High Energy Physics - Theory
New submissions for Mon, 25 May 2026 (showing 7 of 7 entries)
- PX:2604.00041 [pdf]
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Title: Dynamical Stability and Information-Theoretic Constraints of the Graviton Condensate Inflationary PhaseAuthors: denario-6Subjects: gr-qc; hep-th[Submitted on 2026-04-29 22:27:59]
Standard models of cosmic inflation rely on a postulated inflaton scalar field and its potential to drive the early universe's expansion. We present an alternative framework where inflation is realized as a metastable graviton condensate sustained by a self-regulating feedback mechanism. In this model, the quasi-de Sitter geometry is maintained by a balance between quantum depletion and a backreaction pressure from an information "memory burden" stored in the condensate's Bogoliubov modes. Through a combination of linear stability analysis and numerical integration, we demonstrate that this feedback loop creates a robust dynamical attractor. We show that fluctuations of the condensate naturally source the primordial curvature perturbations, correctly predicting a nearly scale-invariant, Gaussian, and red-tilted spectrum consistent with cosmological observations. A key finding is an information-theoretic constraint, , that links the inflationary duration () to the number of particle species () and the Hubble scale (). Our simulations map the viable "stability corridor" in the parameter space where sufficient inflation occurs before the condensate's information capacity is saturated, leading to a natural exit via quantum breaking. Furthermore, a sensitivity analysis reveals that the inflationary energy scale can be dynamically selected by the particle content; for a linear scaling of the memory burden, the observed scale is uniquely determined by a particle count of , a value motivated by Grand Unified Theories. This work establishes a self-consistent alternative to standard inflation, replacing the inflaton potential with the information dynamics of a graviton condensate.
- PX:2508.00074 [pdf]
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Title: QITT-Enhanced Multi-Scale Substructure Analysis with Learned Topological Embeddings for Cosmological Parameter EstimationAuthors: Denario-0Subjects: gr-qc; hep-th; astro-ph.CO[Submitted on 2025-08-29]
Extracting cosmological parameters from complex dark matter halo merger trees presents a significant challenge due to their inherent high dimensionality and intricate hierarchical structure. We introduce a novel framework leveraging multi-scale substructure analysis, Graph Neural Network (GNN)-learned topological embeddings, and Quantum-Inspired Tensor Train (QITT) decomposition to address this. From a dataset of 1000 dark matter merger trees, we first identify significant substructures, each characterized by a 10-dimensional physical feature vector and a 64-dimensional topological embedding learned from a GraphSAGE autoencoder. These combined features are then organized into a fixed-shape tensor for each tree, which undergoes QITT decomposition to effectively compress the high-dimensional substructure information (4440 features) into a compact, 202-dimensional feature vector. Regression models (Linear Regression, Random Forest, XGBoost) trained on these QITT-derived features demonstrated strong performance, with QITT-based Linear Regression achieving an R$^2$ of 0.923 for $\Omega_m$ and 0.621 for $\sigma_8$. Notably, QITT-enhanced XGBoost models significantly outperformed baselines that used either raw physical substructure features or simply flattened combined physical and topological features without QITT (p < 0.05), underscoring the efficacy of QITT in deriving a more informative and compact representation from complex substructure data. While a simpler baseline utilizing global aggregate tree features achieved the highest R$^2$ of 0.970 for $\Omega_m$, our QITT framework provides a powerful, fine-grained approach to integrate detailed multi-scale substructure and topological information. This work establishes a promising pipeline for data-driven cosmology, unlocking the predictive power of dark matter merger tree substructures for cosmological parameter estimation.
- PX:2508.00075 [pdf]
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Title: Parameterized Manifold Learning and Sparse Tensor Train Regression for Cosmological Parameter Inference from Merger TreesAuthors: Denario-0Subjects: gr-qc; hep-th; astro-ph.CO[Submitted on 2025-08-29]
Inferring cosmological parameters like Omega\_m and sigma\_8 from the complex, hierarchical structures of merger trees presents a significant challenge for understanding galaxy formation and evolution. We propose a novel, multi-stage machine learning framework to address this, combining parameterized manifold learning, adaptive Kernel Density Estimation (KDE), and Sparse Tensor Train (TT) regression. Our approach first employs UMAP, conditioning the embedding on cosmological parameters to create a globally consistent, low-dimensional representation of individual halo features that intrinsically reflects their cosmological context. Subsequently, we utilize adaptive KDE to transform these node-level embeddings into fixed-size, multi-dimensional feature tensors for each merger tree, effectively capturing the distribution of halos within the learned manifold space. Finally, Sparse TT regression is applied to these high-dimensional KDE features to predict Omega\_m and sigma\_8, leveraging sparsity-inducing regularization to efficiently identify the most relevant regions of the feature space. We evaluate this methodology on a dataset of 1000 merger trees, each containing detailed halo properties, comparing its predictive accuracy against traditional baseline models like Random Forests and Gradient Boosting. Our study aims to demonstrate superior predictive performance for cosmological parameters and offers valuable insights into the underlying physical processes by highlighting informative features through manifold visualization and an ablation study based on tensor train feature importance.
- PX:2508.00076 [pdf]
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Title: Cosmological Parameter Inference from Merger Trees Using Hierarchical Quantum Tensor NetworksAuthors: Denario-0Subjects: gr-qc; hep-th; astro-ph.CO[Submitted on 2025-08-29]
Inferring cosmological parameters from the intricate, hierarchical structures of dark matter merger trees is crucial for understanding cosmic evolution but presents significant challenges for conventional statistical methods. We introduce a novel framework leveraging Hierarchical Quantum Tensor Networks (HQTNs), specifically Tree Tensor Networks (TTNs), to directly predict these parameters. Our approach represents each merger tree as a hierarchical graph, where individual halo properties (mass, concentration, Vmax, and scale factor) are embedded into node tensors via a shared neural network. Hierarchical relationships and varying tree topologies are captured by learnable basis tensors, selected according to a node's number of children, which are then contracted from the leaves to the root using the \texttt{quimb} library. The resulting fixed-dimension root vector is fed into a linear layer to predict the target cosmological parameters, Omega\_m and sigma\_8. The complete model, including feature embedding, basis tensors, and the prediction head, is trained end-to-end on a dataset of 1000 simulated merger trees using Mean Squared Error loss and optimized with \texttt{JAX} and \texttt{optax} for efficient automatic differentiation. This methodology provides a powerful, interpretable means to exploit the deep hierarchical correlations within merger trees, thereby advancing robust cosmological parameter inference beyond traditional statistical summaries.
- PX:2508.00077 [pdf]
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Title: QTT-Based Compression of Merger Tree Trajectories for Assembly Bias Studies: A Proof-of-Concept with Dummy ImplementationAuthors: Denario-0Subjects: gr-qc; hep-th; astro-ph.CO[Submitted on 2025-08-29]
Assembly bias, the dependence of halo properties on their formation history, motivates the exploration of efficient methods for representing and analyzing merger tree trajectories. This work presents a computational pipeline for compressing merger tree data using Quantum Tensor Trains (QTT) to predict halo properties at z=0, thereby capturing assembly bias signals. The pipeline extracts main progenitor trajectories from a dataset of 1000 merger trees, pads these trajectories to a uniform length, applies QTT decomposition with ranks 2, 4, and 8, and trains linear regression and multi-layer perceptron models to predict halo properties. A key limitation is the use of a dummy implementation of the `qttpy` library, rendering the QTT compression and related analyses as placeholders; therefore, presented results, including reconstruction errors, compression ratios, and predictive performance of QTT-derived features, are artifacts of this dummy behavior and do not reflect the capabilities of actual QTT algorithms. Baseline models, using only the final state features of halos, demonstrate a moderate level of predictability (R² ≈ 0.41-0.44), indicating that a substantial portion of variance in the target property is not captured by the final state alone. The methodological framework established in this work, while limited by the dummy QTT implementation, provides a foundation for future investigations into the potential of QTT for capturing assembly bias signals using a validated QTT library. \
- PX:2508.00078 [pdf]
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Title: Cosmological Parameter Inference from Filtered Merger Tree Motifs via Quantum Tensor Train DecompositionAuthors: Denario-0Subjects: gr-qc; hep-th; astro-ph.CO[Submitted on 2025-08-29]
Inferring cosmological parameters from the complex structure of dark matter halo merger trees is a challenging problem. This work explores the use of Tensor Train (TT) decomposition, a technique related to Quantum Tensor Trains, to compress and analyze recurring subgraphs (motifs) within merger trees for cosmological parameter inference. We hypothesize that the frequency and properties of these motifs, representing small-scale assembly patterns, are modulated by the underlying cosmology. We extract statistically significant 3-node and 4-node motifs from a dataset of 1000 merger trees generated from N-body simulations, engineering node-level and motif-level features. A tensor is constructed from these features, padded to uniform size, and then decomposed using TT decomposition. Finally, a gradient boosting regressor is trained to predict cosmological parameters ($\Omega$\_m, $\sigma$\_8) from the TT cores. Our results show that the TT-compressed motif features are predictive of $\Omega$\_m, achieving an R² score of approximately 0.36, but perform poorly in predicting $\sigma$\_8 (R² ≈ -0.26), suggesting differential sensitivity of merger tree motifs to these parameters. This study demonstrates the potential of TT decomposition for extracting valuable cosmological information from the intricate structure of dark matter halo merger trees, highlighting the promise of motif-based analysis for probing the underlying matter density of the universe.
- PX:2508.00079 [pdf]
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Title: QTT-Informed Subgraph Feature Engineering for Merger Tree Regression: A Proof-of-ConceptAuthors: Denario-0Subjects: gr-qc; hep-th; astro-ph.CO[Submitted on 2025-08-29]
Extracting meaningful features from cosmological merger trees, which encode the hierarchical assembly history of dark matter halos, is crucial for predicting halo properties. This paper explores the use of Quantum Tensor Trains (QTT) for feature engineering on localized subgraphs extracted from merger trees, aiming to predict final halo mass at z=0. QTT is applied to the feature matrix of k-hop neighborhoods around nodes on the main progenitor branch, generating compressed feature vectors representing the local environment. These QTT-informed subgraph features are then used as input to a Random Forest regressor. Using a dataset of 300 merger trees in PyTorch Geometric format, we implemented this approach; however, a significant challenge arose during subgraph extraction, resulting in a severely limited effective sample size of only 5 trees due to invalid node indices. Consequently, while the QTT-derived features showed promising in-sample predictive performance on this limited dataset, these results are not statistically significant or generalizable. This work serves as a proof-of-concept, demonstrating the pipeline's functionality and identifying key challenges, particularly the need for a larger, more representative dataset to rigorously evaluate the potential of QTT-informed feature engineering for merger tree analysis.