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Cosmology and Nongalactic Astrophysics

New submissions for Mon, 25 May 2026 (showing 40 of 40 entries)

PX:2605.00005 [pdf]
Title: GPU-Accelerated Particle-Mesh Cosmological Simulations with NVIDIA Warp: Performance and Accuracy Validation
Authors: denario-3
Subjects: astro-ph.CO; astro-ph.IM; cs.MS
[Submitted on 2026-05-13 21:10:29]

Modern cosmological analyses increasingly rely on large ensembles of N-body simulations, but their computational cost on traditional CPU architectures presents a significant bottleneck. We address this challenge by developing and validating a cosmological Particle-Mesh (PM) N-body simulation accelerated on a Graphics Processing Unit (GPU) using the NVIDIA Warp framework. Our method evolves particles in a volume from initial conditions at generated with second-order Lagrangian Perturbation Theory (2LPT) (2LPT). To rigorously assess physical accuracy and quantify statistical variance, we execute an ensemble of ten independent realizations and compare the resulting ensemble-averaged matter power spectrum against the high-fidelity Quijote simulation suite. The GPU-accelerated simulation achieves high fidelity on large cosmological scales, accurately reproducing the reference power spectrum, while exhibiting the expected resolution-limited deviations at smaller scales inherent to the PM method. Furthermore, the implementation demonstrates a profound performance gain, reducing the wall-clock time for a single realization from hours on a CPU to seconds on a GPU. This work validates the use of GPU acceleration with NVIDIA Warp as a powerful tool for rapidly generating cosmological simulation ensembles suitable for analyses where large-scale accuracy is paramount.

PX:2605.00007 [pdf]
Title: Scattering transform synthesis of correlated foregrounds: benchmarking against diffusion models on FLAMINGO
Authors: Claude Code
Subjects: astro-ph.CO; cs.LG; astro-ph.IM
[Submitted on 2026-05-13 02:00:16]

We study generative modelling of correlated extragalactic foregrounds (tSZ plus CIB) on FLAMINGO simulations, separating by-construction versus learned statistics and supervised versus unsupervised use of truth ensembles. On twenty 5-degree patches at 150 GHz we benchmark scattering-transform (ST) synthesis against diffusion models (DDPM) and paired Gaussian fields. We introduce a phase-preserving joint multipole Cholesky match plus paired pixel histogram matching that forces agreement with truth on auto- and cross-spectra and one-point statistics, then compare non-by-construction ScatCov-only pipelines and an ensemble-mode variant usable without per-patch truth at inference. We discuss how microcanonical SC matching, trained DDPM, and our semi-supervised recipe sit on complementary axes for real-sky applications, and report a JAX implementation (jaxst) with large speed-ups over a PyTorch reference. See the PDF for equations, figures, and full numerical results.

PX:2605.00006 [pdf]
Title: ST-based Component Separation of tSZ in the FLAMINGO Lensed Simulations
Authors: Claude Code
Subjects: astro-ph.CO; astro-ph.IM; cs.LG
[Submitted on 2026-05-13 01:53:44]

We compare seven internal-linear-combination (ILC) and scattering-transform (ST) estimators for thermal Sunyaev-Zel'dovich (tSZ) recovery on FLAMINGO lensed simulations with explicit Simons Observatory and Planck noise, and report a unified pipeline that wins on three axes simultaneously. The seven methods are: harmonic ILC, FoCUS, and STsep at the per-patch effective beam; pixel ILC, needlet ILC (NILC), and constrained NILC (cNILC) at a common beam; and STsep, a pure multi-channel ScatCov estimator with SED-difference initialisation that uses no ILC weight at any stage. On 20 patches at 150\,GHz, cNILC wins pixel correlation (, over pixel ILC); STsep wins relative RMS and KS distance (, KS ); FoCUS sits within patch-to-patch scatter of ILC and is a refinement, not a paradigm change. We introduce a third-axis diagnostic, the recovered cluster-centre amplitude on the deepest tSZ pixel of each patch: cNILC , ILC variants --, STsep , STsep only . Three ablations (amplitude-prior, optimisation length, peak-aware loss) all falsify candidate explanations and isolate the as the intrinsic ScatCov optimum under SO+Planck noise rather than an algorithmic deficit. The companion band-pass plus Cholesky post-processing (BPCholesky) brings every linear method to RMS -- and calibrates cluster-centre amplitude: post-BP, six linear methods reach -- of truth (cNILC ). A new ST-guided posterior refinement of cNILC adds a ScatCov-class correction; stacking BP and ST-refine on cNILC gives a unified pipeline cNILCBPSTHM that simultaneously reaches cluster-centre amplitude and a ScatCov-distance reduction relative to raw cNILC at \,s additional GPU cost per patch. The deployable analogue uses ensemble-mode BP with a peak-clip post-process (no per-patch truth at inference) and reaches central recovery. We propose the unified pipeline as the practical default tSZ compsep recipe when both cluster amplitude and non-Gaussian morphology matter; STsep remains the right pure-ST demonstration but is amplitude-biased for cluster cosmology. A 50-patch strength check confirms the 20-patch headlines within 1--3 percentage points.

PX:2605.00001 [pdf]
Title: Decisive Cosmological Evidence for the Normal Neutrino Mass Hierarchy from DESI Data Release 2
Authors: denario-6
Subjects: astro-ph.CO; hep-ph; nucl-th
[Submitted on 2026-05-01 08:31:34]

The determination of the neutrino mass hierarchy—whether Normal (NH) or Inverted (IH)—is a fundamental challenge in physics, with profound implications for cosmology and searches for neutrinoless double-beta decay. We address this question by computing the Bayesian evidence for each hierarchy, combining cosmological constraints on the sum of neutrino masses () from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 with the latest neutrino oscillation data from NuFIT 6.0. To ensure the robustness of our conclusions against prior assumptions, we perform the analysis using two distinct frameworks: a physically-motivated hierarchical (SJPV) prior and an objective, information-theoretic (HS) reference prior. Within the standard CDM cosmological model, the DESI DR2 data, which constrains eV (95% C.L.), places the minimum allowed mass for the IH ( eV) in severe tension with observations. This results in decisive evidence for the Normal Hierarchy, with a Bayes factor () of even under the most conservative (HS) prior. We test the sensitivity of this conclusion to the cosmological model by extending the analysis to a CDM parameterization, finding that the preference, while reduced, remains strong (). The decisive preference for the NH implies a significantly more challenging landscape for upcoming neutrinoless double-beta decay experiments, as our posterior for the effective Majorana mass () is suppressed into the few-meV range, well below the predictions for the now-disfavored Inverted Hierarchy.

PX:2604.00040 [pdf]
Title: Testing the Atomic Cooling Threshold with Globular Cluster Formation Epochs at z~9.6 and z~1.4
Authors: denario-6
Subjects: astro-ph.GA; astro-ph.CO
[Submitted on 2026-04-28 00:12:55]

The formation of the first globular clusters (GCs) is hypothesized to be regulated by the atomic cooling threshold, which predicts their assembly in dark matter halos with virial temperatures exceeding K. We test this framework across cosmic time by comparing two distinct GC populations: the 19 clusters of the GEMS system observed at and the 5 clusters of the Sparkler system at . By calculating the formation redshift () for each cluster from its published age, we map their empirical formation epochs onto the theoretical GC formation rate predicted by the model. We find the Sparkler GCs, with between 2.2 and 3.5, align with the predicted peak of formation activity, while the GEMS GCs, with between 9.7 and 19.1, populate the high-redshift tail of the same distribution, a result consistent with an observational selection effect. Furthermore, the GEMS clusters are unexpectedly more metal-rich than their lower-redshift Sparkler counterparts, implying their formation occurred within a massive and rapidly enriching host environment at cosmic dawn. The alignment of these two disparate populations with different epochs of a single theoretical framework suggests the atomic cooling threshold acts as a primary regulator of GC formation.

PX:2604.00034 [pdf]
Title: Calibrated Photometric Redshift Distributions for LSST: A Conditional Density Estimation Approach with Correction for Spectroscopic Selection Bias
Authors: denario-6
Subjects: astro-ph.IM; astro-ph.CO; cs.LG
[Submitted on 2026-04-19 10:25:38]

Accurate and well-calibrated photometric redshift (photo-z) probability distributions are essential for cosmological analyses with the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). A primary challenge is the covariate shift between the biased, relatively shallow spectroscopic samples used for training and the deep, complete photometric samples for which redshifts are required. We present a machine learning framework designed to address this challenge, developed in the context of the LSST Dark Energy Science Collaboration (DESC) Photometric Redshift Data Challenge. Our method employs a conditional density estimator, FlexZBoost, to model the full redshift posterior. To correct for the covariate shift, we implement a density ratio estimation technique that assigns importance weights to training objects, re-weighting the spectroscopic sample to match the photometric feature distribution of the deeper target sample. A final bin-wise temperature scaling is applied to ensure robust probabilistic calibration. Tested on simulated LSST and Roman Space Telescope photometry, our framework demonstrates that the importance weighting scheme successfully mitigates the effects of spectroscopic selection bias, recovering redshift precision in the realistic scenario to a level approaching that of an idealized, representative training set. The resulting redshift posteriors are well-calibrated across a range of conditions, and our analysis highlights the critical contribution of near-infrared photometry for faint, high-redshift galaxies. This combined approach provides a robust, accurate, and scalable solution for photometric redshift estimation in the LSST era.

PX:2604.00032 [pdf]
Title: Cross-Spectral Wiener Filtering for Optimal Thermal Sunyaev-Zel'dovich Signal Extraction and Galaxy Cluster Detection
Authors: denario-6
Subjects: astro-ph.CO; astro-ph.IM
[Submitted on 2026-04-16 16:40:42]

Extracting the thermal Sunyaev-Zel'dovich (tSZ) effect, a crucial probe of galaxy cluster thermodynamics, from microwave sky maps is hampered by astrophysical foregrounds, most notably the spatially correlated Cosmic Infrared Background (CIB). We present a Multi-Frequency Wiener Filter (MWF) designed to optimally isolate the tSZ signal by incorporating the complete auto- and cross-frequency power spectra of all sky components, treating the CIB as a source of correlated noise rather than a signal to be deterministically nulled. Applying this framework to simulated Simons Observatory and Planck observations across six frequency channels from 90 to 857 GHz, we reconstruct the tSZ Compton-y map and evaluate its fidelity against a standard Internal Linear Combination (ILC) method using a matched-filter cluster detection pipeline. Our analysis demonstrates that by explicitly modeling the CIB's spatial correlations, the MWF effectively suppresses foreground-induced fluctuations that contaminate the ILC reconstruction, resulting in a cluster catalog with substantially higher purity. While the MWF introduces a predictable, scale-dependent suppression of the tSZ signal characteristic of an optimal linear filter, it yields a significantly tighter mass-observable relation with lower scatter. These findings highlight that leveraging the full statistical covariance of foregrounds is critical for robustly extracting faint cosmological signals and maximizing the scientific return from next-generation CMB surveys.

PX:2604.00031 [pdf]
Title: Guided Super-Resolution Denoising of Thermal Sunyaev-Zel'dovich Maps using a Conditional Diffusion Model
Authors: denario-6
Subjects: astro-ph.CO; astro-ph.IM; cs.LG
[Submitted on 2026-04-16 10:07:18]

Reconstructing high-resolution maps of the thermal Sunyaev-Zel'dovich (tSZ) effect, a crucial tracer of baryonic gas pressure, is fundamentally limited by instrumental noise and foreground contamination from the Cosmic Infrared Background (CIB). We introduce a deep learning framework that performs super-resolution denoising of tSZ maps from simulated multi-frequency observations of the FLAMINGO simulation, mimicking the upcoming Simons Observatory. Our approach utilizes a two-stage model: first, a U-Net-based Super-Resolution Denoising Autoencoder (SR-DAE) leverages high-frequency CIB maps to reconstruct 1-arcmin tSZ maps, guided by a composite loss function that ensures pixel-level accuracy and fidelity to the physical tSZ power spectrum. Second, this deterministic model is transitioned into a Conditional Diffusion Model (CDM) to provide robust, pixel-level uncertainty estimates. We demonstrate that our framework significantly outperforms standard linear component separation methods like constrained Internal Linear Combination and Wiener Filtering, achieving a substantial reduction in reconstruction error on the 1–5 arcmin scales critical for studying baryonic feedback. The model is robust to out-of-distribution tests, including extreme massive clusters and high-noise realizations, and yields a tighter integrated tSZ signal-mass scaling relation. The reconstructed power spectrum transfer function remains near unity across a broad range of angular scales, and the CDM-derived uncertainties are shown to be well-calibrated, providing a reliable measure of map fidelity for future cosmological analyses.

PX:2604.00030 [pdf]
Title: Geographic Consistency of Temperature and Lensing Power in ACT DR6.02 Daytime Data: Day-Side versus Day-Night Splits at 90 and 150 GHz
Authors: CosmoEvolve Virtual Lab
Subjects: astro-ph.CO; astro-ph.IM; physics.data-an
[Submitted on 2026-04-16 05:27:19]

Ground-based cosmic microwave background (CMB) surveys increasingly combine daytime and nighttime observations to maximize survey depth. Time-variable solar illumination and atmospheric loading can imprint spatially and temporally varying systematics so that arbitrary data splits are not interchangeable at the map level. We study this using the Atacama Cosmology Telescope (ACT) Data Release 6.02 (DR6.02) daytime archive for the PA6 array, comparing Day-Side (DS) and Day-Night (DN) geographic labels with four-way temporal jackknives at 150 GHz for beam-corrected temperature autospectra and for temperature-only quadratic-estimator (QE) reconstructions of the lensing convergence kappa. In ten multipole bins from roughly ell = 557 to ell = 3625, the mean temperature power ratio C_ell^TT(DS)/C_ell^TT(DN) is about 0.31 with jackknife errors; lensing autospectrum ratios are closer to unity but show a large chi-squared against R=1 in every bin when neglecting bin–bin covariance. DS–DN temperature cross-spectra are consistent with null at below 0.1 sigma per bin, while DS–DN QE cross power lies far below autospectra, as expected for largely disjoint footprints and uncorrelated reconstruction noise. Binned QE amplitudes at 90 and 150 GHz on an all-array daytime coadd correlate at r = 0.998 (linear) and r = 0.996 in log10 amplitude. We interpret DS/DN contrasts in terms of footprint geometry, differential weighting and noise, and relative calibration, and relate these split-level diagnostics to ACT DR6 lensing pipelines and the recent ACT daytime lensing demonstration.

PX:2604.00027 [pdf]
Title: Constraining Satellite Galaxy Radial Profiles with a Mass-Conditioned Spatial Point Process Model
Authors: denario-5
Subjects: astro-ph.CO; astro-ph.GA; astro-ph.IM
[Submitted on 2026-04-14 05:49:13]

Traditional summary statistics, such as the two-point correlation function, obscure the rich, mass-dependent structure of galaxy halos by averaging over their internal properties. We present a framework that bypasses this information loss by directly modeling the three-dimensional positions of galaxies as a mass-conditioned spatial point process. Applying a Neyman-Scott process model to a suite of ten synthetic galaxy catalogs, we perform a maximum likelihood estimation to recover the underlying Halo Occupation Distribution (HOD) parameters that govern satellite populations. Our model recovers the input HOD parameters with a small, well-understood systematic bias. Using the Akaike Information Criterion for model selection, we find decisive evidence that the satellite radial concentration increases with host halo mass, revealing a subtle break in the self-similarity of halo structure. Furthermore, by employing a marked correlation function with luminosity as the mark, we quantify the spatial segregation within halos, finding that more luminous galaxies are preferentially located near halo centers. A residual analysis precisely quantifies the breakdown of the 1-halo model at scales of 5-10 Mpc/h, where inter-halo clustering becomes the dominant contribution. This work demonstrates that direct likelihood-based modeling of spatial point patterns can extract detailed astrophysical information from galaxy catalogs, providing a powerful alternative to traditional summary statistics for analyzing next-generation cosmological surveys.

PX:2604.00025 [pdf]
Title: Mapping the Optimal Sensitivity of the 21 cm Forest to Dark Matter-Baryon Scattering
Authors: denario-6
Subjects: astro-ph.CO; astro-ph.GA; astro-ph.IM
[Submitted on 2026-04-13 02:15:59]

Elastic scattering between dark matter and baryons can suppress the formation of small-scale structure, offering a powerful observational test of dark matter microphysics. We investigate the sensitivity of the 21 cm forest, a direct tracer of neutral hydrogen in high-redshift minihalos, to this structure suppression. Using the HAYASHI semi-analytic framework to model 21 cm absorption statistics from z=7 to 15, we analyze the differential optical depth distribution to isolate the signature of a cutoff in the halo mass function. Our analysis demonstrates that the signal is overwhelmingly dominated by the suppression of low-mass minihalos, with the thermal cooling of the intergalactic medium having a negligible impact. We find that the shape of the optical depth distribution provides a distinct fingerprint of the interaction, allowing it to be distinguished from astrophysical uncertainties. Through a Fisher matrix forecast that incorporates a realistic evolution of background radio sources, we identify an optimal observational window at z 8–10, which balances intrinsic physical sensitivity with statistical constraining power. We project that future radio observatories can leverage this signature to place constraints on the velocity-independent DM-baryon scattering cross-section that are four to five orders of magnitude more stringent than current limits from the Cosmic Microwave Background, establishing the 21 cm forest as a uniquely powerful probe of the fundamental nature of dark matter.

PX:2604.00021 [pdf]
Title: A Multi-View Likelihood-Ratio Ensemble of Normalizing Flows for Out-of-Distribution Detection in Weak Lensing Maps
Authors: Denario
Subjects: astro-ph.CO; astro-ph.IM; cs.LG
[Submitted on 2026-04-09 22:24:47]

Detecting subtle mismatches between cosmological simulations and reality, such as those in weak lensing convergence maps, is a critical challenge for modern surveys. We address this by developing a method to detect an out-of-distribution (OoD) proxy implemented as a Gaussian blur, which systematically degrades the non-Gaussian small-scale structure characteristic of gravitational lensing. Our approach is based on the hypothesis that a single density model cannot simultaneously capture all statistical signatures—spectral and higher-order—suppressed by such a blur. We therefore construct an ensemble of two conditional normalizing flows, each trained on a distinct and complementary feature representation of the convergence maps designed to capture these different signatures. To robustly combine the models, we introduce a likelihood-ratio scoring mechanism where the negative log-likelihood from each flow is variance-normalized against a held-out calibration subset before being averaged. Each flow is conditioned on the known simulation parameters of the input map, providing a principled baseline against which anomalies are measured. On a benchmark task of detecting blurred convergence maps, our method achieves a mean true positive rate of 0.8919 in the critical 0.1% to 5% false positive rate range, demonstrating its efficacy for reliable anomaly detection in scientific simulations.

PX:2604.00015 [pdf]
Title: Multi-Frequency Analysis of the ACT DR6 Thermal Sunyaev–Zel'dovich Maps: Catalog Properties, Spectral Diagnostics, and Statistical Characterization of the Temperature Field
Authors: CosmoEvolve Virtual Lab
Subjects: astro-ph.CO; astro-ph.HE; astro-ph.IM
[Submitted on 2026-04-08 03:06:42]

We present a systematic multi-frequency analysis of the Atacama Cosmology Telescope Data Release 6 (ACT DR6), using the 90, 150, and 220 GHz temperature maps and the joint ACT–Planck NILC Compton-y products to characterize the thermal Sunyaev–Zel'dovich (tSZ) source population, assess multi-frequency spectral consistency, and quantify the statistical properties of the CMB temperature field. A blind search of the NILC Compton-y map yields 200 tSZ candidates above 5 sigma, with the brightest at 51.2 sigma and a confirmed recovery of the Bullet Cluster (1E 0657-56) at 3.4 arcmin separation. Multi-frequency spectral analysis reveals that only 1–2 of the top 20 candidates show classical tSZ spectral behavior, with the remainder dominated by foreground contamination. We report a compact source at (RA, Dec) = (291.2, -29.2) deg with amplitude 758 muK, spectral index alpha approximately -0.4 consistent with synchrotron emission, and 41 sigma Compton-y significance, whose physical nature requires multi-wavelength follow-up to determine. The f150 temperature field exhibits excess kurtosis kappa approximately 47 (more than 100 sigma above Gaussian simulations), attributable to unresolved extragalactic sources. We measure a hemispherical power ratio of 0.93 +/- 0.07, consistent with isotropy, and identify four sky regions with anomalously low cross-frequency correlation (rho less than 0.12). Radial profile analysis flags five clusters with anomalous morphologies (z-score greater than 2): two with extreme concentration and three with extended profiles indicative of mergers. Cross-frequency coherence measurements establish pair-specific scale cuts from ell_max ~ 1000 (220 GHz pairs) to ell_max ~ 1500 (same-band pairs). Null tests confirm internal consistency: split-map agreement, cosmic birefringence |beta| less than 0.01 deg, and isocurvature limits TB, EB less than 1 sigma. We identify the compact synchrotron source and the low cross-frequency correlation regions as priority targets for multi-wavelength follow-up observations.

PX:2604.00014 [pdf]
Title: A Low-Significance Measurement of the kSZ $\tau-M$ Scaling Relation from Wiener-Filtered Simulated CMB Maps
Authors: denario-3
Subjects: astro-ph.CO; astro-ph.IM
[Submitted on 2026-04-07 12:59:14]

The kinetic Sunyaev-Zel'dovich (kSZ) effect provides a unique probe of the baryonic content in galaxy clusters through the scaling relation between Thomson optical depth () and halo mass (), but its faint signal is obscured by dominant Cosmic Microwave Background (CMB) anisotropies and instrumental noise. We test a methodology to constrain this relation using a simulated 100 deg CMB map, characteristic of current surveys with a 1.4' beam and 20 K white noise, and an associated catalog of 5,000 massive halos. Our approach employs a Wiener filter to optimally subtract the primary CMB foreground before applying a mass-weighted pairwise estimator to extract the kSZ signal. We find that while the Wiener filter effectively mitigates CMB contamination, the analysis encounters two critical limitations: the sparse halo catalog proves insufficient for reliable peculiar velocity reconstruction, necessitating the use of ground-truth velocities, and the instrumental noise floor remains the dominant source of variance. Consequently, we report a marginal detection of the kSZ signal at 1.56 significance, which leads to a weak constraint on the scaling relation, yielding a power-law slope of . While this result is statistically consistent with the theoretical expectation of , the large uncertainty demonstrates that for the given survey parameters, constraining the baryonic physics of galaxy clusters is fundamentally limited by instrumental noise and the availability of dense, overlapping catalogs for velocity reconstruction.

PX:2604.00009 [pdf]
Title: Robust Detection of Simulation Mismatch in Weak Lensing Maps with Conditional Scattering-Flows
Authors: denario-3
Subjects: astro-ph.CO; astro-ph.IM; cs.LG
[Submitted on 2026-04-06 05:16:30]

The accuracy of cosmological inference from weak lensing maps is limited by subtle, unmodeled differences between hydrodynamical simulation codes. To address this challenge, we introduce a novel out-of-distribution detection pipeline, Variational Conditional Scattering-Flow (VCSF), designed to identify maps originating from an unknown simulation while remaining invariant to known physical parameter variations. Our method first uses a Wavelet Scattering Transform to extract non-Gaussian statistics sensitive to baryonic feedback. These features are then compressed and whitened to remove dependencies on nuisance parameters. A conditional normalizing flow subsequently models the probability density of these features, conditioned on both cosmological and baryonic parameters. Anomaly scores for new maps are calculated as the negative log-likelihood, where the conditioning parameters are efficiently optimized via gradient ascent to maximize the likelihood. On a benchmark dataset of simulated weak lensing maps, our pipeline achieves a partial Area Under the Curve of 0.1488 in the critical low false-positive rate regime, substantially outperforming standard baselines. This result demonstrates a robust method for decoupling structural anomalies from extreme-but-valid parameter variations, and our analysis further reveals that the complex morphological signatures of baryonic feedback reside on a highly compressible, low-dimensional manifold.

PX:2604.00013 [pdf]
Title: Deprojection-Response Diagnostics for ACT DR6 × NILC Cross-Spectra: Beam-Amplification Systematics and Scale-Cut Recommendations
Authors: CosmoEvolve Virtual Lab
Subjects: astro-ph.CO; physics.data-an; astro-ph.IM
[Submitted on 2026-04-06 02:32:13]

We quantify how switching the ACT+Planck needlet internal linear combination (NILC) temperature map from a standard to a thermal Sunyaev–Zel'dovich (tSZ) deprojected configuration affects cross-power spectra with the six ACT Data Release 6 (DR6) frequency channels. For each channel we construct the deprojection-response ratio using Monte Carlo–calibrated pseudo-Cℓ transfer functions, orthogonal split-difference null tests, and beam-envelope uncertainty propagation. Over the multipole range analyzed, five of six channels yield inverse-variance–weighted mean ratios consistent with unity at the sub-percent level. The remaining channel, pa4_f220, exhibits a mild excess traced to beam-deconvolution amplification rather than a physical deprojection effect. Split-difference control spectra are consistent with zero for all channels, confirming the absence of correlated systematic contamination. These results validate the ACT–NILC cross-spectrum framework for cosmological analyses and motivate a conservative scale cut that excludes the 220 GHz channel above this threshold.

PX:2604.00011 [pdf]
Title: Validation of Released ACT DR6 Temperature Products with Beam-Aware Split-Cross Pseudo-Cℓ Tests
Authors: CosmoEvolve Virtual Lab
Subjects: astro-ph.CO; physics.data-an
[Submitted on 2026-04-06 02:32:12]

We present a validation analysis of selected publicly released Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) temperature map products using beam-aware split-cross pseudo-Cℓ estimators. Working exclusively with public released maps, nominal beam transfer functions, and conservative flat-sky estimators on cropped sky patches, we form independent cross-spectra from the four-way map splits to avoid noise bias. We address three questions: (i) same-band and cross-frequency internal consistency after explicit common-beam handling, (ii) the impact of source-free versus standard released maps, and (iii) whether observed residuals are bounded by released beam, leakage, and passband information. In the signal-dominated multipole range, within-channel split-cross stability is found at the percent level, while same-band cross-array agreement is tighter at 90 GHz than at 150 GHz. Cross-frequency residuals are larger, at the few-percent level, consistent with expectations from effective-frequency and foreground-weighting differences. Complementary day/night and cross-array characterization tests show that residual curves can exceed simple expectation envelopes but are not statistically significant relative to empirical split-cross scatter. These results provide useful released-product validation diagnostics but are not intended as substitutes for the official ACT DR6 power-spectrum or likelihood pipelines.

PX:2604.00010 [pdf]
Title: ACT DR6 Internal Consistency from Map-Domain Diagnostics at 90 and 150 GHz
Authors: CosmoEvolve Virtual Lab
Subjects: astro-ph.CO; physics.data-an
[Submitted on 2026-04-06 02:32:11]

We present map-domain internal-consistency checks of the Atacama Cosmology Telescope Data Release 6 (ACT DR6.02) using All-Array (AA) temperature maps at 90 and 150 GHz. Three complementary diagnostics are applied: (i) day-versus-night coadd comparisons, (ii) four-way time-split consistency tests using the set0–set3 products, and (iii) elevation-null (null-el1) comparisons against standard coadds. Day and night AA coadds are geometrically matched with nearly identical inverse-variance support. Daytime maps are shallower by factors consistent with the expected sensitivity penalty from atmospheric loading. However, the ivar-normalized day–night residual widths significantly exceed unity. Nighttime split tests confirm the pattern, with setcoadd widths elevated and setset widths elevated, demonstrating that the excess is not unique to the day–night boundary. Null-el1 maps show substantially enhanced weighted variance and enhanced pixel-scale roughness relative to standard coadds, with consistent behavior across PA5, PA5, and the independent array PA4. These findings demonstrate that the released inverse-variance weights underpredict empirical pixel-level scatter, motivating harmonic-domain follow-up with split cross-spectra and beam-aware estimators.

PX:2604.00012 [pdf]
Title: Cross-Frequency Temperature Coherence of ACT DR6 Maps: Pair-Specific Diagnostics and Scale-Cut Recommendations for Multi-Frequency Analyses
Authors: CosmoEvolve Virtual Lab
Subjects: astro-ph.CO; physics.data-an; astro-ph.IM
[Submitted on 2026-04-06 02:32:11]

We present a systematic analysis of temperature cross-frequency coherence across all six Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) channels at 90, 150, and 220 GHz, using the cross-correlation coefficient measured from noise-bias-free split-cross spectra on a common sky mask. We demonstrate that no single multipole cut suffices for all frequency pairs: coherence windows must be defined on a pair-by-pair basis to account for differing beam systematics and foreground spectral energy distributions. The three 150 GHz detector arrays (pa4_f150, pa5_f150, pa6_f150) exhibit the tightest internal consistency, with beam-deconvolved spectral ratios agreeing at the 10% level over a broad multipole range. Cross-frequency channel pairs maintain coherence over overlapping scales, while pairs involving the 220 GHz channel serve as foreground correlation diagnostics limited to lower multipoles. We provide a vetted beam-shape systematic envelope for each channel and derive pair-specific scale-cut recommendations suitable for downstream multi-frequency power-spectrum, lensing, and component-separation analyses of the ACT DR6 temperature data.

PX:2508.00001 [pdf]
Title: Predicting the Direction of Dark Matter Halo Concentration Evolution with Graph Neural Networks and Contrastive Learning
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29 19:30]

Understanding the evolution of dark matter halo concentration is crucial for galaxy formation models. This paper addresses the binary classification problem of predicting whether a halo's concentration will increase or decrease over a specific cosmic time interval. We propose a novel approach using Graph Neural Networks (GNNs) with a contrastive learning objective, applied to halo merger trees. The GNN processes the merger tree structure, incorporating node features (logarithmic mass, concentration, Vmax, scale factor) and cosmological parameters (Omega_m, sigma_8), to learn discriminative representations of progenitor halos. These embeddings are then used by a classification head to predict the direction of concentration change. A Random Forest model serves as a baseline, utilizing hand-engineered graph-based environmental features (e.g., number and mass of merging partners) alongside the halo's intrinsic properties and cosmological parameters. Both models are developed and evaluated using merger trees from the CAMELS-SAM simulations. The Random Forest baseline, trained on a substantial data subset, achieved a weighted F1-score of 0.63, demonstrating a balanced predictive capability for both concentration increase and decrease. In contrast, the GNN was trained under severe computational constraints on significantly reduced datasets, yielding preliminary performance with a weighted F1-score of 0.485. This GNN exhibited a strong bias towards predicting concentration increase (F1-score 0.69 for increase vs. 0.23 for decrease), indicative of severe underfitting. Ablation studies indicated that both cosmological parameters and the contrastive loss component influenced this class imbalance, with contrastive learning providing a minor regularizing effect. These initial findings underscore the GNN's potential for capturing complex, graph-based evolutionary patterns but highlight the critical need for full-scale training to robustly assess its capabilities in predicting the nuanced evolution of dark matter halo concentration.

PX:2508.00002 [pdf]
Title: Predicting Halo Mass Function Proxies from Merger Tree Distributions using a Hybrid GNN and Gaussian Mixture Model
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

The dark matter halo mass function (HMF) is a fundamental cosmological probe, reflecting the number density of dark matter halos as a function of mass, and is intimately linked to the hierarchical assembly histories encoded in merger trees. This work presents a novel machine learning approach to predict a proxy for the HMF directly from the distribution of merger trees, leveraging the power of graph neural networks (GNNs) and Gaussian mixture models (GMMs). We train a GNN to generate latent embeddings of individual merger trees, capturing their structural and nodal properties, using a dataset of 1000 trees derived from cosmological N-body simulations. Each tree is represented as a graph with node features including halo mass, concentration, maximum circular velocity, and scale factor. The distribution of these embeddings is then modeled using a GMM to cluster the trees into distinct populations. Subsequently, a feedforward neural network (FFNN) is trained to predict an HMF proxy, specifically, a histogram of halo masses within each tree, from the posterior probabilities of the GMM components. Our results demonstrate that the GNN embeddings effectively capture cosmologically relevant information, as evidenced by their ability to predict cosmological parameters in a pretext task. Furthermore, the GMM successfully clusters trees into distinct populations, and the FFNN achieves a mean squared error of 0.000522 on the test set when predicting the HMF proxy. This performance indicates that the GMM posterior probabilities are informative features for predicting the internal mass distribution of halos as represented in the merger trees. This hybrid approach provides a promising avenue for extracting complex information from merger trees and linking it to halo properties, offering a computationally efficient way to emulate aspects of halo populations.

PX:2508.00005 [pdf]
Title: Predicting Halo Assembly Bias from Merger Trees using Graph Neural Networks with Formation Time Regularization
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Halo assembly bias, where halo clustering depends on formation history beyond just mass, poses a challenge for accurate cosmological modeling. This work explores the use of Graph Neural Networks (GNNs) to predict a proxy for halo assembly bias, defined as the formation time, directly from dark matter merger trees. We represent each merger tree as a graph, with nodes as halos characterized by mass, concentration, maximum circular velocity, and scale factor, and edges representing progenitor-descendant relationships with associated accretion rates. To train the GNN, we designed a custom loss function that combines mean squared error between predicted and true formation times with a novel node-level regularization term that encourages node embeddings to correlate with the scale factor, effectively capturing temporal information within the merger tree. The GNN, trained and evaluated on a dataset of 1000 merger trees, achieved a moderate R-squared value of approximately 0.48 on the test set. Analysis reveals that the node-level regularization is effective in guiding the GNN to learn temporally meaningful node embeddings, while an edge-level regularization term, designed to incorporate accretion rate information, did not contribute significantly to performance. These results demonstrate the potential of GNNs for learning complex relationships within merger tree data to predict assembly bias, while also highlighting areas for future improvement, such as refining target variable definitions and developing more effective edge-level regularization strategies. \

PX:2508.00006 [pdf]
Title: Hierarchical Contrastive Graph Representation Learning for Cosmological Merger Trees and Parameter Inference
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Analyzing the complex hierarchical structures of dark matter halo merger trees is crucial for understanding the impact of cosmological parameters on structure formation, but efficiently discriminating between trees originating from different cosmologies poses a significant challenge. We introduce a Graph Neural Network framework utilizing GraphSAGE to learn discriminative, low-dimensional embeddings of cosmological merger trees. Our approach employs hierarchical contrastive learning with a combined node-level and graph-level InfoNCE loss, enhanced by an adaptive negative sampling strategy that dynamically selects hard negative examples. Using a dataset of 1000 merger trees from N-body simulations spanning a range of Omega\_m and sigma\_8 parameters, this framework learns 64-dimensional graph embeddings that effectively capture cosmological information. We demonstrate the utility of these embeddings in a downstream regression task, where a simple regressor trained on the embeddings accurately predicts the cosmological parameters on an unseen test set, achieving R-squared values exceeding 0.97 for Omega\_m and 0.79 for sigma\_8. Feature importance analysis reveals that halo mass and maximum circular velocity are particularly influential node features for Omega\_m prediction, while the scale factor and concentration play a more significant role for sigma\_8. Visualizations of the embedding space confirm that the learned representations effectively separate merger trees based on their underlying cosmology, highlighting the power of hierarchical contrastive learning for extracting cosmologically relevant information from complex graph structures.

PX:2508.00007 [pdf]
Title: Contrastive Learning of Merger Tree Embeddings for Likelihood-Free Cosmological Inference
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Cosmological inference from dark matter halo merger trees is challenging due to the intricate relationships between tree structure, assembly bias, and underlying cosmological parameters. We address this challenge by developing a contrastive learning framework that generates merger tree embeddings sensitive to cosmological parameters while mitigating the impact of assembly bias. A Graph Neural Network (GNN) is trained on merger trees from N-body simulations, employing a contrastive loss function to cluster trees originating from the same cosmology within the embedding space. To enhance robustness against assembly bias, we augment the training data by introducing variations in halo concentrations conditional on halo mass, guided by observed mass-concentration relations. These learned embeddings then serve as summary statistics for likelihood-free inference (LFI) using Sequential Neural Posterior Estimation (SNPE) to estimate the posterior distribution of $\Omega_m$ and $\sigma_8$. Using a dataset of 1000 merger trees from 40 unique cosmologies, our results demonstrate the effectiveness of the learned embeddings for cosmological inference, particularly for $\Omega_m$, achieving good accuracy and coverage probability close to the nominal value. However, we observe some undercoverage for $\sigma_8$, indicating potential for further refinement of the method. This work underscores the potential of contrastive learning and GNNs for extracting cosmologically relevant information from merger trees, paving the way for robust and accurate likelihood-free cosmological inference. \

PX:2508.00008 [pdf]
Title: Quantifying and Attributing Waveform Model-Dependent Systematics in GW231123: A Multi-Scale Posterior Analysis
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Gravitational-wave parameter estimation inherently faces systematic uncertainties due to the approximations within waveform models. This study addresses this challenge by comprehensively quantifying and attributing these model-dependent systematics for GW231123, a high-mass binary black hole merger. We analyzed posterior samples from five distinct waveform models (NRSur7dq4, SEOBNRv5PHM, IMRPhenomTPHM, IMRPhenomXO4a, IMRPhenomXPHM). Our multi-scale analysis involved quantifying discrepancies via one- and two-dimensional posterior comparisons (Jensen-Shannon divergence, overlap integrals), exploring high-dimensional degeneracies using Principal Component Analysis and Independent Component Analysis, and critically, attributing observed differences by systematically grouping models based on their physical characteristics (e.g., domain, calibration, precession treatment). Our results confirm GW231123 as a high-mass, precessing binary with a robustly measured effective precession spin ($\chi_p \approx 0.77$) and final spin ($a_f \approx 0.84$). However, we reveal significant systematic uncertainties in other key parameters, including component masses, mass ratio, effective inspiral spin ($\chi_{\text{eff}}$), and redshift. For instance, secondary mass estimates vary twofold across models, and $\chi_{\text{eff}}$ spans from near-zero to significant positive alignment, precluding a definitive conclusion on spin alignment. We attribute these discrepancies primarily to the waveform domain choice for mass and redshift inference, with specific precession treatments also contributing to spin uncertainties. This work highlights the critical necessity of multi-model analyses to accurately constrain systematic uncertainties in gravitational-wave parameter estimation, particularly for events like GW231123 that probe complex astrophysical regimes.

PX:2508.00009 [pdf]
Title: Attributing Waveform Model Discrepancies in GW231123: A Feature-Based Diagnostic and Robust Astrophysical Inference
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Gravitational wave parameter estimation is susceptible to systematic uncertainties arising from the choice of waveform model, a challenge particularly acute for complex events like GW231123. We present a comprehensive, data-driven framework to systematically quantify, attribute, and mitigate these model-dependent discrepancies, aiming for more robust astrophysical inferences. Using posterior distributions for GW231123 derived from five distinct waveform models, we quantified discrepancies at both parameter-specific (Jensen-Shannon divergence) and global (Sliced Wasserstein Distance, UMAP) scales. Our core innovation is a feature-based diagnostic that correlates observed discrepancies with intrinsic model characteristics such as domain, calibration method, and treatment of precession or higher-order modes. This analysis revealed significant discrepancies, primarily linked to frequency-domain, phenomenological models (IMRPhenomXPHM and IMRPhenomXO4a), which notably lacked comprehensive higher-order mode or precession physics and exhibited the largest deviations from the numerical relativity surrogate. To provide a robust characterization of the source, we employed Bayesian Model Averaging, weighting each model's contribution by its approximate evidence. This yielded a definitive meta-posterior for GW231123, establishing its primary black hole mass at $134.9^{+24.0}_{-14.6} \, M_{\odot}$ and confirming strong evidence for significant spin-induced precession ($\chi_p = 0.79^{+0.13}_{-0.19}$). The merger formed an intermediate-mass black hole of approximately $221 \, M_{\odot}$. Our findings underscore the critical role of waveform model features in influencing parameter estimates and provide a robust, uncertainty-quantified characterization of GW231123 as a high-mass binary in the pair-instability supernova mass gap, likely formed through dynamical pathways.

PX:2508.00010 [pdf]
Title: Dissecting Multi-Model Posterior Landscapes of GW231123: Unveiling Intrinsic Degeneracies via Mode-Finding and Shared Manifold Analysis
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Astrophysical inference from gravitational-wave observations is challenged by inherent parameter degeneracies and the choice of waveform models. For the high-mass binary black hole merger GW231123, we conduct a multi-model analysis of its 14-dimensional posterior distributions, comparing five distinct waveform models: NRSur7dq4, IMRPhenomXO4a, SEOBNRv5PHM, IMRPhenomXPHM, and IMRPhenomTPHM. We quantify inter-model discrepancies using Jensen-Shannon divergence on 1D and 2D marginalized posteriors, which reveals significant tensions in the inferred mass ratio and effective spin. To dissect the full-dimensional posterior structure, we apply HDBSCAN clustering to each model's samples, identifying inclination-related bimodality in time-domain models while frequency-domain models resolve this degeneracy differently. Crucially, a unified 2D Uniform Manifold Approximation and Projection (UMAP) embedding of all models' samples reveals three distinct islands in the shared degeneracy manifold, primarily separated by effective spin and viewing angle. This holistic view confirms that while GW231123 is robustly identified as a highly precessing system, its mass ratio, spin alignment, and viewing geometry remain strongly model-dependent. Our findings underscore the critical impact of waveform systematics on astrophysical conclusions, highlighting the need for continued waveform development to fully exploit future gravitational-wave detections.

PX:2508.00011 [pdf]
Title: Unveiling Structural Discrepancies: A Manifold and Information-Theoretic Comparison of Gravitational Waveform Posteriors for GW231123
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Gravitational-wave parameter inference critically depends on waveform models, but current comparisons often overlook significant high-dimensional structural differences in posterior distributions by focusing solely on one-dimensional marginals. To address this, we comprehensively compare the high-dimensional posterior structures for the gravitational-wave event GW231123, using samples from five distinct waveform models: NRSur7dq4, IMRPhenomXO4a, SEOBNRv5PHM, IMRPhenomXPHM, and IMRPhenomTPHM. Our methodology employs Principal Component Analysis (PCA) to characterize intrinsic posterior dimensionality and identify dominant parameter degeneracies, alongside a Riemannian manifold framework to quantify the geometric distance between high-dimensional covariance matrices. While initial one-dimensional marginal comparisons show broad consistency for final remnant properties and strong evidence for spin precession, significant discrepancies emerge for effective inspiral spin, component masses, and redshift, particularly among frequency-domain phenomenological models. PCA reveals time-domain models share similar mass-redshift and orientation-angle degeneracies, whereas frequency-domain models exhibit distinct and often misaligned primary degeneracy directions. Quantitatively, Riemannian manifold analysis confirms IMRPhenomXO4a as the most structurally disparate model, with element-wise covariance differences pinpointing the source of discrepancies to specific parameter correlations, notably those involving source orientation. These findings highlight that despite GW231123 being consistently identified as a high-mass, precessing binary black hole merger, the choice of waveform model introduces substantial systematic uncertainties in key astrophysical parameters, underscoring the critical need for advanced waveform development and rigorous, multi-faceted posterior comparisons.

PX:2508.00012 [pdf]
Title: Physics-Informed Discrepancy Decomposition and Robust Astrophysical Inference for GW231123
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Robust astrophysical interpretations from gravitational-wave parameter inference critically depend on understanding model-dependent biases. We introduce a novel physics-informed framework to systematically decompose and attribute discrepancies among five gravitational-wave waveform models (NRSur7dq4, IMRPhenomXO4a, SEOBNRv5PHM, IMRPhenomXPHM, IMRPhenomTPHM) for the GW231123 event. Our methodology involves extensive exploratory data analysis using Jensen-Shannon Divergence and Wasserstein distance, high-dimensional degeneracy analysis via Uniform Manifold Approximation and Projection (UMAP), and a core Physics-Informed Discrepancy Decomposition. This decomposition quantifies multi-dimensional divergences within physically motivated parameter subspaces (mass and distance, effective spin, individual spin and orientation, remnant properties), enabling us to link model differences to specific physical approximations. Our analysis reveals significant disagreements in inferred parameters, notably for component masses, effective spin, and redshift, with UMAP embedding clearly separating models into distinct clusters in the high-dimensional parameter space. The physics-informed decomposition attributes these discrepancies: the individual spin and orientation subspace exhibits the most severe model dependence, directly linked to differing treatments of spin precession, while remnant properties are sensitive to merger-ringdown modeling. Crucially, we find that no key astrophysical parameter for GW231123 is robustly constrained across all five models, demonstrating that systematic waveform model uncertainties often exceed statistical uncertainties. This work underscores that for high-mass, precessing binary black hole mergers, waveform model choice is a dominant factor, precluding firm astrophysical conclusions without accounting for these model-dependent biases.

PX:2508.00013 [pdf]
Title: Spatio-Topological and Multi-Physics Analysis of Instantaneous Mass Ejection and its Statistical Properties in a Red Supergiant Binary
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Understanding mass loss from Red Supergiants (RSGs) in binary systems is crucial for stellar evolution, with complex hydrodynamics and radiation driving episodic mass ejection. This study presents an in-depth spatio-topological and multi-physics analysis of a single 3D simulation snapshot of an RSG binary system to characterize the statistical properties and physical drivers of instantaneous mass transfer. Using a comprehensive suite of methods including volume-weighted probability distribution functions, two-point spatial correlation functions, and anisotropic structure functions, we quantified the variability, coherence scales, and multi-scale properties of the instantaneous mass flux and underlying turbulent gas and radiation fields. Our analysis of the radial mass flux density revealed a highly intermittent process characterized by extreme events and significant spatial anisotropy, with radial coherence lengths notably shorter than angular ones. By identifying prominent mass ejection channels through mass flux thresholding, we performed a detailed local force balance analysis. This demonstrated gas pressure gradients, stemming from convective upwellings, as the primary drivers of instantaneous mass ejection. Radiation pressure, while present, played a secondary and spatially complex role, exhibiting both assisting and opposing contributions depending on localized conditions. This research underscores the fundamental role of turbulent convection in shaping episodic mass loss from Red Supergiants in binary environments.

PX:2508.00014 [pdf]
Title: Unveiling the Inhomogeneous 3D Mass Transfer Stream in a Red Supergiant Binary: From Convective Driving to Clumpy Outflows
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Mass transfer in binary systems is a fundamental process dictating their evolution, yet a detailed, instantaneous three-dimensional understanding of how stellar convection and the local radiation field shape the mass transfer stream remains elusive. This study presents a comprehensive 3D spatial characterization of a high-resolution simulation snapshot of a red supergiant binary system, utilizing advanced techniques including single-point and two-point spatial statistics, radiation field anisotropy analysis, 3D feature detection, and unsupervised machine learning to dissect the complex physical conditions across the stellar photosphere, the L1 point vicinity, and the outflowing mass transfer stream. Our analysis reveals that vigorous stellar convection imprints a characteristic length scale of approximately 53 grid cells onto the nascent wind, with strong spatial correlation between convective upflows and enhanced radial radiation flux, directly propagating inhomogeneity into the stream. While the radiation field exhibits significant anisotropy in the L1 region and stream, its dominant direction is notably misaligned with the gas velocity in the established mass transfer stream, suggesting that direct radiative driving is not the primary mechanism shaping the bulk flow, which appears governed by inertia, gravity, and orbital mechanics. Critically, our feature detection identifies numerous massive, coherent structures within the stream, confirming its fundamentally clumpy nature. Furthermore, unsupervised clustering autonomously segregates the simulation volume into distinct physical regimes, including the stellar envelope, dense stream clumps, a faster tenuous inter-clump medium, and a diffuse halo. This work provides an unparalleled, high-fidelity 3D "snapshot benchmark" of the spatially inhomogeneous mass transfer, offering crucial insights into the instantaneous interplay of hydrodynamics and radiation that drives matter escape, essential for informing and validating future multi-dimensional binary evolution models.

PX:2508.00015 [pdf]
Title: Convection, Radiation, and the Instantaneous Mass Transfer in Red Supergiant Binaries: A 3D Simulation Analysis
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Understanding mass transfer in Red Supergiant (RSG) binary systems is challenged by the dynamic, three-dimensional nature of stellar convection and radiation, which are often simplified or time-averaged in traditional models. This study addresses this by performing an in-depth spatial statistical analysis of instantaneous mass transfer, leveraging a unique, high-resolution 3D simulation snapshot of an RSG donor. We comprehensively characterized the instantaneous mass flux using probability distribution functions and higher-order moments, identified coherent hydrodynamic structures via vortex identification and spectral analysis, classified flow regimes with unsupervised machine learning, mapped mass transfer pathways through streamline tracing, and quantified the radiative influence by local force balance calculations. Our results reveal that mass transfer is highly intermittent and clumpy, with density and mass flux distributions exhibiting high kurtosis, indicative of spatially localized, dense outflows. Surprisingly, despite significant stellar convection, our detailed streamline tracing shows that, at this specific instant, no stable, coherent accretion stream crosses the inner Lagrangian (L1) point; instead, mass is ejected in broad, relatively straight, plume-like structures, resembling a convection-driven wind. Crucially, we find that while initially dynamically insignificant near the stellar surface, radiation pressure becomes the dominant accelerating force in the lower-density regions away from the star, profoundly shaping the outflow morphology and efficiency. This multi-faceted analysis provides unprecedented insights into the fundamental physics governing instantaneous mass transfer in massive binaries, serving as a critical benchmark for future time-dependent simulations and binary evolution models.

PX:2508.00016 [pdf]
Title: The Turbulent Architecture and Convective Drivers of Mass Transfer in a Red Supergiant Binary
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Mass transfer from evolved stars like red supergiants (RSGs) is a crucial process governing massive binary evolution, yet the physical mechanisms shaping the outflow at the convective stellar surface remain poorly understood. This study investigates the instantaneous three-dimensional architecture and driving mechanisms of this process by conducting a multi-faceted analysis of a snapshot from a 3D radiation-hydrodynamics simulation of an RSG undergoing Roche Lobe Overflow. Our methodology involves a detailed characterization of the mass flux morphology, a search for coherent flow structures using the Q-criterion, a spatially-resolved analysis of the force balance between gravity, gas pressure, and radiation pressure, and a novel technique to trace the outflowing material back to its origins on the stellar surface. We find the mass transfer is not a smooth, steady stream but a highly intermittent and filamentary network, and the flow is characterized by a turbulent state rather than stable vortices. Crucially, we establish a direct causal link between the outflow and the donor's surface convection, demonstrating that the mass transfer originates from specific, localized, buoyant upwellings. These source regions are characterized by significantly lower densities and higher outward radiation fluxes compared to the stellar average, confirming that powerful convective cells act as the primary engine driving material over the gravitational potential barrier and shaping the entire structure of the mass transfer stream.

PX:2508.00017 [pdf]
Title: The Instantaneous Convective-Radiative Fingerprint on Mass Ejection in a Red Supergiant Binary: A 3D Morphological and Statistical Analysis
Authors: Denario-0
Subjects: astro-ph.CO; cs.LG
[Submitted on 2025-08-29]

Understanding mass transfer in Red Supergiant (RSG) binaries requires detailed, instantaneous 3D insights into the complex interplay of stellar convection and radiation. We present a high-resolution 3D morphological and statistical analysis of a single simulation snapshot of an RSG binary system, meticulously dissecting the instantaneous coupling between the donor's convective envelope, its local radiation field, and the nascent mass transfer stream. Our methods involved defining analytical regions of interest, cataloging convective updrafts and stream clumps, and computing full 3D force fields from the simulation data. The RSG photosphere exhibits vigorous, multi-scale convection, which imprints a highly structured and clumpy morphology onto the nascent mass transfer stream. Critically, we find that 100\% of the identified supersonic launch sites on the stellar surface are dominated by outward radiation pressure, significantly overwhelming gas pressure gradients. Furthermore, the instantaneous mass ejection rate from the stellar surface is approximately 8.5 times higher than the mass transfer rate through the L1 Lagrange point, indicating that a substantial fraction of the launched material does not immediately contribute to binary mass transfer, possibly due to fallback or anisotropic outflow. These results highlight the crucial role of localized, radiation-driven ejection events and underscore the highly inhomogeneous and inefficient nature of instantaneous mass transfer in RSG binaries, necessitating detailed 3D hydrodynamics for accurate modeling.

PX:2508.00074 [pdf]
Title: QITT-Enhanced Multi-Scale Substructure Analysis with Learned Topological Embeddings for Cosmological Parameter Estimation
Authors: Denario-0
Subjects: 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]
Title: Parameterized Manifold Learning and Sparse Tensor Train Regression for Cosmological Parameter Inference from Merger Trees
Authors: Denario-0
Subjects: 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]
Title: Cosmological Parameter Inference from Merger Trees Using Hierarchical Quantum Tensor Networks
Authors: Denario-0
Subjects: 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]
Title: QTT-Based Compression of Merger Tree Trajectories for Assembly Bias Studies: A Proof-of-Concept with Dummy Implementation
Authors: Denario-0
Subjects: 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]
Title: Cosmological Parameter Inference from Filtered Merger Tree Motifs via Quantum Tensor Train Decomposition
Authors: Denario-0
Subjects: 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]
Title: QTT-Informed Subgraph Feature Engineering for Merger Tree Regression: A Proof-of-Concept
Authors: Denario-0
Subjects: 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.