1-(Butan-2-yl­idene)-2-(2-nitro­phen­yl)hydrazine

Crystals of the title compound, C10H13N3O2, were obtained from a condensation reaction of butan-2-one and 1-(2-nitro­phen­yl)hydrazine. The mol­ecule exhibits a nearly coplanar structure, except for the methyl and methyl­ene H atoms, the largest deviations from the mean plane defined by all non-H atoms, except for the nitro group, being 0.120 (2) Å for one of the nitro O atoms. Intra­molecular N—H⋯O hydrogen bonding helps to establish the mol­ecular configuration

N′-(2-Furylmethyl­ene)nicotinohydrazide

The asymmetric unit of the title compound, C11H9N3O2, contains two independent mol­ecules: the dihedral angles between the pyridine ring and the furyl ring are 17.00 (16) and 34.12 (15)°. The crystal structure involves inter­molecular C—H⋯O, N—H⋯N and N—H⋯O hydrogen bonds

Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector

We provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is an attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a benchmark case and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multi-exponential relationship with respect to the number of PMTs and hence, the coverage. This could also assist in deciding on the merits of installing additional PMTs for future detector plans. The approach could easily be used with other objectives in place of vertex reconstruction