) accrete over 3.3-4.7 Myr from a much larger volume. Although the protostellar accretion time increases weakly with magnetic field strength, the accreting gas radii, velocity dispersions, virial parameters, and magnetic energy ratios are largely insensitive to the global cloud properties. At the time of protostar formation, the unaccreted gas exhibits linewidth-size and mass-size relations characteristic of turbulently regulated, isothermal dense cores, following $σ_v \propto R^{0.47-0.55}$ and $M \propto R^{1.0-1.1}$, respectively. Low- and intermediate-mass stars undergo relatively continuous accretion and their accretion histories are well-fit by either isothermal sphere, turbulent core, or competitive accretion models, where no one model fits all masses. However, many high-mass stars experience intermittent accretion and their accretion histories are not well-fit by any of these models. While the distribution of accreting gas is more extended than typically-defined dense cores, the physical properties and structure of the star-forming gas resemble those of observed cores and are largely regulated by turbulence and feedback.

astro-ph.GA↗

arXiv2021

Star Formation Efficiency and Dispersal of Giant Molecular Clouds with UV Radiation Feedback: Dependence on Gravitational Boundedness and Magnetic Fields

Molecular clouds are supported by turbulence and magnetic fields, but quantifying their influence on cloud lifecycle and star formation efficiency (SFE) remains an open question. We perform radiation MHD simulations of star-forming giant molecular clouds (GMCs) with UV radiation feedback, in which the propagation of UV radiation via ray-tracing is coupled to hydrogen photochemistry. We consider 10 GMC models that vary in either initial virial parameter ($1\leα_{v,0}\le 5$) or dimensionless mass-to-magnetic flux ratio (0.5-8 and $\infty$); the initial mass $10^5M_{\odot}$ and radius 20pc are fixed. Each model is run with five different initial turbulence realizations. In most models, the duration of star formation and the timescale for molecular gas removal (primarily by photoevaporation) are 4-8Myr. Both the final SFE ($ε_*$) and time-averaged SFE per freefall time ($ε_{ff}$) are reduced by strong turbulence and magnetic fields. The median $ε_*$ ranges between 2.1% and 9.5%. The median $ε_{ff}$ ranges between 1.0% and 8.0% and anticorrelates with $α_{v,0}$, in qualitative agreement with previous analytic theory and simulations. However, the time-dependent $α_{v}(t)$ and $ε_{ff,obs}(t)$ based on instantaneous gas properties and cluster luminosity are positively correlated due to rapid evolution, making observational validation of star formation theory difficult. Our median $ε_{ff,obs}(t)\approx$ 2% is similar to observed values. We show that the traditional virial parameter estimates the true gravitational boundedness within a factor of 2 on average, but neglect of magnetic support and velocity anisotropy can sometimes produce large departures. Magnetically subcritical GMCs are unlikely to represent sites of massive star formation given their unrealistic columnar outflows, prolonged lifetime, and low escape fraction of radiation.

astro-ph.GA↗

arXiv2020

Deep learning for intensity mapping observations: Component extraction

Line intensity mapping (LIM) is an emerging observational method to study the large-scale structure of the Universe and its evolution. LIM does not resolve individual sources but probes the fluctuations of integrated line emissions. A serious limitation with LIM is that contributions of different emission lines from sources at different redshifts are all confused at an observed wavelength. We propose a deep learning application to solve this problem. We use conditional generative adversarial networks to extract designated information from LIM. We consider a simple case with two populations of emission line galaxies; H$\rmα$ emitting galaxies at $z = 1.3$ are confused with [OIII] emitters at $z = 2.0$ in a single observed waveband at 1.5 $\rmμ$m. Our networks trained with 30,000 mock observation maps are able to extract the total intensity and the spatial distribution of H$\rmα$ emitting galaxies at $z = 1.3$. The intensity peaks are successfully located with 74% precision. The precision increases to 91% when we combine the results of 5 networks. The mean intensity and the power spectrum are reconstructed with an accuracy of $\sim$10%. The extracted galaxy distributions at a wider range of redshift can be used for studies on cosmology and on galaxy formation and evolution.

astro-ph.GA↗