Benjamin Horowitz
In this paper, we introduce Mujic{\Lambda} (Mapping the Universe with Jax-based Initial Condition Reconstr{\Lambda}ction), an optimization-based framework for reconstructing initial conditions from realistic galaxy spectroscopic redshift…
This article introduces Generalized Hyperderivative Reed-Solomon codes (GHRS codes), which generalize NRT Reed-Solomon codes. Its main results are as follows: 1) every GHRS code is MDS, 2) the dual of a GHRS code is also an GHRS code, 3)…
We present the extension of the differentiable hydrodynamics code, diffhydro, enabling scalable PDE-constrained inference and integrated hybrid physics-ML models for a wide range of astrophysical applications. New physics additions include…
We present jFoF, a fully GPU-native Friends-of-Friends (FoF) halo finder designed for both high-performance simulation analysis and differentiable modeling. Implemented in JAX, jFoF achieves end-to-end acceleration by performing all…
Hydrodynamical simulations are the most accurate way to model structure formation in the universe, but they often involve a large number of astrophysical parameters modeling subgrid physics, in addition to cosmological parameters. This…
Constructing a general-purpose framework for mapping between dark matter simulations and observable hydrodynamical simulation outputs is a long-standing problem in modern astrophysics. In this work, we present a new approach utilizing…
We introduce the new cosmological simulation project cosmosTNG, a first-of-its-kind suite of constrained galaxy formation simulations for the universe at Cosmic Noon ($z\sim 2$). cosmosTNG simulates a $0.2$ deg$^2$ patch of the COSMOS field…
We introduce the Python program THALAS (TensorFlow Hydrodynamics Analysis for Lyman-Alpha Simulations), which maps baryon fields (baryon density, temperature, and velocity) to Ly$\alpha$ optical depth fields in both real space and redshift…
We study the environmental effect of galaxy evolution as a function of the underlying 3D dark matter density for the first time at $z=2-2.5$, in which the underlying matter density is reconstructed from observed galaxies through dynamical…
We present a novel maximum a posteriori estimator to jointly estimate band-powers and the covariance of the three-dimensional power spectrum (P3D) of Lyman-alpha forest flux fluctuations, called MAPLE. Our Wiener-filter based algorithm…
We reconstruct the dark matter density field from spatially overlapping spectroscopic and photometric redshift catalogs through a forward modelling approach. Instead of directly inferring the underlying density field, we find the best…
We present the Differentiable Lensing Lightcone (DLL), a fully differentiable physical model designed for being used as a forward model in Bayesian inference algorithms requiring access to derivatives of lensing observables with respect to…
Galaxy formation theories predict that galaxy shapes and angular momenta have non-random alignments with the cosmic web. This leads to so-called intrinsic alignment between pairs of galaxies, which is important to quantify as a nuisance…
The Nancy Grace Roman Space Telescope is capable of delivering an unprecedented all-sky, high-spatial resolution, multi-epoch infrared map to the astronomical community. This opportunity arises in the midst of numerous ground- and…
We report a $z=2.30$ galaxy protocluster (COSTCO-I) in the COSMOS field, where the Lyman-$\alpha$ forest as seen in the CLAMATO IGM tomography survey does not show significant absorption. This departs from the transmission-density…
We present the second data release of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) Survey conducted with the LRIS spectrograph on the Keck-I telescope. This project used Lyman-alpha forest absorption in the spectra…
Generating large volume hydrodynamical simulations for cosmological observables is a computationally demanding task necessary for next generation observations. In this work, we construct a novel fully convolutional variational auto-encoder…
In this abstract we explore the possibility of introducing biases in physical parameter inference models from adversarial-type attacks. In particular, we inject small amplitude systematics into inputs to a mixture density networks tasked…
In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep Reinforcement Learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models.…
The fluctuating Gunn-Peterson approximation (FGPA) is a commonly-used method to generate mock Lyman-$\alpha$ (Ly$\alpha$) forest absorption skewers at Cosmic Noon ($z\gtrsim 2$) from the matter-density field of $N$-body simulations without…