Past exam of the mathematics course of the University of Cambridge 2009 iii Paper 18 4 ii Solution Created 2026-10-03 Updated 2026-10-14
Let now denote the degree-zero torsion line bundle represented by the point of , and put . Again the Riemann-Roch theorem gives and . For translation, use as a translated subset. With ordinary , this is ; the notation therefore uses .
Let and be the evaluation determinants for the two chosen bases, interpreted as sections of and . The kernel for the first determinant is . By Serre duality and the Riemann-Roch theorem its dimension is . Thus the determinant vanishes along , including multiplicities by the same determinant-complex argument as in part (i). The quotient is a rational sectionwith zero-minus-pole Cartier divisor . The diagonal factors in the two determinants cancel.
Choose a trivialization . This is possible because . Taking the th tensor power of and using produces a scalar rational function on withOn , the Theorem of the square and implyConsequently there is a rational function with . The map is surjective, since every degree- line bundle has a section by the Riemann-Roch theorem. The two rational functions and have the same Cartier divisor on the smooth projective connected product ; their quotient is an everywhere invertible regular function and hence a constant. Rescale to remove that constant. Away from the diagonals, ordinary evaluation giveswhere the th power is interpreted with the chosen torsion trivialization. Equality as rational functions then extends from this dense open set to the whole product.
The torsion trivialization in a determinant quotient cannot be omitted if one uses raw meromorphic representatives. Explicitly, write and choose with . If is the quotient of the evaluation matrices in these meromorphic frames, thenThus the displayed formula suppresses bundle trivializations; read as an unframed quotient of arbitrary meromorphic representatives, it needs precisely this extra product. Changes of bases or rescaling only change the allowable nonzero scalar normalization of .