Transitmethode - SensorTag *Astrolabor Universität zu Köln 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 Suche nach Exoplaneten mit der Transitmethode. In diesem Experiment wird die Veränderung der Beleuchtungsstärke eines Sterns bei einem Transit eines Planeten gemessen und somit der Alltag eines Astrophysikers simuliert. Dafür werden Analogieexperimente, wie ein die Lichtquelle (Stern) umkreisender Gegenstand (Planet), genutzt. Für die Messung wird mit dem Lichtsensor des TI SensorTags die Beleuchtungsstärke [lx] in Abhängigkeit von der Zeit [s] gemessen. Die Gesamtdauer der Verdunkelung (totale Transitdauer) und der Zeitraum zwischen zwei Transits (Umlaufzeit um den Stern) wird mit der in phyphox berets integrierten optischen Stoppuhr bestimmt. threshold_low threshold_high amplitude t search_a search_t trigger_t trigger_last on0 on1 on2 on3 on4 on5 off0 off1 off2 off3 off4 off5 don0 don1 don2 don3 don4 don5 dt01 dt02 dt03 dt04 dt05 dt12 dt23 dt34 dt45 reset RawInt e m countI tmax factor max_amplitude min_amplitude transit_depth R_star R_planet Quot_R avg_transit_time max_t avg_year_duration Transit method - SensorTag *Astrolab University of Cologne Search for exoplanets using the transit method. In this experiment, the change in the illuminance of a star during the transit of a planet is measured, thus simulating the everyday life of an astrophysicist. For this purpose analogy experiments are used, such as an object (planet) orbiting the light source (star). For the measurement, the light sensor of TI SensorTag measures the illuminance [lx] as a function of time [s]. The total duration of the eclipse (total transit time) and the time between two transits (orbiting time around the star) is determined with the optical stopwatch already integrated in phyphox. Illuminance Illuminance-time diagram Limited by the standard 800ms refresh rate of the CC2650. Measuring duration Maximum illuminance (without transit) Minimum illuminance (for transit) Transit depth ⌀ Transit duration ⌀ duration of one year Reset Transit duration Measures the total transit time, i.e. the time between the beginning and end of the star's eclipse due to the transit of the planet. Zeroth transit First transit Second transit Third transit Fourth transit Fifth transit Note: The ⌀ transit duration is only displayed after six transits. The zeroth transit pass is neglected, because especially in the case of the hidden measurement it is not known whether the planet is already in front of the star at the beginning of the measurement. Period of circulation Measures the time between two transits and calculates the ⌀ duration of one year on the planet Year 1 Year 2 Year 3 Year 4 Year 5 Note: The ⌀ duration of one year on the planet is only given after five years. Planet size The star radius is already known in many cases. For example, it can be approximated by the luminosity and temperature of the star using the Stefan-Boltzmann law. Enter a suitable star radius here Star radius Using the results from the transit method for the transit depth, the radius of the planet can now be estimated. For the planet follows: Radius of the planet Size Planet relative to star Trigger configuration Place the smartphone at the later measuring distance from the light source and determine the maximum luminosity. We now define this as the luminosity of the star. Maximum luminosity This is the recommended value below which the time measurement is triggered and above which it is stopped. This way the total transit time is measured. After entering the threshold for the transit measurement, press Reset and start the measurement. Threshold for transit measurement By default, the stopwatch is set to measure darkening. If the time between two light flashes is to be measured, set the upper value to zero and select a threshold in the following field. Threshold for brightness measurement Attention: for the transit experiment it is important that this value remains set to the default of 1000000.0 and only the threshold for the transit measurement is varied. These are calculations of our model star and model planet. In reality, the dimensions are much larger: Earth's radius is 6 371 kilometres! The radius of the sun is 109 times greater. For the size ratio it results that the earth is therefore only 0.000077% of the sun. 50 01 RawInt RawInt e e RawInt m 2 e factor factor m amplitude RawInt countI countI tmax 0 tmax countI t amplitude max_amplitude amplitude min_amplitude max_amplitude min_amplitude transit_depth R_star transit_depth R_planet R_planet R_star Quot_R don1 don2 don3 don4 don5 avg_transit_time t max_t dt01 dt12 dt23 dt34 dt45 avg_year_duration 0 on0 0 on1 0 on2 0 on3 0 on4 0 on5 0 off0 0 off1 0 off2 0 off3 0 off4 0 off5 reset 0 amplitude reset 0 t 0 reset 0 trigger_last t 0.001 amplitude search_t search_a search_t search_a threshold_low trigger_t search_t search_a threshold_high trigger_t trigger_t on0 on0 trigger_last on0 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_high threshold_low trigger_t trigger_t off0 off0 trigger_last off0 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_low trigger_t search_t search_a threshold_high trigger_t trigger_t on1 on1 trigger_last on1 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_high threshold_low trigger_t trigger_t off1 off1 trigger_last off1 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_low trigger_t search_t search_a threshold_high trigger_t trigger_t on2 on2 trigger_last on2 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_high threshold_low trigger_t trigger_t off2 off2 trigger_last off2 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_low trigger_t search_t search_a threshold_high trigger_t trigger_t on3 on3 trigger_last on3 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_high threshold_low trigger_t trigger_t off3 off3 trigger_last off3 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_low trigger_t search_t search_a threshold_high trigger_t trigger_t on4 on4 trigger_last on4 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_high threshold_low trigger_t trigger_t off4 off4 trigger_last off4 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_low trigger_t search_t search_a threshold_high trigger_t trigger_t on5 on5 trigger_last on5 t trigger_last trigger_last 0.001 trigger_t t trigger_t amplitude search_t search_a search_t search_a threshold_high threshold_low trigger_t trigger_t off5 on1 on0 dt01 on2 on0 dt02 on3 on0 dt03 on4 on0 dt04 on5 on0 dt05 on2 on1 dt12 on3 on2 dt23 on4 on3 dt34 on5 on4 dt45 off0 on0 don0 off1 on1 don1 off2 on2 don2 off3 on3 don3 off4 on4 don4 off5 on5 don5 dt01 0 dt01 dt02 0 dt02 dt03 0 dt03 dt04 0 dt04 dt05 0 dt05 dt12 0 dt12 dt23 0 dt23 dt34 0 dt34 dt45 0 dt45 don0 0 don0 don1 0 don1 don2 0 don2 don3 0 don3 don4 0 don4 don5 0 don5 t amplitude max_t max_amplitude min_amplitude transit_depth avg_transit_time avg_year_duration don0 don1 don2 don3 don4 don5 avg_transit_time dt01 dt12 dt23 dt34 dt45 avg_year_duration R_star transit_depth R_planet Quot_R max_amplitude threshold_low threshold_high t amplitude on0 off0 on1 off1 on2 off2 on3 off3 on4 off4 on5 off5