恆星閃焰事件是磁重聯作用的結果，尤其是光譜型態晚期恆星。具有對流層的太陽 型恆星會產生磁場，而那些閃焰即是在恆星表面上，有著相當大的磁通量的區域。一些 M 矮星，光譜類型越晚，對流性越強，更容易發生這種恆星閃焰，與沒有表面活動時期 的亮度相比，它們閃焰的發生頻率更高，而且能量顯著的更強。
在這裡，我們報告了 Wolf 359 (GJ409; CN Leo) 的可見光波段閃焰事件。這顆年輕 (< 1 Gyr)的紅矮星(M6.5 Ve)是距離太陽第五近的恆星系統(2.4 pc)，已知有頻繁的 的閃焰活動，伴隨著伽馬射線和 X 射線波段的爆發。我們的數據包括 2020 年 4 月共 7 天，有效時間為 27 小時在新疆使用兩台小型望遠鏡進行的觀測，其中一台是南山一米 廣角望遠鏡，另外一台則是最近從鹿林天文台搬遷的 TAOS 0.5 m 望遠鏡。觀測到 13 次能量大於 1029 ergs 的閃焰，包括一次“超級閃焰”事件(∼ 1031 ergs)，意味著每兩 小時平均發生一次閃焰事件。每個閃焰事件我們進行參數化，包括用指數衰退去擬合衰 減階段。對於由兩個望遠鏡同時觀測到的“超級閃焰”事件，我們透過模擬兩台望遠鏡 採樣函數，恢復這個事件的“可能真相”。我們還討論了採樣函數如何改變真實的閃焰
形狀，從而得出不同對能量估算的影響。

Flare events are eruptive brightening observed on the surface of late-type stars. The solar-type stars known to have convection layers produce magnetic fields, and those areas exposed to considerable magnetic fluxes are visible as spots on the surface. Some M dwarfs, the later spectral types, the more so, being more convective, are even more prone to such stellar flares, with higher occurrence rates and significantly more energetic during the flares in comparison to the quiescent photometric luminosities.
Here we report the optical flare activity of Wolf 359 (GJ409; CN Leo). This relatively young (< 1 Gyr) red dwarf (M6.5 Ve), being the fifth nearest stellar system (2.4 pc) to the Sun, is known for its frequent optical flares, along with gamma-ray and X-ray bursts. Our data consist of 27 hours spanning seven days in April 2020 of photometric monitoring with two small telescopes in Xinjiang, including a one-meter and one of the TAOS 0.5 m telescopes recently relocated from Lulin Observatory. A total of 13 flares with energies greater than 1029 ergs were detected, including one ”superflare” event (∼ 1031 ergs), implying an average occurrence rate of one flare per two hours. Each flare is parameterized and fitted the decay phase with exponential templates. For the ”superflare” event, which was observed simultaneously by two telescopes, we simulate how do the sampling functions influence the profiles and recover the ”possible truth” of this particular event. We also discuss how the sampling function reshapes the underlying profile, and therefore different energy budgets are derived.

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