DOI: 10.57760/sciencedb.24650
title: The CUSUM-based Method and Its Products for Traversing Extinction Jump Points Along the Line of Sight
title: The CUSUM-based Method and Its Products for Traversing Extinction Jump Points Along the Line of Sight

description: CUSUM is a type of control chart designed to detect deviations from a target value and monitor the mean of a process. The concept of CUSUM is remarkably straightforward, making it a versatile tool for various applications.For the first time, our study applies the CUSUM-based jump point detection (JPD) method to identify extinction jump points along the line of sight. Through comprehensive validation using mock data combined with Gaia parallax and extinction data, we demonstrate that this method can effectively detect weak extinction jump points caused by interstellar dust in distance–extinction (D-A) diagrams, even in complex regions near the Galactic disk containing multiple overlapping jump features. The key scientific outcomes of this work include:(1) tables of extinction components (from which the 3D extinction maps and distances of molecular clouds can be derived), (2) all-sky dust maps, and (3) scripts for simple implementations of this algorithm. It should be noted that these products are also based on some publicly available catalogs, such as Gaia DR3 parallax and G-band extinction data, V-band extinction values obtained by applying the StarHorse algorithm to the Gaia EDR3 and other catalogs (SHEDR3), and the parameters of 220 million stars calculated by Zhang et al. (2023) using Gaia XP spectra. The algorithm's implementation is intentionally designed with simplicity and flexibility in mind, allowing for straightforward customization to meet diverse research needs. As such, we have opted not to provide a dedicated interface/installation package. The script is developed in Python, leveraging NumPy's efficient numerical computation capabilities for the core jump point detection functionality.​​System Requirements:​​To execute the script, the following Python packages must be installed:​​Core Dependencies​​: Python 3.x, NumPy​​Recommended Packages​​: SciPy (for advanced calculations), Matplotlib (for visualization), AstroPy (for astronomical computations)
description: CUSUM is a type of control chart designed to detect deviations from a target value and monitor the mean of a process. The concept of CUSUM is remarkably straightforward, making it a versatile tool for various applications.For the first time, our study applies the CUSUM-based jump point detection (JPD) method to identify extinction jump points along the line of sight. Through comprehensive validation using mock data combined with Gaia parallax and extinction data, we demonstrate that this method can effectively detect weak extinction jump points caused by interstellar dust in distance–extinction (D-A) diagrams, even in complex regions near the Galactic disk containing multiple overlapping jump features. The key scientific outcomes of this work include:(1) tables of extinction components (from which the 3D extinction maps and distances of molecular clouds can be derived), (2) all-sky dust maps, and (3) scripts for simple implementations of this algorithm. It should be noted that these products are also based on some publicly available catalogs, such as Gaia DR3 parallax and G-band extinction data, V-band extinction values obtained by applying the StarHorse algorithm to the Gaia EDR3 and other catalogs (SHEDR3), and the parameters of 220 million stars calculated by Zhang et al. (2023) using Gaia XP spectra. The algorithm's implementation is intentionally designed with simplicity and flexibility in mind, allowing for straightforward customization to meet diverse research needs. As such, we have opted not to provide a dedicated interface/installation package. The script is developed in Python, leveraging NumPy's efficient numerical computation capabilities for the core jump point detection functionality.​​System Requirements:​​To execute the script, the following Python packages must be installed:​​Core Dependencies​​: Python 3.x, NumPy​​Recommended Packages​​: SciPy (for advanced calculations), Matplotlib (for visualization), AstroPy (for astronomical computations)