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ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1. 

Abstract We present a high-significance cross-correlation of CMB lensing maps from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) with luminous red galaxies (LRGs) from the Dark Energy Spectroscopic Instrument (DESI) Legacy Survey spectroscopically calibrated by DESI. We detect this cross-correlation at a significance of 38σ; combining our measurement with thePlanck Public Release 4 (PR4) lensing map, we detect the cross-correlation at 50σ. Fitting this jointly with the galaxy auto-correlation power spectrum to break the galaxy bias degeneracy withσ₈, we perform a tomographic analysis in four LRG redshift bins spanning 0.4 ≤z≤ 1.0 to constrain the amplitude of matter density fluctuations through the parameter combinationS₈^×=σ₈(Ω_m/ 0.3)^0.4. Prior to unblinding, we confirm with extragalactic simulations that foreground biases are negligible and carry out a comprehensive suite of null and consistency tests. Using a hybrid effective field theory (HEFT) model that allows scales as small ask_max= 0.6 h/ Mpc, we obtain a 3.3% constraint onS₈^×=σ₈(Ω_m/ 0.3)^0.4= 0.792^+0.024_-0.028from ACT data, as well as constraints onS₈^×(z) that probe structure formation over cosmic time.Our result is consistent with the early-universe extrapolation from primary CMB anisotropies measured byPlanck PR4 within 1.2σ. Jointly fitting ACT andPlanck lensing cross-correlations we obtain a 2.7% constraint ofS₈^×= 0.776^+0.019_-0.021, which is consistent with the Planck early-universe extrapolation within 2.1σ, with the lowest redshift bin showing the largest difference in mean. The latter may motivate further CMB lensing tomography analyses atz< 0.6 to assess the impact of potential systematics or the consistency of the ΛCDM model over cosmic time. 

Many astrophysical analyses depend on estimates of redshifts (a proxy for distance) determined from photometric (i.e., imaging) data alone. Inaccurate estimates of photometric redshift uncertainties can result in large systematic errors. However, probability distribution outputs from many photometric redshift methods do not follow the frequentist definition of a Probability Density Function (PDF) for redshift -- i.e., the fraction of times the true redshift falls between two limits z1 and z2 should be equal to the integral of the PDF between these limits. Previous works have used the global distribution of Probability Integral Transform (PIT) values to re-calibrate PDFs, but offsetting inaccuracies in different regions of feature space can conspire to limit the efficacy of the method. We leverage a recently developed regression technique that characterizes the local PIT distribution at any location in feature space to perform a local re-calibration of photometric redshift PDFs. Though we focus on an example from astrophysics, our method can produce PDFs which are calibrated at all locations in feature space for any use case. 

Abstract We utilize ∼17,000 bright luminous red galaxies (LRGs) from the novel Dark Energy Spectroscopic Instrument Survey Validation spectroscopic sample, leveraging its deep (∼2.5 hr galaxy⁻¹exposure time) spectra to characterize the contribution of recently quenched galaxies to the massive galaxy population at 0.4 <z< 1.3. We useProspectorto infer nonparametric star formation histories and identify a significant population of recently quenched galaxies that have joined the quiescent population within the past ∼1 Gyr. The highest-redshift subset (277 atz> 1) of our sample of recently quenched galaxies represents the largest spectroscopic sample of post-starburst galaxies at that epoch. At 0.4 <z< 0.8, we measure the number density of quiescent LRGs, finding that recently quenched galaxies constitute a growing fraction of the massive galaxy population with increasing look-back time. Finally, we quantify the importance of this population among massive ( $\log \left(M_{\star }/M_{\odot }\right)$> 11.2) LRGs by measuring the fraction of stellar mass each galaxy formed in the gigayear before observation,f_{1 Gyr}. Although galaxies withf_{1 Gyr}> 0.1 are rare atz∼ 0.4 (≲0.5% of the population), byz∼ 0.8, they constitute ∼3% of massive galaxies. Relaxing this threshold, we find that galaxies withf_{1 Gyr}> 5% constitute ∼10% of the massive galaxy population atz∼ 0.8. We also identify a small but significant sample of galaxies atz= 1.1–1.3 that formed withf_{1 Gyr}> 50%, implying that they may be analogs to high-redshift quiescent galaxies that formed on similar timescales. Future analysis of this unprecedented sample promises to illuminate the physical mechanisms that drive the quenching of massive galaxies after cosmic noon.