Low-redshift measurement of the sound horizon through gravitational time-delays

The matter sound horizon can be inferred from the cosmic microwave background within the Standard Model. Independent direct measurements of the sound horizon are then a probe of possible deviations from the Standard Model. We aim at measuring the sound horizon $r_s$ from low-redshift indicators, which are completely independent of CMB inference. We used the measured product $H(z)r_s$ from baryon acoustic oscillations (BAO) together with supernovae~\textsc{I}a to constrain $H(z)/H_{0}$ and time-delay lenses analysed by the H0LiCOW collaboration to anchor cosmological distances ($\propto H_{0}^{-1}$). {Additionally, we investigated the influence of adding a sample of quasars with higher redshift with standardisable UV-Xray luminosity distances. We adopted polynomial expansions in $H(z)$ or in comoving distances} so that our inference was completely independent of any cosmological model on which the expansion history might be based. Our measurements are independent of Cepheids and systematics from peculiar motions {to within percent-level accuracy.} The inferred sound horizon $r_s$ varies between $(133 \pm 8)$~Mpc and $(138 \pm 5)$~Mpc across different models. The discrepancy with CMB measurements is robust against model choice. Statistical uncertainties are comparable to systematics. The combination of time-delay lenses, supernovae, and BAO yields a distance ladder that is independent of cosmology (and of Cepheid calibration) and a measurement of $r_s$ that is independent of the CMB. These cosmographic measurements are then a competitive test of the Standard Model, regardless of the hypotheses on which the cosmology is based.Comment: 2019A&A...632A..91A (subm. 28/05/2019), 6 pages, 3 figure

Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks

We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions in the sky and their line-of-sight velocities. By formulating the mass estimation problem within this simulation-based inference framework, we are able to quantify the uncertainties on the inferred masses in a straightforward and robust way. We generate a realistic mock catalogue emulating the Sloan Digital Sky Survey (SDSS) Legacy spectroscopic observations (the main galaxy sample) for redshifts $z \lesssim 0.09$ and explicitly illustrate the challenges posed by interloper (non-member) galaxies for cluster mass estimation from actual observations. Our approach constitutes the first optimal machine learning-based exploitation of the information content of the full 3D projected phase-space distribution, including both the virialized and infall cluster regions, for the inference of dynamical cluster masses. We also present, for the first time, the application of a simulation-based inference machinery to obtain dynamical masses of around $800$ galaxy clusters found in the SDSS Legacy Survey, and show that the resulting mass estimates are consistent with mass measurements from the literature.Comment: 14 pages, 11 figures. Accepted for publication in MNRAS. Contains non-peer reviewed supplementary material on cluster mass function in appendi

AI-driven spatio-temporal engine for finding gravitationally lensed supernovae

We present a spatio-temporal AI framework that concurrently exploits both the spatial and time-variable features of gravitationally lensed supernovae in optical images to ultimately aid in the discovery of such exotic transients in wide-field surveys. Our spatio-temporal engine is designed using recurrent convolutional layers, while drawing from recent advances in variational inference to quantify approximate Bayesian uncertainties via a confidence score. Using simulated Young Supernova Experiment (YSE) images as a showcase, we find that the use of time-series images yields a substantial gain of nearly 20 per cent in classification accuracy over single-epoch observations, with a preliminary application to mock observations from the Legacy Survey of Space and Time (LSST) yielding around 99 per cent accuracy. Our innovative deep learning machinery adds an extra dimension in the search for gravitationally lensed supernovae from current and future astrophysical transient surveys.Comment: 6+8 pages, 10 figures, 2 tables. For submission to a peer-reviewed journal. Comments welcom

SN Zwicky: uncovering a population of gravitational lens galaxies with magnified "standard candles"

We report the discovery of a very rare phenomenon, a multiply-imaged gravitationally lensed Type Ia supernova (SNe Ia), "SN Zwicky", a.k.a. SN 2022qmx, magnified nearly twenty-five times by a foreground galaxy. The system was identified as intrinsically bright thanks to the "standard candle" nature of SNe Ia. Observations with high-spatial resolution instruments resolved a system with four nearly simultaneous images, with an Einstein radius of only $\theta_E =0.167"$, corresponding to a lens mass of $8\cdot 10^9$ solar masses within a physical size below $0.8$ kiloparsecs. A smooth lens model fails to reproduce the image flux ratios, suggesting significant additional magnification from compact objects. Given the small image splitting and a relatively faint deflecting galaxy, the lensing system would not have been found through the angular separation technique generally used in large imaging surveys

Complete Genome Sequences of Cluster A Mycobacteriophages BobSwaget, Fred313, KADY, Lokk, MyraDee, Stagni, and StepMih

Seven mycobacteriophages from distinct geographical locations were isolated, using Mycobacterium smegmatis mc2155 as the host, and then purified and sequenced. All of the genomes are related to cluster A mycobacteriophages, BobSwaget and Lokk in subcluster A2; Fred313, KADY, Stagni, and StepMih in subcluster A3; and MyraDee in subcluster A18, the first phage to be assigned to that subcluster

Cosmology intertwined III: fσ8 and S8

The standard Λ Cold Dark Matter cosmological model provides a wonderful fit to current cosmological data, but a few statistically significant tensions and anomalies were found in the latest data analyses. While these anomalies could be due to the presence of systematic errors in the experiments, they could also indicate the need for new physics beyond the standard model. In this Letter of Interest we focus on the tension between Planck data and weak lensing measurements and redshift surveys, in the value of the matter energy density Ωm and the amplitude σ8 (or the growth rate fσ8) of cosmic structure. We list a few promising models for solving this tension, and discuss the importance of trying to fit multiple cosmological datasets with complete physical models, rather than fitting individual datasets with a few handpicked theoretical parameters