HOLISMOKES Schuldt, S; Suyu, S H; Meinhardt, T ...
Astronomy and astrophysics (Berlin),
02/2021, Letnik:
646
Journal Article
Recenzirano
Odprti dostop
Modeling the mass distributions of strong gravitational lenses is often necessary in order to use them as astrophysical and cosmological probes. With the large number of lens systems (≳105) expected ...from upcoming surveys, it is timely to explore efficient modeling approaches beyond traditional Markov chain Monte Carlo techniques that are time consuming. We train a convolutional neural network (CNN) on images of galaxy-scale lens systems to predict the five parameters of the singular isothermal ellipsoid (SIE) mass model (lens center x and y, complex ellipticity ex and ey, and Einstein radius θE). To train the network we simulate images based on real observations from the Hyper Suprime-Cam Survey for the lens galaxies and from the Hubble Ultra Deep Field as lensed galaxies. We tested different network architectures and the effect of different data sets, such as using only double or quad systems defined based on the source center and using different input distributions of θE. We find that the CNN performs well, and with the network trained on both doubles and quads with a uniform distribution of θE > 0.5″ we obtain the following median values with 1σ scatter: Δx = (0.00−0.30+0.30)″, Δy = (0.00−0.29+0.30)″, ΔθE = (0.07−0.12+0.29)″, Δex = −0.01−0.09+0.08, and Δey = 0.00−0.09+0.08. The bias in θE is driven by systems with small θE. Therefore, when we further predict the multiple lensed image positions and time-delays based on the network output, we apply the network to the sample limited to θE > 0.8″. In this case the offset between the predicted and input lensed image positions is (0.00−0.29+0.29)″ and (0.00−0.31+0.32)″ for the x and y coordinates, respectively. For the fractional difference between the predicted and true time-delay, we obtain 0.04−0.05+0.27. Our CNN model is able to predict the SIE parameter values in fractions of a second on a single CPU, and with the output we can predict the image positions and time-delays in an automated way, such that we are able to process efficiently the huge amount of expected galaxy-scale lens detections in the near future.
TRANSFAC is a database on transcription factors, their genomic binding sites and DNA-binding profiles (http://transfac.gbf.de/TRANSFAC/). Its content has been enhanced, in particular by information ...about training sequences used for the construction of nucleotide matrices as well as by data on plant sites and factors. Moreover, TRANSFAC has been extended by two new modules: PathoDB provides data on pathologically relevant mutations in regulatory regions and transcription factor genes, whereas S/MARt DB compiles features of scaffold/matrix attached regions (S/MARs) and the proteins binding to them. Additionally, the databases TRANSPATH, about signal transduction, and CYTOMER, about organs and cell types, have been extended and are increasingly integrated with the TRANSFAC data sources.
TRANSFAC is a database on transcription factors, their genomic binding sites and DNA-binding profiles. In addition to being updated and extended by new features, it has been complemented now by a ...series of additional database modules. among them, modules which provide data about signal transduction pathways (TRANSPATH) or about cell types/organs/developmental stages (CYTOMER) are available as well as an updated version of the previously described compel database. the databases are available on the www at http://transfac.gbf.de/
HOLISMOKES Cañameras, R.; Schuldt, S.; Suyu, S. H. ...
Astronomy and astrophysics (Berlin),
12/2020, Letnik:
644
Journal Article
Recenzirano
Odprti dostop
We present a systematic search for wide-separation (with Einstein radius
θ
E
≳ 1.5″), galaxy-scale strong lenses in the 30 000 deg
2
of the Pan-STARRS 3
π
survey on the Northern sky. With long time ...delays of a few days to weeks, these types of systems are particularly well-suited for catching strongly lensed supernovae with spatially-resolved multiple images and offer new insights on early-phase supernova spectroscopy and cosmography. We produced a set of realistic simulations by painting lensed COSMOS sources on Pan-STARRS image cutouts of lens luminous red galaxies (LRGs) with redshift and velocity dispersion known from the sloan digital sky survey (SDSS). First, we computed the photometry of mock lenses in
g
r
i
bands and applied a simple catalog-level neural network to identify a sample of 1 050 207 galaxies with similar colors and magnitudes as the mocks. Second, we trained a convolutional neural network (CNN) on Pan-STARRS
g
r
i
image cutouts to classify this sample and obtain sets of 105 760 and 12 382 lens candidates with scores of
p
CNN
> 0.5 and > 0.9, respectively. Extensive tests showed that CNN performances rely heavily on the design of lens simulations and the choice of negative examples for training, but little on the network architecture. The CNN correctly classified 14 out of 16 test lenses, which are previously confirmed lens systems above the detection limit of Pan-STARRS. Finally, we visually inspected all galaxies with
p
CNN
> 0.9 to assemble a final set of 330 high-quality newly-discovered lens candidates while recovering 23 published systems. For a subset, SDSS spectroscopy on the lens central regions proves that our method correctly identifies lens LRGs at
z
∼ 0.1–0.7. Five spectra also show robust signatures of high-redshift background sources, and Pan-STARRS imaging confirms one of them as a quadruply-imaged red source at
z
s
= 1.185, which is likely a recently quenched galaxy strongly lensed by a foreground LRG at
z
d
= 0.3155. In the future, high-resolution imaging and spectroscopic follow-up will be required to validate Pan-STARRS lens candidates and derive strong lensing models. We also expect that the efficient and automated two-step classification method presented in this paper will be applicable to the ∼4 mag deeper
g
r
i
stacks from the
Rubin
Observatory Legacy Survey of Space and Time (LSST) with minor adjustments.
Galaxy redshifts are a key characteristic for nearly all extragalactic studies. Since spectroscopic redshifts require additional telescope and human resources, millions of galaxies are known without ...spectroscopic redshifts. Therefore, it is crucial to have methods for estimating the redshift of a galaxy based on its photometric properties, the so-called photo-
z
. We have developed NetZ, a new method using a convolutional neural network (CNN) to predict the photo-
z
based on galaxy images, in contrast to previous methods that often used only the integrated photometry of galaxies without their images. We use data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) in five different filters as the training data. The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-
z
range, where it fares better than other methods on the same data. We obtained a precision |
z
pred
−
z
ref
| of
σ
= 0.12 (68% confidence interval) with a CNN working for all galaxy types averaged over all galaxies in the redshift range of 0 to ∼4. We carried out a comparison with a network trained on point-like sources, highlighting the importance of morphological information for our redshift estimation. By limiting the scope to smaller redshift ranges or to luminous red galaxies, we find a further notable improvement. We have published more than 34 million new photo-
z
values predicted with NetZ. This shows that the new method is very simple and swift in application, and, importantly, it covers a wide redshift range that is limited only by the available training data. It is broadly applicable, particularly with regard to upcoming surveys such as the
Rubin
Observatory Legacy Survey of Space and Time, which will provide images of billions of galaxies with similar image quality as HSC. Our HSC photo-
z
estimates are also beneficial to the
Euclid
survey, given the overlap in the footprints of the HSC and
Euclid
.
HOLISMOKES Schuldt, S.; Suyu, S. H.; Meinhardt, T. ...
Astronomy and astrophysics (Berlin),
02/2021, Letnik:
646
Journal Article
Recenzirano
Odprti dostop
Modeling the mass distributions of strong gravitational lenses is often necessary in order to use them as astrophysical and cosmological probes. With the large number of lens systems (≳10
5
) ...expected from upcoming surveys, it is timely to explore efficient modeling approaches beyond traditional Markov chain Monte Carlo techniques that are time consuming. We train a convolutional neural network (CNN) on images of galaxy-scale lens systems to predict the five parameters of the singular isothermal ellipsoid (SIE) mass model (lens center
x
and
y
, complex ellipticity
e
x
and
e
y
, and Einstein radius
θ
E
). To train the network we simulate images based on real observations from the Hyper Suprime-Cam Survey for the lens galaxies and from the
Hubble
Ultra Deep Field as lensed galaxies. We tested different network architectures and the effect of different data sets, such as using only double or quad systems defined based on the source center and using different input distributions of
θ
E
. We find that the CNN performs well, and with the network trained on both doubles and quads with a uniform distribution of
θ
E
> 0.5″ we obtain the following median values with 1
σ
scatter: Δ
x
= (0.00
−0.30
+0.30
)″, Δ
y
= (0.00
−0.29
+0.30
)″, Δ
θ
E
= (0.07
−0.12
+0.29
)″, Δ
e
x
= −0.01
−0.09
+0.08
, and Δ
e
y
= 0.00
−0.09
+0.08
. The bias in
θ
E
is driven by systems with small
θ
E
. Therefore, when we further predict the multiple lensed image positions and time-delays based on the network output, we apply the network to the sample limited to
θ
E
> 0.8″. In this case the offset between the predicted and input lensed image positions is (0.00
−0.29
+0.29
)″ and (0.00
−0.31
+0.32
)″ for the
x
and
y
coordinates, respectively. For the fractional difference between the predicted and true time-delay, we obtain 0.04
−0.05
+0.27
. Our CNN model is able to predict the SIE parameter values in fractions of a second on a single CPU, and with the output we can predict the image positions and time-delays in an automated way, such that we are able to process efficiently the huge amount of expected galaxy-scale lens detections in the near future.
HOLISMOKES Cañameras, R.; Schuldt, S.; Shu, Y. ...
Astronomy and astrophysics (Berlin),
09/2021, Letnik:
653
Journal Article
Recenzirano
Odprti dostop
We have carried out a systematic search for galaxy-scale strong lenses in multiband imaging from the Hyper Suprime-Cam (HSC) survey. Our automated pipeline, based on realistic strong-lens ...simulations, deep neural network classification, and visual inspection, is aimed at efficiently selecting systems with wide image separations (Einstein radii
θ
E
∼ 1.0–3.0″), intermediate redshift lenses (
z
∼ 0.4–0.7), and bright arcs for galaxy evolution and cosmology. We classified
gri
images of all 62.5 million galaxies in HSC Wide with
i
-band Kron radius ≥0.8″ to avoid strict preselections and to prepare for the upcoming era of deep, wide-scale imaging surveys with Euclid and Rubin Observatory. We obtained 206 newly-discovered candidates classified as definite or probable lenses with either spatially-resolved multiple images or extended, distorted arcs. In addition, we found 88 high-quality candidates that were assigned lower confidence in previous HSC searches, and we recovered 173 known systems in the literature. These results demonstrate that, aided by limited human input, deep learning pipelines with false positive rates as low as ≃0.01% can be very powerful tools for identifying the rare strong lenses from large catalogs, and can also largely extend the samples found by traditional algorithms. We provide a ranked list of candidates for future spectroscopic confirmation.
HOLISMOKES Schuldt, S.; Cañameras, R.; Shu, Y. ...
Astronomy and astrophysics (Berlin),
03/2023, Letnik:
671
Journal Article
Recenzirano
Odprti dostop
Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. With the large number of detections in current and upcoming surveys, such as the
Rubin
...Legacy Survey of Space and Time (LSST), it is pertinent to investigate automated and fast analysis techniques beyond the traditional and time-consuming Markov chain Monte Carlo sampling methods. Building upon our (simple) convolutional neural network (CNN), 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
x
and
y
, ellipticity
e
x
and
e
y
, Einstein radius
θ
E
) and the external shear (
γ
ext, 1
,
γ
ext, 2
) from ground-based imaging data. In contrast to our previous CNN, this ResNet further predicts the 1
σ
uncertainty for each parameter. To train our network, we use our improved pipeline 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 very good recoveries overall for the SIE parameters, especially for the lens center in comparison to our previous CNN, while significant differences remain in predicting the external shear. From our multiple tests, it appears that most likely the low ground-based image resolution is the limiting factor in predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to quickly predict the next appearing image and time delays of lensed transients. Therefore, we use the network-predicted mass model to estimate these quantities and compare to those values obtained from our simulations. Unfortunately, the achieved precision allows only a first-order estimate of time delays on real lens systems and requires further refinement through follow-up modeling. Nonetheless, our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU, meaning that we are able to efficiently process the huge amount of galaxy-scale lenses expected in the near future.
One lock for different keys: A flexible arginine in the active site allows γ‐aminobutyrate:pyruvate transaminases to bind the chemically different substrates L‐alanine and γ‐aminobutyric acid. ...Moreover, a flexible arginine residue facilitates the promiscuous conversion of (S)‐amines and ketones. The degree of promiscuity can be related to distinct key amino acids lying at the surface of the active site.
PDF
