4 research outputs found
The Galaxy Assembly and Interaction Neural Networks (GAINN) for high-redshift JWST observations
We present the Galaxy Assembly and Interaction Neural Networks (GAINN), a
series of artificial neural networks for predicting the redshift, stellar mass,
halo mass, and mass-weighted age of simulated galaxies based on JWST
photometry. Our goal is to determine the best neural network for predicting
these variables at . The parameters of the optimal neural
network can then be used to estimate these variables for real, observed
galaxies. The inputs of the neural networks are JWST filter magnitudes of a
subset of five broadband filters (F150W, F200W, F277W, F356W, and F444W) and
two medium-band filters (F162M and F182M). We compare the performance of the
neural networks using different combinations of these filters, as well as
different activation functions and numbers of layers. The best neural network
predicted redshift with normalized root mean squared error NRMS =
, stellar mass with RMS = ,
halo mass with MSE = , and mass-weighted age with RMS
= . We also test the performance of GAINN on real
data from MACS0647-JD, an object observed by JWST. Predictions from GAINN for
the first projection of the object (JD1) have mean absolute errors , which is significantly smaller than with
template-fitting methods. We find that the optimal filter combination is F277W,
F356W, F162M, and F182M when considering both theoretical accuracy and
observational resources from JWST.Comment: 19 pages, 6 figures, submitted to Ap
Milky Way White Dwarfs as Sub-GeV to Multi-TeV Dark Matter Detectors
We show that Milky Way white dwarfs are excellent targets for dark matter
(DM) detection. Using Fermi and H.E.S.S. Galactic center gamma-ray data, we
investigate sensitivity to DM annihilating within white dwarfs into long-lived
or boosted mediators and producing detectable gamma rays. Depending on the
Galactic DM distribution, we set new constraints on the spin-independent
scattering cross section down to cm in the sub-GeV DM
mass range, which is multiple orders of magnitude stronger than existing
limits. For a generalized NFW DM profile, we find that our white dwarf
constraints exceed spin-independent direct detection limits across most of the
sub-GeV to multi-TeV DM mass range, achieving sensitivities as low as about
cm. In addition, we improve earlier versions of the DM capture
calculation in white dwarfs, by including the low-temperature distribution of
nuclei when the white dwarf approaches crystallization. This yields smaller
capture rates than previously calculated by a factor of a few up to two orders
of magnitude, depending on white dwarf size and the astrophysical system.Comment: 29 pages, 5 figure
JWST reveals a possible galaxy merger in triply-lensed MACS0647JD
MACS0647JD is a triply-lensed galaxy originally discovered with
the Hubble Space Telescope. Here we report new JWST imaging, which clearly
resolves MACS0647JD as having two components that are either merging
galaxies or stellar complexes within a single galaxy. Both are very small, with
stellar masses and radii . The brighter
larger component "A" is intrinsically very blue (), likely due
to very recent star formation and no dust, and is spatially extended with an
effective radius . The smaller component "B" appears redder
(), likely because it is older () with mild dust
extinction (), and a smaller radius . We
identify galaxies with similar colors in a high-redshift simulation, finding
their star formation histories to be out of phase. With an estimated stellar
mass ratio of roughly 2:1 and physical projected separation ,
we may be witnessing a galaxy merger 400 million years after the Big Bang. We
also identify a candidate companion galaxy C away, likely
destined to merge with galaxies A and B. The combined light from galaxies A+B
is magnified by factors of 8, 5, and 2 in three lensed images JD1, 2, and
3 with F356W fluxes , , (AB mag 25.1, 25.6, 26.6).
MACS0647JD is significantly brighter than other galaxies recently discovered
at similar redshifts with JWST. Without magnification, it would have AB mag
27.3 (). With a high confidence level, we obtain a photometric
redshift of based on photometry measured in 6 NIRCam filters
spanning , out to rest-frame. JWST NIRSpec
observations planned for January 2023 will deliver a spectroscopic redshift and
a more detailed study of the physical properties of MACS0647JD.Comment: 27 pages, 14 figures, submitted to Natur
JWST Reveals a Possible z ∼ 11 Galaxy Merger in Triply Lensed MACS0647–JD
MACS0647–JD is a triply lensed z ∼ 11 galaxy originally discovered with the Hubble Space Telescope. The three lensed images are magnified by factors of ∼8, 5, and 2 to AB mag 25.1, 25.6, and 26.6 at 3.5 μ m. The brightest is over a magnitude brighter than other galaxies recently discovered at similar redshifts z > 10 with JWST. Here, we report new JWST imaging that clearly resolves MACS0647–JD as having two components that are either merging galaxies or stellar complexes within a single galaxy. The brighter larger component “A” is intrinsically very blue ( β ∼ −2.6 ± 0.1), likely due to very recent star formation and no dust, and is spatially extended with an effective radius ∼70 ± 24 pc. The smaller component “B” ( r ∼ 20 pc) appears redder ( β ∼ −2 ± 0.2), likely because it is older (100–200 Myr) with mild dust extinction ( A _V ∼ 0.1 mag). With an estimated stellar mass ratio of roughly 2:1 and physical projected separation ∼400 pc, we may be witnessing a galaxy merger 430 million years after the Big Bang. We identify galaxies with similar colors in a high-redshift simulation, finding their star formation histories to be dissimilar, which is also suggested by the spectral energy distribution fitting, suggesting they formed further apart. We also identify a candidate companion galaxy “C” ∼3 kpc away, likely destined to merge with A and B. Upcoming JWST Near Infrared Spectrograph observations planned for 2023 January will deliver spectroscopic redshifts and more physical properties for these tiny magnified distant galaxies observed in the early universe