Nonlinear plasma physics problems are usually simulated through comprehensive modeling of phase space. The extreme computational cost of such simulations has motivated the development of multi-moment fluid models. However, a major challenge has been finding a suitable fluid closure for these fluid models. Recent developments in physics-informed machine learning have led to a renewed interest in constructing accurate fluid closure terms. In this study, we take an approach that integrates kinetic physics from the first-principles Vlasov simulations into a fluid model (through the heat flux closure term) using the Fourier neural operator - a neural network architecture. Without resolving the phase space dynamics, this new fluid model is capable of capturing the nonlinear evolution of the Landau damping process that exactly matches the Vlasov simulation results. This machine learning-assisted new approach provides a computationally affordable framework that surpasses previous fluid models in accurately modeling the kinetic evolution of complex plasma systems.
We present a new machine learning algorithm for classifying short-duration features in raw time ordered data (TODs) of cosmic microwave background survey observations. The algorithm, specifically designed for the Atacama Cosmology Telescope (ACT), works in conjunction with the previous TOD preprocessing techniques that employ statistical thresholding to indiscriminately remove all large spikes in the data, whether they are due to noise features, cosmic rays, or true astrophysical sources in a process called "data cuts". This has the undesirable effect of excising real astrophysical sources, including transients, from the data. The machine learning algorithm demonstrated in this work uses the output from these data cuts and is able to differentiate between electronic noise, cosmic rays, and point sources, enabling the removal of undesired signals while retaining true astrophysical signals during TOD pre-processing. We achieve an overall accuracy of 90% in categorizing data spikes of different origin and, importantly, 94% for identifying those caused by astrophysical sources. Our algorithm also measures the amplitude of any detected source seen more than once and produces a sub-minute to minute light curve, providing information on its short timescale variability. This automated algorithm for source detection and amplitude estimation will be particularly useful for upcoming surveys with large data volumes, such as the Simons Observatory.
Comparative studies of young and old exoplanet populations offer a glimpse into how planets may form and evolve with time. We present an occurrence rate study of short-period ($<$12 days) planets between 1.8--10 Rearth around 1374 FGK stars in nearby (200 pc) young clusters ($<$1 Gyr), utilizing data from the Transiting Exoplanet Survey Satellite (TESS) mission. These planets represent a population closer to their primordial state. We find that the occurrence rate of young planets is higher ($64^{+32}_{-22}$%) compared to the Gyr-old population observed by \kepler ($7.98^{+0.37}_{-0.35}$%). Dividing our sample into bins of young (10--100 Myr) and intermediate (100\,Myr--1 Gyr) ages, we also find that the occurrence distribution in orbital period remains unchanged while the distribution in planet radius changes with time. Specifically, the radius distribution steepens with age, indicative of a larger planet population shrinking due to the atmospheric thermal cooling and mass loss. We also find evidence for an increase (1.9$\sigma$) in occurrence after 100 Myr, possibly due to tidal migration driving planets inside of 12 days. While evidence suggests post-disk migration and atmospheric mass loss shape the population of short-period planets, more detections of young planets are needed to improve statistical comparisons with older planets. Detecting long-period young planets and planets $<$1.8 Rearth will help us understand these processes better. Additionally, studying young planetary atmospheres provides insights into planet formation and the efficiency of atmospheric mass loss mechanisms on the evolution of planetary systems.
With about 1.5 billion galaxies expected to be observed, the very large number of objects in the \textit{Euclid}\xspace photometric survey will allow for precise studies of galaxy clustering from a single survey, over a large range of redshifts $0.2 < z < 2.5$. In this work, we use photometric redshifts to extract the baryon acoustic oscillation signal (BAO) from the Flagship galaxy mock catalogue with a tomographic approach to constrain the evolution of the Universe and infer its cosmological parameters. We measure the two-point angular correlation function in 13 redshift bins. A template-fitting approach is applied to the measurement to extract the shift of the BAO peak through the transverse Alcock--Paczynski parameter $\alpha$. A joint analysis of all redshift bins is performed to constrain $\alpha$ at the effective redshift $z_\mathrm{eff}=0.77$ with MCMC and profile likelihood techniques. We also extract one $\alpha_i$ parameter per redshift bin to quantify its evolution as a function of time. From these 13 $\alpha_i$, which are directly proportional to the ratio $D_\mathrm{A}/\,r_\mathrm{s,\,drag}$, we constrain $h$, $\Omega_\mathrm{b}$, and $\Omega_\mathrm{cdm}$. From the joint analysis, we constrain $\alpha(z_\mathrm{eff}=0.77)=1.0011^{+0.0078}_{-0.0079}$, which represents a three-fold improvement over current constraints from the Dark Energy Survey. As expected, the constraining power in the analysis of each redshift bin is lower, with an uncertainty ranging from $\pm\,0.13$ to $\pm\,0.024$. From these results, we constrain $h$ at 0.45 %, $\Omega_\mathrm{b}$ at 0.91 %, and $\Omega_\mathrm{cdm}$ at 7.7 %. We quantify the influence of analysis choices like the template, scale cuts, redshift bins, and systematic effects like redshift-space distortions over our constraints both at the level of the extracted $\alpha_i$ parameters and at the level of cosmological inference.
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this https URL and this https URL . 12 pages + appendices, 12 figures. Submitted to A&A