arXiv subjects

Aayush Gautam

Publications and source records attributed to Aayush Gautam.

7 recordsLinked to original sources

arXiv2026

Star cluster formation from turbulent clumps. V. Stellar clustering around massive stars

Massive stars (> 8 $M_\odot$) are known to have high degrees of multiplicity, e.g., with about 60% in triples or higher-order multiples. Such high levels of multiplicity may arise during formation (primary multiplicity) or through dynamical processing of already formed stars in dense clusters (secondary multiplicity). The level of primary multiplicity is an important metric to help distinguish between different formation scenarios, such as core accretion and competitive accretion. The level of secondary multiplicity is expected to evolve with time and be sensitive to local cluster environment. Here we analyze a suite of $N$-body simulations to study bound multiplicity and local projected stellar density, $N_*$, around massive stars within gradually forming star clusters with 50% primordial binaries in the Turbulent Clump Core Accretion (TCCA) paradigm. We find that massive stars rapidly gather triple or higher-order bound companions and enhancements in local $N_*$ via dynamical processes. We study these metrics as a function of environment in a given cluster, quantifying the increasing multiplicity that arises towards cluster centers. We find that secondary multiplicity tends to decrease in more massive clusters due to their higher velocity dispersions, but rises as the mean density of the bound cluster increases. We find our $N_*$ radial profiles are shallower compared to those in the STARFORGE simulations, which form massive stars via competitive accretion. A comparison to the AFGL 5180 system suggests it is better described by TCCA models. However, a larger number of observed systems is needed to better discriminate between these formation models.

astro-ph.GA↗

arXiv2026

Exoplanet System Architecture: Sculpting the Inner Regions

In this study, we seek to improve our understanding of the competing roles of disk-driven and planet-planet dynamical migration in sculpting planetary system architecture in the inner $\lesssim 1.5$ au of protoplanetary disks. Over a range of host star masses, we compare the orbit semimajor axis values of transiting multi-planet and resonant systems to observationally-derived estimates of protoplanetary disk inner truncation radius $R_{i}$, corotation radius $R_{co}$, and dust sublimation radius $R_{dust}$. We find that disk-driven migration is primarily responsible for setting the inner edge of planetary systems near $R_{co}$ and that subsequent dynamical migration shapes the distribution of planetary semimajor axis values over the range $\approx 20-300$ $R_{\star}$. If multi-planet systems form in a way similar to the resonant chain systems, either a zone of highly efficient planet formation at $\gtrsim 100 R_{\star}$, followed by subsequent disk-driven migration, is implied, or a modified in-situ mechanism operating over a region from $\simeq 15-100 R_{\star}$ and incorporating disk-driven migration is needed. There are indications that after disk dispersal, dynamical migration causes a subset of planets to migrate to locations inside $R_{co}$.

astro-ph.EP↗

arXiv2026

Fast Forward: Accelerating LLM Prefill with Predictive FFN Sparsity

The prefill stage of large language model (LLM) inference is a key computational bottleneck for long-context workloads. At short-to-moderate context lengths (1K--16K tokens), Feed-Forward Networks (FFNs) dominate this cost, accounting for most of the total FLOPs. Existing FFN sparsification methods, designed for autoregressive decoding, fail to exploit the prefill stage's parallelism and often degrade accuracy. To address this, we introduce FastForward, a predictive sparsity framework that accelerates LLM prefill through block-wise, context-aware FFN sparsity. FastForward combines (1) a lightweight expert predictor to select high-importance neurons per block, (2) an error compensation network to correct sparsity-induced errors, and (3) a layer-wise sparsity scheduler to allocate compute based on token-mixing importance. Across LLaMA and Qwen models up to 8B parameters, FastForward delivers up to 1.45$\times$ compute-bound speedup at 50% FFN sparsity with $<$ 6% accuracy loss compared to the dense baseline on LongBench, substantially reducing Time-to-First-Token (TTFT) for efficient, long-context LLM inference on constrained hardware.

cs.LG↗

arXiv2025

Token-Driven GammaTune: Adaptive Calibration for Enhanced Speculative Decoding

Speculative decoding accelerates large language model (LLM) inference by using a smaller draft model to propose tokens, which are then verified by a larger target model. However, selecting an optimal speculation length is critical for maximizing speedup while minimizing wasted computation. We introduce \textit{GammaTune} and \textit{GammaTune+}, training-free adaptive algorithms that dynamically adjust speculation length based on token acceptance rates using a heuristic-based switching mechanism. Evaluated on SpecBench across multiple tasks and model pairs, our method outperforms other heuristic-based approaches and fixed-length speculative decoding, achieving an average speedup of 15\% ($\pm$5\%) with \textit{GammaTune} and 16\% ($\pm$3\%) with \textit{GammaTune+}, while reducing performance variance. This makes \textit{GammaTune} a robust and efficient solution for real-world deployment.

cs.CL↗

arXiv2025

Are LLMs Good Annotators for Discourse-level Event Relation Extraction?

Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks. However, its effectiveness over discourse-level event relation extraction (ERE) tasks remains unexplored. In this paper, we assess the effectiveness of LLMs in addressing discourse-level ERE tasks characterized by lengthy documents and intricate relations encompassing coreference, temporal, causal, and subevent types. Evaluation is conducted using an commercial model, GPT-3.5, and an open-source model, LLaMA-2. Our study reveals a notable underperformance of LLMs compared to the baseline established through supervised learning. Although Supervised Fine-Tuning (SFT) can improve LLMs performance, it does not scale well compared to the smaller supervised baseline model. Our quantitative and qualitative analysis shows that LLMs have several weaknesses when applied for extracting event relations, including a tendency to fabricate event mentions, and failures to capture transitivity rules among relations, detect long distance relations, or comprehend contexts with dense event mentions. Code available at: https://github.com/WeiKangda/LLM-ERE.git.

cs.CL↗

arXiv2024

Star cluster formation from turbulent clumps. IV. Protoplanetary disc evolution

Most stars are born in the crowded environments of gradually forming star clusters. Dynamical interactions between close-passing stars and the evolving UV radiation fields from proximate massive stars are expected to sculpt the protoplanetary discs in these clusters, potentially contributing to the diversity of planetary systems that we observe. Here, we investigate the impact of cluster environment on disc demographics by implementing simple protoplanetary disc evolution models within $N$-body simulations of gradual star cluster formation. We consider a range of star formation efficiency per free-fall time, $ε_{\rm ff}$, and mass surface density of the natal cloud environment, $Σ_{\rm cl}$, both of which affect the overall duration of cluster formation. We track the interaction history of all stars to estimate the dynamical truncation of the discs around stars involved in close encounters. We also track external photoevaporation of the discs due to the ionizing radiation field of the nearby high- and intermediate-mass ($> 5 M_\odot$) stars. We find that $ε_{\rm ff}$, $Σ_{\rm cl}$, and the degree of primordial binarity have major influences on the masses and radii of the disc population. In particular, external photo-evaporation has a greater impact than dynamical interactions in determining the fate of discs in our clusters.

astro-ph.GA↗

arXiv2020

EinsteinPy: A Community Python Package for General Relativity

This paper presents EinsteinPy (version 0.3), a community-developed Python package for gravitational and relativistic astrophysics. Python is a free, easy to use a high-level programming language which has seen a huge expansion in the number of its users and developers in recent years. Specifically, a lot of recent studies show that the use of Python in Astrophysics and general physics has increased exponentially. We aim to provide a very high level of abstraction, an easy to use interface and pleasing user experience. EinsteinPy is developed keeping in mind the state of a theoretical gravitational physicist with little or no background in computer programming and trying to work in the field of numerical relativity or trying to use simulations in their research. Currently, EinsteinPy supports simulation of time-like and null geodesics and calculates trajectories in different background geometries some of which are Schwarzschild, Kerr, and KerrNewmann along with coordinate inter-conversion pipeline. It has a partially developed pipeline for plotting and visualization with dependencies on libraries like Plotly, matplotlib, etc. One of the unique features of EinsteinPy is a sufficiently developed symbolic tensor manipulation utilities which are a great tool in itself for teaching yourself tensor algebra which for many beginner students can be overwhelmingly tricky. EinsteinPy also provides few utility functions for hypersurface embedding of Schwarzschild spacetime which further will be extended to model gravitational lensing simulation.

gr-qc↗