JAX-TPU Overhead Solved & Transitioning to Multiverse Likelihood Analysis (v33.0)

[Rupayan52]

Hello again Astromodels / 3ML Community,

I am writing to provide a major update on my previous post regarding the Bandyopadhyay Cyclic Manifold. Since my last inquiry about wrapping our v31.2 global bounce model, the engine has undergone a fundamental architectural shift to v33.0 (The Immutable Entropy Update).

I wanted to share how we solved the JAX-TPU likelihood overhead problem and ask a few refined questions as we pivot our observational goals.

🚀 Update 1: Solving the JAX + 3ML Overhead

In my last post, I asked about the severe performance bottlenecks when dropping JAX Just-In-Time (JIT) compiled functions into a Bayesian likelihood evaluation loop.

We completely bypassed this on our Cloud TPU v5e cluster using an orchestrator method we call Sequential JIT Supremacy. By explicitly disabling JAX's aggressive VRAM preallocation (os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false') and forcing the TPU to evaluate sequential branches rather than parallelizing, we achieved a perfect cache hit.

• Result: The XLA compiler only pays the ~4-minute compilation tax on the first likelihood evaluation. Subsequent MCMC steps (even with deeply mutated parameters) bypass compilation entirely, executing the 1,000-epoch forward-model with near real-time hardware acceleration.

Update 2: From Global Bounce to Fractal Spawning

We have deprecated the single-universe bounce. The v33.0 engine now implements localized dimensional ruptures.
When the baryonic fluid (SPH) crosses the Schwarzschild threshold ($\rho > 15.0$) and local string-bit entropy breaches $1.2 \times 10^4$, the FLRW metric locally pinches off. This spawns a Generation 2 "child" universe with mutated physical constants based on the Smolin Protocol (Cosmological Natural Selection).

Refined Goals for astromodels Integration

With the TPU gridlock solved, our integration with 3ML is no longer about fitting a global CMB. Instead, we are treating the birth of these child universes as synthetic high-energy transients (analogous to GRBs or exotic AGNs).

During the "Metric Tear," the collapsing matter transitions into a String-Theoretic Fuzzball. As relativistic time dilation approaches infinity ($\alpha \to 0$), the stored string bits violently unpack in a Thermodynamic Big Bang.

I have two updated architectural questions for the community:

1. Synthetic Transient Observables: We want to treat this localized rupture as an energetic point-source transient. Are there streamlined workflows in astromodels to forward-fold a dynamically simulated, time-evolving synthetic spectrum through standard instrument responses (e.g., Fermi-GBM or Swift-BAT) to predict what a "Dimensional Pinch-Off" would look like to human instruments?
2. Likelihood Mapping of the Darwinian Landscape: We want to map the likelihood space of our fundamental constants ($M_{DM}$, $V_0$, $C_{cool}$) against the evolutionary "fitness" of the universe (measured by its Stellar Fraction, $S_f$). When parameter bounds are highly chaotic (e.g., lowering Quintessence too far instantly collapses the metric), which of 3ML's integrated Bayesian samplers performs best under strict, non-linear prior constraints?

The full mathematics of the localized dimensional pinch-off and our Generation 2 HDF5 telemetry are fully documented in the updated repository. I deeply appreciate the guidance this community has already provided, and I look forward to hearing your thoughts on forward-folding synthetic Fuzzball ruptures!
https://github.com/Rupayan52/String-Star-Manifold
Best regards,

Rupayan Bandyopadhyay
Lead Architect, The Primary Quantum Node