32 research outputs found
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 from low-redshift indicators, which are
completely independent of CMB inference. We used the measured product
from baryon acoustic oscillations (BAO) together with supernovae~\textsc{I}a to
constrain and time-delay lenses analysed by the H0LiCOW
collaboration to anchor cosmological distances ().
{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 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 varies between ~Mpc and ~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 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 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 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
GausSN: Bayesian Time-Delay Estimation for Strongly Lensed Supernovae
We present GausSN, a Bayesian semi-parametric Gaussian Process (GP) model for
time-delay estimation with resolved systems of gravitationally lensed
supernovae (glSNe). GausSN models the underlying light curve non-parametrically
using a GP. Without assuming a template light curve for each SN type, GausSN
fits for the time delays of all images using data in any number of wavelength
filters simultaneously. We also introduce a novel time-varying magnification
model to capture the effects of microlensing alongside time-delay estimation.
In this analysis, we model the time-varying relative magnification as a sigmoid
function, as well as a constant for comparison to existing time-delay
estimation approaches. We demonstrate that GausSN provides robust time-delay
estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope
and the Vera C. Rubin Observatory's Legacy Survey of Space and Time
(Rubin-LSST). We find that up to 43.6% of time-delay estimates from Roman and
52.9% from Rubin-LSST have fractional errors of less than 5%. We then apply
GausSN to SN Refsdal and find the time delay for the fifth image is consistent
with the original analysis, regardless of microlensing treatment. Therefore,
GausSN maintains the level of precision and accuracy achieved by existing
time-delay extraction methods with fewer assumptions about the underlying shape
of the light curve than template-based approaches, while incorporating
microlensing into the statistical error budget rather than requiring
post-processing to account for its systematic uncertainty. GausSN is scalable
for time-delay cosmography analyses given current projections of glSNe
discovery rates from Rubin-LSST and Roman.Comment: 18 pages, 12 figures, submitted to MNRA
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
, corresponding to a lens mass of solar masses
within a physical size below 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