LLM embeddings condition generative networks for LHC events, yielding faster convergence, higher quality, and generalization to unseen processes.
The Fast Fourier Transform Telescope
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
We propose an all-digital telescope for 21 cm tomography, which combines key advantages of both single dishes and interferometers. The electric field is digitized by antennas on a rectangular grid, after which a series of Fast Fourier Transforms recovers simultaneous multifrequency images of up to half the sky. Thanks to Moore's law, the bandwidth up to which this is feasible has now reached about 1 GHz, and will likely continue doubling every couple of years. The main advantages over a single dish telescope are cost and orders of magnitude larger field-of-view, translating into dramatically better sensitivity for large-area surveys. The key advantages over traditional interferometers are cost (the correlator computational cost for an N-element array scales as N log N rather than N^2) and a compact synthesized beam. We argue that 21 cm tomography could be an ideal first application of a very large Fast Fourier Transform Telescope, which would provide both massive sensitivity improvements per dollar and mitigate the off-beam point source foreground problem with its clean beam. Another potentially interesting application is cosmic microwave background polarization.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Forecasts show that future 21 cm surveys can deliver moderate constraints on the scale-dependent growth index and HI bias in viable f(R) models.
citing papers explorer
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition generative networks for LHC events, yielding faster convergence, higher quality, and generalization to unseen processes.
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Constraining Scale-Dependent Growth in $f(R)$ Gravity with Future 21 cm Surveys
Forecasts show that future 21 cm surveys can deliver moderate constraints on the scale-dependent growth index and HI bias in viable f(R) models.