278,436 research outputs found
Halo assembly bias from a deep learning model of halo formation
We build a deep learning framework that connects the local formation process
of dark matter halos to the halo bias. We train a convolutional neural network
(CNN) to predict the final mass and concentration of dark matter halos from the
initial conditions. The CNN is then used as a surrogate model to derive the
response of the halos' mass and concentration to long-wavelength perturbations
in the initial conditions, and consequently the halo bias parameters following
the "response bias" definition. The CNN correctly predicts how the local
properties of dark matter halos respond to changes in the large-scale
environment, despite no explicit knowledge of halo bias being provided during
training. We show that the CNN recovers the known trends for the linear and
second-order density bias parameters and , as well as for the local
primordial non-Gaussianity linear bias parameter . The expected
secondary assembly bias dependence on halo concentration is also recovered by
the CNN: at fixed mass, halo concentration has only a mild impact on , but
a strong impact on . Our framework opens a new window for discovering
which physical aspects of the halo's Lagrangian patch determine assembly bias,
which in turn can inform physical models of halo formation and bias.Comment: 11 pages, 5 figures, to be submitted to MNRAS, comments welcom
Cosmoparticle constraints with large-scale structure
Precisely measuring the large-scale structure of the universe is key to learning about fundamental physics. This thesis focuses on two of the most pressing problems in fundamental physics; massive neutrinos and dark energy, and explores what can be learnt from precise measurements of the large-scale structure of the universe. First, I examine the precision required for large-scale structure measurements to determine the neutrino hierarchy when combined with current particle physics results. The neutrino hierarchy refers to the ordering of the neutrino masses, and is a key question in neutrino physics. Particle physics and cosmology provide complementary information about neutrinos so a joint analysis is highly desirable. However, the method of incorporating prior knowledge about neutrinos into the analysis can strongly influence any results. I therefore developed a prior which is agnostic to the hierarchy by design, and used it to set a conclusive target precision for upcoming cosmological experiments. Second, I forecast whether including weak lensing magnification in future large-scale structure analyses can improve the constraints on dark energy and dark matter. Weak gravitational lensing is one of the key probes in forthcoming galaxy surveys, such as the Vera Rubin Observatory. Usually, the signal is detected by measuring distortions to the shapes of millions of galaxies - weak lensing shear. However, it can also be detected by measuring fluctuations in the number density of galaxies across the sky - weak lensing magnification. Weak lensing magnification only requires a count of galaxies to be made, as opposed to a measurement of their shape so is therefore traceable even for the very faint, small, and distant galaxies. In this thesis, I determined whether including weak lensing magnification in upcoming deep large-scale structure analyses improves the final cosmological constraints
Wide-field Multi-object Spectroscopy to Enhance Dark Energy Science from LSST
LSST will open new vistas for cosmology in the next decade, but it cannot
reach its full potential without data from other telescopes. Cosmological
constraints can be greatly enhanced using wide-field ( deg total
survey area), highly-multiplexed optical and near-infrared multi-object
spectroscopy (MOS) on 4-15m telescopes. This could come in the form of
suitably-designed large surveys and/or community access to add new targets to
existing projects. First, photometric redshifts can be calibrated with high
precision using cross-correlations of photometric samples against spectroscopic
samples at that span thousands of sq. deg. Cross-correlations of
faint LSST objects and lensing maps with these spectroscopic samples can also
improve weak lensing cosmology by constraining intrinsic alignment systematics,
and will also provide new tests of modified gravity theories. Large samples of
LSST strong lens systems and supernovae can be studied most efficiently by
piggybacking on spectroscopic surveys covering as much of the LSST
extragalactic footprint as possible (up to square degrees).
Finally, redshifts can be measured efficiently for a high fraction of the
supernovae in the LSST Deep Drilling Fields (DDFs) by targeting their hosts
with wide-field spectrographs. Targeting distant galaxies, supernovae, and
strong lens systems over wide areas in extended surveys with (e.g.) DESI or MSE
in the northern portion of the LSST footprint or 4MOST in the south could
realize many of these gains; DESI, 4MOST, Subaru/PFS, or MSE would all be
well-suited for DDF surveys. The most efficient solution would be a new
wide-field, highly-multiplexed spectroscopic instrument in the southern
hemisphere with m aperture. In two companion white papers we present gains
from deep, small-area MOS and from single-target imaging and spectroscopy.Comment: Submitted to the call for Astro2020 science white papers; tables with
estimates of telescope time needed for a supernova host survey can be seen at
http://d-scholarship.pitt.edu/id/eprint/3604
Analyzing {\gamma}-rays of the Galactic Center with Deep Learning
We present a new method to interpret the -ray data of our inner
Galaxy as measured by the Fermi Large Area Telescope (Fermi LAT). We train and
test convolutional neural networks with simulated Fermi-LAT images based on
models tuned to real data. We use this method to investigate the origin of an
excess emission of GeV -rays seen in previous studies. Interpretations
of this excess include rays created by the annihilation of dark matter
particles and rays originating from a collection of unresolved point
sources, such as millisecond pulsars. Our new method allows precise
measurements of the contribution and properties of an unresolved population of
-ray point sources in the interstellar diffuse emission model.Comment: 24 pages, 11 figure
Synergy between the Large Synoptic Survey Telescope and the Square Kilometre Array
We provide an overview of the science benefits of combining information from
the Square Kilometre Array (SKA) and the Large Synoptic Survey Telescope
(LSST). We first summarise the capabilities and timeline of the LSST and
overview its science goals. We then discuss the science questions in common
between the two projects, and how they can be best addressed by combining the
data from both telescopes. We describe how weak gravitational lensing and
galaxy clustering studies with LSST and SKA can provide improved constraints on
the causes of the cosmological acceleration. We summarise the benefits to
galaxy evolution studies of combining deep optical multi-band imaging with
radio observations. Finally, we discuss the excellent match between one of the
most unique features of the LSST, its temporal cadence in the optical waveband,
and the time resolution of the SKA.Comment: SKA Synergies Chapter, Advancing Astrophysics with the SKA (AASKA14)
Conference, Giardini Naxos (Italy), June 9th-13th 201
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