90 research outputs found
Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique
Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and
Euclid necessitate automatic and efficient identification methods of strong
lensing systems. We present a strong lensing identification approach that
utilizes a feature extraction method from computer vision, the Histogram of
Oriented Gradients (HOG), to capture edge patterns of arcs. We train a
supervised classifier model on the HOG of mock strong galaxy-galaxy lens images
similar to observations from the Hubble Space Telescope (HST) and LSST. We
assess model performance with the area under the curve (AUC) of a Receiver
Operating Characteristic (ROC) curve. Models trained on 10,000 lens and
non-lens containing images images exhibit an AUC of 0.975 for an HST-like
sample, 0.625 for one exposure of LSST, and 0.809 for 10-year mock LSST
observations. Performance appears to continually improve with the training set
size. Models trained on fewer images perform better in absence of the lens
galaxy light. However, with larger training data sets, information from the
lens galaxy actually improves model performance, indicating that HOG captures
much of the morphological complexity of the arc finding problem. We test our
classifier on data from the Sloan Lens ACS Survey and find that small scale
image features reduces the efficiency of our trained model. However, these
preliminary tests indicate that some parameterizations of HOG can compensate
for differences between observed mock data. One example best-case
parameterization results in an AUC of 0.6 in the F814 filter image with other
parameterization results equivalent to random performance.Comment: 18 pages, 14 figures, summarizing results in figure
MultiCAM: A multivariable framework for connecting the mass accretion history of haloes with their properties
Models that connect galaxy and halo properties often summarize a halo's mass
accretion history (MAH) with a single value, and use this value as the basis
for predictions. However, a single-value summary fails to capture the
complexity of MAHs and information can be lost in the process. We present
MultiCAM, a generalization of traditional abundance matching frameworks, which
can simultaneously connect the full MAH of a halo with multiple halo and/or
galaxy properties. As a first case study, we apply MultiCAM to the problem of
connecting dark matter halo properties to their MAHs in the context of a dark
matter-only simulation. While some halo properties, such as concentration, are
more strongly correlated to the early-time mass growth of a halo, others, like
the virial ratio, have stronger correlations with late-time mass growth. This
highlights the necessity of considering the impact of the entire MAH on halo
properties. For most of the halo properties we consider, we find that MultiCAM
models that use the full MAH achieve higher accuracy than conditional abundance
matching models which use a single epoch. We also demonstrate an extension of
MultiCAM that captures the covariance between predicted halo properties. This
extension provides a baseline model for applications where the covariance
between predicted properties is important.Comment: 16 pages, 7 + 1 figures, comments welcome, to be submitted to MNRA
Merger Response of Halo Anisotropy Properties
Anisotropy properties -- halo spin, shape, position offset, velocity offset,
and orientation -- are an important family of dark matter halo properties that
indicate the level of directional variation of the internal structures of
haloes. These properties reflect the dynamical state of haloes, which in turn
depends on the mass assembly history. In this work, we study the evolution of
anisotropy properties in response to merger activity using the IllustrisTNG
simulations. We find that the response trajectories of the anisotropy
properties significantly deviate from secular evolution. These trajectories
have the same qualitative features and timescales across a wide range of merger
and host properties. We propose explanations for the behaviour of these
properties and connect their evolution to the relevant stages of merger
dynamics. We measure the relevant dynamical timescales. We also explore the
dependence of the strength of the response on time of merger, merger ratio, and
mass of the main halo. These results provide insight into the physics of halo
mergers and their effects on the statistical behaviour of halo properties. This
study paves the way towards a physical understanding of scaling relations,
particularly to how systematics in their scatter are connected to the mass
assembly histories of haloes.Comment: 12+3 pages, 5+2 figures. Fig. 4 and 5 are the main figures. To be
submitted to MNRAS, comments welcom
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