Abstract
This paper presents a comprehensive analysis and comparison of various celestial body detection methods used in astronomy. The research methodology involved gathering data from multiple scientific studies, assigning numerical scores to different characteristics of each detection method, and calculating an overall score to rank them. The study evaluated nine celestial body detection techniques: radial velocity, astrometry, radio wave detection, optical telescopes, direct imaging, remote sensing, microlensing, transits, and pulsar timing. Radio wave detection emerged as the top-performing method with a score of 45, excelling in characteristics like average detection distance, sensitivity to specific characteristics, and temporal sensitivity. The analysis revealed that data processing complexity, accessibility, and average clarity/resolution were shared strengths across many methods, influenced by their longevity of development. The conclusions from this research provide insights that can guide the scientific community’s efforts in optimizing celestial body detection methods, allowing for a better understanding of the cosmos.
Introduction
Deep space is more mysterious than the deepest parts of our world. Planets and stars orbit each other in perfect unison—planets and stars of all shapes and sizes, densities and masses, and temperatures and colors. Scientists around the world ask themselves “What is out there? What can we learn and observe from these countless celestial bodies that are just outside our solar system?” To uncover these unfathomable mysteries, there have been numerous methods that have been created for the sole purpose of discovering extrasolar planets. With the numerous methods created, there must be a method that is the best. However, through my research, this topic has not been researched deeply. For that reason, my research question is “What specific methods of celestial body detection can most efficiently and accurately determine, not only the size of the planet but the composition and other details?”
A celestial body is an aggregation of matter in the universe that can be considered a single unit1. This broad definition allows many objects to be classified as celestial bodies, such as asteroids, stars, planets, and even nebulae. Celestial bodies have been documented and discovered for thousands of years. More specifically the art of detecting these celestial bodies, or radio astronomy, has been an important part of anthropological history since 539 B.C.2. The ancient Babylonians were the first to document specific celestial bodies and record periodic motions of celestial bodies such as Jupiter and the Sun.3
Celestial body detection is an essential aspect of the future of our world, from discovering faraway exoplanets to nearby habitable planets. Additionally, celestial body detection is crucial because Earth will be exhausted of resources, and we will have to seek other habitable planets. Jolene Creighton, a distinguished writer, and researcher for Futurism, argues that our world is headed toward irreversible collapse due to unsustainable resource exploitation.4 Several researchers give our world only a few decades before this problem gets serious. Discovering potential habitable planets, using detection methods, and formulating a plan for a large-scale move will allow for an efficient and effective relocation. Moreover, discovering and properly analyzing said detection methods can allow for the efficiency of discovering celestial objects to greatly increase. The increased efficiency of these detection methods will reduce the ethical considerations about the allocation of resources for celestial body detection missions. Balancing scientific exploration with societal needs and priorities requires careful consideration of resource allocation, funding priorities, and ethical decision-making processes to ensure equitable access to opportunities and benefits derived from space exploration. However, maximizing and enhancing current methods is the best first step towards this goal.
Another aspect of celestial body detection methods is that they could lead us to planets that have unique atmospheres or climate systems. We have already discovered many exoplanets that are habitable for life, exhibiting conditions that we could have never imagined. One instance of this happening occurred at the discovery of the exoplanet HD 209458b5. This exoplanet is a mere seven million kilometers away but still contains molecules such as methane, ammonia, and water vapor. The planet’s composition should be similar to that of its host star, as the intense heat would prevent habitable conditions from forming (Johnson, 2021). This led scientists to conclude that the planet originally formed much farther out, beyond the point where water changes from liquid to gas5. Each discovery teaches us more about planetary formation and brings us closer to finding the first truly habitable exoplanet6. Advancing into the present and future, our quest to distinguish and discover new celestial bodies relies on an arsenal of sophisticated methods, using complex sensors, radio waves, gamma radiation, and more. As we navigate this complex composition of cosmic exploration, this research contributes to the evolving information on celestial body detection, propelling our understanding of the universe to new levels.
Literary Analysis
Celestial body detection is a well-known concept that has been researched for decades by various organizations such as NASA and the ESA2. We have already explored and documented hundreds of different celestial body detection methods ranging from tracking radial velocities to transit photometry. Each of these methods has advantages and disadvantages that are discussed deeply in their respective studies. However, there are not many research studies with the sole purpose of comparing and contrasting these specific methods.
For instance, the study on radio waves that NASA did analyzes radio waves that are often emitted by celestial bodies7. Specifically, when using a radio telescope, you would see distant pulsars, star-forming regions, and supernova remnants. These multiple objects that can be viewed through radio telescopes can give clues about other nearby celestial bodies. Additionally, radio telescopes can detect quasars—remnants of powerful supernovas and can give insight into potential nearby celestial bodies. Quasars emit over 1,000 times as much energy as the entire Milky Way, allowing them to be visible from billions of light years away. Specifically, a very well-known radio telescope that has detected many quasars is the Hubble Space Telescope.
Another study regarding research studies done on celestial body detection methods is the implementation of CubeSats or advanced sensors to determine certain qualities of celestial objects8. CubeSats are small, standardized satellites traditionally used for Earth observation, but advancements are now enabling their use for deep space detection. Their resolving power, which can range from a few meters to sub-meter per pixel, depends on the quality of their optics, spectral sensors, and the altitude or distance from their target. To send and receive signals from celestial bodies and Earth, CubeSats use high-frequency transceivers (such as X-band or Ka-band) and deployable antennas, which provide the necessary data rates and minimize interference over vast distances.
Additionally, the article mentions how small-body missions need to be considered already at the “mission design stage in the case of miniaturized platform and sensors,”8. However, once again, there is no mention of other methods, which would have strengthened the usefulness of their newfound technology. This specific study is a very scientific and mathematical-based approach and goes very in-depth into the background of the formation of the technology. This may justify why there is no comparison done between other methods, but it once again proves how the action of comparing different celestial body detection methods is rather uncommon.
Finally, one study in particular that piqued my interest in detection methods is an article done on the various common detection methods. This research study includes general descriptions of how the methods perform— taking into several different factors such as distance9. Additionally, this article includes equations that are used in each of these methods, for example, the author includes the equation to determine the minimum mass of an exoplanet9. Through immersive and representative graphs, the article is poised in a way to reaches out to the general audience and helps the average reader with the current methods of detecting celestial bodies9. However, there are no statistics or significant numbers that compare these methods with their counterparts. Additionally, the article mainly focuses on the detection of exoplanets, categorizing, and comparing them, however, the article leaves out the detection of other celestial bodies such as asteroids, moons, quasars, and more9. This justifies the need for having a detailed, scientific study, my gap, that takes into account many different types of celestial bodies and seeing if these methods have any notable similarities or differences. By determining what specific celestial body detection methods work on what specific bodies, we will be able to determine any overlap. Furthermore, with this information, we can deduce more methods that can more efficiently and accurately detect numerous celestial bodies.
After reading all of the different related sources, I hypothesize that radio wave detection will be the superior detection method. This is mainly because the use of radio waves for detecting bodies has been utilized for decades more than other celestial body detection methods other than optical telescopes. Additionally, radio waves have been known to detect planetary bodies from great distances with great accuracy.
Methodology
The methodology of my research consisted of many different elements. As I stated before, my research focuses on modeling and ranking different celestial body detection methods. With a literary review, I used pre-existing studies done on celestial body detection methods. I employed both statistical analysis and thematic analysis to reach my conclusion. My research contains both qualitative and quantitative data or a mixed-methods approach, mainly to provide a comprehensive analysis of celestial body detection methods. The combination of qualitative and quantitative data allowed for a deep understanding of the topic. Qualitative data was obtained through a literature review, which included research studies, academic papers, and reports from many different sources. Some examples of qualitative data are certain aspects of the celestial body detection method that make it unique such as the complex machinery required to use CubeSats (Franzese, 2023). Quantitative data includes all of the numerical measurements of performance metrics for each detection method, which includes distance, speed, and accuracy. This data is also going to be extracted from research studies and datasets provided by space agencies or other space-related companies. The qualitative findings were combined with quantitative data to improve the validity of the results. Many patterns were used to identify and confirm the ideas from the sources. For example, qualitative themes related to the challenges of CubeSats were supported by quantitative evidence that indicated a lower accuracy when compared to traditional detection methods (Franzese, 2023). Similarly, qualitative insight into the achievements in modern imaging technologies was consistent with quantitative data that showed that modern imaging has improved detection capabilities with a higher resolution.
With this data, I formulated a rubric or specific scoring guidelines that assigned each celestial body detection method a numerical value. The numerical values were computed by ascertaining the average values for each criterion, which were extracted from other research studies. The average values will be calculated by looking at big organizations such as NASA and ESA and recording their specific data values for each celestial body detection method. If NASA and ESA have not reported on a specific celestial body detection method I will look to the Official Space Journal for articles. Furthermore, for each celestial body detection method, I will reference several different research studies and compare their results. In the case of results being very different, I found more studies on that method and averaged them all together. On the occurrence of a celestial body detection method not having many research studies available, I took research studies done on them and averaged the data. The first step in my research study was to gather scientific research studies that contain data about specific celestial body detection methods. I analyzed every research study to guarantee the accuracy and reliability of each study. This included looking at the source of the article and making sure that the source is reliable, such as the research study done by NASA or ESA. Additionally, I looked at whether that research study was posted in any significant or famous journals as these journals perform extensive research on the credibility and reliability of the contents of the research study. I used a specific form that contains many different conditions each research study must contain, if all of these fields are checked off, then I used this research study for my paper.
My methodology was inspired by the research study done by Wenda Chen that focused on comparing five different celestial body detection methods, transit, radial velocity, microlensing, imaging, and timing9. This research study mainly focuses on the results of each research method with an equation used to model the results. The distinction between my research study and the research study done by Wenda Chen is that Wenda Chen’s research study focuses only on a few celestial body detection methods specifically designed for exoplanetary detection, while my research study encompassed various methods serving different but related purposes9.
Average Detection Distance | Average Clarity or Resolution (arcseconds) |
5: 1000+ light-years | 5: Sub-arcsecond resolution |
4: 500-1000 light-years | 4: 0.1 – 0.5 arcseconds |
3: 100-500 light-years | 3: 0.5 – 1 arcsecond |
2: 50-100 light-years | 2: 1 – 2 arcseconds |
1: <50 light-years | 1: >2 arcseconds |
Technological Requirements: | Accessibility: |
5: Minimal | 5: Most accessible |
4: Low | 4: Highly accessible |
3: Moderate | 3: Moderately accessible |
2: High | 2: Less accessible |
1: Very high | 1: Least accessible |
Sensitivity to Specific Characteristics: | Sensitivity to Interference: |
5: Very sensitive | 5: Very low susceptibility |
4: Highly sensitive | 4: Low susceptibility |
3: Moderately sensitive | 3: Moderate susceptibility |
2: Low sensitivity | 2: High susceptibility |
1: Very low sensitivity | 1: Very high susceptibility |
Temporal Sensitivity (hours): | Data Processing Complexity (floating-point operations per second): |
5: Real-time or near real-time | 5: <1012 FLOPs |
4: Within a day | 4: 1012 – 1014 FLOPs |
3: Within a week | 3: 1014 – 1016 FLOPs |
2: Within a month | 2: 1016 – 1018 FLOPs |
1: Longer than a month | 1: >1018 FLOPs |
Observational Cost (USD): | Average Wavelength Range (nanometers): |
5: <$100,000 | 5: >1000 nm |
4: $100,000 – $1,000,000 | 4: 500 – 1000 nm |
3: $1,000,000 – $10,000,000 | 3: 300 – 500 nm |
2: $10,000,000 – $100,000,000 | 2: 100 – 300 nm |
1: >$100,000,000 | 1: <100 nm |
Additionally, these characteristics are present in every single celestial body detection method allowing for this methodology to be easily replicable. Each characteristic is weighted equally as each characteristic is important for determining a good detection method. Moreover, as I am using several different studies I used meta-analysis, a statistical technique to combine and analyze data, to provide a more comprehensive conclusion.
First, I conducted a thorough literature review using a data evaluation form to identify relevant studies. Next, I meticulously gathered pertinent data from each study. Then, I quantified the significance of the results obtained from each study. Finally, I performed a comprehensive statistical analysis, which enabled me to draw meaningful conclusions based on the synthesized data.
The specific celestial body detection methods that I reviewed were radial velocity, astrometry, radio wave detection, the use of optical telescopes, direct imaging, pulsar timing, microlensing, transits, and remote sensing.
Results
My research resulted in many different types of data. My data included qualitative and quantitative measurements as many celestial body detection methods contain valuable information in both formats. Additionally, having both data types provided unique insights and complemented the other. Integrating both quantitative and qualitative data, allowed me to triangulate my findings. Triangulation is comparing data from different sources or methods to validate findings. Not only does having qualitative and quantitative data provide a vaster data bank, but it also enhances the credibility and validity of the research outcomes. More specifically, my quantitative data provided numerical measurements and statistical analysis, while qualitative data offered rich, detailed descriptions and explanations. Together, they provided a more comprehensive understanding of the research topic, allowing readers to understand every celestial body detection method better. The scorings and total scores for the celestial body detection methods are found below. (Tables 2-11)
Radial Velocity | ||||
Average Detection Distance | Average Clarity or Resolution | Sensitivity to Specific Characteristics | Sensitivity to Interference | Observational Cost |
4 | 4 | 4 | 3 | 4 |
Technological Requirements | Accessibility | Temporal Sensitivity | Data Processing Complexity | Average Wavelength Range |
4 | 4 | 3 | 4 | 4 |
Astrometry | ||||
Average Detection Distance | Average Clarity or Resolution | Sensitivity to Specific Characteristics | Sensitivity to Interference | Observational Cost |
4 | 5 | 3 | 2 | 4 |
Technological Requirements | Accessibility | Temporal Sensitivity | Data Processing Complexity | Average Wavelength Range |
3 | 4 | 3 | 4 | 4 |
Radio Wave Detection | ||||
Average Detection Distance | Average Clarity or Resolution | Sensitivity to Specific Characteristics | Sensitivity to Interference | Observational Cost |
5 | 3 | 5 | 2 | 4 |
Technological Requirements | Accessibility | Temporal Sensitivity | Data Processing Complexity | Average Wavelength Range |
5 | 5 | 5 | 4 | 5 |
Optical Telescopes | ||||
Average Detection Distance | Average Clarity or Resolution | Sensitivity to Specific Characteristics | Sensitivity to Interference | Observational Cost |
4 | 5 | 4 | 3 | 4 |
Technological Requirements | Accessibility | Temporal Sensitivity | Data Processing Complexity | Average Wavelength Range |
4 | 4 | 4 | 3 | 4 |
Direct Imaging | ||||
Average Detection Distance | Average Clarity or Resolution | Sensitivity to Specific Characteristics | Sensitivity to Interference | Observational Cost |
3 | 5 | 3 | 2 | 5 |
Technological Requirements | Accessibility | Temporal Sensitivity | Data Processing Complexity | Average Wavelength Range |
5 | 4 | 4 | 5 | 4 |
Remote Sensing | ||||
Average Detection Distance | Average Clarity or Resolution | Sensitivity to Specific Characteristics | Sensitivity to Interference | Observational Cost |
4 | 4 | 4 | 2 | 2 |
Technological Requirements | Accessibility | Temporal Sensitivity | Data Processing Complexity | Average Wavelength Range |
5 | 4 | 5 | 4 | 5 |
Microlensing | ||||
Average Detection Distance | Average Clarity or Resolution | Sensitivity to Specific Characteristics | Sensitivity to Interference | Observational Cost |
3 | 5 | 4 | 5 | 4 |
Technological Requirements | Accessibility | Temporal Sensitivity | Data Processing Complexity | Average Wavelength Range |
4 | 3 | 4 | 4 | 4 |
Transits | ||||
Average Detection Distance | Average Clarity or Resolution | Sensitivity to Specific Characteristics | Sensitivity to Interference | Observational Cost |
4 | 5 | 4 | 4 | 3 |
Technological Requirements | Accessibility | Temporal Sensitivity | Data Processing Complexity | Average Wavelength Range |
4 | 4 | 4 | 3 | 3 |
Pulsar Timing | ||||
Average Detection Distance | Average Clarity or Resolution | Sensitivity to Specific Characteristics | Sensitivity to Interference | Observational Cost |
5 | 5 | 5 | 4 | 3 |
Technological Requirements | Accessibility | Temporal Sensitivity | Data Processing Complexity | Average Wavelength Range |
4 | 4 | 3 | 3 | 2 |
Method | Total Score |
Radial Velocity | 38 |
Astrometry | 37 |
Radio Wave Detection | 45 |
Optical Telescopes | 39 |
Direct Imaging | 40 |
Remote Sensing | 39 |
Microlensing | 40 |
Transits | 38 |
Pulsar Timing | 36 |
Discussion
A total of 9 different celestial body detection methods were analyzed and interpreted to determine which method is the best for detecting masses. Many different characteristics were tested with each method and were scored on a range of 1-5. I hypothesized that radio wave detection would be the most effective and efficient method of detecting celestial objects. This hypothesis was proven by having a score of 45: five points more than the next highest scoring method. This may seem like a biased view however I used over five different studies to get accurate data for one celestial body detection method. Additionally, I tended to not use studies that talked about many different celestial body detection methods in one research paper. This is due to inaccuracies or biases that could be present when trying to analyze so many different methods.
While initially researching these different celestial body detection methods I already had a perception set of what the results will be. However, I was mildly surprised by the many discoveries that emerged. The methods that I believed were significantly different from each other, had many similarities that I did not expect. For example, I believed that optical telescopes would have the lowest operation cost due to how long optical telescopes have been used. However, optical telescopes are shown as having some of the greatest operational costs ranging from $500,000 to $10,000,000. This is similar to the operational cost for radio wave detection which would easily have the highest operational cost due to the complex machinery needed to operate effectively. Moreover, both methods do not even touch the costs of remote sensing. Remote sensing pertains to the launching of different spacecraft and satellites to closely monitor the conditions or characteristics of planetary objects (Figure 13). This greatly varies from my initial perception of what remote sensing is.
Method | Operation Costs |
Optical Telescopes | $500,000 – $10,000,000 |
Radio Wave Detection | $1,000,000 – $10,000,000 |
Remote Sensing | $10,000,000 – $500,000,000 |
Likewise, when looking at some detection methods that I believed to be similar, many blatant differences appeared. For example, at first glance, radial velocity and astrometry seem to be very similar methods with similar technological requirements, average wavelength range, and accessibility. However, radial velocity and astrometry are very different with significant differences in their temporal sensitivities, observational costs, and data processing complexity (Figure 14).
Radial Velocity | Astrometry | |
Temporal Sensitivity | 100 – 1000 | 500 – 2000 |
Observational Cost | $100,000 – $1,000,000 | $500,000 – $5,000,000 |
Data Processing Complexity | 1012 – 1015 FLOPs | 1013 – 1016 FLOPs |
Celestial body detection methods vary greatly, but many share key characteristics: data processing complexity, accessibility, and average clarity/resolution. High data processing complexity requires a skilled team, but simplified methods, like those used in NASA’s citizen science projects, allow quicker analysis. Most methods now have lower complexity due to years of refinement.
Average clarity or resolution, indicating the detail in observations, depends on factors like instrument quality, atmospheric conditions, and observing wavelengths. Methods in use longer, like optical telescopes, generally achieve higher clarity than newer methods like radio wave detection.
Radio wave detection excels in penetration through interstellar gas and dust, large-scale surveying capabilities, and accurate detection of cold objects, revealing deeper space regions and new sources. This method scores consistently high in characteristics like detection distance and wavelength range, averaging 4.5 overall, despite lower clarity/resolution compared to direct imaging or microlensing.
Ongoing advancements, such as Very Long Baseline Interferometry, are enhancing radio telescopes’ resolutions, making them comparable to optical telescopes. These improvements allow astronomers to study intricate features of celestial objects more effectively than with other methods.10
Conclusions
While my initial hypothesis was that radio wave detection is the best celestial body detection method across all characteristics, after my research and analyzing the resulting data I have learned that all of these celestial body detection methods are complementary techniques, scientists use them in conjunction to gain a more deep understanding of our universe. Each technique has its strengths and weaknesses, and the choice of a specific celestial body detection method depends on the specific scientific goals and characteristics of the objects being studied. Thus, my hypothesis is true but it has to be taken into account that this observation is derived from many different characteristics where some methods perform amazingly and some methods perform poorly. This observation assumes you are comparing every single major characteristic of celestial body detection methods, where some characteristics may not be specifically targeted for some methods.
My main observations are broken down into three main assertions. The first observation is that radio wave detection is the most effective and efficient method for detecting celestial objects, on average. This was tested by performing deep literary analysis and extracting data regarding major shared characteristics that most celestial body detection methods account for. Several studies also agree that radio wave detection is a very good detection method such as several studies done by esteemed scientists at the Carnegie Museum of Natural History and NASA, thus adding more credibility to my claim.11
The second observation is that many different celestial body detection methods exhibit low data processing complexity, indicating that the data is relatively straightforward to analyze. Additionally, the average clarity or resolution of the tested celestial body detection methods tends to be influenced by their longevity or how long they have been implemented in the scientific community.
The third observation is that celestial body detection methods that use radio waves offer many advantages that propel them in front of many different celestial body detection methods. These advantages include longer wavelengths, enabling better penetration through interstellar gas and dust, and large-scale surveying capabilities leading to groundbreaking discoveries. Radio wave detection is also the best method to detect various celestial objects such as pulsars, clouds of gas or dust, and galaxies.12
These observations regarding radio wave detection being the most effective and efficient method for celestial body detection are significant for the field of astronomy. Firstly, it emphasizes the importance of technological advancements in radio astronomy, allowing for further research and development in this area.
Moreover, with radio waves offering advantages such as better penetration through interstellar gas and dust, as well as large-scale surveying capabilities, it opens up new avenues for exploring the universe. Furthermore, radio wave detection’s low data processing complexity hints at the possibility of streamlining and optimizing the analysis of celestial data.
By optimizing radio wave detection methods, astronomers may be able to uncover previously unknown celestial phenomena and gain deeper insights into the nature and evolution of the cosmos.
Limitations
My research study is very complex and contains many intricate parts that require a vast amount of knowledge. My research study began with the idea of performing a scientific experiment testing several different celestial body detection methods and recording specific data such as average wavelength and temporal sensitivity. To test these celestial body detection methods, a laboratory equipped with proper safety features and capabilities for intensive testing is required. After contacting several professors hoping to collaborate, I was turned down due to my limited experience and lack of advanced certification. Even after submitting several forms of verification, I was unable to acquire a mentor who would assist me with my research study. Without a proper laboratory or environment for testing different celestial body detection methods, I switched to a secondary research study where I extracted the information required, to make a well-supported conclusion.
Another limitation I faced was my lack of knowledge regarding advanced topics such as advanced space physics, vector calculus, and dynamic/relativity. With this roadblock, I decided to simplify my research study, making it feasible for a high school student to perform.
The last limitation, one of the most significant, is the lack of a mentor or teaching figure. As I stated earlier, my research study is fairly complex and requires a lot of absorption of new material: material I have never been exposed to before. Having a mentor or teaching figure would have allowed me to make my research study more complex and meaningful to the scientific community. With a mentor, I could find more professional and official sources to extract information from and also the mentor’s knowledge could aid me or steer me in the correct direction. This would not only save a lot of time but make my research more accurate and reliable, as having a professional mentor would make my research more credible. Moreover, if the mentor was affiliated with a specific organization or institution, research studies that would have been hidden from the public, would be available to me resulting in a more accurate analysis.
Ending Statement
This study aimed to research and distinguished what the best detection method is, on average. The research method was a secondary research study that utilized many different published articles to extract information. To mitigate bias or invalid sources, the research study employed a source evaluation form that included many forms of validation, such as a checklist filled with many characteristics that a reliable study would have, to ensure the validity of the research study’s conclusions. From these research papers and articles, I extracted the specific information that would give data that corresponds to the specific characteristics the research study is testing. Each celestial body detection method has data for every single characteristic the research study chose. With this data, a rubric or criteria was created to properly score each celestial body detection method. Analyzing these scores, the research study revealed that radio wave detection is the most effective and efficient method, on average. As well as revealing radio wave detection as the most efficient method for detecting celestial bodies, the analysis of the scores revealed many strengths and weaknesses for several different celestial body detection methods.
While my research study ended with a concrete conclusion, I encountered many limitations. Some of these limitations are the need for a laboratory, a mentor, and advanced knowledge. The original plan for the research study was to experiment with each celestial body detection method for each specific characteristic. However, no mentor or teaching figure accepted this proposal of collaboration. Moreover, during the process of gathering data and doing preliminary research, I faced many challenges regarding my current knowledge of complex topics such as space physics and vector calculus. This switch overcame all of these limitations by switching to a secondary research method. This eliminated many of the problems I would have encountered if I decided to pursue an experimental study.
References
- Definition of CELESTIAL BODY. (2019). Merriam-Webster.com. https://www.merriam-webster.com/dictionary/celestial%20body [↩]
- ESA Science & Technology – A history of astrometry – Part IMapping the sky from ancient to pre-modern times. (2019, September 1). Sci.esa.int.https://sci.esa.int/web/gaia/-/53196-the-oldest-sky-maps#:~:text=The%20first%20documented%20records%20of [↩] [↩]
- Smith, K. N. (2016, January 28). Ancient Babylonian Astronomers Were Way Ahead of Their Time. Discover Magazine; Discover Magazine. https://www.discovermagazine.com/the-sciences/ancient-babylonian-astronomers-were-way-ahead-of-their-time [↩]
- Creighton, J. (2014, July 24). A Timeline of Death: How Long Until We Exhaust All Our Resources? Futurism; Futurism.https://futurism.com/how-long-do-we-have-until-we-exhaust-all-of-our-resources [↩]
- Johnson, B. (2021). Exoplanetary Atmospheres and How to Understand Them. SETI Institute. https://www.seti.org/exoplanetary-atmospheres-and-how-understand-them [↩] [↩]
- Johnson, B. (2021). Exoplanetary Atmospheres and How to Understand Them. SETI Institute. https://www.seti.org/exoplanetary-atmospheres-and-how-understand-them [↩]
- NASA. (2022, April 13). How We Find and Characterize | Discovery. Exoplanet Exploration: Planets Beyond Our Solar System. https://exoplanets.nasa.gov/discovery/how-we-find-and-characterize/ [↩]
- Franzese, V., & Topputo, F. (2023). Celestial Bodies Far-Range Detection with Deep-Space CubeSats. Sensors, 23(9), 4544. https://doi.org/10.3390/s23094544 [↩] [↩]
- Chen, W. (2023). The Comparison of Five Methods of Detecting Exoplanets. Highlights in Science, Engineering and Technology, 38, 235–244. https://doi.org/10.54097/hset.v38i.5812 [↩] [↩] [↩] [↩] [↩] [↩]
- Space Agency, E. (2024). Observations: Very Long Baseline Interferometry (VLBI). Www.esa.int. https://www.esa.int/Science_Exploration/Space_Science/Observations_Very_Long_Baseline_Interferometry_VLBI [↩]
- Detecting Objects with Invisible Waves: Using Radar, Sonar, and Echolocation to “See.” (2020). Carnegiemnh.org. https://carnegiemnh.org/detecting-objects-with-invisible-waves-using-radar-sonar-and-echolocation-to-see/#:~:text=The%20received%20radio%20wave%20information [↩]
- Astronomy Observatory, N. R. (2024). Pulsars Astronomy. National Radio Astronomy Observatory. https://public.nrao.edu/radio-astronomy/pulsars/#:~:text=star%20(yellow). [↩]