The universe speaks in the language of gravitational waves — ripples in the fabric of spacetime itself. When two neutron stars, the ultradense remnants of long-gone supernovae, merge in a final dance, they emit a long, frequency-rising 'chirp.' Neutron stars, first predicted by Fritz Zwicky, sometimes reveal themselves as pulsars — blinking radio beacons, discovered by Jocelyn Bell Burnell. Catching this whisper amid the roar of LIGO and Virgo detectors, built through the efforts of generations of scientists including Rainer Weiss, is a task that for decades relied on sophisticated mathematical filters. But classical methods have an Achilles' heel: speed. While algorithms compute template matches, precious seconds slip away: each one potentially costing the loss of the electromagnetic afterglow, a flash that reveals the secrets of nuclear matter governed by the Standard Model. This is where the Aframe neural network steps in, armed with a concept borrowed from radio engineering.

The key to speed is heterodyning, a method familiar to anyone who has listened to FM radio. Just as a radio receiver mixes an incoming signal with a local oscillator frequency to isolate a desired station, Aframe 'cleans' detector data of fast oscillations by tuning to the system's expected chirp mass. This quantity, \(\mathcal{M} = \frac{(m_1 m_2)^{3/5}}{(m_1+m_2)^{1/5}}\), conducts the entire merger: it determines how quickly the frequency rises. A hundred such 'radio channels' simultaneously comb through the noise, and at the moment of merger the energy flares in a narrow window. A residual convolutional network analyzes just a one-and-a-half-second snippet, and delivers a verdict. The entire chain, from raw data to alert, fits within 4.7 seconds, comparable to the time it takes light to travel one and a half million kilometers. And thanks to the finite speed of light, the signal delay between separated detectors allows pinpointing the source's position in the sky.

Results are staggering: in independent tests, Aframe detected 671 signals at the same false-alarm rate as the traditional pipeline, which found 528. For a canonical pair of 1.4-solar-mass neutron stars, the sensitive volume increased to 120 megaparsecs, and for more massive systems the neural network outright outperformed classical methods. And all this — without tuning to specific detector noise, without simplifying assumptions about stationarity. A model trained on real data retains its knack for months, requiring no retuning. Thus machine learning ceases to be a toy on the sidelines of gravitational astronomy and becomes a full-fledged participant in the hunt for celestial treasures.

This work is more than a technical feat. It cracks open the bottleneck of multimessenger astronomy. When the neural network outputs merger coordinates within 5 seconds, telescopes can slew in time to catch a kilonova — a cosmic crucible where gold and platinum are forged. That brings us closer to solving the equation of state of nuclear matter at densities unattainable in labs. In the future, a unified neural network detector will catch all types of mergers, from black holes to mixed systems, and future observatories like Einstein Telescope will likely embed such algorithms into their sensitive electronics. The universe's radio receiver, tuned to the whisper of neutron stars, is already turned on, and its speakers promise to sound ever louder.