Eli Sanchez

MIT Security Studies Program

Zoom link: https://mit.zoom.us/j/93818875228

Abstract

Ballistic missile submarines (SSBNs) have long been considered invulnerable to disarming strikes due to their weak detectable signatures and the large areas within which they could hold an adversary’s high value assets at risk. Defeating a fleet of highly sophisticated SSBNs before they can launch retaliatory strikes would require a massive, coordinated search effort that is widely believed to be far beyond the capabilities of modern national actors. For this reason, SSBNs have long been considered highly secure second strike forces, and thus a strong deterrent against initiating nuclear war.

However, for roughly the past decade a growing number of analysts have suggested that several strands of emerging technology may soon conspire to render SSBNs vulnerable to disarming strikes. These technologies include novel sensing modalities; uncrewed autonomous vehicles; and advanced computing techniques, particularly artificial intelligence (AI). These analysts worry that such technologies will undermine the foundations of nuclear deterrence, with immense implications for geopolitical security.

This study conducts a broad survey of the sensing technologies most often cited in this discourse. It provides high-level discussions and order-of-magnitude analyses of the relevant technical aspects of each to assess their potential to enhance SSBN detection. The technologies considered in the study include quantum sensors, artificial intelligence (AI), space-based sensors, advanced acoustic sensors, and laser detection (light detection and ranging, or LiDAR).

It is concluded that most of these technologies will not offer any meaningful improvement in SSBN detection, either because they do not address current technical limitations (as in the case of quantum sensors and advanced acoustic sensors) or they would require technological advances far exceeding what appears plausible in the foreseeable future (as in the case of space-based sensors and LiDAR). It is found that the most promising approach to enhancing SSBN detection is the application of AI algorithms to passive sonar signal processing. However, the capabilities that have been demonstrated by the relevant AI algorithms in the unclassified literature are suggestive of evolutionary, rather than revolutionary, advances in SSBN detection.

Bio

Eli Sanchez grew up in Smithville, TX, a small town roughly midway between Houston and Austin. He received his bachelor’s degree in Chemistry with a minor in Physics from the University of Texas at Dallas. He then worked for a year at Oak Ridge National Laboratory, where he used computational models to study the effects of radiation exposure on the human body, before beginning his PhD in the Nuclear Science and Engineering Department at MIT. His doctoral research assessed the implications of hypersonic boost-glide weapons for great power strategic stability. He is now a postdoctoral Nuclear Security Fellow at the Security Studies Program at MIT where he studies the potential for emerging technologies to increase the vulnerability of ballistic missile submarines.