Batched matched filtering and cross-correlation over large template banks are a primary computational cost in gravitational-wave astronomy, seismology, and radio transient searches. Standard pipelines pair a general-purpose inverse Fast Fourier Transform (FFT) with separate multiplication and peak-finding kernels, writing intermediate spectra and full correlation series to memory for every template. We fuse the conjugate product into the first inverse-transform butterflies and the peak reduction into the last so only candidate peaks leave processor registers, and tile batches in 2D so $K^2$ correlations reuse $2K$ cached spectra. This shifts filtering from a DRAM-bandwidth-bound to an on-chip cache-bandwidth-bound regime. In some situations, the signal-to-noise ratio (SNR) is concentrated toward lower frequencies while higher frequencies dominate the transform length. We develop a hierarchical screening method that exploits this via a sequence of short low-frequency transforms before evaluating the full band, deriving stage thresholds from the spectral power profile and matched-filter noise autocovariance to target a specified false-dismissal probability. We demonstrate these methods in the open-source matchedfilter library across eight CPU architectures and two integrated GPUs (AMD Radeon 8060S via Vulkan and Apple M2 via Metal). On an AMD Ryzen AI Max+ 395 at $N=4096$, fused peak-only filtering is $1.4\times$ faster than a standalone AOCL-FFTW inverse transform on one CPU core and $6.2\times$ faster than rocFFT on its Radeon 8060S GPU ($3.1\times$ over MLX FFT on the M2 GPU). For a fiducial gravitational-wave search ($ρ_\mathrm{th}=5.5$, $10^{-2}$ false-dismissal target), hierarchical screening yields a $26\times$ speedup over AOCL-FFTW on CPU, $21\times$ over rocFFT on the 8060S GPU, and $19\times$ over MLX FFT on the M2 GPU.
LLM-based peer review is now used at production scale, but its evaluation remains limited. We introduce a benchmark that injects objective, localisable errors of four types (general issues, mathematics, numerics, citations) at three severity levels into real papers and measures each reviewer's detection rate by error type and severity, together with its false-positive rate on clean copies. We also present Skepthical, a multi-agent reviewer that verifies citation-backed claims by retrieving the cited papers, mathematics with computer algebra, and numerical claims with executed code. We apply the benchmark to two corpora of 15 recent arXiv preprints each, one in astrophysics and one in machine learning (ML), injecting a total of 357 errors, and evaluate twelve reviewer systems: six single-LLM reviewers, two Claude Code configurations, three publicly available reviewers, and Skepthical. The choice of model has the largest impact on detection rate: given the same prompt, three frontier LLMs differ by about 50 percentage points in total detection on both corpora. Tools and scaffolding help when they contribute what the model cannot supply on its own. In particular, on astrophysics, Claude Code improves on a single Claude call in mathematics and numerics detection, and enforced citation retrieval gives Skepthical the highest citation error detection rate of all systems by a large margin. This citation lead, however, is not observed on ML, where the models appear to know the literature better and Skepthical's citation detection rate is statistically indistinguishable from that of the best single LLM. False-positive rates are below 10% for most systems, but vary with prompt, model, and scaffolding, with mathematics flags the main source of false positives. The evaluation pipeline, the injected errors, and the adjudication logs are released as a benchmark.
The optical continuum variability of quasars is often described by a damped random walk (DRW) model, with characteristic damping timescales $τ_{\rm DRW}$ of hundreds of days and amplitudes $σ_{\rm DRW}$ of $\mathcal{O}(0.1)$ magnitudes. This model has been widely used as a null hypothesis to assess the significance of periodic signals in quasar light curves in the search for supermassive black hole binaries, the significance of extreme quasar variability like tidal disruption events (TDEs) and changing-look (CL) quasars, or to model time lags in reverberation mapping. However, the DRW model is known to be incomplete, and an incorrect null hypothesis can lead to biased significance estimates. We investigate the impact of such null-hypothesis mis-specification on the detection of periodic signals in synthetic quasar light curves and introduce the generative model Nullasar, capable of learning quasar variability directly from time-series data. We demonstrate that Nullasar, trained with flow matching, can learn delicate statistical properties of stochastic light-curve variability without explicitly assuming any noise model. We further demonstrate that Nullasar can learn correlated distributions of noise parameters and generate new light-curve realizations from a range of deviations from the Gaussian DRW noise model, approximating parametric descriptions of stochastic processes. When used as a null hypothesis for detecting periodic signals in synthetic light curves, Nullasar performs comparably to fitting the correct DRW model to each light curve, and outperforms parametric fitting when the variability follows a more flexible AR(2) model. Finally, we identify practical limitations, including sensitivity to contamination of the training set by non-null signals, and show that projecting the conditioning vector of the model onto its leading principal components largely mitigates it.
Foundation models are increasingly used across the sciences, but whether their internal computations correspond to known scientific operations or proceed through different mechanisms remains unclear. Understanding these mechanisms is essential for assessing reliability, identifying failure modes, and learning new science from the models. We present, to our knowledge, the first causal mechanistic interpretability study of an astronomy foundation model, AION-1, focusing on its multimodal advantage in redshift prediction, where combining imaging with photometry outperforms photometry alone. We find that this advantage is largest for spatially extended and dust-attenuated galaxies, and that most of it is localized to the first of the encoder's twelve blocks. Within that block, how well a property is linearly decodable does not predict whether it can be localized. Spatial extent is causally used, and one attention head dominates linear probing for it, yet ablating that head has negligible effect on redshift prediction: the information is distributed across heads and is decodable again one layer after we erase it. Dust attenuation shows the opposite pattern: it is only weakly decodable anywhere in the encoder, yet a single head is necessary for most of the multimodal advantage, particularly for the most dust-attenuated galaxies, and keeping that head as the only active head in its block preserves nearly all of the advantage. None of the astrophysically motivated operations we tested explains what this head computes. Whether this reflects a limitation of current interpretability methods or a computation that does not correspond to any known astrophysical operation remains open, and resolving it is a prerequisite for using interpretability as a tool for scientific discovery.