A method, system and readable storage medium for searching for white dwarf stars using a multicolor photometric system

Abstract

This invention provides a method, system, and readable storage medium for searching white dwarfs using a multicolor photometric system, comprising: S1: acquiring a multicolor magnitude table containing extinction-corrected magnitudes for the u, g, and i bands; S2: calculating the color index g-i and composite color index (u-g)-1.5(g-i) for each star based on the multicolor magnitude table; S3: marking the positions of all stars on a two-color map with g-i as the abscissa and (u-g)-1.5(g-i) as the ordinate; S4: selecting stars falling within a pre-defined white dwarf distribution region as white dwarf candidates. This invention analyzes SAGES multicolor photometric data to find an optimal color combination, constructs a two-color map, and delineates exclusive distribution regions for white dwarfs, thereby achieving rapid and high-purity screening of white dwarfs from large-sample survey data.

Description

Technical Field

[0001] This invention relates to the field of astronomical object classification technology, and in particular to a method, system, and readable storage medium for searching for white dwarfs using a multicolor photometric system. Background Technology

[0002] White dwarfs are the final fate of approximately 95% of stars in the Milky Way, and large-scale statistical studies of them are crucial for understanding the structure and evolution of the Milky Way. Traditional white dwarf search methods primarily rely on proper motion or parallax information obtained from spectroscopic observations or high-precision astrometry. However, spectroscopic observations are extremely time-consuming and difficult to apply to large-scale sky surveys; while methods based on proper motion and parallax are limited by the accuracy of astrometry and the long time span. Existing general-purpose photometric systems (such as SDSS ugriz) can provide multicolor information, but their color separation is limited when distinguishing white dwarfs from other compact objects (such as subdwarfs), resulting in low search efficiency and purity. Therefore, there is an urgent need for a new method that does not rely on spectra and proper motion / parallax, but can achieve efficient and high-purity white dwarf searches solely through photometry.

[0003] The use of SAGES multicolor photometry for white dwarf searching is an independent method that does not rely on traditional criteria. In the early stages of this work, Gaia DR2 was used for white dwarf determination, which utilized the parallax provided by Gaia. Other current white dwarf determination methods, such as LAMOST spectroscopy, require spectroscopic observations. This invention does not rely on parallax or spectroscopic observations; it is a purely photometric method for white dwarf determination.

[0004] Therefore, it is necessary to study a method, system, and readable storage medium for searching white dwarfs using a multicolor photometric system to address the shortcomings of existing technologies and to solve or mitigate one or more of the aforementioned problems. Summary of the Invention

[0005] In view of this, the present invention provides a method, system and readable storage medium for searching white dwarfs using a multicolor photometric system. By analyzing SAGES multicolor photometric data, an optimal set of color combinations is found, a two-color map is constructed, and the exclusive distribution area of ​​white dwarfs is delineated, thereby achieving rapid and high-purity screening of white dwarfs in large-sample sky survey data.

[0006] On one hand, the present invention provides a method for searching for white dwarfs using a multicolor photometric system, the method comprising the following steps: S1: Obtain a multicolor magnitude table containing the star to be measured, wherein the multicolor magnitude table contains extinction-corrected magnitudes in the u, g, and i bands; S2: Based on the multicolor magnitude table, calculate the color index gi and composite color index (ug)-1.5(gi) for each star to be tested; S3: Mark the positions of all the stars to be measured on a two-color graph with gi as the horizontal axis and (ug)-1.5(gi) as the vertical axis; S4: Based on the pre-defined white dwarf distribution region, select the stars to be tested that fall within the region as white dwarf candidates.

[0007] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the pre-defined white dwarf distribution region in S4 specifically includes: Using astrometric data and Hertzsprung-Russell diagrams, the stellar samples were divided into known white dwarf groups and non-white dwarf groups; The known white dwarf groups and non-white dwarf groups are matched with the polychromatic magnitude table to obtain the corresponding polychromatic magnitude data; On the two-color diagram, the known white dwarf groups and non-white dwarf groups are drawn using different markers; Analyze the distribution characteristics of white dwarfs and delineate the region that separates white dwarfs from non-white dwarfs as the pre-delineated white dwarf distribution area.

[0008] In addition to the aspects and any possible implementations described above, a further implementation is provided in which, in step S4, the stellar sample is divided into known white dwarf groups and non-white dwarf groups using astrometric data combined with Hertzsprung-Russell diagram positions. Specifically, this includes: Acquire color index, magnitude, and parallax data from astrometric data samples; Hertzsprung-Russell diagrams were plotted based on the color index, magnitude, and parallax data. The known white dwarf groups and the non-white dwarf groups are determined based on the positions of the stars on the Hertzsprung-Russell diagram.

[0009] In addition to the aspects described above and any possible implementation, a further implementation is provided in which the white dwarf distribution region specifically includes: determining a large-scale region and determining a small-scale region located in the upper right corner of the large-scale region.

[0010] In addition to the aspects described above and any possible implementation, a further implementation is provided, wherein determining the small region located in the upper right corner of the large region specifically includes: defining a triangular region as the small region based on the distribution on the duochrome image; and incorporating the triangular region into the white dwarf distribution region.

[0011] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the multicolor magnitude table specifically includes: matching the u and vs band data of the first survey data with the g, r, and i band data of the second survey data; and performing extinction processing on the matched data to obtain the extinction-corrected magnitudes of the u, g, and i bands.

[0012] In addition to the aspects described above and any possible implementation, a further implementation is provided in which the calculation of the color index gi and composite color index (ug)-1.5(gi) for each star to be tested in S2 specifically includes: for each star to be tested, obtaining the u, g, and i band magnitudes from the multicolor magnitude table; calculating the gi as the g band magnitude minus the i band magnitude; and calculating the (ug)-1.5(gi) as the u band magnitude minus the g band magnitude minus 1.5 times the gi.

[0013] In addition to the aspects described above and any possible implementations, a further implementation is provided in which the method for searching for white dwarfs using a multicolor photometric system is based on the SAGES multicolor photometric system and is performed by constructing a specific color-color map.

[0014] As described above and in any possible implementation, a system for searching for white dwarfs using a multicolor photometric system is further provided, for use in the method of searching for white dwarfs using a multicolor photometric system, wherein the system for searching for white dwarfs using a multicolor photometric system comprises: Data acquisition module: used to acquire a multicolor magnitude table containing the star to be measured, the multicolor magnitude table including extinction-corrected magnitudes in the u, g, and i bands; Data calculation module: Based on the multicolor magnitude table, calculate the color index gi and composite color index (ug)-1.5(gi) for each star to be tested; Position calibration module: Mark the positions of all stars to be measured on a two-color graph with gi as the horizontal axis and (ug)-1.5(gi) as the vertical axis; Star identification module: Based on the pre-defined white dwarf distribution region, select the stars to be tested that fall within the region as white dwarf candidates.

[0015] In addition to the aspects described above and any possible implementation thereof, a computer-readable storage medium is further provided, wherein program code is stored therein, which can be invoked by a processor to execute the method of searching for white dwarfs using a multicolor photometric system.

[0016] Compared with the prior art, the present invention can achieve the following technical effects: 1. High efficiency: This method is a pure photometric method, which can cover all targets in the entire field of view in a single observation, greatly improving the speed compared to the spectral method.

[0017] 2. High hit rate: Through optimized color combination (gi) vs. (ug)-1.5 (gi), the separation between white dwarfs and non-white dwarfs is good, and the hit rate of candidate bodies in the defined area is as high as 84.5% or more.

[0018] 3. No selection effect: It does not depend on parallax or specific spectral features, and is applicable to the search for white dwarfs of various distances and types, avoiding the selection bias of traditional methods.

[0019] Highly applicable: It is particularly suitable for large-scale sky survey projects such as SAGES, and can quickly extract tens of thousands of high-quality white dwarf candidates from massive amounts of data, providing a valuable sample library for subsequent spectroscopic identification and scientific research.

[0020] Of course, any product implementing this invention does not necessarily need to achieve all of the technical effects described above at the same time. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is an embodiment of the present invention providing an optimal color-color map for searching for white dwarfs; Figure 2 This is a flowchart of a method for searching for white dwarfs using a multicolor photometric system, provided by an embodiment of the present invention. Detailed Implementation

[0023] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0025] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0026] This invention provides a method, system, and readable storage medium for searching white dwarfs using a multicolor photometric system. The embodiments of this invention are described in detail below with reference to specific implementation methods to make the objectives, technical solutions, and advantages of this invention clearer. It should be noted that the following embodiments are only used to explain the technical solutions of this invention and are not intended to limit the scope of protection of this invention.

[0027] The method provided in this invention aims to efficiently search for white dwarf candidates through multicolor photometric data and duochromatic image analysis. The core of this method lies in constructing a duochromatic image using a specific color combination gi and (ug)-1.5(gi) from the SAGES (Stellar Abundance and Galactic Evolution Survey) multicolor photometric system. This allows for pure photometric screening by pre-delineating exclusive distribution regions for white dwarfs. This approach clearly distinguishes itself from methods relying on parallax spectroscopy or general photometric systems with insufficient separation, requiring only extinction data in the u, g, and i bands to process massive survey samples.

[0028] By designing a composite color index (ug)-1.5 (gi) to capture the unique spectral characteristics of white dwarfs and improve the separation, and by matching Gaia Hertzsprung-Russell diagram samples with SAGES data to define the region, a large-sample search without selection effects is achieved.

[0029] like Figure 2 As shown, the overall process includes steps such as acquiring multicolor magnitude data, calculating color indices, locating star positions on a two-color map, and screening candidate stars based on predetermined regions. The following will describe this process in detail, following the logical order of the technical implementation.

[0030] Step S1 involves obtaining a multicolor magnitude table containing the magnitudes of the stars to be measured. This table includes extinction-corrected magnitudes in the u, g, and i bands. Specifically, the stars to be measured refer to celestial objects that need to be classified and identified. The data for these stars comes from photometric data acquired during astronomical observation projects. The multicolor magnitude table is a table compiled based on photometric measurements in different bands, containing the magnitude values ​​of each star in the u, g, and i bands. These magnitude values ​​have undergone extinction correction, taking into account the absorption and scattering effects of interstellar dust on light, thus obtaining values ​​closer to their true brightness. Obtaining the multicolor magnitude table can be achieved by accessing publicly available astronomical databases or by collecting and processing data using specific astronomical observation equipment. For example, relevant information can be extracted from datasets of large-scale sky surveys to ensure data integrity and accuracy.

[0031] In one embodiment, when obtaining a multicolor magnitude table, the target sky region, i.e., the region of the sky containing the star to be measured, must first be determined. For this region, a star catalog containing photometric data in the u, g, and i bands can be downloaded from an existing astronomical database. These databases are typically maintained by international astronomical institutions or research teams, and the data has a wide coverage and has undergone preliminary calibration. After downloading, the data needs to be initially screened to remove stellar records with missing magnitude values ​​or excessive measurement errors to ensure the reliability of subsequent analysis. In addition, if the magnitude values ​​provided in the database have not been extinction corrected, they need to be corrected according to the interstellar extinction model of the target sky region. The extinction correction process involves using dust distribution data within the sky region, combined with a standard extinction coefficient, to adjust the magnitude values ​​of each band to obtain the final extinction-corrected magnitude. This process ensures the accuracy of subsequent color index calculations.

[0032] Furthermore, in another possible implementation, if data is acquired through autonomous observation, photometric observations of the target sky region are required using an astronomical telescope equipped with multicolor filters. The observation equipment can be a ground-based telescope or a space telescope, the specific choice depending on the observation conditions and accuracy requirements. During observation, photometric measurements are performed separately for the u, g, and i bands, recording the brightness data of each star in different bands. After observation, the raw data needs to be preprocessed, including dark-field correction, flat-field correction, and instrument response correction, to eliminate equipment noise and environmental interference. Subsequently, the measured magnitude values ​​are corrected for extinction by incorporating extinction parameters within the sky region, forming the final multicolor magnitude table. It should be noted that autonomous observation is suitable for specific research needs or areas with insufficient data coverage, providing customized data support for subsequent analysis.

[0033] Step S11, regarding the sources of the multicolor magnitude tables, can be further subdivided into two methods: utilizing existing survey data and autonomous observation data. For existing survey data, large-scale survey projects with broad coverage and high data quality can be selected, such as certain well-known astronomical data platforms. The magnitude data provided by these platforms has usually undergone preliminary processing, but still requires secondary calibration according to research needs. For example, when processing U-band data, due to the significant influence of atmospheric absorption in this band, additional calibration steps may be needed to improve data accuracy. For autonomous observation data, strict control is required throughout the entire process from equipment selection to data processing. For example, in terms of observation equipment, a larger aperture telescope can be selected to improve the signal-to-noise ratio; in terms of data processing, the standard star calibration method can be used, that is, calibrating the instrument response by observing standard stars of known brightness, thereby ensuring the reliability of the measurement results.

[0034] Step S12 involves selecting different extinction models based on the interstellar dust distribution characteristics of the sky region for specific extinction correction. For example, in the sky region near the Milky Way disk, the dust distribution is more complex, requiring a three-dimensional dust distribution-based extinction model for correction. This model comprehensively analyzes the density and distance distribution of dust within the sky region to estimate the light loss at different wavelengths, thereby adjusting the magnitude values. In high-latitude sky regions, where dust distribution is relatively sparse, a simplified uniform extinction model can be used, combining the standard extinction coefficient to directly calculate the correction value. It should be noted that the accuracy of extinction correction directly affects the subsequent color index calculation results; therefore, in practice, a suitable model must be selected based on the sky region characteristics to reduce systematic errors.

[0035] In one possible implementation, extinction correction can be validated using multi-band data. For example, after acquiring magnitude data in the u, g, and i bands, additional r-band data can be introduced. By comparing the color index variation trends across different bands, the rationality of the extinction correction can be verified. If a star is found to have significant color index deviations across multiple bands, its extinction correction parameters may need to be re-examined, or the star's data may need to be removed. This validation method can effectively improve the quality of multi-color magnitude tables, laying the foundation for subsequent analysis.

[0036] Step S2: Based on the multicolor magnitude table, calculate the color index gi and composite color index (ug)-1.5(gi) for each star to be tested. Specifically, the color index is an important parameter characterizing the spectral characteristics of a star, reflecting its color and temperature characteristics through the difference in magnitude values ​​across different wavelengths. In this step, for each star to be tested, its magnitude values ​​in the u, g, and i bands are extracted from the multicolor magnitude table, and then the two indices gi and (ug)-1.5(gi) are calculated according to their definitions. gi represents the difference between the g-band magnitude and the i-band magnitude, used to characterize the star's color characteristics in the visible light range; while (ug)-1.5(gi) is a composite color index that, by incorporating u-band data and weighting gi, further highlights the color differences of the star in the ultraviolet to visible light range. This composite index design can better separate the distribution characteristics of white dwarfs from other types of stars.

[0037] In one embodiment, when calculating the color index, the magnitude values ​​of the u, g, and i bands for each star to be measured are first read from a multi-color magnitude table. Assuming a star has magnitude values ​​of u=18.5, g=17.8, and i=17.2, the calculation of gi is a direct subtraction, i.e., gi=17.8-17.2=0.6. Then, the composite color index (ug)-1.5(gi) is calculated. First, ug=18.5-17.8=0.7, then 1.5 times gi is calculated, i.e., 1.5×0.6=0.9, finally obtaining (ug)-1.5(gi)=0.7-0.9=-0.2. It should be noted that the accuracy of the magnitude values ​​must be ensured during the calculation process, typically by retaining two decimal places to avoid accumulated errors affecting subsequent analysis.

[0038] Furthermore, in another possible implementation, for star catalogs with large datasets, color indices can be calculated in batches using automated tools. For example, a script can be written to read the magnitude data line by line from a multi-color magnitude catalog, generate the gi and (ug)-1.5(gi) values ​​for each star according to the aforementioned calculation rules, and store the results in a new table file. This approach is suitable for processing tens of thousands or even millions of star data points, significantly improving computational efficiency. In addition, a data anomaly detection mechanism can be set up during the calculation process. For example, if a star's magnitude value is missing or the calculated result exceeds a reasonable range, the star is automatically marked as abnormal data for subsequent manual verification. This anomaly detection mechanism further ensures data quality.

[0039] Step S21 involves calculating the color index gi, which allows for further analysis of its physical meaning. gi reflects the difference in luminosity between the g-band and i-band of a star and is typically closely related to the star's surface temperature. White dwarfs, due to their high surface temperatures, have a spectral energy distribution biased towards the blue end, thus their gi values ​​are usually small or even negative. Calculating gi allows for a preliminary distinction between hot and cold stars, providing a basis for subsequent screening. For example, if a star in a certain region of the sky has a gi value of -0.3, it indicates that it may have a high surface temperature, consistent with the typical characteristics of a white dwarf.

[0040] Step S22, the calculation of the composite color index (ug) - 1.5 (gi), is designed to enhance the distinction between white dwarfs and other types of stars. U-band data reflects the brightness of stars in the ultraviolet range, and white dwarfs typically exhibit strong radiative characteristics in the ultraviolet band; therefore, introducing U-band data can highlight the spectral features of white dwarfs. Simultaneously, by applying a 1.5-fold weighted adjustment to gi, the color differences can be further amplified, making the distribution of white dwarfs more concentrated on the two-color map. For example, when processing a batch of stellar data, if a star has an ug value of 0.5 and a gi value of -0.2, its composite color index is 0.5 - 1.5 × (-0.2) = 0.8. This positive value may indicate that the star deviates from the typical distribution of white dwarfs and requires further confirmation in subsequent steps.

[0041] In one possible implementation, preliminary statistical analysis can be performed on the calculated color index to understand the overall distribution characteristics of the stars being tested. For example, the distribution range and mean of the gi values ​​can be statistically analyzed to determine if the data meets expectations. If it is found that the gi values ​​of most stars are concentrated in the positive range, it may indicate that the sky region is dominated by low-temperature stars with a low proportion of white dwarfs; conversely, if the gi values ​​are mostly negative or close to zero, there may be many high-temperature stars, worthy of further screening. This statistical analysis can provide a reference for subsequent two-color map drawing and help optimize the screening strategy.

[0042] Step S3: Plot the positions of all the stars to be tested on a two-color plot with gi as the x-axis and (ug)-1.5(gi) as the y-axis. Specifically, a two-color plot is a two-dimensional scatter plot used to analyze the color characteristics of stars. By using different color indices as coordinate axes, it can visually display the distribution patterns of stars in color space. In this step, a two-color plot coordinate system is constructed with gi as the x-axis and (ug)-1.5(gi) as the y-axis. Then, based on the color index value of each star to be tested, the corresponding point is plotted on the plot. The two-color plot can be drawn using data visualization tools, such as astronomical data processing software or general plotting software. After importing the calculated color index data, a scatter plot is automatically generated. By observing the distribution of star points on the two-color plot, it is possible to preliminarily determine which stars may belong to the white dwarf category.

[0043] In one embodiment, when plotting a two-color graph, the range and scale of the coordinate axes must first be determined. To ensure the readability of the graph, the range of the horizontal axis gi can be set according to the color index distribution range of the stars being measured, for example, from -1.0 to 3.0, and the range of the vertical axis (ug)-1.5 (gi) can be set, for example, from -2.0 to 4.0. Then, the color index value of each star is used as a coordinate point and plotted on the graph one by one. For example, if a star has a gi value of 0.6 and an (ug)-1.5 (gi) value of -0.2, the corresponding point on the two-color graph is (0.6, -0.2). After plotting, the concentration of star distribution can be characterized by point density or color intensity to facilitate subsequent region division and filtering.

[0044] Furthermore, in another possible implementation, for large-scale stellar data, a layered rendering approach can be used to optimize the visualization of the two-color map. For example, the stars to be tested can be divided into multiple subsets based on their magnitude or celestial location, and plotted on different layers. This avoids visual confusion caused by overly dense points and facilitates the analysis of the distribution characteristics of different subsets. In addition, auxiliary lines or reference areas can be added to the two-color map, such as plotting the positions of standard stellar sequences, to more intuitively compare the distribution differences between the stars to be tested and known stellar types. This auxiliary information can provide a reference for subsequent screening of white dwarf candidates.

[0045] Step S31, regarding the selection of the tool for drawing the two-color plot, can be adjusted according to the data scale and research needs. For example, for small-scale data, simple spreadsheet software can be used to quickly generate a two-color plot using its built-in scatter plot function. For large-scale data, it is recommended to use professional astronomical data processing software. These software programs support batch data import and custom plotting parameters, enabling efficient processing of complex datasets. In addition, professional software also supports interactive functions, such as viewing the stellar information corresponding to a specific point by clicking with the mouse. This function helps to quickly locate potential white dwarf candidates.

[0046] Step S32 involves setting the coordinate range of the duochrome plot, which can be dynamically adjusted based on the distribution characteristics of the stars being analyzed. For example, when processing data for a certain celestial region, if it is found that the gi values ​​of most stars are concentrated between -0.5 and 1.5, the horizontal axis range can be reduced to -1.0 to 2.0 to improve the plot resolution. Simultaneously, if the distribution range of (ug)-1.5(gi) values ​​is wide, the vertical axis range can be appropriately expanded to ensure that all points are displayed within the plot. This dynamic adjustment method optimizes the display effect of the duochrome plot, facilitating subsequent analysis.

[0047] In one possible implementation, after creating the two-color map, preliminary clustering analysis can be performed on the stellar points to identify possible distribution patterns. For example, by observing clustered areas of points, it can be determined whether there are obvious concentrated distribution characteristics. If the point density in a certain area is found to be significantly higher than in other areas, it may indicate that the area corresponds to a specific type of star, such as a white dwarf or a main-sequence star. This preliminary analysis can provide clues for the subsequent delineation of white dwarf distribution areas and help verify the rationality of the color index calculation. The intuitive display and preliminary analysis of the two-color map can lay the foundation for the next step of candidate selection.

[0048] Step S4 involves selecting stars falling within a pre-defined white dwarf distribution region as white dwarf candidates. Specifically, the white dwarf distribution region is a pre-determined area on a two-color map based on the color index distribution characteristics of known stellar samples. The positions of the stars on the two-color map have already been drawn in the preceding steps. In this step, the coordinates of each star are compared with the boundary of the predetermined region to determine if it falls within that region. If a star's coordinates fall within the white dwarf distribution region, it is marked as a white dwarf candidate for further verification or research. This method allows for the rapid selection of potential white dwarf-like stars from a large pool of potential candidates.

[0049] In one embodiment, the process of screening white dwarf candidates can be automated. First, the boundary conditions of a pre-defined white dwarf distribution region are converted into mathematical expressions; for example, the region boundary can be represented by a set of inequalities. Then, the coordinates of each star to be tested on the two-color map are substituted into these inequalities to determine whether the conditions are met. For example, if a star has a gi value of -0.2 and a composite color index (ug) -1.5 (gi) value of 0.5, while the conditions for the predetermined region are gi less than 0.0 and composite color index greater than 0.3, then the star meets the conditions and is marked as a candidate. After screening, a list containing information on all candidates can be generated, recording their numbers, celestial locations, and color index values ​​for subsequent analysis.

[0050] Furthermore, in another possible implementation, the screening process can be combined with manual verification to improve accuracy. Candidates selected through automated screening can be manually checked using a two-color visualization interface, focusing on stellar points located near region boundaries. For example, if a star's coordinates are very close to the boundary of a white dwarf distribution region, it may be necessary to combine other observational data, such as spectral characteristics or parallax information, to further confirm its classification. This manual verification method is suitable for scenarios with a small number of candidates or high research precision requirements, effectively reducing the possibility of misclassification.

[0051] Step S41: Determine the large-scale region. Specifically, the large-scale region is a preliminary range of the white dwarf's distribution area, typically covering the main regions where white dwarfs are likely to be distributed on the two-color map. This range is determined based on the known distribution patterns of white dwarf color indices, usually within regions with lower gi values ​​and higher composite color indices. When determining the large-scale region, statistical results on the color characteristics of white dwarfs in astronomical literature can be referenced, or analysis can be performed based on existing stellar sample data. By delineating the large-scale region, the selection range can be initially narrowed, laying the foundation for more precise regional division later.

[0052] In one embodiment, determining a large region can be achieved by analyzing the distribution characteristics of known white dwarf samples on a two-color map. For example, assuming that the gi values ​​of known white dwarf samples are mainly distributed between -0.5 and 0.5, and the composite color index is mainly distributed between 0.2 and 1.5, this range can be used as the preliminary boundary of the large region. When drawing the two-color map, this region can be marked with a rectangle or polygon, and all stars to be tested can be compared with this region to initially screen out stars that may meet the criteria. This method is simple and intuitive, and suitable for rapidly processing large-scale data.

[0053] Step S42 involves identifying a smaller region located in the upper right corner of the larger region. Specifically, this smaller region is a subset of the larger region, typically located in the upper right corner of the duochrome image, corresponding to the distribution location of white dwarfs where color index characteristics are more pronounced. Further narrowing the region improves the accuracy of the screening and reduces the possibility of non-white dwarfs being misclassified as candidates. Determining this smaller region requires a comprehensive analysis combining the typical color characteristics of white dwarfs and their distribution density on the duochrome image.

[0054] In one possible implementation, determining a small area can be achieved by observing the density distribution of points on a duochrome map. For example, if the point density in the upper right corner of a large area is significantly higher than in other parts, this high-density area can be designated as a small area. This designation can be based on the outline of the point distribution or by defining boundaries using a more stringent color index range. This approach allows for further focusing on the region most likely to contain white dwarfs, improving screening efficiency.

[0055] Step S421: Based on the distribution on the two-color image, a triangular region is delineated as the small-scale region. Specifically, the triangular region is a geometric shape designed based on the point distribution characteristics on the two-color image, usually located in the upper right corner of a large-scale region, whose vertices and edges can effectively cover the high-density distribution area of ​​white dwarfs. When delineating the triangular region, the positions of the three vertices of the triangle can be determined based on the known distribution boundaries of white dwarf samples and the point clustering of the stars to be tested on the two-color image. For example, if it is found that the white dwarf samples are mainly concentrated in the range of gi values ​​from -0.1 to 0.3 and composite color index from 0.4 to 1.0, then the triangular region can be drawn based on this.

[0056] In one embodiment, when defining the triangular region, three key points can be identified as vertices. For example, the first vertex can be located at a gi value of -0.1 and a composite color index of 0.4, the second vertex at a gi value of 0.3 and a composite color index of 0.4, and the third vertex at a gi value of 0.1 and a composite color index of 1.0. After connecting these three vertices to form a triangular region, this region is used as a small area for further screening of candidates. This triangular region design can better match the distribution trend of white dwarfs on the duochrome map, avoiding the inclusion of too many non-white dwarf objects.

[0057] Step S422: The triangular region is included in the white dwarf distribution region. Specifically, after determining the triangular region, it is used as the core part of the white dwarf distribution region, combined with a larger area to form the final screening range. After being included in the triangular region, the stars to be tested that fall within this region can be marked as high-priority candidates for white dwarfs. At the same time, stars located in the larger area but not within the triangular region can be used as secondary-priority candidates for further verification. Through this hierarchical screening method, the comprehensiveness of the screening can be ensured while highlighting candidates in key areas.

[0058] In one possible implementation, after incorporating the triangular region into the white dwarf distribution area, data processing tools can automatically identify the stellar locations falling within this region. For example, the boundary conditions of the triangular region can be transformed into a set of linear inequalities, and then the color index value of each star being tested can be evaluated to determine whether it meets the conditions. High-priority candidates can be stored as a separate subset, with detailed observational data recorded for subsequent confirmation using other methods. This hierarchical management approach helps optimize resource allocation and improve research efficiency.

[0059] Step S5, the process of determining the pre-defined white dwarf distribution region, can be further refined into analysis and verification steps based on sample data. Specifically, the delineation of the white dwarf distribution region relies on the classification results and color index distribution characteristics of known stellar samples. By comparing the positional differences between white dwarfs and non-white dwarfs on the two-color map, a reasonable region boundary is determined. The implementation of this process will be described in detail below.

[0060] Step S21 involves using astrometric data combined with Hertzsprung-Russell (HR) chart positions to divide the stellar sample into known white dwarf groups and non-white dwarf groups. Specifically, the astrometric data includes information such as stellar magnitude, color index, and parallax. This data can be used to construct a HR chart, a two-dimensional graph with color index on the x-axis and absolute magnitude on the y-axis. The HR chart visually reflects the evolutionary stages and physical properties of stars. White dwarfs are typically located in specific regions of the HR chart and are clearly distinguishable from other types of stars such as main-sequence stars and giants. By analyzing the positions of stars on the HR chart, the sample stars can be divided into known white dwarf groups and non-white dwarf groups, providing a reference for subsequent region delineation.

[0061] In one embodiment, classifying known white dwarf groups into non-white dwarf groups first requires obtaining astrometric data of sample stars from an astronomical database, including their magnitudes and parallax data across multiple wavelengths. The parallax data allows for the calculation of stellar distances, which, combined with the magnitude values, leads to the deduction of absolute magnitudes. Subsequently, a Hertzsprung-Russell diagram is plotted using color index and absolute magnitude as coordinates, and the stars are classified according to their positions on the diagram. For example, white dwarfs are typically located in the lower left corner of the Hertzsprung-Russell diagram, exhibiting higher surface temperatures and lower luminosity, while main-sequence stars are distributed in the diagonal regions. In this way, sample stars can be accurately divided into different groups.

[0062] Step S211 involves acquiring the color index, magnitude, and parallax data of the astrometric data sample. Specifically, the color index can be calculated from the difference in magnitude values ​​across different spectral bands; magnitude values ​​reflect the brightness of a star; and parallax data is used to determine the distance to a star. This data can typically be obtained from publicly available astronomical databases or through a combination of independent observations. When acquiring the data, it is crucial to ensure its integrity and consistency, for example, by removing records with significant measurement errors, to guarantee the reliability of subsequent analysis.

[0063] In one possible implementation, when acquiring astrometric data, a large astronomical database with broad coverage and high update frequency can be selected. These databases store a large number of observational records of stars, including multi-band magnitude and parallax information. By downloading data from the target sample stars, the dataset required for analysis can be quickly constructed. Furthermore, if some data is missing from the database, it can be supplemented through cross-matching with other observational projects, such as correlating magnitude and parallax data from different sources to generate a complete record.

[0064] Step S212: A Hertzsprung-Russell diagram is drawn based on the color index, magnitude, and parallax data. Specifically, drawing the Hertzsprung-Russell diagram requires first calculating the absolute magnitude of the stars, i.e., combining the magnitude value and parallax data to estimate the brightness of the stars at a standard distance. Then, using the color index as the x-axis and the absolute magnitude as the y-axis, each sample star is plotted as a point on the diagram. By observing the distribution of these points, clusters of different types of stars can be identified, providing a basis for classification.

[0065] In one embodiment, when plotting the Hertzsprung-Russell diagram, specialized astronomical data processing software can be used to automatically generate the chart after importing data from sample stars. For example, the horizontal axis range can be set to -1.0 to 3.0, and the vertical axis range to -5.0 to 15.0 to cover the distribution range of most stars. After plotting, stars from different data sources can be distinguished by color or symbols to facilitate the analysis of data consistency. This visualization method can intuitively show the differences in the physical properties of stars.

[0066] Step S213: Based on the stars' positions on the Hertzsprung-Russell diagram, determine the known white dwarf group and the non-white dwarf group. Specifically, white dwarfs are typically located in the lower left corner of the Hertzsprung-Russell diagram, corresponding to high temperature and low luminosity characteristics, while main-sequence stars and giants are distributed in other regions. By setting positional boundaries, sample stars can be divided into the known white dwarf group and the non-white dwarf group. For example, if a star has a color index less than 0.0 and an absolute magnitude greater than 10.0, it is classified as a white dwarf; otherwise, it is classified as a non-white dwarf.

[0067] In one possible approach, the classification boundaries can be determined by referencing statistical patterns of stellar evolution stages found in astronomical literature. For example, the typical color index range and absolute magnitude range of white dwarfs can serve as criteria for classification. Furthermore, adjustments can be made based on the distribution density of the sample data. If a region is found to have a high density of points that match the characteristics of white dwarfs, that region can be included in the white dwarf group. This method ensures the scientific validity and accuracy of the classification results.

[0068] Step S22 involves matching the known white dwarf groups and non-white dwarf groups with the polychromatic magnitude table to obtain the corresponding polychromatic magnitude data. Specifically, the stellar samples from the known white dwarf groups and non-white dwarf groups need to be associated with the polychromatic magnitude table obtained in the preceding steps to extract their magnitude values ​​in the u, g, and i bands. This matching provides data support for subsequent two-color map drawing and region delineation. The matching process can be achieved through star numbering or celestial coordinates to ensure accurate data correspondence.

[0069] In one embodiment, data matching can be performed using unique stellar identifiers. For example, if both the sample star table and the multicolor magnitude table contain star numbers, the two sets of data can be linked by comparing these numbers. After matching, the magnitude values ​​of each star in the u, g, and i bands are extracted, and its color index gi and composite color index (ug)-1.5 (gi) are calculated. These color index data will be used subsequently to plot the distribution of known groups on a two-color map.

[0070] Step S23: On the two-color map, the known white dwarf groups and non-white dwarf groups are plotted using different markers. Specifically, in the coordinate system of the two-color map constructed in the previous steps, with gi as the abscissa and (ug)-1.5(gi) as the ordinate, the stellar points of the known white dwarf groups and non-white dwarf groups are plotted separately. To facilitate differentiation, different colors or symbols can be used to mark the two groups of stars; for example, blue dots represent the white dwarf group, and red triangles represent the non-white dwarf group. In this way, the differences in the distribution of the two groups of stars on the two-color map can be visually observed.

[0071] In one possible implementation, plotting known groups of stars can be automated using data visualization tools. For example, color index data for known white dwarf groups and non-white dwarf groups can be imported into the software, and scatter plots can be automatically generated after setting different marker styles. After plotting, the distribution range and overlap of the two groups of points can be observed to preliminarily determine whether the color index characteristics of white dwarfs have significant distinguishability. This visualization analysis provides an intuitive basis for subsequent region delineation.

[0072] Step S24: Analyze the distribution characteristics of white dwarfs and delineate the region that separates white dwarfs from non-white dwarfs as the pre-defined white dwarf distribution region. Specifically, by comparing the distribution positions of known white dwarf groups and non-white dwarf groups on a two-color diagram, analyze the color index distribution characteristics of white dwarfs to determine a boundary region that can maximally separate the two groups of stars. For example, if it is found that white dwarf groups are mainly concentrated in regions with gi values ​​less than 0.0 and composite color indices greater than 0.3, while non-white dwarf groups are mostly distributed in other regions, this range can be defined as the white dwarf distribution region.

[0073] In one embodiment, the distribution region of white dwarfs can be delineated by manually drawing boundary lines. For example, by observing the clustered areas of known white dwarf groups on a two-color map and determining their approximate range, these areas can be enclosed using polygons or rectangles. Simultaneously, to reduce misjudgments, the boundary range can be appropriately expanded to include areas that may contain white dwarfs. After delineation, the boundary conditions of this region are recorded for subsequent screening of stars to be tested.

[0074] Furthermore, in another possible implementation, statistical methods can be used to assist decision-making when delineating the region. For example, the mean and standard deviation of the color index of known white dwarf star clusters can be calculated to determine a statistically significant distribution range as the initial boundary of the white dwarf distribution area. Subsequently, the separation effect of the region division can be evaluated by calculating the proportion of non-white dwarf star clusters within this region. If the proportion of non-white dwarf star clusters is found to be too high, the boundary range can be adjusted appropriately to improve the separation. In this way, the region delineation can be made more scientific and reasonable.

[0075] Step S6, the construction process of the multicolor magnitude table, can be further refined into data matching and extinction processing steps. Specifically, the data sources for the multicolor magnitude table may involve multiple sky survey projects, and the wavebands covered and observation conditions of different projects may differ. Therefore, a unified multicolor magnitude table needs to be generated through data matching and processing. The implementation method of this process will be described in detail below.

[0076] Step S61 involves matching the u and vs band data from the first sky survey with the g, r, and i band data from the second sky survey. Specifically, the first and second sky survey data come from different astronomical observation projects, and may cover different bands and sky regions. By matching the two sets of data, magnitude information from different bands can be integrated to form a complete dataset covering the u, g, and i bands. The matching process can be achieved using the celestial coordinates or numbers of stars, ensuring accurate data correspondence.

[0077] In one embodiment, data matching first requires preprocessing the two sets of data, such as unifying the coordinate system and data format. Then, by comparing the star's celestial coordinates, the magnitude values ​​of the u and vs bands in the first survey data are associated with the magnitude values ​​of the g, r, and i bands in the second survey data. For example, if a star has a certain coordinate in the first survey data, and a record with the same coordinates is found in the second survey data, its magnitude value is merged into one record. After matching is complete, a temporary star catalog containing multi-band magnitude values ​​can be generated.

[0078] Step S62 involves performing extinction processing on the matched data to obtain the extinction-corrected magnitudes for the u, g, and i bands. Specifically, extinction processing refers to correcting the magnitude values ​​to eliminate the absorption and scattering effects of interstellar dust on light. Extinction processing requires selecting appropriate extinction models and parameters based on the dust distribution characteristics of the target sky region, adjusting the magnitude values ​​for each band to obtain the extinction-corrected results.

[0079] In one possible implementation, different extinction models can be selected based on the galactic latitude of the sky region during extinction processing. For example, in low-latitude sky regions where dust distribution is more complex, a model based on three-dimensional dust distribution can be used to calculate the light loss in different wavelengths by estimating dust density and distance. In high-latitude sky regions where dust distribution is sparser, a simplified uniform extinction model can be used, directly applying the standard extinction coefficient for correction. After processing, the extinction-corrected magnitude values ​​of the u, g, and i bands are stored as the final multicolor magnitude table.

[0080] Step S7, the calculation process for the color index, can be further refined into data extraction and calculation steps. Specifically, the calculation of the color index requires extracting the magnitude value of each star from the multi-color magnitude table, and then performing a difference operation according to the definition. The implementation of this process will be described in detail below.

[0081] Step S71: For each star to be measured, obtain the u, g, and i band magnitudes from the multicolor magnitude table. Specifically, the multicolor magnitude table stores the magnitude values ​​of each star in different bands. By reading the table data, the u, g, and i band magnitude information of the target star can be obtained. When obtaining the data, it is necessary to ensure the integrity of the data. For example, if a certain band magnitude value of a star is missing, it can be marked as invalid data and excluded from subsequent calculations.

[0082] In one embodiment, when acquiring star magnitude data, a multi-color star magnitude table can be read line by line using a data processing tool. For example, for a certain star, its U-band magnitude is read as 18.3, its G-band magnitude as 17.6, and its I-band magnitude as 17.1, and these values ​​are recorded for subsequent calculations. During the reading process, a data verification mechanism can also be set up, such as checking whether the star magnitude values ​​are within a reasonable range. If outliers are found, they are marked or removed to ensure the reliability of the calculation results.

[0083] Step S72: Calculate gi as the difference between the g-band magnitude and the i-band magnitude. Specifically, gi is an index characterizing the color characteristics of a star, calculated as the difference between the g-band magnitude and the i-band magnitude. For example, if a star has a g-band magnitude of 17.6 and an i-band magnitude of 17.1, then the gi value is 17.6 - 17.1 = 0.5. This value reflects the color characteristics of a star in the visible light range; a smaller value generally indicates a higher surface temperature of the star.

[0084] Step S73: Calculate (ug)-1.5(gi), which is the U-band magnitude minus the G-band magnitude minus 1.5 times the gi. Specifically, the composite color index (ug)-1.5(gi) further highlights the color differences of stars in the ultraviolet to visible light range by incorporating U-band data and weighting gi. For example, if a star has a U-band magnitude of 18.3, a G-band magnitude of 17.6, and a gi value of 0.5, then first calculate ug as 18.3-17.6=0.7, then calculate gi by 1.5 times as 1.5×0.5=0.75, finally obtaining (ug)-1.5(gi)=0.7-0.75=-0.05. This composite index can better separate white dwarfs from other types of stars.

[0085] Step S8, regarding the automated implementation of the method, can be further refined into the development and application steps of a computer program product. Specifically, by developing a computer program product, the aforementioned steps S1 to S4 can be integrated into the automated process, improving data processing and filtering efficiency. The implementation method of this process will be described in detail below.

[0086] Step S81: Generate a computer program product for executing S1 to S4. When the computer program product is run on a computer, the computer executes S1 to S4. Specifically, the computer program product can be a software tool or a collection of scripts that includes the entire process from data acquisition, color index calculation, duochrome plotting to candidate selection. By running this program product on the computer, the search process for white dwarf candidates can be completed automatically, reducing the time cost of manual operation.

[0087] In one embodiment, when developing a computer program product, each step can be designed as an independent module, such as a data acquisition module, a color index calculation module, a two-color graph drawing module, and a candidate selection module. Each module is responsible for performing a specific function and passing the processing results to the next module. For example, the data acquisition module is responsible for downloading a multi-color magnitude table from a database, the color index calculation module is responsible for calculating the GI and composite color index, the two-color graph drawing module is responsible for generating a visualization chart, and the candidate selection module selects candidates based on a predetermined area. Modular design can improve the maintainability and scalability of the program.

[0088] Furthermore, in another possible implementation, the computer program product can also provide a user interface, allowing users to customize parameters or view intermediate results. For example, users can select a target sky region, set a color index range, or adjust the boundary conditions of the white dwarf distribution region through the interface. During program operation, the two-color image and filtering results can be displayed in real time, facilitating user monitoring and adjustments. This interactive design can meet the needs of different research scenarios and improve the applicability of the program.

[0089] Example 1: This invention provides a method for efficiently searching for white dwarfs using the SAGES photometric system. This method analyzes SAGES multicolor photometric data to find an optimal color combination, constructs a two-color map, and delineates the specific distribution regions of white dwarfs, thereby achieving rapid and high-purity screening of white dwarfs in large-sample sky survey data. To achieve the above objective, this invention provides a method for searching for white dwarfs using the SAGES photometric system, comprising the following steps: Step 1: Obtain the SAGES multi-band magnitude table containing the star to be measured. This magnitude table includes extinction-corrected magnitudes in the u, g, and i bands. Step 2: Based on the magnitude table, calculate the color index gi and composite color index (ug) - 1.5(gi) for each star to be tested; Step 3: On a two-color graph with gi as the x-axis and (ug) - 1.5(gi) as the y-axis, mark the positions of all the stars to be measured; Step 4: Based on the pre-defined white dwarf distribution region, select the stars to be tested that fall within the region as white dwarf candidates.

[0090] The pre-defined white dwarf distribution region is determined by analyzing the distribution of known white dwarf and non-white dwarf samples on the duochromatic image. Specifically, the samples can be initially classified using the Hertzsprung-Russell diagram of Gaia DR2, and then matched with SAGES photometric data to finally determine the region with the best separation.

[0091] Specific operational examples are as follows: I. Determination of White Dwarf Distribution Regions Before applying this method, it is necessary to first use known samples to determine the distribution area of ​​white dwarfs on a specific color-color map.

[0092] 1. Sample Preparation: Using Gbp−Grp color index, magnitude, and parallax data released by Gaia DR2, Hertzsprung-Russell diagrams were constructed. Based on the positions of stars on the Hertzsprung-Russell diagrams, the samples were clearly divided into the "known white dwarf group" and the "non-white dwarf group".

[0093] 2. Data Matching: The u and vs band photometric data from SAGES DR1 were cross-matched with the g, r, and i band data from the Pan-STARRS survey to construct a SAGES multi-band magnitude table containing five bands: u / vs / g / r / i. This magnitude table was then subjected to interstellar extinction processing.

[0094] 3. Color-Color Map Construction and Analysis: The "known white dwarf group" and "non-white dwarf group" were matched with the extinction-processed SAGES multi-band magnitude table to obtain SAGES multi-band magnitude data for the two groups of samples. Various color combinations (such as ug vs gi, vs-g vs gi, etc.) were experimented with to create two-color maps. Analysis revealed that the best separation between white dwarfs (usually marked as blue dots) and non-white dwarfs was achieved when the horizontal axis was gi and the vertical axis was (ug) - 1.5(gi).

[0095] 4. Area delineation: such as Figure 1 As shown, on this optimal two-color map, a large rhomboid region and a small triangular region located in its upper right corner are designated as the exclusive distribution area for white dwarfs. The horizontal axis of the map is gi, and the vertical axis is (ug) - 1.5(gi). The blue dots in the map represent known white dwarfs. Calculations show that the hit rate is 84.5% within the large region and 86.3% within the small region.

[0096] Based on the above work, a total of 24,266 (large range) or 21,567 (small range) white dwarf candidates were obtained from the SAGES project, as shown in Table 1 below. Further spectroscopic work is needed to confirm whether they are indeed white dwarfs.

[0097] Table 1 scope Number of stars within the target range Number of white dwarfs within the target range Hit rate Rhombus-shaped large area 9681 8181 84.5% Small triangle 8679 7490 86.3% II. Once the search area for white dwarf candidates has been marked, a large-scale search for unknown targets can be conducted.

[0098] 1. Data preparation: Obtain the complete SAGES multi-band magnitude table for the sky area to be searched (which also needs to be extinct).

[0099] 2. Color Calculation: For each source in the table, calculate its gi and (ug) - 1.5(gi) color index.

[0100] 3. Plotting and Filtering: Plot all sources onto the pre-labeled two-color graph. Filter out all sources that fall within the aforementioned large or small range.

[0101] 4. Results Output: The sources falling into these regions are high-confidence white dwarf candidates. For example, applying this method to the full SAGES project data yields 24,266 (large-scale) or 21,567 (small-scale) white dwarf candidates. These candidates can be submitted to spectroscopic telescopes for final verification.

[0102] Through the above-described embodiments, this invention successfully transforms SAGES photometric data into a highly efficient white dwarf search tool, achieving rapid, high-purity, and unbiased large-sample white dwarf search. Compared to parallax and spectral observation methods, the pure photometric method of this invention provides faster observation speed, acquires more sources, has higher photometric accuracy, and a higher hit rate for white dwarf identification. It can rapidly obtain a large number of white dwarf candidates in a short time, thus providing an important sample library for white dwarf research. The foregoing has provided a detailed description of a method, system, and readable storage medium for searching white dwarfs using a multicolor photometric system, as provided in the embodiments of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas; furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

[0103] Certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that hardware manufacturers may use different names to refer to the same component. This specification and claims do not distinguish components based on differences in name, but rather on differences in function. The terms "comprising" and "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising / including but not limited to". "Approximately" means that within an acceptable margin of error, those skilled in the art can solve the technical problem and substantially achieve the technical effect within a certain margin of error. The following descriptions in the specification are preferred embodiments for carrying out this application; however, these descriptions are for the purpose of illustrating the general principles of this application and are not intended to limit the scope of this application. The scope of protection of this application shall be determined by the appended claims.

[0104] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0105] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0106] The foregoing description illustrates and describes several preferred embodiments of this application. However, as previously stated, it should be understood that this application is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the application concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of this application should be within the protection scope of the appended claims.

Claims

1. A method for searching for white dwarfs using a multicolor photometric system, characterized in that, The method for searching for white dwarfs using a multicolor photometric system includes the following steps: S1: Obtain a multicolor magnitude table containing the star to be measured, wherein the multicolor magnitude table contains extinction-corrected magnitudes in the u, g, and i bands; S2: Based on the multicolor magnitude table, calculate the color index gi and composite color index (ug)-1.5(gi) for each star to be tested; S3: Mark the positions of all the stars to be measured on a two-color graph with gi as the horizontal axis and (ug)-1.5(gi) as the vertical axis; S4: Based on the pre-defined white dwarf distribution region, select the stars to be tested that fall within the region as white dwarf candidates.

2. The method according to claim 1, characterized in that, The pre-defined white dwarf distribution region in S4 specifically includes: Using astrometric data and Hertzsprung-Russell diagrams, the stellar samples were divided into known white dwarf groups and non-white dwarf groups; The known white dwarf groups and non-white dwarf groups are matched with the polychromatic magnitude table to obtain the corresponding polychromatic magnitude data; On the two-color diagram, the known white dwarf groups and non-white dwarf groups are drawn using different markers; Analyze the distribution characteristics of white dwarfs and delineate the region that separates white dwarfs from non-white dwarfs as the pre-delineated white dwarf distribution area.

3. The method for searching white dwarfs using a multicolor photometric system according to claim 2, characterized in that, In S4, the stellar sample is divided into known white dwarf groups and non-white dwarf groups using astrometric data combined with Hertzsprung-Russell diagram positions. Specifically, this includes: Acquire color index, magnitude, and parallax data from astrometric data samples; Hertzsprung-Russell diagrams were plotted based on the color index, magnitude, and parallax data. The known white dwarf groups and the non-white dwarf groups are determined based on the positions of the stars on the Hertzsprung-Russell diagram.

4. The method for searching for white dwarfs using a multicolor photometric system according to claim 1, characterized in that, The white dwarf distribution area specifically includes: a large area and a small area located in the upper right corner of the large area.

5. The method for searching for white dwarfs using a multicolor photometric system according to claim 4, characterized in that, The determination of the small region located in the upper right corner of the large region specifically includes: defining a triangular region as the small region based on the distribution on the duochrome image; and incorporating the triangular region into the white dwarf distribution region.

6. The method for searching for white dwarfs using a multicolor photometric system according to claim 1, characterized in that, The multicolor magnitude table specifically includes: matching the u and vs band data of the first survey data with the g, r, and i band data of the second survey data; and performing extinction processing on the matched data to obtain the extinction-corrected magnitudes of the u, g, and i bands.

7. The method for searching white dwarfs using a multicolor photometric system according to claim 1, characterized in that, The calculation of the color index gi and composite color index (ug)-1.5(gi) for each star to be tested in S2 specifically includes: for each star to be tested, obtaining the u, g, and i band magnitudes from the multicolor magnitude table; calculating gi as the g band magnitude minus the i band magnitude; and calculating (ug)-1.5(gi) as the u band magnitude minus the g band magnitude minus 1.5 times the gi.

8. The method for searching white dwarfs using a multicolor photometric system according to claim 1, characterized in that, The method for searching white dwarfs using a multicolor photometric system is based on the SAGES multicolor photometric system and is performed by constructing a specific color-color map.

9. A system for searching white dwarfs using a multicolor photometric system, used in the method for searching white dwarfs using a multicolor photometric system as described in any one of claims 1-8, characterized in that, The system for searching white dwarfs using a multicolor photometric system includes: Data acquisition module: used to acquire a multicolor magnitude table containing the star to be measured, the multicolor magnitude table including extinction-corrected magnitudes in the u, g, and i bands; Data calculation module: Based on the multicolor magnitude table, calculate the color index gi and composite color index (ug)-1.5(gi) for each star to be tested; Position calibration module: Mark the positions of all stars to be measured on a two-color graph with gi as the horizontal axis and (ug)-1.5(gi) as the vertical axis; Star identification module: Based on the pre-defined white dwarf distribution region, select the stars to be tested that fall within the region as white dwarf candidates.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be invoked by a processor to execute the method of searching for white dwarfs using a multicolor photometric system as described in any one of claims 1 to 8.

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