Jump to main content

Quicklinks

Following Tracer Particles to Identify Filaments in Star-Forming Cores with HDBSCAN

  • Fig. 1 Comparison of filament spines: DisPerSE skeleton of the gas density field (grey dots) versus the pruned spine from skeletonize (black dots), applied to a density-threshold mask of simulation cells mapped from clustered tracers. The COM is indicated with a cyan colored point. The found clusters are represented by the differently colored point clouds.

  • Fig. 2 Relative validity (blue points) as a function of the scaling factor α for multiple snapshots, where the feature space is defined as (x, y, z, α·vx, α·vy, α·vz). The optimal value in each case is marked by a green point. The noise fraction (orange points) is defined as the fraction of points classified as noise relative to the total number of points.

Author: Nuray Ortaköse

Filaments appear on all scales of the cold, dense ISM. Observations show that star-forming cores are interlaced in complex networks of filaments. These structures are thought to funnel material onto forming cores through gravitationally driven accretion, yet the influence of filamentary dynamics on star formation remains uncertain. Observations struggle with line-of-sight confusion: structures that appear continuous in Position-Position-Velocity (PPV) space may not be connected in PPP (Clarke et al. 2018). While recent work has advanced filament identification and kinematic analysis (e.g. Chen et al. 2020), robust methods for identifying velocity-coherent filaments in simulations are still limited.

Simulations of colliding flows demonstrate that filaments can form self-consistently and act as accretion channels (e.g. Gómez & Vázquez-Semadeni 2014). In this work, I analyse tracer particles in a colliding-flow simulation from Weis et al. (2024). These tracer particles are massless, passive particles that follow the gas velocity field while recording physical quantities such as density and temperature.

To identify filamentary structures, I cluster tracer particles in a six-dimensional phase space (PPPVVV) using HDBSCAN. HDBSCAN is a non-parametric clustering algorithm that works well on data with various shapes and densities, while also identifying noise (i.e. not assigned to any cluster). This approach allows the identification of structures that are coherent in both position and velocity space. As a first validation, I compare filament spines extracted from the gas density field using DisPerSE (Sousbie 2011) with spines derived from clustered tracer particles (See Fig. 1). Near the center of mass (COM), both methods show strong agreement, demonstrating that tracer particles can reliably recover structures present in the underlying gas distribution.

In addition, I find that the scaling factor relating positional and velocity coordinates is critical for identifying physically meaningful structures through clustering (See Fig. 2). In particular, the dependence on α varies significantly between snapshots: some exhibit a broad range of high relative validity, indicating robust and well-defined clustering, while others show a narrow, isolated peak, suggesting that the clustering solution is highly sensitive to the choice of α. In some cases, clustering becomes extremely robust and largely independent of α. This diversity reflects differences in the dynamical state of the system rather than a simple temporal evolution.

Monthly Highlights

May 2026

Following Tracer Particles to Identify Filaments in Star-Forming Cores with HDBSCAN (Nuray Ortaköse)

April 2026

Estimating ionization fractions in SILCC simulations (Lennart Buhlmann)

March 2026

1D protostellar disk sub-grid model for star formation 3D MHD simulations (Anaïs Pauchet)

January 2026

Protostellar Outflows: From Simulations to Synthetic Observations (Taishi Ushirogi)

December 2025

Prestellar Core Formation in Colliding Gas Flows: Simulations and Synthetic Observations (Felix Rauprich)

November 2025

Measuring the Velocity Dispersion of the Warm Neutral Medium of the Milky Way at Galactic Scale using the Code FLASH (Wajdee Chayeb)

October 2025

Higher-order finite volume method for modelling shock waves in the interstellar medium (Mervin Yap)

June 2025

Simulating [CII] emission in high-redshift galaxies and their halos (Clarissa Immisch)

May 2025

Episodic Accretion onto Low Mass Protostars (Christian Riesop)

April 2025

Molecular cloud formation: Impact of metallicity, far-UV and CR heating using SILCC-Zoom simulations (Sanjit Pal)

March 2025

Modelling Protostellar Outflows in ISM Simulations (Michael Weis)

February 2025

Multi-band ray-tracing of ionizing photons (Sebastian Vider)