Joint Estimation of Properties of the Lunar Subsurface and Galactic Foregrounds with LuSEE-night
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Engineering topics
Slosar, Anže
Publications and source records attributed to Slosar, Anže.
Superhorizon isocurvature fluctuations relax tensions
Here, we present a new class of models that have potential to alleviate tensions present in the cosmological data today. We postulate about variation in the sound horizon scale on superhorizon scales, i.e., on scales that are larger than that of the present observable low-redshift Universe (≳ 1Gpc) while at the same time smaller than the largest scales probed by the cosmic microwave background (CMB) (≲ 10Gpc). In this scenario, CMB peaks are naturally smoothed as preferred by the Planck data, while at the same time the low-redshift baryon acoustic oscillation calibration is partially decoupled from the CMB. Taking superhorizon variations in baryon fraction as an example and using approximate modeling, we find improvement in the best fit Planck power spectrum model Δχ 2 ~ 6 for 1 extra degree of freedom with the relevant extension parameter 10 3 σ b = 2.10 ± 0.60, implying about 10% variations in baryon fraction across the Universe. At the same time, S 8 drops by about 1 sigma, easing tension with weak lensing surveys. We find that the combination of Planck 2018 data, eBOSS BAO data, and Riess et al. distance ladder Hubble parameter determination produce a satisfactory fit in our model if we allow for a phantom dark energy equation of state.
79 ASTRONOMY AND ASTROPHYSICS↗
Point spread function deconvolution using a convolutional autoencoder
A major issue in optical astronomical image analysis is the combined effect of the instrument’s point spread function (PSF) and the atmospheric seeing that blurs images and changes their shape in a way that is band and time-of-observation dependent. In this work we present a very simple neural network based approach to nonblind image deconvolution that relies on feeding a convolutional autoencoder (CAE) input images that have been preprocessed by convolution with the corresponding PSF and its regularized inverse, a method which is both conceptually simple and computationally less intensive. We also present here, a new approach for dealing with limited input dynamic range of neural networks compared to the dynamic range present in astronomical images.
79 ASTRONOMY AND ASTROPHYSICS↗
Searching for parity violation in SDSS DR16 Lyman-α forest data
The four-point correlation function is the lowest order correlation function for scalar fields that can be used to probe statistical parity invariance in an isotropic universe. There are intriguing claims of detection of parity violation in the 4-point function of BOSS galaxy clustering data. We apply the same estimator to the public SDSS Data Release 16 Lyman-α forest data. Lyman-α forest data probes a different redshift range and is sensitive to a different density regime using a completely different technique. A detection would therefore be a strong indication of new physics. We identify an accurate covariance matrix as a crucial impediment to performing this measurement accurately, consistent with existing literature on galaxy 4-point function. Here, we discuss several approaches to estimating the covariance matrix, several of which produce spurious detection. Using a robust, but very suboptimal, covariance matrix derived from subsample bootstrapping, we find no evidence for parity violation.
79 ASTRONOMY AND ASTROPHYSICS↗