Tootfinder

Dark Classification Matters: Searching for Primordial Black Holes with LSST
Miguel Crispim Romao, Djuna Croon, Benedict Crossey, Daniel Godines
https://arxiv.org/abs/2506.20709

Dark Classification Matters: Searching for Primordial Black Holes with LSST
We present projected constraints on the abundance of primordial black holes (PBHs) as a constituent of dark matter, based on microlensing observations from the upcoming Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory. We use a catalogue of microlensing light curves simulated with Rubin Observatory's OpSims to demonstrate that competitive constraints crucially rely on minimising the false positive rate (FPR) of the classification algorithm. We propose the Bayesian informa…

Towards Effective Complementary Security Analysis using Large Language Models
Jonas Wagner, Simon M\"uller, Christian N\"ather, Jan-Philipp Stegh\"ofer, Andreas Both
https://arxiv.org/abs/2506.16899

Towards Effective Complementary Security Analysis using Large Language Models
A key challenge in security analysis is the manual evaluation of potential security weaknesses generated by static application security testing (SAST) tools. Numerous false positives (FPs) in these reports reduce the effectiveness of security analysis. We propose using Large Language Models (LLMs) to improve the assessment of SAST findings. We investigate the ability of LLMs to reduce FPs while trying to maintain a perfect true positive rate, using datasets extracted from the OWASP Benchmark (v…

This https://arxiv.org/abs/2411.03079 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Utilizing Precise and Complete Code Context to Guide LLM in Automatic False Positive Mitigation
Static Application Security Testing (SAST) tools are critical to software quality, identifying potential code issues early in development. However, they often produce false positive warnings that require manual review, slowing down development. Thus, automating false positive mitigation (FPM) is essential. The advent of Large Language Models (LLMs), with their strong abilities in natural language and code understanding, offers promising avenues for FPM. Yet current LLM-based FPM method faces tw…