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We present SNIascore, a deep-learning based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R ∼100) data. The goal of SNIascore is fully automated classification of SNe Ia with a very low false-positive rate (FPR) so that human intervention can be greatly reduced in large-scale SN classification efforts, such as that undertaken by the public Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS). We utilize a recurrent neural network (RNN) architecture with a combination of bidirectional long short-term memory and gated recurrent unit layers. SNIascore achieves a <0.6% FPR while classifying up to 90% of the low-resolution SN Ia spectra obtained by the BTS. SNIascore simultaneously performs binary classification and predicts the redshifts of secure SNe Ia via regression (with a typical uncertainty of <0.005 in the range from z=0.01 to z=0.12). For the magnitude-limited ZTF BTS survey (≈70% SNe Ia), deploying SNIascore reduces the amount of spectra in need of human classification or confirmation by ≈60%. Furthermore, SNIascore allows SN Ia classifications to be automatically announced in real-time to the public immediately following a finished observation during the night. 

Using Zwicky Transient Facility (ZTF) observations, we identify a pair of "sibling" Type Ia supernovae (SNe Ia), i.e., hosted by the same galaxy at z = 0.0541. They exploded within 200 days from each other at a separation of 0.6″ corresponding to a projected distance of only 0.6 kpc. Performing SALT2 light curve fits to the gri ZTF photometry, we show that for these equally distant "standardizable candles", there is a difference of 2 magnitudes in their rest frame B-band peaks, and the fainter SN has a significantly red SALT2 colour c=0.57± 0.04, while the stretch values x1 of the two SNe are similar, suggesting that the fainter SN is attenuated by dust in the interstellar medium of the host galaxy. We use these measurements to infer the SALT2 colour standardization parameter, β = 3.5 ± 0.3, independent of the underlying cosmology and Malmquist bias. Assuming the colour excess is entirely due to dust, the result differs by 2σ from the average Milky-Way total-to-selective extinction ratio, but is in good agreement with the colour-brightness corrections empirically derived from the most recent SN Ia Hubble-Lemaitre diagram fits. Thus we suggest that SN "siblings", which will increasingly be discovered in the coming years, can be used to probe the validity of the colour and lightcurve shape corrections using in SN Ia cosmology while avoiding important systematic effects in their inference from global multi-parameter fits to inhomogeneous data-sets, and also help constrain the role of interstellar dust in SN Ia cosmology.