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When multiple star-forming gas structures overlap along the line-of-sight andemit optically thin emission at significantly different radial velocities, theemission can become non-Gaussian and often exhibits two distinct peaks.Traditional line-fitting techniques can fail to account adequately for thesedouble-peaked profiles, providing inaccurate cloud kinematics measurements. Wepresent a new method called Convnet Line-fitting Of Velocities in Emission-lineRegions (CLOVER) for distinguishing between one-component, two-component, andnoise-only emission lines using 1D convolutional neural networks trained withsynthetic spectral cubes. CLOVER utilizes spatial information in spectral cubesby predicting on $3\times3$ pixel sub-cubes, using both the central pixel'sspectrum and the average spectrum over the $3\times3$ grid as input. On anunseen set of 10,000 synthetic spectral cubes in each predicted class, CLOVERhas classification accuracies of $\sim99\%$ for the one-component class and$\sim97\%$ for the two-component class. For the noise-only class, which isanalogous to a signal-to-noise cutoff of four for traditional line-fittingmethods, CLOVER has classification accuracy of $100\%$. CLOVER also hasexceptional performance on real observations, correctly distinguishing betweenthe three classes across a variety of star-forming regions. In addition, CLOVERquickly and accurately extracts kinematics directly from spectra identified astwo-component class members. Moreover, we show that CLOVER is easily scalableto emission lines with hyperfine splitting, making it an attractive tool in thenew era of large-scale NH$_3$ and N$_2$H$^+$ mapping surveys.

CLOVER: Convnet Line-fitting Of Velocities in Emission-line Regions