PANOPTICON: A novel deep learning model to detect single transit events with no prior data filtering in PLATO light curves

Open Access

Table 1

Typical number of parameters for each architecture.

Kernel	Unet	Unet++	Unet3+
11	562 713	651 025	516 385
31	1 543 353	1 778 865	1 452 225
61	3 014 313	3 470 625	2 855 985

Notes. The kernel here refers to the number of learnable parameters k_l, and the number of initial feature channels is set to eight.

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