First author: Chethan Krishnan
We show that the Friedmann-Lema^{i}tre-Robertson-Walker (FLRW) framework has an instability towards the growth of fluid flow anisotropies, even if the Universe is accelerating. This flow (tilt) instability in the matter sector is invisible to Cosmic No-Hair Theorem-like arguments, which typically only flag shear anisotropies in the metric. We illustrate our claims in the setting of ``dipole cosmology’’, the maximally Copernican generalization of FLRW that can accommodate a flow.
First author: Peter Melchior
We introduce the neural network architecture SPENDER as a core differentiable building block for analyzing, representing, and creating galaxy spectra. It combines a convolutional encoder, which pays attention to up to 256 spectral features and compresses them into a low-dimensional latent space, with a decoder that generates a restframe representation, whose spectral range and resolution exceeds that of the observing instrument. The decoder is followed by explicit redshift, resampling, and convolution transformations to match the observations.
First author: Zidu Lin
We perform a novel multi-messenger analysis for the identification and parameter estimation of the Standing Accretion Shock Instability (SASI) in a core collapse supernova with neutrino and gravitational wave (GW) signals. In the neutrino channel, this method performs a likelihood ratio test for the presence of SASI in the frequency domain. For gravitational wave signals we process an event with a modified constrained likelihood method. Using simulated supernova signals, the properties of the Hyper-Kamiokande neutrino detector, and O3 LIGO Interferometric data, we produce the two-dimensional probability density distribution (PDF) of the SASI activity indicator and calculate the probability of detection $P_\mathrm{D}$ as well as the false identification probability $P_\mathrm{FI}$.
First author: Makoto Sakuda
We first discuss and determine the isospin mixing of the two $2^-$ states (12.53 MeV and 12.97 MeV) of $^{16}$O nucleus using the inelastic electron scattering data. We then evaluate the cross section of 4.4-MeV $\gamma$ rays produced in the neutrino neutral-current (NC) reaction $^{16}$O($\nu, \nu^{\prime}$)$^{16}$O$(12.97~{\rm MeV}, 2^-$) in a water Cherenkov detector at the low energy below 100 MeV. The detection of $\gamma$ rays for $E_{\gamma}>5$~MeV from the NC reaction $^{16}$O($\nu, \nu^{\prime}$)$^{16}$O$(E_x>16\ {\rm MeV}, T=1$) with a water Cherenkov detector in the supernova neutrino bursts has been proposed and discussed by several authors previously.
First author: Daichi Kashino
We present a first sample of 117 [OIII]$\lambda\lambda$4960,5008-selected star-forming galaxies at $5.33 < z < 6.93$ detected in JWST/NIRCam 3.5$\mu$m slitless spectroscopy of a $6.5 \times 3.4$ arcmin$^2$ field centered on the hyperluminous quasar SDSS J0100+2802, obtained as part of the EIGER (Emission-line galaxies and Intergalactic Gas in the Epoch of Reionization) survey. Three prominent galaxy overdensities are observed, one of them at the redshift of the quasar.
First author: Jorryt Matthee
We present emission-line measurements and physical interpretations for a sample of 117 [OIII] emitting galaxies at $z=5.33-6.93$, using the first deep JWST/NIRCam wide field slitless spectroscopic observations. Our 9.7-hour integration is centered upon the $z=6.3$ quasar J0100+2802 – the first of six fields targeted by the EIGER survey – and covers $\lambda=3-4$ microns. We detect 133 [OIII] doublets, but merge pairs within $\approx$10 kpc and 600 km s$^{-1}$, motivated by their small scale clustering excess.
First author: Dominic Smith
Galaxies broadly fall into two categories: star-forming (blue) galaxies and quiescent (red) galaxies. In between, one finds the less populated ``green valley". Some of these galaxies are suspected to be in the process of ceasing their star-formation through a gradual exhaustion of gas supply or already dead and are experiencing a rejuvenation of star-formation through fuel injection. We use the Galaxy And Mass Assembly database and the Galaxy Zoo citizen science morphological estimates to compare the morphology of galaxies in the green valley against those in the red sequence and blue cloud.
First author: Atsuhisa Ota
The nonlinear Lagrangian displacement field and initial linear density field are highly correlated. Therefore, reconstructing the nonlinear displacement field could better extract the primordial cosmological information from the late time density field. Continuing from Ref. [1], we investigate to what extent the iterative displacement reconstruction in Ref. [2] can recover the true displacement field with a particular emphasis on improving the numerical discreteness effect and improving the perturbation theory model for the postreconstructed field.
First author: Siddharth Chaini
We present MargNet, a deep learning-based classifier for identifying stars, quasars and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) catalogue. MargNet consists of a combination of Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) architectures. Using a carefully curated dataset consisting of 240,000 compact objects and an additional 150,000 faint objects, the machine learns classification directly from the data, minimising the need for human intervention.
First author: Josh Borrow
All modern galaxy formation models employ stochastic elements in their sub-grid prescriptions to discretise continuous equations across the time domain. In this paper, we investigate how the stochastic nature of these models, notably star formation, black hole accretion, and their associated feedback, that act on small ($<$ kpc) scales, can back-react on macroscopic galaxy properties (e.g. stellar mass and size) across long ($>$ Gyr) timescales. We find that the scatter in scaling relations predicted by the EAGLE model implemented in the SWIFT code can be significantly impacted by random variability between re-simulations of the same object, even when galaxies are resolved by tens of thousands of particles.
