47 citations · 112 across the 20 of their papers we have counts for
21 papers
Evidence for renormalized instantons in real-time simulations of vacuum decay
Emilie Hertig, Alexander C. Jenkins, Hiranya V. Peiris +2
While vacuum decay is traditionally described by Euclidean instanton methods, lattice simulations enable real-time modeling of dynamical observables relevant to cosmology and upcom…
pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches
Benedict Van den Bussche, Sinan Deger, Hiranya V. Peiris +6
The physical processes that shape a galaxy's spectrum are strongly degenerate in observations, obscuring which processes act independently. Leveraging the pop-cosmos generative gal…
pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web
Madalina N. Tudorache, Hiranya V. Peiris, Stephen Thorp +7
Galaxy sizes are correlated with stellar mass and redshift, as characterised by size scaling relations. The inferred forms of these scaling relations are sensitive to how galaxies…
Thermostats, Not Engines: A New Picture of Halo Gas Regulation
Hiranya V. Peiris, Andrew Pontzen, Madalina N. Tudorache +5
We propose that black hole feedback regulates gas in massive halos by establishing an entropy ceiling; the resulting buoyant gas migrates to the virial radius with no additional en…
StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow
Gurjeet Jagwani, Stephen Thorp, Sinan Deger +1
Diffusion and flow-based models are ubiquitously used for generative modelling and density estimation. They admit a deterministic probability flow ordinary differential equation (P…
The limits of feedback from active galactic nuclei
Andrew Pontzen, Hiranya V. Peiris, Joop Schaye +1
We use FLAMINGO to investigate why feedback from active galactic nuclei (AGN) significantly depletes gas in galaxy groups but is ineffective in clusters. We delineate three radial…
Large language models are not the problem
Hiranya V. Peiris
If a Large Language Model (LLM) can replicate your scientific contribution, the problem is not the LLM. What does it say about our field that so much of the anxiety about AI comes…
pop-cosmos: Forward modeling KiDS-1000 redshift distributions using realistic galaxy populations
Boris Leistedt, Hiranya V. Peiris, Anik Halder +13
The accuracy of the cosmological constraints from Stage~IV galaxy surveys will be limited by how well the galaxy redshift distributions can be inferred. We have addressed this chal…
pop-cosmos: Redshifts and physical properties of KiDS-1000 galaxies
Anik Halder, Hiranya V. Peiris, Stephen Thorp +8
Principled Bayesian inference of galaxy properties has not previously been performed for wide-area weak lensing surveys with millions of sources. We address this gap by applying th…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
Uniform Rolling: An LSST Observing Cadence Offering Sufficient Survey Uniformity for Comprehensive Cosmological Analysis
Boris Leistedt, Matthew R. Becker, Humna Awan +14
The Legacy Survey of Space and Time (LSST) that will be carried out by the NSF-DOE Vera C. Rubin Observatory promises to be the defining survey of the next decade, supplying unprec…
Galaxies as stochastic systems: why the next breakthrough in galaxy evolution requires one hundred million spectra
Sandro Tacchella, Vasily Belokurov, Harry T. J. Bevins +4
Each galaxy is observed only once along its life, making galaxy evolution fundamentally an inverse statistical problem: time-dependent physics must be inferred from ensembles of si…
pop-cosmos: Star formation over 12 Gyr from generative modelling of a deep infrared-selected galaxy catalogue
Sinan Deger, Hiranya V. Peiris, Stephen Thorp +5
We study star formation over 12 Gyr using pop-cosmos, a generative model trained on 26-band photometry of 420,000 COSMOS2020 galaxies (IRAC Ch.1 ). The model learns distributi…
pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population
Stephen Thorp, Hiranya V. Peiris, Gurjeet Jagwani +6
We present an extension of the pop-cosmos model for the evolving galaxy population up to redshift . The model is trained on distributions of observed colors and magnitudes,…
Cosmological feedback from a halo assembly perspective
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +6
The impact of feedback from galaxy formation on cosmological probes is typically quantified in terms of the suppression of the matter power spectrum in hydrodynamical compared to g…
Bubbles in a box: Eliminating edge nucleation in cold-atom simulators of vacuum decay
Alexander C. Jenkins, Hiranya V. Peiris, Andrew Pontzen
The decay of metastable 'false vacuum' states via bubble nucleation plays a crucial role in many cosmological scenarios. Cold-atom analog experiments will soon provide the first em…
Deep learning insights into non-universality in the halo mass function
Ningyuan Guo, Luisa Lucie-Smith, Hiranya V. Peiris +2
The abundance of dark matter haloes is a key cosmological probe in forthcoming galaxy surveys. The theoretical understanding of the halo mass function (HMF) is limited by our incom…
An Anti-halo Void Catalogue of the Local Super-Volume
Stephen Stopyra, Hiranya V. Peiris, Andrew Pontzen +2
We construct an anti-halo void catalogue of voids with radii in the Local Super-Volume ( from the Milky Way), using…
The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals
A. I. Malz, R. Hložek, T. Allam +22
Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of their underlying physical processes. Ho…
Wide-Field Lensing Mass Maps from DES Science Verification Data
C. Chang, V. Vikram, B. Jain +81
We present a mass map reconstructed from weak gravitational lensing shear measurements over 139 sq. deg from the Dark Energy Survey (DES) Science Verification data. The mass map pr…
Wide-Field Lensing Mass Maps from DES Science Verification Data: Methodology and Detailed Analysis
V. Vikram, C. Chang, B. Jain +91
Weak gravitational lensing allows one to reconstruct the spatial distribution of the projected mass density across the sky. These "mass maps" provide a powerful tool for studying c…