arXiv subjects

Narges Ahmadi

Publications and source records attributed to Narges Ahmadi.

8 recordsLinked to original sources

arXiv2026

An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction

Travel behavior research increasingly combines digital data collection with predictive modeling, yet these stages are often developed and evaluated separately. This study proposes a three-agent workflow integrating conversational data collection, structured data processing, and behavioral prediction. A chatbot-administered, image-augmented stated-preference survey collected mode choices from student commuters across five predefined weather scenarios, yielding 454 respondent-scenario observations. Weather-related associations were analyzed using a multinomial logit model, while logistic regression and random forest provided machine-learning benchmarks. Nine locally deployed large language models (LLMs), ranging from 2 to 35 billion parameters, were evaluated across four zero-shot prompt-and-context conditions and extended through persona, few-shot, and vision-based configurations. Random forest achieved 69.6% five-class accuracy, while the best text-only zero-shot LLM reached 69.9% without task-specific fitting. Habitual travel information produced the most consistent gains, Expert framing generally outperformed Role-Play, and persona information was most useful when habitual travel information was unavailable. Few-shot prompting improved prediction for several models, with gains stabilizing after a small number of examples. Using the same weather images shown to respondents, the best vision-based configuration reached 71.5% five-class accuracy, indicating that visual context may provide additional predictive information for selected models. Overall, the study shows how conversational surveys, structured data processing, conventional behavioral modeling, machine learning, and multimodal LLM prediction can be coordinated within an auditable multi-agent workflow.

cs.AI↗

arXiv2026

Automatic Identification of Magnetospheric Regions using Supervised Machine Learning Models

We present an automated approach for identifying magnetospheric regions using supervised machine learning techniques applied to Magnetospheric MultiScale mission data. Our method utilizes ion energy spectra, total magnetic field, total ion temperature, ion velocity component, ion density and spacecraft position data to classify five distinct plasma environments: solar wind, magnetosheath, inner magnetosphere, plasma sheet, and lobe regions. The approach combines a convolutional neural network (CNN) for analyzing ion energy spectrogram data with a Random Forest classifier for scalar plasma parameters. The CNN method employs 2D convolution to identify spatial and temporal patterns in the ion energy spectrogram treated as image-like data, while the Random Forest model processes averaged magnetic field, temperature, velocity, density and position parameters. Our hybrid model achieves 99% accuracy on test dataset with an F1 score of 0.99, providing reliable automated region identification at 3-minute temporal resolution. This lightweight approach requires minimal manual data labeling and can be readily applied to other magnetospheric missions with similar data products.

physics.space-ph↗

arXiv2026

Statistical study of energy dissipation in magnetic structures during turbulent reconnection in the Earth's magnetotail

Magnetic reconnection is a ubiquitous plasma phenomenon that plays a critical role in particle heating and energization. During reconnection, the topology of magnetic field rearranges, depositing energy into the surrounding plasma through bulk flow, thermal heating, or non-thermal particle acceleration. While the pathways of this transformation from magnetic energy into kinetic have been studied extensively in recent years through theoretical or case-by-case observations, comprehensive statistical studies remain limited. In this paper, we present a statistical investigation using data from the Magnetospheric Multiscale (MMS) mission, and detail the particle energization mechanisms in magnetic structures found near reconnecting regions in turbulent Earth's magnetotail. We find that electrons with motion perpendicular to the magnetic field dominate $\vec{j}\cdot\vec{E}$ dissipation. In contrast to the conventional picture of unidirectional energy transfer to particles by laminar two-dimensional (2D) reconnection, we find that energy exchange within magnetic structures during turbulent reconnection tends to be bidirectional with only a small positive bias from electromagnetic fields to particles. Specific electron energization mechanisms are quantified, including those due to parallel electric field, Fermi energization from curvature drift, betatron heating from magnetic field inhomogeneity, and polarization drift.

physics.space-ph↗

arXiv2019

In situ spacecraft observations of a structured electron diffusion region during magnetopause reconnection

The Electron Diffusion Region (EDR) is the region where magnetic reconnection is initiated and electrons are energized. Because of experimental difficulties, the structure of the EDR is still poorly understood. A key question is whether the EDR has a homogeneous or patchy structure. Here we report Magnetospheric MultiScale (MMS) novel spacecraft observations providing evidence of inhomogeneous current densities and energy conversion over a few electron inertial lengths within an EDR at the terrestrial magnetopause, suggesting that the EDR can be rather structured. These inhomogenenities are revealed through multi-point measurements because the spacecraft separation is comparable to a few electron inertial lengths, allowing the entire MMS tetrahedron to be within the EDR most of the time. These observations are consistent with recent high-resolution and low-noise kinetic simulations.

physics.plasm-ph↗

arXiv2018

Generation of Electron Whistler Waves at the Mirror Mode Magnetic Holes: MMS Observations and PIC Simulation

The Magnetospheric Multiscale (MMS) mission has observed electron whistler waves at the center and at the edges of magnetic holes in the dayside magnetosheath. The magnetic holes are nonlinear mirror structures since their magnitude is anti-correlated with particle density. In this article, we examine the growth mechanisms of these whistler waves and their interaction with the host magnetic hole. In the observations, as magnetic holes develop and get deeper, an electron population gets trapped and develops a temperature anisotropy favorable for whistler waves to be generated. In addition, the decrease in magnetic field magnitude and the increase in density reduces the electron resonance energy, which promotes the electron cyclotron resonance. To investigate this process, we used an expanding box particle-in-cell simulations to produce the mirror instability, which then evolves into magnetic holes. The simulation shows that whistler waves can be generated at the center and edges of magnetic holes, which reproduces the primary features of the MMS observations. The simulation shows that the electron temperature anisotropy develops in the center of the magnetic hole once the mirror instability reaches its nonlinear stage of evolution. The plasma is then unstable to whistler waves at the minimum of the magnetic field structures. In the saturation regime of mirror instability, when magnetic holes are developed, the electron temperature anisotropy appears at the edges of the holes and electron distributions become more isotropic at the magnetic field minimum. At the edges, the expansion of magnetic holes decelerates the electrons which leads to temperature anisotropies.

physics.space-ph↗

arXiv2017

Simulation of Observed Magnetic Holes in the Magnetosheath

Magnetic holes have been frequently observed in the magnetosheath of Earth and it is believed that these structures are the result of nonlinear evolution of mirror instability. Mirror mode fluctuations mostly appear as magnetic holes in regions where plasma is marginally mirror stable with respect to the linear instability. We present an expanding box particle in cell simulation to mimic the magnetosheath plasma and produce the mirror mode magnetic holes. We show that magnetic peaks are dominant when plasma is mirror unstable and mirror fluctuations evolve to deep magnetic holes when plasma is marginally mirror stable. Although, the averaged plasma parameters in the simulation are marginally close to mirror instability threshold, the plasma in the magnetic holes is highly unstable to mirror instability and mirror stable in the magnetic peaks.

physics.space-ph↗

arXiv2016

Effects of electron temperature anisotropy on proton mirror instability evolution

Proton mirror modes are large amplitude nonpropagating structures frequently observed in the magnetosheath. It has been suggested that electron temperature anisotropy can enhance the proton mirror instability growth rate while leaving the proton cyclotron instability largely unaffected, therefore causing the proton mirror instability to dominate the proton cyclotron instability in Earth's magnetosheath. Here, we use particle-in-cell simulations to investigate the electron temperature anisotropy effects on proton mirror instability evolution. Contrary to the hypothesis, electron temperature anisotropy leads to excitement of the electron whistler instability. Our results show that the electron whistler instability grows much faster than the proton mirror instability and quickly consumes the electron free energy, so that there is no electron temperature anisotropy left to significantly impact the evolution of the proton mirror instability.

physics.plasm-ph↗

arXiv2015

The Plasma Simulation Code: A modern particle-in-cell code with load-balancing and GPU support

Recent increases in supercomputing power, driven by the multi-core revolution and accelerators such as the IBM Cell processor, graphics processing units (GPUs) and Intel's Many Integrated Core (MIC) technology have enabled kinetic simulations of plasmas at unprecedented resolutions, but changing HPC architectures also come with challenges for writing efficient numerical codes. This paper describes the Plasma Simulation Code (PSC), an explicit, electromagnetic particle-in-cell code with support for different order particle shape functions. We focus on two distinguishing feature of the code: patch-based load balancing using space-filling curves, and support for Nvidia GPUs, which achieves substantial speed-up of up to more than 6x on the Cray XK7 architecture compared to a CPU-only implementation.

physics.plasm-ph↗