# Rapid Detection and Inference of Extreme-Mass-Ratio Inspirals in LISA: Divide and Conquer

- Institution: CERN
- Speaker: Rahul Srinivasan
- Year: 2026
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3025586)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Extreme-mass-ratio inspirals (EMRIs) are key gravitational-wave sources for the Laser Interferometer Space Antenna (LISA), but their detection and parameter inference are computationally challenging due to the extreme concentration of posterior distributions within vast prior volumes. In this work, we introduce a novel divide-and-conquer strategy that reformulates global inference as a hierarchical identification problem. Our approach iteratively localizes the posterior mode through a coarse-to-fine procedure based on ordinal classification, progressively restricting the parameter space while preserving the true signal region. A transformer-based neural network is trained at each stage to identify the most probable parameter subregions, enabling exponential reduction of the search volume with only a few refinement steps. Once the parameter space is reduced to the Fisher-information scale, standard local sampling methods efficiently recover the full joint posterior. We demonstrate that this method achieves rapid and accurate intrinsic parameter estimation for EMRIs in simulated LISA data, dramatically reducing computational costs compared to traditional global sampling techniques. This framework provides a scalable and efficient pathway for real-time EMRI detection and inference in the LISA era.

00:00:00 Slide 1

00:00:17 Slide 2

00:00:32 Slide 3

00:02:01 Slide 4

00:02:37 Slide 5

00:03:09 Slide 6

00:03:25 Slide 7

00:04:13 Slide 8

00:04:34 Slide 9

00:05:33 Slide 10

00:06:11 Slide 11

00:07:13 Slide 12

00:07:47 Slide 13

00:08:22 Slide 14

00:09:15 Slide 15

00:10:14 Slide 16

00:10:43 Slide 17

00:11:09 Slide 18

00:11:22 Slide 19

00:12:24 Slide 20

00:12:39 Slide 21

00:13:05 Slide 22

00:17:26 Slide 23

## Lectures

1. [Rapid Detection and Inference of Extreme-Mass-Ratio Inspirals in LISA: Divide and Conquer](https://invisiblenetwork.ai/learn/cern/3025586/eed0ae03444c), 18 min (CERN's player)