arXiv subjects

Junyang Lu

Publications and source records attributed to Junyang Lu.

5 recordsLinked to original sources

arXiv2026

Detectable subhalo impacts in Milky Way streams

Dark matter subhalos leave gravitational imprints in the stellar streams of the Milky Way. Observing individual strong impacts of subhalos offers a compelling way to constrain and discover potentially dark subhalos down to $10^6 M_\odot$, allowing for new tests of the particle physics properties of dark matter. We develop a pipeline and statistical framework to forecast the expected number of detectable subhalo impacts on stellar streams, based on morphological and kinematic data from surveys such as LSST and Via. Starting from a catalog of confirmed stellar streams, we focus our efforts on 14 promising streams that are relatively well-modeled with a particle spray algorithm. Our criteria for a detectable impact is a deviation at 95% CL from the best-fit polynomial proxy model for the stream, which accounts for stream modeling uncertainties and regulates the effect of distant impacts that are degenerate with these uncertainties. Among the 14 streams studied, we find that 5 streams have an expected number of detectable impacts greater than 0.2. With LSST and Via data, the stream Jet has $5.15^{+1.10}_{-0.95}$ expected detectable impacts, followed by Orphan-Chenab ($1.40^{+0.62}_{-0.47}$), ATLAS-Aliqa Uma ($1.25^{+0.60}_{-0.44}$), GD-1 ($0.55^{+0.43}_{-0.28}$), and Palomar 5 ($0.40^{+0.39}_{-0.23}$), where error bars are the 95% containment on the Poisson mean. These values rely on the assumed subhalo population, which can give a factor of few systematic uncertainty in the predictions. We also consider effects of different particle dark matter models on the number of impacts, finding a suppression by a factor of $\sim 4$ for warm dark matter and fuzzy dark matter models at their current mass bounds and an $O(1)$ enhancement for a toy model of self-interacting dark matter.

astro-ph.GA↗

arXiv2026

A Multi-Agent Approach to Validate and Refine LLM-Generated Personalized Math Problems

Students benefit from math problems contextualized to their interests. Large language models (LLMs) offer promise for efficient personalization at scale. However, LLM-generated personalized problems may often have problems such as unrealistic quantities and contexts, poor readability, limited authenticity with respect to students' experiences, and occasional mathematical inconsistencies. To alleviate these problems, we propose a multi-agent framework that formalizes personalization as an iterative generate--validate--revise process; we use four specialized validator agents targeting the criteria of solvability, realism, readability, and authenticity, respectively. We evaluate our framework on 600 problems drawn from a popular online mathematics homework platform, ASSISTments, personalizing each problem to a fixed set of 20 student interest topics. We compare three refinement strategies that differ in how validation feedback is coordinated into revisions. Results show that authenticity and realism are the most frequent failure modes in initial LLM-personalized problems, but that a single refinement iteration substantially reduces these failures. We further find that different refinement strategies have different strengths on different criteria. We also assess validator reliability via human evaluation. Results show that reliability is highest on realism and lowest on authenticity, highlighting the need for better evaluation protocols that consider teachers' and students' personal characteristics.

cs.CY↗

arXiv2025

BBAL: A Bidirectional Block Floating Point-Based Quantisation Accelerator for Large Language Models

Large language models (LLMs), with their billions of parameters, pose substantial challenges for deployment on edge devices, straining both memory capacity and computational resources. Block Floating Point (BFP) quantisation reduces memory and computational overhead by converting high-overhead floating point operations into low-bit fixed point operations. However, BFP requires aligning all data to the maximum exponent, which causes loss of small and moderate values, resulting in quantisation error and degradation in the accuracy of LLMs. To address this issue, we propose a Bidirectional Block Floating Point (BBFP) data format, which reduces the probability of selecting the maximum as shared exponent, thereby reducing quantisation error. By utilizing the features in BBFP, we present a full-stack Bidirectional Block Floating Point-Based Quantisation Accelerator for LLMs (BBAL), primarily comprising a processing element array based on BBFP, paired with proposed cost-effective nonlinear computation unit. Experimental results show BBAL achieves a 22% improvement in accuracy compared to an outlier-aware accelerator at similar efficiency, and a 40% efficiency improvement over a BFP-based accelerator at similar accuracy.

cs.AR↗

arXiv2025

Detectability of dark matter subhalo impacts in Milky Way stellar streams

Stellar streams are a promising way to probe the gravitational effects of low-mass dark matter (DM) subhalos. In recent years, there has been a remarkable explosion in the number of stellar streams detected in the Milky Way, and hundreds more may be discovered with future surveys such as LSST. Studies of DM subhalo impacts on streams have so far focused on a few of the thinnest and brightest streams, and it is not known how much information can be gained from the others. In this work, we develop a method to quickly estimate the minimum detectable DM subhalo mass of a given stream, depending on its width, length, distance, and stellar density. We use an analytic model for the impacts and apply a test statistic to determine whether they are detectable. We consider several observational scenarios, based on current and future surveys including Gaia, DESI, Via, and LSST. We find that at 95% confidence level, a stream like GD-1 has a minimum detectable subhalo mass of $\sim 6\times 10^6~\mathrm{M}_\odot$ in Gaia data and $\sim 8\times 10^5~\mathrm{M}_\odot$ with LSST 10 year sensitivity. Applying our results to confirmed Milky Way streams, we rank order them by their sensitivity to DM subhalos and identify promising ones for further study.

astro-ph.GA↗

arXiv2015

Energy-aware Fixed-Priority Multi-core Scheduling for Real-time Systems

Multi-core processors are becoming more and more popular in embedded and real-time systems. While fixed-priority scheduling with task-splitting in real-time systems are widely applied, current approaches have not taken into consideration energy-aware aspects such as dynamic voltage/frequency scheduling (DVS). In this paper, we propose two strategies to apply dynamic voltage scaling (DVS) to fixed-priority scheduling algorithms with task-splitting for periodic real-time tasks on multi-core processors. The first strategy determines voltage scales for each processor after scheduling (Static DVS), which ensures all tasks meet the timing requirements on synchronization. The second strategy adaptively determines the frequency of each task before scheduling (Adaptive DVS) according to the total utilization of task-set and number of cores available. The combination of frequency pre-allocation and task-splitting makes it possible to maximize energy savings with DVS. Simulation results show that it is possible to achieve significant energy savings with DVS while preserving the schedulability requirements of real-time schedulers for multi-core processors.

cs.OS↗