SIMBAD is an astronomical database. It is a database consisting of cross-identifications, bibliography, and measurements of selected objects. It is developed and maintained by CDS, Strasbourg. Several institutes contribute to the database's contents [1-3]. This data is useful to anyone who wishes to test theories about dark energy, dark matter, quantum redshift, quantum cosmic microwave background, and other astronomical subjects. Also, data could be used for statistical studies such as finding a correlation between distance, temperature, and redshift.

SIMBAD uses the Parallax method. Hence, you can study space objects in a universe without expansion, using Parallax data. By choosing nearby stars, you can calculate their distances more precisely than other methods. This data can be used for a variety of investigations. The Parallax data could be used to test the predictions of Quantum Redshift. This theory suggests that redshift occurs because of sharing of energy between light periods.

Although it is possible to write a query to extract data from SIMBAD Astronomical Database, there are some issues. There is a problem with repeating the name of stars in different records. Also, there is a cap on the number of records in each query. On the other hand, the format of the output records is unsuitable to use in Excel. Therefore, we need to write a script and execute it at different ranges. Additionally, we need to write a program to group data and remove repeated records.

TAP protocol from the Virtual Observatory is available from the URL: http://simbad.u-strasbg.fr/simbad/sim-tap to extract the data of the nearby space objects. It allows researchers to build complex queries which give them access to a database like SQL output syntax.

The SIMBAD’s TAP service lets us write our script and execute it for extracting necessary data. We wrote many scripts with different conditions; the source of the scripts is represented in the text files in the “Script Examples” folder.

We have used this software to report a positive correlation (0.914384) between the possibility of finding an unexpected high Redshift (Z > 0.001) and the average temperature of nearby stars in different categories []. Also, we have realized that almost 20 percent of the stars with temperatures more than 12,000 K have a Redshift greater than 0.001.

Description

This software has three parts. In the first part, in the script folder, there are some scripts, whose content must be copied and inserted into the Tap service.

The second part is data files in text format, and the third part includes tables and programs written in Microsoft Access.

Fig.1 illustrates the structure of the software:

In the TAP Service, complex queries can be written, and data can be extracted in a variety of formats. Here is the direct link to the TAP Service:  https://simbad.cds.unistra.fr/simbad/sim-tap.

To see the structure and names of the tables, click the "Simbad table" button or use its direct link at the bottom-left of the page: https://simbad.cds.unistra.fr/simbad/tap/tapsearch.html

On the left side of the page, programmers can specify the format of the output data and the number of the output records. In this paper, we choose the text format. We can write our scripts and run them by clicking the start button just below the "ADQL query to execute" at the center of the page.

We have written some sample scripts. They are in the script folder. We can copy and paste them into the Tap service and run it by clicking the start button. Here are the codes of the first sample:

SELECT top 20000 main_id, plx_value, rvz_redshift, teff

FROM basic JOIN ident ON oid=ident.oidref

LEFT JOIN mesFe_h ON oid=mesFe_h.oidref

WHERE plx_value >1 and rvz_redshift >0 and  teff>100

GROUP BY  main_id, plx_value , rvz_redshift, teff Data

These lines have written in the standard query language. Line 1 introduces field names, while lines 2 and 3 introduce relationships and sequences of tables. In the fourth line, conditions have been introduced, and in the fifth line, a group output record prevents duplicates.

To retrieve more data, we can use different temperature limits and increase them in each execution, such as (teff>100 and teff<1000) and then (teff>=1000 and teff<1300) and so on. In contrast, we can change rvz_redshift >0 to rvz_redshift <0 to retrieve blueshift data or delete it to have all space objects together.

Code Metadata

Data

At the center of the page, just below the “ADQL query to execute” there is an empty box where we can write our scripts and run them by clicking the start button. After clicking the start button, the website opens a new page to show results. Now we should select all output records by clicking CTRL_A and paste them into a text file. The name of these files should be data1, data2, and data3,

Some sample text data files have located in the data folder. The records in the data files are repeated many times. We use the field “main_id” for deleting them. Hence, the field “main_id” is an essential field in the scripts.

There is no limit to the number of data files. Although in the source of the program we check more than 5555 data files, you can change the source for checking more data or even changing the naming method of the data files.

In the SIMBAD Astronomical Database, we can find out more information about the object by using the "Object Name" field. The source page has the following format:

http://simbad.u-strasbg.fr/simbad/sim-id?Ident=ObjectName

We will be redirected to the SIMBAD website if we replace the "ObjectName" at the end of the above link with the actual object name in the table.

Program

Fig.2 shows the main page of the program. On the upper-right side of the page, there is a direct link to the SIMBAD Tap service. The “Open Data Folder” button opens the folder of txt data files to locate and manage the results of the scripts in a text format file. The “Scripts Examples” button opens the folder of script examples. In each file, there is a different script.

The green Button “Read. ta from Data Files and Add to Input Records” transfers the data record from all data files to the input table.

The blue button “Read Input Records and Delete Duplicate Records” read all records from the input record table and deletes duplicate records.  We can select all output records by right-clicking on the upper-left of the output records and copying them into the clipboard to paste into Excel. We prefer to delete all records before starting the process. Red buttons do this job.

 Figure 2: Main Form: For executing the software, open the “Using SIMBAD Database” in the Microsoft Access and DoubleClick on the “Main Form”.

Impact Overview

We have used this software for retrieving information of almost 2,200,000 data records that is equal to 93,060 unique space objects. The objects' temperature is between 671 and 99,575 K. The distance of the objects is between 413.13 and 0.5 (mas). The database has a DOI and is downloadable from this address.

• Nearby Space Objects Name_Distance_ Redshift_Temperature

               https://doi.org/10.6084/m9.figshare.20099951.v2

Here is a list of research articles that have used this software:

• How does the quantum structure of electromagnetic waves describe quantum redshift?

             https://doi.org/10.21203/rs.3.rs-87775/v3

• Quantum Redshift rejects dark energy and the theory of accelerating expansion of the universe

              https://doi.org/10.6084/m9.figshare.19509697.v3

• Unexpected Redshift of nearby stars

               https://doi.org/10.21203/rs.3.rs-234589/v1

• Impact of the temperature of stars on their Redshift

               https://doi.org/10.21203/rs.3.rs-246029/v1

• Rate of losing energy in Quantum Redshift

               https://doi.org/10.21203/rs.3.rs-350615/v1

This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France 2000, A&AS, 143, 9, "The SIMBAD astronomical database", Wenger et al.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

1. Wenger M, Ochsenbein F, Egret D, Dubois P, Bonnarel F, Borde S, et al. 2000. The SIMBAD astronomical database-The CDS reference database for astronomical objects (2000) 143(1) pp.9-22. Astronomy and Astrophysics Supplement Series.
2. Poudel, Manoj, et al. "A Survey of Big Data Archives in Time-Domain Astronomy." Applied Sciences.2022; 12: 6202.
3. Kuhn, L., M. Shubat, and P. Barmby. "Comparing NED and SIMBAD classifications across the contents of nearby galaxies." Monthly Notices of the Royal Astronomical Society. 2022.
4. Kalhor, Bahram, Nearby Space Objects Name_Distance_ Redshift_Temperature. figshare. Dataset. 2022.
5. Kalhor, Bahram. "Data of nearby space objects using SIMBAD astronomical database." Data in Brief. 2023: 108943.