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Abstract ESA and NASA are moving forward with plans to launch LISA around 2034. With data from the Illustris cosmological simulation, we provide analysis of LISA detection rates accompanied by characterization of the merging massive black hole population. Massive black holes of total mass ∼105 − 1010M⊙ are the focus of this study. We evolve Illustris massive black hole mergers, which form at separations on the order of the simulation resolution (∼kpc scales), through coalescence with two different treatments for the binary massive black hole evolutionary process. The coalescence times of the population, as well as physical properties of the black holes, form a statistical basis for each evolutionary treatment. From these bases, we Monte Carlo synthesize many realizations of the merging massive black hole population to build mock LISA detection catalogs. We analyze how our massive black hole binary evolutionary models affect detection rates and the associated parameter distributions measured by LISA. With our models, we find massive black hole binary detection rates with LISA of ∼0.5 − 1 yr−1 for massive black holes with masses greater than 105M⊙. This should be treated as a lower limit primarily because our massive black hole sample does not include masses below 105M⊙, which may significantly add to the observed rate. We suggest reasons why we predict lower detection rates compared to much of the literature.
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This content will become publicly available on September 30, 2026
Improved Local Indicators of Spatial Association Analysis for Zero-Heavy Crack Cocaine Seizure Data
Local Indicators of Spatial Association (LISA) analysis is a useful tool for analyzing and extracting meaningful insights from geographic data. It provides informative statistical analysis that highlights areas of high and low activity. However, LISA analysis methods may not be appropriate for zero-heavy data, as without the correct mathematical context, the meaning of the patterns identified by the analysis may be distorted. We demonstrate these issues through statistical analysis and provide the appropriate context for interpreting LISA results for zero-heavy data. We then propose an improved LISA analysis method for spatial data with a majority of zero values. This work constitutes a possible path to a more appropriate understanding of the underlying spatial relationships. Applying our proposed methodology to crack cocaine seizure data in the United States, we show how our improved methods identify different spatial patterns, which in our context could lead to different real-world law enforcement strategies. As LISA analysis is a popular statistical approach that supports policy analysis and design, and as zero-heavy data are common in these scenarios, we provide a framework that is tailored to zero-heavy contexts, improving interpretations and providing finer categorization of observed data, ultimately leading to better decisions in multiple fields where spatial data are foundational.
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- Award ID(s):
- 2039862
- PAR ID:
- 10642005
- Publisher / Repository:
- INFORMS
- Date Published:
- Journal Name:
- INFORMS Journal on Data Science
- ISSN:
- 2694-4022
- Subject(s) / Keyword(s):
- exploratory spatial data analysis LISA analysis zero-heavy data local Moran’s I U.S. cocaine seizures
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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