Abstract
We introduce the neural network architecture
spender
as a core differentiable building block for analyzing, representing, and creating galaxy spectra. It combines a convolutional encoder, ...which pays attention to up to 256 spectral features and compresses them into a low-dimensional latent space, with a decoder that generates a restframe representation, whose spectral range and resolution exceeds that of the observing instrument. The decoder is followed by explicit redshift, resampling, and convolution transformations to match the observations. The architecture takes galaxy spectra at arbitrary redshifts and is robust to glitches like residuals of the skyline subtraction, so that spectra from a large survey can be ingested directly without additional preprocessing. We demonstrate the performance of
spender
by training on the entire spectroscopic galaxy sample of SDSS-II; show its ability to create highly accurate reconstructions with substantially reduced noise; perform deconvolution and oversampling for a superresolution model that resolves the O
ii
doublet; introduce a novel method to interpret attention weights as proxies for important spectral features; and infer the main degrees of freedom represented in the latent space. We conclude with a discussion of future improvements and applications.
Abstract
We present an unsupervised outlier detection method for galaxy spectra based on the spectrum autoencoder architecture
spender
, which reliably captures spectral features and provides highly ...realistic reconstructions for SDSS galaxy spectra. We interpret the sample density in the autoencoder latent space as a probability distribution, and identify outliers as low-probability objects with a normalizing flow. However, we found that the latent-space position is not, as expected from the architecture, redshift invariant, which introduces stochasticity into the latent space and the outlier detection method. We solve this problem by adding two novel loss terms during training, which explicitly link latent-space distances to data-space distances, preserving locality in the autoencoding process. Minimizing the additional losses leads to a redshift-invariant, nondegenerate latent-space distribution with clear separations between common and anomalous data. We inspect the spectra with the lowest probability and find them to include blends with foreground stars, extremely reddened galaxies, galaxy pairs and triples, and stars that are misclassified as galaxies. We release the newly trained
spender
model and the latent-space probability for the entire SDSS-I galaxy sample to aid further investigations.
Abstract
Stellar activity interferes with precise radial velocity measurements and limits our ability to detect and characterize planets, in particular Earth-like planets. We introduce
AESTRA
...(Auto-Encoding STellar Radial-velocity and Activity), a deep-learning method for precise radial velocity measurements. It combines a spectrum autoencoder, which learns to create realistic models of the star’s rest-frame spectrum, and a radial-velocity estimator, which learns to identify true Doppler shifts in the presence of spurious shifts due to line-profile variations. Being self-supervised,
AESTRA
does not need “ground truth” radial velocities for training, making it applicable to exoplanet host stars for which the truth is unknown. In tests involving 1000 simulated spectra,
AESTRA
can detect planetary signals as low as 0.1 m s
−1
even in the presence of 3 m s
−1
of activity-induced noise and 0.3 m s
−1
of photon noise per spectrum.
Abstract
We present
PopSED
, a framework for the population-level inference of galaxy properties from photometric data. Unlike the traditional approach of first analyzing individual galaxies and then ...combining the results to determine the physical properties of the entire galaxy population, we directly make the population distribution the inference objective. We train normalizing flows to approximate the population distribution by minimizing the Wasserstein distance between the synthetic photometry of the galaxy population and the observed data. We validate our method using mock observations and apply it to galaxies from the GAMA survey.
PopSED
reliably recovers the redshift and stellar mass distribution of 10
5
galaxies using broadband photometry within <1 GPU hr, being 10
5–6
times faster than the traditional spectral energy distribution modeling method. From the population posterior, we also recover the star-forming main sequence for GAMA galaxies at
z
< 0.1. With the unprecedented number of galaxies in upcoming surveys, our method offers an efficient tool for studying galaxy evolution and deriving redshift distributions for cosmological analyses.
Abstract
Previous studies have shown the filamentary structures in the cosmic web influence the alignments of nearby galaxies. We study this effect in the LOWZ sample of the Sloan Digital Sky Survey ...using the ‘Cosmic Web Reconstruction' filament catalogue. We find that LOWZ galaxies exhibit a small but statistically significant alignment in the direction parallel to the orientation of nearby filaments. This effect is detectable even in the absence of nearby galaxy clusters, which suggests it is an effect from the matter distribution in the filament. A non-parametric regression model suggests that the alignment effect with filaments extends over separations of 30–40 Mpc. We find that galaxies that are bright and early-forming align more strongly with the directions of nearby filaments than those that are faint and late-forming; however, trends with stellar mass are less statistically significant, within the narrow range of stellar mass of this sample.
Abstract Protoplanetary disks (PPDs) are sites of vigorous hydrodynamic processes, such as accretion and outflows, and ultimately establish the conditions for the formation of planets. The properties ...of disk outflows are often inferred through the analysis of forbidden emission lines. These lines contain multiple overlapping components, tracing different emission regions with different processes that excite them: a high-velocity component (tracing a jet), a broad low-velocity component (LVC; tracing inner disk wind), and a narrow LVC (tracing the outer disk wind). They are also heavily contaminated by background spectral features. All of these challenges call into question the traditional approach of fitting Gaussian components to the line profiles and cloud the physical interpretation of those components. We introduce a novel statistical technique to analyze emission lines in PPDs. Simulation-based inference is a computationally efficient machine-learning technique that produces posterior distributions of the parameters (e.g., magnetic field, radiation sources, and geometry) of a representative wind model when given a spectrum without any prior assumption about line shapes (e.g., symmetry). In this pathfinder study, we demonstrate that this technique indeed accurately recovers the parameters from simulated spectra without noise and background. Future work will provide an analysis of the observed spectra.
We report the first measurement of the diminutive lensing signal arising from matter underdensities associated with cosmic voids. While undetectable individually, by stacking the weak gravitational ...shear estimates around 901 voids detected in Sloan Digital Sky Survey DR7 by Sutter et al., we find substantial evidence for a depression of the lensing signal compared to the cosmic mean. This depression is most pronounced at the void radius, in agreement with analytical models of void matter profiles. Even with the largest void sample and imaging survey available today, we cannot put useful constraints on the radial dark matter void profile. We invite independent investigations of our findings by releasing data and analysis code to the public at https://github.com/pmelchior/void-lensing.
Mangrove: Learning Galaxy Properties from Merger Trees Jespersen, Christian Kragh; Cranmer, Miles; Melchior, Peter ...
Astrophysical journal/The Astrophysical journal,
12/2022, Letnik:
941, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Abstract
Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics. Although semianalytic models (SAMs) and hydrodynamical simulations have made impressive ...advances in reproducing galaxy observables across cosmologically significant volumes, these methods still require significant computation times, representing a barrier to many applications. Graph neural networks have recently proven to be the natural choice for learning physical relations. Among the most inherently graph-like structures found in astrophysics are the dark matter merger trees that encode the evolution of dark matter halos. In this paper, we introduce a new, graph-based emulator framework,
Mangrove
, and show that it emulates the galactic stellar mass, cold gas mass and metallicity, instantaneous and time-averaged star formation rate, and black hole mass—as predicted by an SAM—with an rms error up to 2 times lower than other methods across a (75 Mpc/
h
)
3
simulation box in 40 s, 4 orders of magnitude faster than the SAM. We show that
Mangrove
allows for quantification of the dependence of galaxy properties on merger history. We compare our results to the current state of the art in the field and show significant improvements for all target properties.
Mangrove
is publicly available:
https://github.com/astrockragh/Mangrove
.
Abstract We present the first cosmological constraints using only the observed photometry of galaxies. Villaescusa-Navarro et al. recently demonstrated that the internal physical properties of a ...single simulated galaxy contain a significant amount of cosmological information. These physical properties, however, cannot be directly measured from observations. In this work, we present how we can go beyond theoretical demonstrations to infer cosmological constraints from actual galaxy observables (e.g., optical photometry) using neural density estimation and the CAMELS suite of hydrodynamical simulations. We find that the cosmological information in the photometry of a single galaxy is limited. However, we combine the constraining power of photometry from many galaxies using hierarchical population inference and place significant cosmological constraints. With the observed photometry of ∼20,000 NASA-Sloan Atlas galaxies, we constrain Ω m = 0.323 − 0.095 + 0.075 and σ 8 = 0.799 − 0.085 + 0.088 .
Abstract
We examine the cosmological information available from the 1-point probability density function (PDF) of the weak-lensing convergence field, utilizing fast l-picola simulations and a Fisher ...analysis. We find competitive constraints in the Ω
m
–σ8 plane from the convergence PDF with 188 arcmin2 pixels compared to the cosmic shear power spectrum with an equivalent number of modes (ℓ < 886). The convergence PDF also partially breaks the degeneracy cosmic shear exhibits in that parameter space. A joint analysis of the convergence PDF and shear 2-point function also reduces the impact of shape measurement systematics, to which the PDF is less susceptible, and improves the total figure of merit by a factor of 2–3, depending on the level of systematics. Finally, we present a correction factor necessary for calculating the unbiased Fisher information from finite differences using a limited number of cosmological simulations.
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