266 research outputs found
HOLISMOKES -- XII. Time-delay Measurements of Strongly Lensed Type Ia Supernovae using a Long Short-Term Memory Network
Strongly lensed Type Ia supernovae (LSNe Ia) are a promising probe to measure
the Hubble constant () directly. To use LSNe Ia for cosmography, a
time-delay measurement between the multiple images, a lens-mass model, and a
mass reconstruction along the line of sight are required. In this work, we
present the machine learning network LSTM-FCNN which is a combination of a Long
Short-Term Memory Network (LSTM) and a fully-connected neural network (FCNN).
The LSTM-FCNN is designed to measure time delays on a sample of LSNe Ia
spanning a broad range of properties, which we expect to find with the upcoming
Rubin Observatory Legacy Survey of Space and Time (LSST) and for which
follow-up observations are planned. With follow-up observations in band
(cadence of one to three days with a single-epoch depth of 24.5 mag),
we reach a bias-free delay measurement with a precision around 0.7 days over a
large sample of LSNe Ia. The LSTM-FCNN is far more general than previous
machine learning approaches such as the Random Forest (RF), where a RF has to
be trained for each observational pattern separately, and yet the LSTM-FCNN
outperforms the RF by a factor of roughly three. Therefore, the LSTM-FCNN is a
very promising approach to achieve robust time delays in LSNe Ia, which is
important for a precise and accurate constraint on Comment: 14 pages, 13 figures; submitted to A&
The inner dark matter distribution of the Cosmic Horseshoe (J1148+1930) with gravitational lensing and dynamics
We present a detailed analysis of the inner mass structure of the Cosmic
Horseshoe (J1148+1930) strong gravitational lens system observed with the
Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3). In addition to the
spectacular Einstein ring, this systems shows a radial arc. We obtained the
redshift of the radial arc counter image from
Gemini observations. To disentangle the dark and luminous matter, we consider
three different profiles for the dark matter distribution: a power-law profile,
the NFW, and a generalized version of the NFW profile. For the luminous matter
distribution, we base it on the observed light distribution that is fitted with
three components: a point mass for the central light component resembling an
active galactic nucleus, and the remaining two extended light components scaled
by a constant M/L. To constrain the model further, we include published
velocity dispersion measurements of the lens galaxy and perform a
self-consistent lensing and axisymmetric Jeans dynamical modeling. Our model
fits well to the observations including the radial arc, independent of the dark
matter profile. Depending on the dark matter profile, we get a dark matter
fraction between 60 % and 70 %. With our composite mass model we find that the
radial arc helps to constrain the inner dark matter distribution of the Cosmic
Hoseshoe independently of the dark matter profile.Comment: 19 pages, 14 figures, 8 tables, submitted to A&
Cosmography from two-image lens systems: overcoming the lens profile slope degeneracy
The time delays between the multiple images of a strong lens system, together
with a model of the lens mass distribution, allow a one-step measurement of a
cosmological distance, namely, the "time-delay distance" of the lens (D_dt)
that encodes cosmological information. The time-delay distance depends
sensitively on the radial profile slope of the lens mass distribution;
consequently, the lens slope must be accurately constrained for cosmological
studies. We show that the slope cannot be constrained in two-image systems with
single-component compact sources, whereas it can be constrained in systems with
two-component sources provided the separation between the image components can
be measured with milliarcsecond precisions, which is not feasible in most
systems. In contrast, we demonstrate that spatially extended images of the
source galaxy in two-image systems break the radial slope degeneracy and allow
D_dt to be measured with uncertainties of a few percent. Deep and
high-resolution imaging of the lens systems are needed to reveal the extended
arcs, and stable point spread functions are required for our lens modelling
technique. Two-image systems, no longer plagued by the radial profile slope
degeneracy, would augment the sample of useful time-delay lenses by a factor of
~6, providing substantial advances for cosmological studies.Comment: 14 pages, 9 figures, revisions based on referee's comments, accepted
for publication in MNRA
GLaD: Gravitational Lensing and Dynamics, combined analysis to unveil properties of high-redshift galaxies
Dynamical modelling of Integral-Field-Unit (IFU) stellar kinematics is a
powerful tool to unveil the dynamical structure and mass build-up of galaxies
in the local Universe, while gravitational lensing is nature's cosmic telescope
to explore the properties of galaxies beyond the local Universe. We present a
new approach which unifies dynamical modelling of galaxies with the
magnification power of strong gravitational lensing, to reconstruct the
structural and dynamical properties of high-redshift galaxies. By means of
axisymmetric Jeans modelling, we create a dynamical model of the source galaxy,
assuming a surface brightness and surface mass density profile. We then predict
how the source's surface brightness and kinematics would look like when lensed
by the foreground mass distribution and compare with the mock observed arcs of
strong gravitational lensing systems. For demonstration purposes, we create and
analyse mock data of the strong lensing system RX J1131-1231. By modelling both
the lens and source, we recover the dynamical mass within the effective radius
of strongly lensed high-redshift sources within 5% uncertainty, and we improve
the constraints on the lens mass parameters by up to 50%. This machinery is
particularly well suited for future observations from large segmented-mirror
telescopes, such as the James Webb Space Telescope, that will yield high
sensitivity and angular-resolution IFU data for studying distant and faint
galaxies.Comment: 16 pages, 13 figure
The Hubble Constant determined through an inverse distance ladder including quasar time delays and Type Ia supernovae
Context. The precise determination of the present-day expansion rate of the
Universe, expressed through the Hubble constant , is one of the most
pressing challenges in modern cosmology. Assuming flat CDM,
inference at high redshift using cosmic-microwave-background data from Planck
disagrees at the 4.4 level with measurements based on the local
distance ladder made up of parallaxes, Cepheids and Type Ia supernovae (SNe
Ia), often referred to as "Hubble tension". Independent,
cosmological-model-insensitive ways to infer are of critical importance.
Aims. We apply an inverse-distance-ladder approach, combining strong-lensing
time-delay-distance measurements with SN Ia data. By themselves, SNe Ia are
merely good relative distance indicators, but by anchoring them to strong
gravitational lenses one can obtain an measurement that is relatively
insensitive to other cosmological parameters. Methods. A cosmological parameter
estimate is performed for different cosmological background models, both for
strong-lensing data alone and for the combined lensing + SNe Ia data sets.
Results. The cosmological-model dependence of strong-lensing measurements
is significantly mitigated through the inverse distance ladder. In combination
with SN Ia data, the inferred consistently lies around 73-74 km s
Mpc, regardless of the assumed cosmological background model. Our
results agree nicely with those from the local distance ladder, but there is a
>2 tension with Planck results, and a ~1.5 discrepancy with
results from an inverse distance ladder including Planck, Baryon Acoustic
Oscillations and SNe Ia. Future strong-lensing distance measurements will
reduce the uncertainties in from our inverse distance ladder.Comment: 5 pages, 3 figures, A&A letters accepted versio
HOLISMOKES -- X. Comparison between neural network and semi-automated traditional modeling of strong lenses
Modeling of strongly gravitationally lensed galaxies is often required in
order to use them as astrophysical or cosmological probes. With current and
upcoming wide-field imaging surveys, the number of detected lenses is
increasing significantly such that automated and fast modeling procedures for
ground-based data are urgently needed. This is especially pertinent to
short-lived lensed transients in order to plan follow-up observations.
Therefore, we present in a companion paper (submitted) a neural network
predicting the parameter values with corresponding uncertainties of a Singular
Isothermal Ellipsoid (SIE) mass profile with external shear. In this work, we
present a newly-developed pipeline glee_auto.py to model consistently any
galaxy-scale lensing system. In contrast to previous automated modeling
pipelines that require high-resolution images, glee_auto.py is optimized for
ground-based images such as those from the Hyper-Suprime-Cam (HSC) or the
upcoming Rubin Observatory Legacy Survey of Space and Time. We further present
glee_tools.py, a flexible automation code for individual modeling that has no
direct decisions and assumptions implemented. Both pipelines, in addition to
our modeling network, minimize the user input time drastically and thus are
important for future modeling efforts. We apply the network to 31 real
galaxy-scale lenses of HSC and compare the results to the traditional models.
In the direct comparison, we find a very good match for the Einstein radius
especially for systems with ". The lens mass center and
ellipticity show reasonable agreement. The main discrepancies are on the
external shear as expected from our tests on mock systems. In general, our
study demonstrates that neural networks are a viable and ultra fast approach
for measuring the lens-galaxy masses from ground-based data in the upcoming era
with lenses expected.Comment: 17+28 pages, 7+31 figures, 2+5 tables, submitted to A&
Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements
The upcoming Large Synoptic Survey Telescope (LSST) will detect many strongly
lensed Type Ia supernovae (LSNe Ia) for time-delay cosmography. This will
provide an independent and direct way for measuring the Hubble constant ,
which is necessary to address the current tension in between
the local distance ladder and the early Universe measurements. We present a
detailed analysis of different observing strategies for the LSST, and quantify
their impact on time-delay measurement between multiple images of LSNe Ia. For
this, we produced microlensed mock-LSST light curves for which we estimated the
time delay between different images. We find that using only LSST data for
time-delay cosmography is not ideal. Instead, we advocate using LSST as a
discovery machine for LSNe Ia, enabling time delay measurements from follow-up
observations from other instruments in order to increase the number of systems
by a factor of 2 to 16 depending on the observing strategy. Furthermore, we
find that LSST observing strategies, which provide a good sampling frequency
(the mean inter-night gap is around two days) and high cumulative season length
(ten seasons with a season length of around 170 days per season), are favored.
Rolling cadences subdivide the survey and focus on different parts in different
years; these observing strategies trade the number of seasons for better
sampling frequency. In our investigation, this leads to half the number of
systems in comparison to the best observing strategy. Therefore rolling
cadences are disfavored because the gain from the increased sampling frequency
cannot compensate for the shortened cumulative season length. We anticipate
that the sample of lensed SNe Ia from our preferred LSST cadence strategies
with rapid follow-up observations would yield an independent percent-level
constraint on .Comment: 25 pages, 22 figures; accepted for publication in A&
The Halos of Satellite Galaxies: the Companion of the Massive Elliptical Lens SL2S J08544-0121
Strong gravitational lensing by groups or clusters of galaxies provides a
powerful technique to measure the dark matter properties of individual lens
galaxies. We study in detail the mass distribution of the satellite lens galaxy
in the group-scale lens SL2S J08544-0121 by modelling simultaneously the
spatially extended surface brightness distribution of the source galaxy and the
lens mass distribution using Markov chain Monte Carlo methods. In particular,
we measure the dark matter halo size of the satellite lens galaxy to be
6.0^{+2.9}_{-2.0} kpc with a fiducial velocity dispersion of 127^{+21}_{-12}
km/s. This is the first time the size of an individual galaxy halo in a galaxy
group has been measured using strong gravitational lensing without assumptions
of mass following light. We verify the robustness of our halo size measurement
using mock data resembling our lens system. Our measurement of the halo size is
compatible with the estimated tidal radius of the satellite galaxy, suggesting
that halos of galaxies in groups experience significant tidal stripping, a
process that has been previously observed on galaxies in clusters. Our mass
model of the satellite galaxy is elliptical with its major axis misaligned with
that of the light by ~50 deg. The major axis of the total matter distribution
is oriented more towards the centre of the host halo, exhibiting the radial
alignment found in N-body simulations and observational studies of satellite
galaxies. This misalignment between mass and light poses a significant
challenge to modified Newtonian dynamics.Comment: 13 pages, 10 figures, minor revisions based on referee's comments,
accepted for publication in A&
HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images
Modeling of strong gravitational lenses is a necessity for further
applications in astrophysics and cosmology. Especially with the large number of
detections in current and upcoming surveys such as the Rubin Legacy Survey of
Space and Time (LSST), it is timely to investigate in automated and fast
analysis techniques beyond the traditional and time consuming Markov chain
Monte Carlo sampling methods. Building upon our convolutional neural network
(CNN) presented in Schuldt et al. (2021b), we present here another CNN,
specifically a residual neural network (ResNet), that predicts the five mass
parameters of a Singular Isothermal Ellipsoid (SIE) profile (lens center
and , ellipticity and , Einstein radius ) and the
external shear (, ) from ground-based imaging
data. In contrast to our CNN, this ResNet further predicts a 1
uncertainty for each parameter. To train our network, we use our improved
pipeline from Schuldt et al. (2021b) to simulate lens images using real images
of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra
Deep Field as lens galaxies and background sources, respectively. We find
overall very good recoveries for the SIE parameters, while differences remain
in predicting the external shear. From our tests, most likely the low image
resolution is the limiting factor for predicting the external shear. Given the
run time of milli-seconds per system, our network is perfectly suited to
predict the next appearing image and time delays of lensed transients in time.
Therefore, we also present the performance of the network on these quantities
in comparison to our simulations. Our ResNet is able to predict the SIE and
shear parameter values in fractions of a second on a single CPU such that we
are able to process efficiently the huge amount of expected galaxy-scale lenses
in the near future.Comment: 16 pages, including 11 figures, accepted for publication by A&
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