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1-DREAM: 1D Recovery, Extraction and Analysis of Manifolds in noisy environments
Authors:
Marco Canducci,
Petra Awad,
Abolfazl Taghribi,
Mohammad Mohammadi,
Michele Mastropietro,
Sven De Rijcke,
Reynier Peletier,
Rory Smith,
Kerstin Bunte,
Peter Tino
Abstract:
Filaments are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied system and hold essential information on its history and future evolution. However, the recovery of such structures is often complicated by the presence of a large amount of background and transverse noise in the observa…
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Filaments are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied system and hold essential information on its history and future evolution. However, the recovery of such structures is often complicated by the presence of a large amount of background and transverse noise in the observation space. While the former is generally detrimental to the analysis, the latter can be attributed to measurement errors and it can hold essential information about the structure. To further complicate the scenario, 1D manifolds (filaments) are generally non-linear and their geometry difficult to extract and model. In order to study hidden manifolds within the dataset, particular care has to be devoted to background noise removal and transverse noise modelling, while still maintaining accuracy in the recovery of their geometrical structure. We propose 1-DREAM: a toolbox composed of five main Machine Learning methodologies whose aim is to facilitate manifold extraction in such cases. Each methodology has been designed to address issues when dealing with complicated low-dimensional structures convoluted with noise and it has been extensively tested in previously published works. In this work all methodologies are presented in detail, joint within a cohesive framework and demonstrated for three interesting astronomical cases: a simulated jellyfish galaxy, a filament extracted from a simulated cosmic web and the stellar stream of Omega-Centauri as observed with the GAIA DR2. Two newly developed visualization techniques are also proposed, that take full advantage of the results obtained with 1-DREAM. The code is made publicly available to benefit the community. The controlled experiments on a purposefully built data set prove the accuracy of the pipeline in recovering the hidden structures.
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Submitted 27 March, 2025;
originally announced March 2025.
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More than a void? The detection and characterization of cavities in a simulated galaxy's interstellar medium
Authors:
Abolfazl Taghribi,
Marco Canducci,
Michele Mastropietro,
Sven De Rijcke,
Reynier Frans Peletier,
Peter Tino,
Kerstin Bunte
Abstract:
The interstellar medium of galaxies is filled with holes, bubbles, and shells, typically interpreted as remnants of stellar evolution. There is growing interest in the study of their properties to investigate stellar and supernova feedback. So far, the detection of cavities in observational and numerical data is mostly done visually and, hence, is prone to biases. Therefore, we present an automate…
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The interstellar medium of galaxies is filled with holes, bubbles, and shells, typically interpreted as remnants of stellar evolution. There is growing interest in the study of their properties to investigate stellar and supernova feedback. So far, the detection of cavities in observational and numerical data is mostly done visually and, hence, is prone to biases. Therefore, we present an automated, objective method for discovering cavities in particle simulations, with demonstrations using hydrodynamical simulations of a dwarf galaxy. The suggested technique extracts holes based on the persistent homology of particle positions and identifies tight boundary points around each. With a synthetic ground-truth analysis, we investigate the relationship between data density and the detection radius, demonstrating that higher data density also allows for the robust detection of smaller cavities. By tracking the boundary points, we can measure the shape and physical properties of the cavity, such as its temperature. In this contribution, we detect 808 holes in 21 simulation snapshots. We classified the holes into supernova-blown bubbles and cavities unrelated to stellar feedback activity based on their temperature profile and expansion behaviour during the 100 million years covered by the simulation snapshots analysed for this work. Surprisingly, less than 40% of the detected cavities can unequivocally be linked to stellar evolution. Moreover, about 36% of the cavities are contracting, while 59% are expanding. The rest do not change for a few million years. Clearly, it is erroneous to interpret observational data based on the premise that all cavities are supernova-related and expanding. This study reveals that supernova-driven bubbles typically exhibit smaller diameters, larger expansion velocities, and lower kinetic ages (with a maximum of 220 million years) compared to other cavities.
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Submitted 9 January, 2025;
originally announced January 2025.
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$S^5$: New insights from deep spectroscopic observations of the tidal tails of the globular clusters NGC 1261 and NGC 1904
Authors:
Petra Awad,
Ting S. Li,
Denis Erkal,
Reynier F. Peletier,
Kerstin Bunte,
Sergey E. Koposov,
Andrew Li,
Eduardo Balbinot,
Rory Smith,
Marco Canducci,
Peter Tino,
Alexandra M. Senkevich,
Lara R. Cullinane,
Gary S. Da Costa,
Alexander P. Ji,
Kyler Kuehn,
Geraint F. Lewis,
Andrew B. Pace,
Daniel B. Zucker,
Joss Bland-Hawthorn,
Guilherme Limberg,
Sarah L. Martell,
Madeleine McKenzie,
Yong Yang,
Sam A. Usman
Abstract:
As globular clusters (GCs) orbit the Milky Way, their stars are tidally stripped forming tidal tails that follow the orbit of the clusters around the Galaxy. The morphology of these tails is complex and shows correlations with the phase of the orbit and the orbital angular velocity, especially for GCs on eccentric orbits. Here, we focus on two GCs, NGC 1261 and NGC 1904, that have potentially been…
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As globular clusters (GCs) orbit the Milky Way, their stars are tidally stripped forming tidal tails that follow the orbit of the clusters around the Galaxy. The morphology of these tails is complex and shows correlations with the phase of the orbit and the orbital angular velocity, especially for GCs on eccentric orbits. Here, we focus on two GCs, NGC 1261 and NGC 1904, that have potentially been accreted alongside Gaia-Enceladus and that have shown signatures of having, in addition of tidal tails, structures formed by distributions of extra-tidal stars that are misaligned with the general direction of the clusters' respective orbits. To provide an explanation for the formation of these structures, we make use of spectroscopic measurements from the Southern Stellar Stream Spectroscopic Survey ($S^5$) as well as proper motion measurements from Gaia's third data release (DR3), and apply a Bayesian mixture modeling approach to isolate high-probability member stars. We recover extra-tidal features similar to those found in Shipp et al. (2018) surrounding each cluster. We conduct N-body simulations and compare the expected distribution and variation in the dynamical parameters along the orbit with those of our potential member sample. Furthermore, we use Dark Energy Camera (DECam) photometry to inspect the distribution of the member stars in the color-magnitude diagram (CMD). We find that the potential members agree reasonably with the N-body simulations and that the majority of them follow a simple stellar population-like distribution in the CMD which is characteristic of GCs. In the case of NGC 1904, we clearly detect the tidal debris escaping the inner and outer Lagrange points which are expected to be prominent when at or close to the apocenter of its orbit. Our analysis allows for further exploration of other GCs in the Milky Way that exhibit similar extra-tidal features.
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Submitted 13 November, 2024;
originally announced November 2024.
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The large-scale structure around the Fornax-Eridanus Complex
Authors:
Maria Angela Raj,
Petra Awad,
Reynier F. Peletier,
Rory Smith,
Ulrike Kuchner,
Rien van de Weygaert,
Noam I. Libeskind,
Marco Canducci,
Peter Tino,
Kerstin Bunte
Abstract:
Our objectives are to map the filamentary network around the Fornax-Eridanus Complex and probe the influence of the local environment on galaxy morphology. We employ the novel machine-learning tool, 1-DREAM (1-Dimensional, Recovery, Extraction, and Analysis of Manifolds) to detect and model filaments around the Fornax cluster. We then use the morphology-density relation of galaxies to examine the…
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Our objectives are to map the filamentary network around the Fornax-Eridanus Complex and probe the influence of the local environment on galaxy morphology. We employ the novel machine-learning tool, 1-DREAM (1-Dimensional, Recovery, Extraction, and Analysis of Manifolds) to detect and model filaments around the Fornax cluster. We then use the morphology-density relation of galaxies to examine the variation in the galaxies' morphology with respect to their distance from the central axis of the detected filaments. We detect 27 filaments that vary in length and galaxy-number density around the Fornax-Eridanus Complex. These filaments showcase a variety of environments; some filaments encompass groups/clusters, while others are only inhabited by galaxies in pristine filamentary environments. We also reveal a well-known structure -- the Fornax Wall, that passes through the Dorado group, Fornax cluster, and Eridanus supergroup. Regarding the morphology of galaxies, we find that early-type galaxies (ETGs) populate high-density filaments and high-density regions of the Fornax Wall. Furthermore, the fraction of ETGs decreases as the distance to the filament spine increases. Of the total galaxy population in filaments, ~7% are ETGs and ~24% are late-type galaxies (LTGs) located in pristine environments of filaments, while ~27% are ETGs and ~42% are LTGs in groups/clusters within filaments. This study reveals the Cosmic Web around the Fornax Cluster and asserts that filamentary environments are heterogeneous in nature. When investigating the role of the environment on galaxy morphology, it is essential to consider both, the local number-density and a galaxy's proximity to the filament spine. Within this framework, we ascribe the observed morphological segregation in the Fornax Wall to pre-processing of galaxies within groups embedded in it.
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Submitted 3 July, 2024;
originally announced July 2024.
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Swarming in stellar streams: Unveiling the structure of the Jhelum stream with ant colony-inspired computation
Authors:
Petra Awad,
Marco Canducci,
Eduardo Balbinot,
Akshara Viswanathan,
Hanneke C. Woudenberg,
Orlin Koop,
Reynier Peletier,
Peter Tino,
Else Starkenburg,
Rory Smith,
Kerstin Bunte
Abstract:
The halo of the Milky Way galaxy hosts multiple dynamically coherent substructures known as stellar streams that are remnants of tidally disrupted systems such as globular clusters (GCs) and dwarf galaxies (DGs). A particular case is that of the Jhelum stream, which is known for its complex morphology. Using the available data from Gaia DR3, we extracted a region on the sky that contains Jhelum. W…
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The halo of the Milky Way galaxy hosts multiple dynamically coherent substructures known as stellar streams that are remnants of tidally disrupted systems such as globular clusters (GCs) and dwarf galaxies (DGs). A particular case is that of the Jhelum stream, which is known for its complex morphology. Using the available data from Gaia DR3, we extracted a region on the sky that contains Jhelum. We then applied the novel Locally Aligned Ant Technique (LAAT) on the position and proper motion space of stars belonging to the selected region to highlight the stars that are closely aligned with a local manifold in the data and the stars belonging to regions of high local density. We find that the overdensity representing the stream in proper motion space is composed of two components, and show the correspondence of these two signals to the previously reported narrow and broad spatial components of Jhelum. We made use of the radial velocity measurements provided by the $S^5$ survey to confirm, for the first time, a separation between the two components in radial velocity. We show that the narrow and broad components have velocity dispersions of $4.84^{+1.23}_{-0.79}$~km/s and $19.49^{+2.19}_{-1.84}$~km/s, and metallicity dispersions of $0.15^{+0.18}_{-0.10}$ and $0.34^{+0.13}_{-0.09}$, respectively. These measurements, and the difference in component widths, could be explained with a scenario where Jhelum is the remnant of a GC embedded within a DG that were accreted onto the Milky Way during their infall. Although the properties of Jhelum can be explained with this merger scenario, other progenitors of the narrow component remain possible such as a nuclear star cluster or a DG. To rule these possibilities out, we would need more observational data of member stars of the stream. Our analysis highlights the importance of the internal structure of streams with regards to their formation history.
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Submitted 19 December, 2023;
originally announced December 2023.
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Swarm Intelligence-based Extraction and Manifold Crawling Along the Large-Scale Structure
Authors:
Petra Awad,
Reynier Peletier,
Marco Canducci,
Rory Smith,
Abolfazl Taghribi,
Mohammad Mohammadi,
Jihye Shin,
Peter Tino,
Kerstin Bunte
Abstract:
The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network, several techniques have been developed that are able to describe its properties and the properties of groups of galaxies as a function of their environment. In this w…
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The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network, several techniques have been developed that are able to describe its properties and the properties of groups of galaxies as a function of their environment. In this work we analyze the previously introduced framework: 1-Dimensional Recovery, Extraction, and Analysis of Manifolds (1-DREAM) on N-body cosmological simulation data of the Cosmic Web. The 1-DREAM toolbox consists of five Machine Learning methods, whose aim is the extraction and modelling of 1-dimensional structures in astronomical big data settings. We show that 1-DREAM can be used to extract structures of different density ranges within the Cosmic Web and to create probabilistic models of them. For demonstration, we construct a probabilistic model of an extracted filament and move through the structure to measure properties such as local density and velocity. We also compare our toolbox with a collection of methodologies which trace the Cosmic Web. We show that 1-DREAM is able to split the network into its various environments with results comparable to the state-of-the-art methodologies. A detailed comparison is then made with the public code DisPerSE, in which we find that 1-DREAM is robust against changes in sample size making it suitable for analyzing sparse observational data, and finding faint and diffuse manifolds in low density regions.
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Submitted 7 February, 2023;
originally announced February 2023.
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Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques
Authors:
Mohammad Mohammadi,
Jarvin Mutatiina,
Teymoor Saifollahi,
Kerstin Bunte
Abstract:
Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Therefore, identifying such systems allows to study galaxies mass assembly, formation and evolution. However, in the lack of spectroscopic information detecting UCDs/GCs using imaging data is very uncertain.…
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Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Therefore, identifying such systems allows to study galaxies mass assembly, formation and evolution. However, in the lack of spectroscopic information detecting UCDs/GCs using imaging data is very uncertain. Here, we aim to train a machine learning model to separate these objects from the foreground stars and background galaxies using the multi-wavelength imaging data of the Fornax galaxy cluster in 6 filters, namely u, g, r, i, J and Ks. The classes of objects are highly imbalanced which is problematic for many automatic classification techniques. Hence, we employ Synthetic Minority Over-sampling to handle the imbalance of the training data. Then, we compare two classifiers, namely Localized Generalized Matrix Learning Vector Quantization (LGMLVQ) and Random Forest (RF). Both methods are able to identify UCDs/GCs with a precision and a recall of >93 percent and provide relevances that reflect the importance of each feature dimension %(colors and angular sizes) for the classification. Both methods detect angular sizes as important markers for this classification problem. While it is astronomical expectation that color indices of u-i and i-Ks are the most important colors, our analysis shows that colors such as g-r are more informative, potentially because of higher signal-to-noise ratio. Besides the excellent performance the LGMLVQ method allows further interpretability by providing the feature importance for each individual class, class-wise representative samples and the possibility for non-linear visualization of the data as demonstrated in this contribution. We conclude that employing machine learning techniques to identify UCDs/GCs can lead to promising results.
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Submitted 7 January, 2022; v1 submitted 5 January, 2022;
originally announced January 2022.
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GEO debris and interplanetary dust: fluxes and charging behavior
Authors:
Amara L. Graps,
Simon F. Green,
Neil McBride,
J. A. M. McDonnell,
Kalle Bunte,
Hakan Svedhem,
Gerhard Drolshagen
Abstract:
In September 1996, a dust/debris detector: GORID was launched into the geostationary (GEO) region as a piggyback instrument on the Russian Express-2 telecommunications spacecraft. The instrument began its normal operation in April 1997 and ended its mission in July 2002. The goal of this work was to use GORID's particle data to identify and separate the space debris to interplanetary dust partic…
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In September 1996, a dust/debris detector: GORID was launched into the geostationary (GEO) region as a piggyback instrument on the Russian Express-2 telecommunications spacecraft. The instrument began its normal operation in April 1997 and ended its mission in July 2002. The goal of this work was to use GORID's particle data to identify and separate the space debris to interplanetary dust particles (IDPs) in GEO, to more finely determine the instrument's measurement characteristics and to derive impact fluxes. While the physical characteristics of the GORID impacts alone are insufficient for a reliable distinction between debris and interplanetary dust, the temporal behavior of the impacts are strong enough indicators to separate the populations based on clustering. Non-cluster events are predominantly interplanetary, while cluster events are debris. The GORID mean flux distributions (at mass thresholds which are impact speed dependent) for IDPs, corrected for dead time, are 1.35x10^{-4} m^{-2} s^{-1} using a mean detection rate: 0.54 d^{-1}, and for space debris are 6.1x10^{-4} m^{-2} s^{-1} using a mean detection rate: 2.5 d^{-1}. Beta-meteoroids were not detected. Clusters could be a closely-packed debris cloud or a particle breaking up due to electrostatic fragmentation after high charging.
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Submitted 12 September, 2006;
originally announced September 2006.