For solar-type stars, spots and their associated magnetic regions induce radial velocity perturbations through the Doppler rotation signal and the suppression of convective blueshift -- collectively known as rotation-modulation. We developed the Rotation-Convection (RC) model: a method of detrending and characterizing rotation-modulation, using only cross-correlation functions or 1-dimensional spectra, without the need for continuous high cadence measurements. The RC method uses a simple model for the anomalous radial velocity induced by an active region and has two inputs: stellar flux (or a flux proxy) and the relative radial velocity between strongly and weakly absorbed wavelengths (analogous to the bisector-inverse slope). On NEID solar data (three month baseline), the RC model lowers the amplitude of rotationally-modulated stellar activity to below the meter-per-second level. For the standard star HD 26965, the RC model detrends the activity signal to the meter-per-second level for HARPS, EXPRES, and NEID observations, even though the temporal density and timespan of the observations differs by an order of magnitude between the three datasets. In addition to detrending, the RC model also characterizes the rotation-modulation signal. From comparison with the Solar Dynamics Observatory, we confirmed that the model accurately recovers and separates the rotation and convection radial velocity components. We also mapped the amplitude of the rotation and convection perturbations as a function of height within the stellar atmosphere. Probing stellar atmospheres with our revised spot model will fuel future innovations in stellar activity mitigation, enabling robust exoplanet detection.
Distribution functions of collisionless systems are known to show non-thermal power law tails. Interestingly, collisionless plasmas in various physical scenarios, (e.g., the ion population of the solar wind) feature a $v^{-5}$ tail in the velocity ($v$) distribution, whose origin has been a long-standing mystery. We show this power law tail to be a natural outcome of the self-consistent collisionless relaxation of driven electrostatic plasmas. We perform a quasilinear analysis of the perturbed Vlasov-Poisson equations to show that the coarse-grained mean distribution function (DF), $f_0$, follows a quasilinear diffusion equation with a diffusion coefficient $D(v)$ that depends on $v$ through the plasma dielectric constant. If the plasma is isotropically forced on scales much larger than the Debye length with a white noise-like electric field, then $D(v)\sim v^4$ for $\sigma<v<\omega_{\mathrm{P}}/k$, with $\sigma$ the thermal velocity, $\omega_{\mathrm{P}}$ the plasma frequency and $k$ the maximum wavenumber of the perturbation; the corresponding $f_0$, in the quasi-steady state, develops a $v^{-\left(d+2\right)}$ tail in $d$ dimensions ($v^{-5}$ tail in 3D), while the energy ($E$) distribution develops an $E^{-2}$ tail irrespective of the dimensionality of space. Any redness of the noise only alters the scaling in the high $v$ end. Non-resonant particles moving slower than the phase-velocity of the plasma waves ($\omega_{\mathrm{P}}/k$) experience a Debye-screened electric field, and significantly less (power law suppressed) acceleration than the near-resonant particles. Thus, a Maxwellian DF develops a power law tail. The Maxwellian core ($v<\sigma$) eventually also heats up, but over a much longer timescale than that over which the tail forms. We definitively show that self-consistency (ignored in test-particle treatments) is crucial for the development of the universal $v^{-5}$ tail.
We present a field-level emulator for large-scale structure, capturing the cosmology dependence and the time evolution of cosmic structure formation. The emulator maps linear displacement fields to their corresponding nonlinear displacements from N-body simulations at specific redshifts. Designed as a neural network, the emulator incorporates style parameters that encode dependencies on $\Omega_{\rm m}$ and the linear growth factor $D(z)$ at redshift $z$. We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model's displacement outputs. This innovation results in significant improvements in training efficiency and model accuracy. Tested on diverse cosmologies and redshifts not seen during training, the emulator achieves percent-level accuracy on scales of $k\sim~1~{\rm Mpc}^{-1}~h$ at $z=0$, with improved performance at higher redshifts. We compare predicted structure formation histories with N-body simulations via merger trees, finding consistent merger event sequences and statistical properties.