Galaxy formation and evolution

Abstract:

JWST has shed light on galaxy formation and metal enrichment within 300 Myr of the Big Bang. While luminous galaxies at z > 10 often show significant [O iii] λλ4959, 5007 emission lines, it remains unclear whether such features are prevalent among fainter, more typical galaxies due to observational limits. We present deep imaging and spectroscopy of JADES-GS-z14-1 at zspec=13.86−0.05+0.04 , currently the faintest spectroscopically confirmed galaxy at z ≈ 14. It serendipitously received 70.7 hr of MIRI/F770W imaging in the JWST Advanced Deep Extragalactic Survey (JADES), the deepest MIRI exposure for any high-redshift galaxy to date. Nonetheless, we detect only tentative F770W emission of 7.9 ± 2.8 nJy at 2.8σ significance, constraining the total equivalent width of [O iii] λλ4959, 5007 + Hβ to 520−380+400 Å, weaker than most z > 10 galaxies with MIRI detections. This source is unresolved across 16 NIRCam bands, implying a physical radius ≲50 pc. NIRSpec/PRISM spectroscopy totaling 56 hr reveals no rest-frame ultraviolet emission lines above 3σ. Stellar population synthesis suggests a stellar mass ∼4 × 107 M⊙ and a star formation rate ∼2 M⊙ yr−1. The absence of strong metal emission lines despite intense star formation suggests a gas-phase metallicity below 10% solar and potentially a high escape fraction of ionizing photons. These deep observations provide rare constraints on faint, early galaxies, tracing the onset of chemical enrichment and ionization in the early Universe.

Abstract:

Adaptive binning is a crucial step in the analysis of large astronomical data sets, such as those from integral-field spectroscopy, to ensure a sufficient signal-to-noise ratio () for reliable model fitting. However, the widely used Voronoi-binning method and its variants suffer from two key limitations: they scale poorly with data size, often as , creating a computational bottleneck for modern surveys, and they can produce undesirable non-convex or disconnected bins. I introduce PowerBin, a new algorithm that overcomes these issues. I frame the binning problem within the theory of optimal transport, for which the solution is a Centroidal Power Diagram (CPD), guaranteeing convex bins. Instead of formal CPD solvers, which are unstable with real data, I develop a fast and robust heuristic based on a physical analogy of packed soap bubbles. This method reliably enforces capacity constraints even for non-additive measures like with correlated noise. I also present a new bin-accretion algorithm with complexity, removing the previous bottleneck. The combined PowerBin algorithm scales as , making it about two orders of magnitude faster than previous methods on million-pixel data sets. I demonstrate its performance on a range of simulated and real data, showing it produces high-quality, convex tessellations with excellent uniformity. The public python implementation provides a fast, robust, and scalable tool for the analysis of modern astronomical data.