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We present a new methodology for the estimation of stellar atmosphericparameters from narrow- and intermediate-band photometry of the JavalambrePhotometric Local Universe Survey (J-PLUS), and propose a method for targetpre-selection of low-metallicity stars for follow-up spectroscopic studies.Photometric metallicity estimates for stars in the globular cluster M15 aredetermined using this method. By development of a neural-network-basedphotometry pipeline, we aim to produce estimates of effective temperature,$T_{\rm eff}$, and metallicity, [Fe/H], for a large subset of stars in theJ-PLUS footprint. The Stellar Photometric Index Network Explorer, SPHINX, isdeveloped to produce estimates of $T_{\rm eff}$ and [Fe/H], after training on acombination of J-PLUS photometric inputs and synthetic magnitudes computed formedium-resolution (R ~ 2000) spectra of the Sloan Digital Sky Survey. Thismethodology is applied to J-PLUS photometry of the globular cluster M15.Effective temperature estimates made with J-PLUS Early Data Release photometryexhibit low scatter, \sigma($T_{\rm eff}$) = 91 K, over the temperature range4500 < $T_{\rm eff}$ (K) < 8500. For stars from the J-PLUS First Data Releasewith 4500 < $T_{\rm eff}$ (K) < 6200, 85 $\pm$ 3% of stars known to have [Fe/H]<-2.0 are recovered by SPHINX. A mean metallicity of [Fe/H]=-2.32 $\pm$ 0.01,with a residual spread of 0.3 dex, is determined for M15 using J-PLUSphotometry of 664 likely cluster members. We confirm the performance of SPHINXwithin the ranges specified, and verify its utility as a stand-alone tool forphotometric estimation of effective temperature and metallicity, and forpre-selection of metal-poor spectroscopic targets.

J-PLUS: Identification of low-metallicity stars with artificial neural networks using SPHINX