  The advent of next-generation survey instruments, such as the Vera C. Rubin
Observatory and its Legacy Survey of Space and Time (LSST), is opening a window
for new research in time-domain astronomy. The Extended LSST Astronomical
Time-Series Classification Challenge (ELAsTiCC) was created to test the
capacity of brokers to deal with a simulated LSST stream. We describe ATAT, the
Astronomical Transformer for time series And Tabular data, a classification
model conceived by the ALeRCE alert broker to classify light-curves from
next-generation alert streams. ATAT was tested in production during the first
round of the ELAsTiCC campaigns. ATAT consists of two Transformer models that
encode light curves and features using novel time modulation and quantile
feature tokenizer mechanisms, respectively. ATAT was trained on different
combinations of light curves, metadata, and features calculated over the light
curves. We compare ATAT against the current ALeRCE classifier, a Balanced
Hierarchical Random Forest (BHRF) trained on human-engineered features derived
from light curves and metadata. When trained on light curves and metadata, ATAT
achieves a macro F1-score of 82.9 +- 0.4 in 20 classes, outperforming the BHRF
model trained on 429 features, which achieves a macro F1-score of 79.4 +- 0.1.
The use of Transformer multimodal architectures, combining light curves and
tabular data, opens new possibilities for classifying alerts from a new
generation of large etendue telescopes, such as the Vera C. Rubin Observatory,
in real-world brokering scenarios.
