Distinguishing a Slowly Accelerating Black Hole by Differential Time Delays of Images
Amjad Ashoorioon; Mohammad Bagher Jahani Poshteh; Robert B. Mann · 2022 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
The finding, in one line
“Such a slow acceleration has no significant effect on the shadow of such supermassive black holes.”
Abstract (excerpt)
Accelerating supermassive black holes, connected to cosmic strings, could contribute to structure formation and get captured by galaxies if their velocities are small. This would mean that the acceleration of these black holes is small too. Such a slow acceleration has no significant effect on the shadow of such supermassive black holes. We also show that, for slowly accelerating black holes, the angular position of images in the gravitational lensing effects do not change significantly. We propose a method to observe the acceleration of these black holes through the gravitational lensing. The
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Leakage and the reproducibility crisis in machine-learning-based science
Negative / Null Result ReportDefining and detecting quantum speedup
Negative / Null Result ReportService robots in hotels: understanding the service quality perceptions of human-robot interaction
Negative / Null Result ReportBoosting methods for multi-class imbalanced data classification: an experimental review
Negative / Null Result ReportFINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: A META‐ANALYSIS
Negative / Null Result ReportThe impact of site-specific digital histology signatures on deep learning model accuracy and bias
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: arXiv
