Day 1719

Monday.

2006.06706
Extreme data compression while searching for new physics
Heavens, Sellentin, Jaffe

Bringing a high-dimensional dataset into science-ready shape is a formidable challenge that often necessitates data compression. Compression has accordingly become a key consideration for contemporary cosmology, affecting public data releases, and reanalyses searching for new physics. However, data compression optimized for a particular model can suppress signs of new physics, or even remove them altogether. We therefore provide a solution for exploring new physics \emph{during} data compression. In particular, we store additional agnostic compressed data points, selected to enable precise constraints of non-standard physics at a later date. Our procedure is based on the maximal compression of the MOPED algorithm, which optimally filters the data with respect to a baseline model. We select additional filters, based on a generalised principal component analysis, which are carefully constructed to scout for new physics at high precision and speed. We refer to the augmented set of filters as MOPED-PC. They enable an analytic computation of Bayesian evidences that may indicate the presence of new physics, and fast analytic estimates of best-fitting parameters when adopting a specific non-standard theory, without further expensive MCMC analysis. As there may be large numbers of non-standard theories, the speed of the method becomes essential. Should no new physics be found, then our approach preserves the precision of the standard parameters. As a result, we achieve very rapid and maximally precise constraints of standard and non-standard physics, with a technique that scales well to large dimensional datasets.

2006.06741

the imprint of dark sub haloes on the circumgalactic medium

McCarthy, Font

The standard model of cosmology, the LCDM model, robustly predicts the existence of a multitude of dark matter 'subhaloes' around galaxies like the Milky Way. A wide variety of observations have been proposed to look for the gravitational effects such subhaloes would induce in observable matter. Most of these approaches pertain to the stellar or cool gaseous phases of matter. Here we propose a new approach, which is to search for the perturbations that such dark subhaloes would source in the warm/hot circumgalactic medium (CGM) around normal galaxies. With a combination of analytic theory, carefully-controlled high-resolution idealised simulations, and full cosmological hydrodynamical simulations, we calculate the expected signal and how it depends on important physical parameters (subhalo mass, CGM temperature, and relative velocity). We find that dark subhaloes enhance the local CGM pressure, density, and temperature, in order of decreasing magnitude of the effect. For the pressure, the fluctuations can vary in magnitude from tens of percent (for subhaloes with M_sub=10^10 Msun) to a few percent (for subhaloes with M_sub=10^8 Msun), although this depends strongly on the CGM temperature. The subhaloes also induce fluctuations in the velocity field ranging in magnitude from a few km/s up to 25 km/s. We propose that X-ray, Sunyaev-Zel'dovich effect, radio dispersion measure, and quasar absorption line observations can be used to measure these fluctuations and place constraints on the abundance and distribution of dark subhaloes, thereby placing constraints on the nature of dark matter.

2006.07011

Shear measurement bias II: a fast machine learning calibration method

Pujol, et al

We present a new shear calibration method based on machine learning. The method estimates the individual shear responses of the objects from the combination of several measured properties on the images using supervised learning. The supervised learning uses the true individual shear responses obtained from copies of the image simulations with different shear values. On simulated GREAT3 data, we obtain a residual bias after the calibration compatible with $0$ and beyond Euclid requirements for SNR>20 within $\sim 15$ CPU hours of training using only $\sim 10^5$ objects. This efficient machine learning approach can use a smaller data set since the method avoids the contribution from shape noise. Also, the low dimensionality of the input data leads to simple neural network architectures. We compare it to the state-of-the-art method Metacalibration, showing similar performances, and their different methodologies and systematics suggest them to be very good complementary methods. Our method could therefore be fastly applied to any survey such as Euclid or LSST, with less than a million images to simulate to learn the calibration function.