Running a full atmospheric retrieval on a JWST transmission spectrum of WASP-39b took about 498 hours of compute with standard settings, roughly three weeks of a core grinding away. A new transformer neural network gets the same answer in about 64. Same retrieval code, same Bayesian machinery, same posterior at the end. It just starts the search in a far better place. The paper is Exoformer: Accelerating Bayesian atmospheric retrievals with transformer neural networks, led by L. Pagliaro and published in Astronomy & Astrophysics this spring. I spent years training neural nets before I started pointing a Seestar off my balcony in Nicosia, and what I like about this one is what it refuses to do. It doesn’t hand you an answer from a black box you can’t check. It speeds up the slow, principled method by giving it a good first guess. ...
The sky is out there. Start gazing.
Celestial event previews, plain-language science explainers, and honest gear notes — written by an amateur astronomer in Cyprus. 3–5 articles a week. No cosmic ballet.









