arXiv subjects

Aman N. Raju

Publications and source records attributed to Aman N. Raju.

3 recordsLinked to original sources

arXiv2026

A Meta-Learning Framework for Multitask Reverberation Mapping in Active Galactic Nuclei

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe active galactic nuclei (AGN) at sky densities of approximately 1000-4000 per sq. deg, enabling photometric reverberation mapping on an unprecedented scale. We present a meta-learning framework for AGN photometric reverberation mapping based on Attentive Latent Neural Processes (ALNP), developed by the SER-SAG-S1 directable software in-kind team for LSST. The framework clusters AGN light curves with similar topologies using Self-Organizing Maps and combines ALNPs with Mixture Density Models to learn light-curve structure, supermassive black hole (SMBH) properties, and accretion-disk transfer functions in an unsupervised manner. We evaluate the framework on simulated AGN light curves spanning a range of cadences and transfer functions, as well as on real data from the Zwicky Transient Facility. The learned latent representations encode information on both transfer functions and SMBH parameters. Relative to ensemble-trained baseline regressors, including Gaussian-process models, the framework improves light-curve reconstruction by 60-70%. The transfer function recovery improves by approximately 35% relative to the training prior in a low-variability cluster, while recovery of intrinsic SMBH and red-noise parameters improves by approximately 34%. We further demonstrate that models trained on simulated data can be applied to real AGN light curves. These results indicate that ALNP-based representations provide a flexible and scalable approach to photometric reverberation mapping and are well suited to the diverse AGN population expected from LSST and future time-domain surveys.

astro-ph.GA↗

arXiv2022

Effects of the environment on the multiplicity properties of stars in the STARFORGE simulations

Most observed stars are part of a multiple star system, but the formation of such systems and the role of environment and various physical processes is still poorly understood. We present a suite of radiation-magnetohydrodynamic simulations of star-forming molecular clouds from the STARFORGE project that include stellar feedback with varied initial surface density, magnetic fields, level of turbulence, metallicity, interstellar radiation field, simulation geometry and turbulent driving. In our fiducial cloud the raw simulation data reproduces the observed multiplicity fractions for Solar-type and higher mass stars, similar to previous works. However, after correcting for observational incompleteness the simulation under-predicts these values. The discrepancy is likely due to the lack of disk fragmentation, as the simulation only resolves multiples that form either through capture or core fragmentation. The raw mass distribution of companions is consistent with randomly drawing from the initial mass function for the companions of $>1\,\mathrm{M_\odot}$ stars, however, accounting for observational incompleteness produces a flatter distribution similar to observations. We show that stellar multiplicity changes as the cloud evolves and anti-correlates with stellar density. This relationship also explains most multiplicity variations between runs, i.e., variations in the initial conditions that increase stellar density (increased surface density, reduced turbulence) decrease multiplicity. While other parameters, such as metallicity, interstellar radiation, and geometry significantly affect the star formation history or the IMF, varying them produces no clear trend in stellar multiplicity properties.

astro-ph.GA↗

arXiv2022

The dynamics and outcome of star formation with jets, radiation, winds, and supernovae in concert

We analyze the first giant molecular cloud (GMC) simulation to follow the formation of individual stars and their feedback from jets, radiation, winds, and supernovae, using the STARFORGE framework in the GIZMO code. We evolve the GMC for $\sim 9 \rm Myr$, from initial turbulent collapse to dispersal by feedback. Protostellar jets dominate feedback momentum initially, but radiation and winds cause cloud disruption at $\sim 8\%$ star formation efficiency (SFE), and the first supernova at $8.3 \rm Myr$ comes too late to influence star formation significantly. The per-freefall SFE is dynamic, accelerating from 0 to $\sim 18\%$ before dropping quickly to <1%, but the estimate from YSO counts compresses it to a narrower range. The primary cluster forms hierarchically and condenses to a brief ($\sim 1\,\mathrm{Myr}$) compact ($\sim 1 \rm pc$) phase, but does not virialize before the cloud disperses, and the stars end as an unbound expanding association. The initial mass function resembles the Chabrier (2005) form with a high-mass slope $α=-2$ and a maximum mass of $55 M_\odot$. Stellar accretion takes $\sim 400 \rm kyr$ on average, but $\gtrsim 1\rm Myr$ for $>10 M_\odot$ stars, so massive stars finish growing latest. The fraction of stars in multiples increases as a function of primary mass, as observed. Overall, the simulation much more closely resembles reality, compared to variations which neglect different feedback physics entirely. But more detailed comparison with synthetic observations is necessary to constrain the theoretical uncertainties.

astro-ph.GA↗