Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.31778

Wavefront-Conditioned Photon Measurements for Exoplanet Spectroscopy: Contrast Limits and Conditional Gains

Slava G. Turyshev
View original source ↗Read source document ↗Export metadata ↓

Abstract

A wavefront-sensor (WFS) prediction can select the photon measurement before detection or condition its likelihood afterward. This matters for reflected-light spectra requiring hundreds of hours, because subtracting residual starlight cannot remove its photon noise. In a two-mode, low-occupation model, isotropically distributed leakage makes planet-mode projection---ideal single-mode injection matched to the planet---the optimal fixed single-photon measurement, including unequal aligned backgrounds and equal Poisson detector-event rates per output. For nonzero leakage, an attainable two-output conditioned measurement is optimal and strictly more informative before differential loss. For an isotropic background, a contrast bound holds for any distribution of recorded leakage directions: relative transmission $0.80$ and relative admitted-time duty $0.90$ limit the Fisher information per scheduled time to $0.81$ of the fixed optimum at equal predictable-leakage and planet contrasts. Haar averaging sharpens this ceiling to $0.78$. The necessary leakage-to-planet ratios for gain are approximately $2.25$ for any direction law and $3.26$ for Haar directions. A concentration-model certificate identifies directional diversity sufficient to beat recalibrated fixed measurements. Independently gated fixed-basis fallback guarantees no loss of continuum-profiled spectral information when rates and costs are known. For the Habitable Worlds Observatory, the candidate regime is predictable, directionally diverse, leakage-dominated residuals, not a general sensitivity improvement.

Explore related subjects

astro-ph.IM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Slava G. Turyshev. 2026-09-24. Wavefront-Conditioned Photon Measurements for Exoplanet Spectroscopy: Contrast Limits and Conditional Gains. https://arxiv.org/abs/2609.31778

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Platonic Universe: Do Foundation Models See the Same Sky?

We investigate when foundation models converge towards shared representations, and how this convergence depends on model capacity, training regime, and model architecture. We take a `science-for-AI' approach, using astronomy as an experimental instrument to test the Platonic Representation Hypothesis and its Aristotelian refinement against an external physical reference. The historical success of astrophysics is evidence that a compact, modality-invariant description of galaxy observables exists, and so representation convergence toward reality should be measurable against the physical parameters astronomers already use. Given this framework, we evaluate eleven foundation model families (spanning classification, self-distillation, joint-embedding prediction, autoencoding, vision-language pre-training, and astro-specific architectures from $\mathcal{O}$(10M)${\to}\mathcal{O}$(10B) parameters) on crossmatched JWST, HSC, and Legacy imagery, and DESI spectroscopy. All models are evaluated frozen, with no astronomy-specific fine-tuning. We probe redshift, stellar mass, and sSFR via linear probes, and local (MKNN) and global (CKA) embedding geometry within families, between modalities, and across architectures. We find that physics performance scales predictably with capacity; probe directions align consistently with expected astrophysical correlations and selection effects; and local (not global) embedding alignment tracks physics performance, including between DESI spectra and HSC imagery---modalities that share essentially no low-level statistics. Our results support the ARH over the strict PRH, demonstrate astronomy's value as an experimental framework for neural representation learning, and suggest that astro-foundation models can build on general-purpose pre-trained architectures, capitalizing on the broader open machine learning community's already-spent computational investment.

astro-ph.IM↗
arXiv2026

nufftcf: Fast Auto- and Cross-Correlation Function Estimation for Irregularly-Sampled Time Series via the Non-Uniform FFT

Estimating the auto-correlation function (ACF) and cross-correlation function (CCF) of sampled series is a standard task in many physics fields, including astronomy and environmental sciences. We present nufftcf, an ACF/CCF estimator designed primarily for irregularly sampled series, numerically consistent with established kernel-weighted definitions and scaling as O(nK) for K requested lags, rather than O(n^2). nufftcf was initially developed to evaluate the Wiener-Khinchin theorem for irregularly sampled data using the Non-Uniform Fast Fourier Transform (NUFFT), through the Flatiron Institute FINUFFT library, with Gaussian and rectangle kernels. An O(n)-per-lag two-pointer scan replaces the naive O(n^2) computation of the effective pair count used to normalize each lag bin. This enabled real-space estimators using the same kernels, providing exact reference implementations with the same O(nK) complexity and, in some cases, lower cost than the NUFFT path. The library also provides dedicated classical-FFT estimators for regularly sampled data. All estimators share a common calling convention. Using synthetic time series, we validate nufftcf for ACF against pastas, a library used in groundwater time-series analysis, and for CCF against pyZDCF, used in astronomical time-series analysis. Benchmarks confirm O(nK) scaling for the NUFFT and real-space nufftcf estimators, compared with O(n^2) for the pastas slotting technique. nufftcf is already advantageous at moderate series lengths because of its low millisecond-scale overhead. For regularly sampled data, the FFT path further reduces computational cost. We also demonstrate nufftcf on a simulated ground-based stellar light curve combining quasi-periodic rotation, correlated flicker noise, seasonal sampling gaps, and heteroscedastic measurement errors. Notebooks and scripts are provided to reproduce the examples and explore other use cases.

astro-ph.IM↗
arXiv2026

The best of all worlds: gravitational-wave transient detection with multiple pipelines

The search for gravitational-wave transients (modeled and unmodeled) as performed by the LIGO-Virgo-KAGRA collaborations and the broader community typically involves multiple detection algorithms, often redundant and sometimes complementary. We here address the problem of combining such detection algorithms in order to maximize the noise vs signal discrimination power of such an astrophysical search that utilizes multiple analysis pipelines. Using a machine learning approach, we construct a neural network-based classifier that utilizes information (or lack thereof) from all participating pipelines in the search for compact binary coalescences in LIGO-Virgo-KAGRA's realtime (and offline) processing of gravitational-wave data. We benchmark the method with simulated (astrophysical) signals and real noise from the instruments and obtain receiver operating characteristic curves to quantify its performance. We show how this leads to an effectively new, combined pipeline that outperforms any single pipeline alone or other logical combinations of them. This is particularly critical for real-time operations of the LIGO-Virgo-KAGRA detectors as such combination of detection methods can lead to increased robustness in identifying astrophysical sources and to reduction of the number of false alerts that may otherwise be sent out as astronomical telegrams. Our method also aims in simplifying the presentation of astrophysical results out of the LIGO-Virgo-KAGRA detectors by combining all participating pipelines in the astrophysical searches as well as optimizing resources by the broader multi-messenger astrophysics community in following up astronomical alerts for gravitational-wave transient events.

astro-ph.IM↗