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Abstract:

We present a novel method for anomaly detection in solar system object data in preparation for the Legacy Survey of Space and Time. We train a deep autoencoder for anomaly detection and use the learned latent space to search for other interesting objects. We demonstrate the efficacy of the autoencoder approach by finding interesting examples, such as interstellar objects, and show that by using the autoencoder, further examples of interesting classes can be found. We also investigate the limits of classic unsupervised approaches to anomaly detection through the generation of synthetic anomalies and evaluate the feasibility of using a supervised learning approach. Future work should consider expanding the feature space to increase the variety of anomalies that can be uncovered during the survey using an autoencoder

Abstract:

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale of k_max = 0.4 Mpc^-1 for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data.

Abstract:

We present tensor-to-scalar ratio forecasts for GreenPol, a hypothetical ground-based B-mode experiment aiming to survey the cleanest regions of the Northern Galactic hemisphere at five frequencies between 10 and 44 GHz. Its primary science goal would be to measure large-scale CMB polarization fluctuations at multipoles $\ell \lesssim 500$, and thereby constrain the primordial tensor-to-scalar ratio. The observations for the suggested experiment would take place at the Summit Station (72deg N, 38deg W) on Greenland, at an altitude of 3216 meters above sea level. In this paper we simulate various experimental setups, and derive limits on the tensor-to-scalar ratio after CMB component separation using a Bayesian component separation implementation called Commander. When combining the proposed experiment with Planck HFI observations for constraining polarized thermal dust emission, we find a projected limit of r<0.02 at 95 % confidence for the baseline configuration. This limit is very robust with respect to a range of important experimental parameters, including sky coverage, detector weighting, foreground priors etc. Overall, GreenPol would have the possibility to provide deep CMB polarization measurements of the Northern Galactic hemisphere at low frequencies.Comment: 10 pages, 8 figures. To be submitted to A&