25 research outputs found
The low-redshift circumgalactic medium in SIMBA
We examine the properties of the low-redshift circumgalactic medium (CGM)
around star-forming and quenched galaxies in the Simba cosmological
hydrodynamic simulations, focusing on comparing HI and metal line absorption to
observations from the COS-Halos and COS-Dwarfs surveys. Halo baryon fractions
are always well below the cosmic fraction due to stellar feedback at low
masses, and jet-mode AGN feedback at high masses. Baryons and metals in the CGM
of quenched galaxies are mostly in hot gas, while the CGM of star-forming
galaxies is more multiphase. Hot CGM gas has low metallicity, while warm and
cool CGM gas have metallicity close to that of galactic gas. Equivalent widths,
covering fractions and total path absorption of HI and selected metal lines
(MgII, SiIII, CIV and OVI) around a matched sample of Simba galaxies are
broadly consistent with COS-Halos and COS-Dwarfs observations. Absorption is
higher around star forming galaxies, and drops with radius. HI absorption
around Simba star-forming galaxies is in good agreement with observations,
however around quenched galaxies HI absorption is under-predicted. Metal-line
absorption is sensitive to choice of photo-ionising background; assuming recent
backgrounds, Simba matches OVI but under-predicts low ions, while an older
background matches low ions but under-predicts OVI. Simba reproduces the
observed dichotomy of OVI absorption around star forming and quenched galaxies.
CGM metals primarily come from stellar feedback, while jet-mode AGN feedback
reduces absorption particularly for lower ions.Comment: 23 pages, 12 figures, submitted to MNRAS. Comments welcom
The Aemulus Project VI: Emulation of beyond-standard galaxy clustering statistics to improve cosmological constraints
There is untapped cosmological information in galaxy redshift surveys in the
non-linear regime. In this work, we use the AEMULUS suite of cosmological
-body simulations to construct Gaussian process emulators of galaxy
clustering statistics at small scales () in
order to constrain cosmological and galaxy bias parameters. In addition to
standard statistics -- the projected correlation function
, the redshift-space monopole of the correlation
function , and the quadrupole -- we emulate statistics
that include information about the local environment, namely the underdensity
probability function and the density-marked correlation
function . This extends the model of AEMULUS III for redshift-space
distortions by including new statistics sensitive to galaxy assembly bias. In
recovery tests, we find that the beyond-standard statistics significantly
increase the constraining power on cosmological parameters of interest:
including and improves the precision of our
constraints on by 33%, by 28%, and the growth of
structure parameter, , by 18% compared to standard statistics. We
additionally find that scales below contain as much
information as larger scales. The density-sensitive statistics also contribute
to constraining halo occupation distribution parameters and a flexible
environment-dependent assembly bias model, which is important for extracting
the small-scale cosmological information as well as understanding the
galaxy-halo connection. This analysis demonstrates the potential of emulating
beyond-standard clustering statistics at small scales to constrain the growth
of structure as a test of cosmic acceleration. Our emulator is publicly
available at https://github.com/kstoreyf/aemulator.Comment: Submitted to the Astrophysical Journal; comments welcom
Aemulus : Precise Predictions for Matter and Biased Tracer Power Spectra in the Presence of Neutrinos
We present the Aemulus simulations: a suite of 150 -body simulations with a mass resolution of in a CDM cosmological
parameter space. The simulations have been explicitly designed to span a broad
range in to facilitate investigations of tension between large scale
structure and cosmic microwave background cosmological probes. Neutrinos are
treated as a second particle species to ensure accuracy to , the
maximum neutrino mass that we have simulated. By employing Zel'dovich control
variates, we increase the effective volume of our simulations by factors of
depending on the statistic in question. As a first application of
these simulations, we build new hybrid effective field theory and matter power
spectrum surrogate models, demonstrating that they achieve accuracy
for and , and accuracy for
for the matter power spectrum. We publicly release
the trained surrogate models, and estimates of the surrogate model errors in
the hope that they will be broadly applicable to a range of cosmological
analyses for many years to come.Comment: 37 pages, 15 figures, matching version accepted by JCA
Detecting outliers in astronomical images with deep generative networks
With the advent of future big-data surveys, automated tools for unsupervised
discovery are becoming ever more necessary. In this work, we explore the
ability of deep generative networks for detecting outliers in astronomical
imaging datasets. The main advantage of such generative models is that they are
able to learn complex representations directly from the pixel space. Therefore,
these methods enable us to look for subtle morphological deviations which are
typically missed by more traditional moment-based approaches. We use a
generative model to learn a representation of expected data defined by the
training set and then look for deviations from the learned representation by
looking for the best reconstruction of a given object. In this first
proof-of-concept work, we apply our method to two different test cases. We
first show that from a set of simulated galaxies, we are able to detect
of merging galaxies if we train our network only with a sample of
isolated ones. We then explore how the presented approach can be used to
compare observations and hydrodynamic simulations by identifying observed
galaxies not well represented in the models
Constraining cosmology with the Gaia - unWISE Quasar Catalog and CMB lensing: structure growth
We study the angular clustering of Quaia, a Gaia- and unWISE-based catalog of over a million quasars with an exceptionally well-defined selection function. With it, we derive cosmology constraints from the amplitude and growth of structure across cosmic time. We divide the sample into two redshift bins, centered at z = 1.0 and z = 2.1, and measure both overdensity auto-correlations and cross-correlations with maps of the Cosmic Microwave Background convergence measured by Planck. From these data, and including a prior from measurements of the baryon acoustic oscillations scale, we place constraints on the amplitude of the matter power spectrum σ 8 = 0.766 ± 0.034, and on the matter density parameter Ω m = 0.343+0.017 -0.019. These measurements are in reasonable agreement with Planck at the ∼ 1.4σ level, and are found to be robust with respect to observational and theoretical uncertainties. We find that our slightly lower value of σ 8 is driven by the higher-redshift sample, which favours a low amplitude of matter fluctuations. We present plausible arguments showing that this could be driven by contamination of the CMB lensing map by high-redshift extragalactic foregrounds, which should also affect other cross-correlations with tracers of large-scale structure beyond z ∼ 1.5. Our constraints are competitive with those from state-of-the-art 3×2-point analyses, but arise from a range of scales and redshifts that is highly complementary to those covered by cosmic shear data and most galaxy clustering samples. This, coupled with the unprecedented combination of volume and redshift precision achieved by Quaia, allows us to break the usual degeneracy between Ω m and σ 8
Quaia, the Gaia-unWISE quasar catalog: an all-sky spectroscopic quasar sample
We present a new, all-sky quasar catalog, Quaia, that samples the largest comoving volume of any existing spectroscopic quasar sample. The catalog draws on the 6,649,162 quasar candidates identified by the Gaia mission that have redshift estimates from the space observatory’s low-resolution blue photometer/red photometer spectra. This initial sample is highly homogeneous and complete, but has low purity, and 18% of even the bright (G 0.2) compared to those from the Sloan Digital Sky Survey (SDSS). In this work, we combine the Gaia candidates with unWISE infrared data (based on the Wide-field Infrared Survey Explorer survey) to construct a catalog useful for cosmological and astrophysical quasar studies. We apply cuts based on proper motions and colors, reducing the number of contaminants by approximately four times. We improve the redshifts by training a k-Nearest Neighbor model on SDSS redshifts, and achieve estimates on the G 0.2 (0.1), a reduction of approximately three times (approximately two times) compared to the Gaia redshifts. The final catalog has 1,295,502 quasars with G < 20.5, and 755,850 candidates in an even cleaner G < 20.0 sample, with accompanying rigorous selection function models. We compare Quaia to existing quasar catalogs, showing that its large effective volume makes it a highly competitive sample for cosmological large-scale structure analyses. The catalog is publicly available at 10.5281/zenodo.10403370
Astrobites as a Community-led Model for Education, Science Communication, and Accessibility in Astrophysics
Support for early career astronomers who are just beginning to explore
astronomy research is imperative to increase retention of diverse practitioners
in the field. Since 2010, Astrobites has played an instrumental role in
engaging members of the community -- particularly undergraduate and graduate
students -- in research. In this white paper, the Astrobites collaboration
outlines our multi-faceted online education platform that both eases the
transition into astronomy research and promotes inclusive professional
development opportunities. We additionally offer recommendations for how the
astronomy community can reduce barriers to entry to astronomy research in the
coming decade
From Data to Software to Science with the Rubin Observatory LSST
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset
will dramatically alter our understanding of the Universe, from the origins of
the Solar System to the nature of dark matter and dark energy. Much of this
research will depend on the existence of robust, tested, and scalable
algorithms, software, and services. Identifying and developing such tools ahead
of time has the potential to significantly accelerate the delivery of early
science from LSST. Developing these collaboratively, and making them broadly
available, can enable more inclusive and equitable collaboration on LSST
science.
To facilitate such opportunities, a community workshop entitled "From Data to
Software to Science with the Rubin Observatory LSST" was organized by the LSST
Interdisciplinary Network for Collaboration and Computing (LINCC) and partners,
and held at the Flatiron Institute in New York, March 28-30th 2022. The
workshop included over 50 in-person attendees invited from over 300
applications. It identified seven key software areas of need: (i) scalable
cross-matching and distributed joining of catalogs, (ii) robust photometric
redshift determination, (iii) software for determination of selection
functions, (iv) frameworks for scalable time-series analyses, (v) services for
image access and reprocessing at scale, (vi) object image access (cutouts) and
analysis at scale, and (vii) scalable job execution systems.
This white paper summarizes the discussions of this workshop. It considers
the motivating science use cases, identified cross-cutting algorithms,
software, and services, their high-level technical specifications, and the
principles of inclusive collaborations needed to develop them. We provide it as
a useful roadmap of needs, as well as to spur action and collaboration between
groups and individuals looking to develop reusable software for early LSST
science.Comment: White paper from "From Data to Software to Science with the Rubin
Observatory LSST" worksho
Finishing the euchromatic sequence of the human genome
The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead