Speaker
Description
Within the first billion years of cosmic time, galaxies and supermassive black holes (SMBHs) had already formed. Especially, rapidly growing billion solar mass black holes - shining as quasars - discovered at z > 7 challenge standard models of SMBH growth. To constrain their formation, expanding the quasar redshift frontier is critical.
The deep, wide-area photometry of the Euclid mission, is revolutionizing this frontier and is expected to discover hundreds of new quasars. Over the past two years, about 50 new quasars have been confirmed, more than doubling the number of z > 7 quasars. This success was facilitated by state-of-the-art supervised machine learning methods, efficiently selecting candidates from billions of sources.
However, they are naturally biased toward the training dataset, potentially running the risk of missing quasars with properties different from known populations.
In this work, we explore self-supervised machine learning methods for a less biased census of the quasar population. Specifically, we build a multimodal probabilistic autoencoder (PAE) for anomaly detection and apply it to Euclid data. We combine image cutouts and catalog data via cross-attention in our architecture. The PAE training consists of two stages: first, latent representations are learned using a standard autoencoder; second, the latent space is mapped to a Gaussian using normalizing flow. We define an anomaly score based on the source distribution likelihood.
In current tests, known quasars are well separated from contaminants and form a compact region in the representation space, well consistent with high anomaly scores.
With this study we are laying the foundation for a less biased, more comprehensive quasar selection methodology, which will be applied to the full Euclid DR1 dataset.