| Abstract : | Till now, Earth is the only known planet to humankind that can support intelligent life. One approach to finding extraordinarily advanced intelligent civilizations compared to humans is to search for Dyson Spheres, a hypothetical megastructure built by an advanced civilisation around their host star for harvesting energy. The observational signature of the Dyson sphere is a change in the spectrum of the host star, dim in optical while bright in infrared. But, Young Stellar Objects (YSO) found in nebulae have similar spectral characteristics as a Dyson sphere. Suazo et al. (2022) have used a Machine Learning algorithm to select Dyson sphere candidates based on the infrared photometric observations of the stars in our galaxy. However, this algorithm sometimes fails to detect any nebular feature around a star, even when the star has been identified as a YSO via other observations. In the case of a YSO, we expect a higher fraction of red stars, because of dust reddening, in its surrounding nebular region compared to a random star located in a non-nebular environment. We plan to use this characteristic of YSOs to filter out the YSOs from the list of potential Dyson sphere candidates identified by Suazo et al. (2022). Our initial analysis shows that for a sample of 218 randomly chosen the main sequence, 218 Herbig Ae/Be and 218 T-Tauri stars, the average value of the fraction of red stars for Herbig Ae/Be is 0.330 ± 0.283, 0.669 ± 0.262 and 0.864 ± 0.064, respectively. This significant difference in the fraction of neighbouring red stars could potentially be used to differentiate whether a Dyson Sphere candidate belongs to a nebular region or not. We apply this method to verify potential Dyson sphere candidates identified by Suazo et al. (2022).
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