Julian Mena | Searcharxiv

arXiv2025

Optimized Observation Sequencing in Low-Earth Orbit with the SPHEREx Survey Planning Software

SPHEREx is a NASA infrared astronomy mission that launched on March 12th, 2025 and is operating successfully in low-Earth orbit (LEO). The mission is currently observing the entire sky in 102 spectral channels in four independent all-sky surveys and also achieves enhanced coverage in two deep fields. This data will resolve key science questions about the early universe, galaxy formation, and the origin of water and biogenic molecules. In this paper, we describe the survey planning software (SPS) that enables SPHEREx to observe efficiently while mitigating a range of operational challenges in LEO. Our optimal target selection algorithm achieves the required high coverage in both the All-Sky and Deep Surveys. The algorithm plans observations to stay within our time-varying allowable pointing zone, interleaves required data downlink passes, and mitigates outages due to the South Atlantic Anomaly and other events. As demonstrated by the sky coverage achieved in the first SPHEREx public data release, our approach is performing well in flight. The SPHEREx SPS is a key new capability that enables the mission to deliver groundbreaking science from LEO.

astro-ph.IM↗

arXiv2023

Robust Joint Estimation of Galaxy Redshift and Spectral Templates using Online Dictionary Learning

We present a novel approach to analyzing astronomical spectral survey data using our non-linear extension of an online dictionary learning algorithm. Current and upcoming surveys such as SPHEREx will use spectral data to build a 3D map of the universe by estimating the redshifts of millions of galaxies. Existing algorithms rely on hand-curated external templates and have limited performance due to model mismatch error. We address these limitations by developing a new algorithm that jointly estimates both the underlying spectral features in common across the entire dataset, as well as the redshift of each galaxy. To do this, we significantly extend an existing online dictionary learning algorithm, and apply this approach to redshift estimation for the first time. Our new approach scales well to large datasets since we only process a single spectrum in memory at a time. Our algorithm performs better than a state-of-the-art existing algorithm when analyzing a mock SPHEREx dataset, achieving a normalized median absolute deviation (NMAD) of 0.18% and a catastrophic error rate of 0.40% when analyzing noiseless data. Our algorithm also performs well over a wide range of signal to noise ratios (S/N), delivering sub-percent NMAD and catastrophic error above median S/N of 20. We released our algorithm publicly and it is available on github.

astro-ph.IM↗