Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We present a machine learning framework to simulate realistic galaxies for the Euclid Survey, producing more complex and realistic galaxies than the analytical simulations currently used in Euclid . The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions learned from real Hubble Space Telescope observations by deep generative models. We simulate a galaxy field of 0.4 deg 2 as it will be seen by the Euclid visible imager VIS, and we show that galaxy structural parameters are recovered to an accuracy similar to that for pure analytic Sérsic profiles. Based on these simulations, we estimate that the Euclid Wide Survey (EWS) will be able to resolve the internal morphological structure of galaxies down to a surface brightness of 22.5 mag arcsec −2 , and the Euclid Deep Survey (EDS) down to 24.9 mag arcsec −2 . This corresponds to approximately 250 million galaxies at the end of the mission and a 50% complete sample for stellar masses above 10 10.6 M ⊙ (resp. 10 9.6 M ⊙ ) at a redshift z ∼ 0.5 for the EWS (resp. EDS). The approach presented in this work can contribute to improving the preparation of future high-precision cosmological imaging surveys by allowing simulations to incorporate more realistic galaxies.

Abstract:

Lorentz invariance violation in quantum gravity (QG) models or a nonzero photon mass, mγ, would lead to an energy-dependent propagation speed for photons, such that photons of different energies from a distant source would arrive at different times, even if they were emitted simultaneously. By developing source-by-source, Monte Carlo-based forward models for such time delays from gamma ray bursts, and marginalizing over empirical noise models describing other contributions to the time delay, we derive constraints on mγ and the QG length scale, ℓQG, using spectral lag data from the BATSE satellite. We find mγ<4.0×10-5 h eV/c2 and ℓQG<5.3×10-18 h GeV-1 at 95% confidence, and demonstrate that these constraints are robust to the choice of noise model. The QG constraint is among the tightest from studies which consider multiple gamma ray bursts and the constraint on mγ, although weaker than from using radio data, provides an independent constraint which is less sensitive to the effects of dispersion by electrons.