XGBoost classifier filters interlopers in CSST slitless spectroscopy simulations, retaining 42% of galaxies with 96.6% accurate redshifts and 0.13% outliers.
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Constructs a (D_τ,D_x)-manifold with N-correlators of N_t-objects using field theory, topology, algebra, statistics and Fourier transforms, and discusses applicability across cosmological scales.
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Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy
XGBoost classifier filters interlopers in CSST slitless spectroscopy simulations, retaining 42% of galaxies with 96.6% accurate redshifts and 0.13% outliers.
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A $(D_\tau,D_x)$-manifold with $N$-correlators of $N_t$-objects
Constructs a (D_τ,D_x)-manifold with N-correlators of N_t-objects using field theory, topology, algebra, statistics and Fourier transforms, and discusses applicability across cosmological scales.