Engineering Scientific Assistants using Interactive Structured Induction of Programs

We are interested in the construction of software that can act as scientific assistants to domain specialists. It is expected that such assistants will be needed to accelerate the identification of ways to address complex problems requiring urgent solutions. In this paper, our focus is not on a specific scientific problem, but on the software-engineering of such 'science accelerators'. Recent developments in 'No Code' techniques would seem to suggest that scientist can simply hypothesise solutions simply by conversing with a large language model (LLM). However, for complex scientific problems, this seems unlikely given the current state of LLM technology. What does appear feasible is that a software engineer can use LLMs to rapidly construct programs for use by a domain-specialist, including the specialist's requirements expressed in natural language. We propose the design of an interactive form of 'structured' inductive programming in which a software-engineer and an LLM collaboratively construct an 'assistant' for a scientific data analysis. The paper describes a simple implementation called iStrucInd that adapts a '2-way Intelligibility' protocol to implement the interaction between the software engineer and the LLM. We test the tool on two different non-trivial scientific data analysis tasks. Specifically, we compare the system constructed by iStrucInd against systems constructed manually and by Low Code/No Code methods along dimensions of: (a) program performance; (b) program quality; and (c) programming effort. The results show iStrucInd allows a software engineer to develop better programs faster suggesting interactive structured induction can play a useful role in the rapid construction of scientific assistants.

Predicting star formation properties of galaxies using deep learning

Understanding the star-formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditionally, stellar population synthesis models have been used to obtain best fit parameters that characterise star formation in galaxies. As multiband flux measurements become available for thousands of galaxies, an alternative approach to characterising star formation using machine learning becomes feasible. In this work, we present the use of deep learning techniques to predict three important star formation properties -- stellar mass, star formation rate and dust luminosity. We characterise the performance of our deep learning models through comparisons with outputs from a standard stellar population synthesis code.