Related papers: Robustness to systematics for future dark energy p…
While modelling our late time cosmically accelerated universe, it is popular to involve different dark energy models, the equation of state of which can be taken as a function of the redshift and some unknown parameters. Barboza and Alcaniz…
Observations of supernovae of type Ia require dark energy (some unknown exotic \emph{`matter'} of negative pressure) to explain their unexpected faintness. Whereas the simplest and most favoured candidate of dark energy, the Einsteinian…
The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process ${\sim}10^6$ transient detections per night. For precision measurements of cosmological parameters and rates, it is critical to understand the…
We combine high redshift Type Ia supernovae from the first 3 years of the Supernova Legacy Survey (SNLS) with other supernova (SN) samples, primarily at lower redshifts, to form a high-quality joint sample of 472 SNe (123 low-$z$, 93 SDSS,…
Massive gravity in the presence of doubly coupled matter field via en effective composite metric yields an accelerated expansion of the universe. It has been recently shown that the model admits stable de Sitter attractor solutions and…
Supernova experiments to characterize dark energy require a well designed low redshift program; we consider this for both ongoing/near term (e.g. Supernova Legacy Survey) and comprehensive future (e.g. SNAP) experiments. The derived…
Distance measurements to Type Ia supernovae (SNe Ia) indicate that the Universe is accelerating and that two-thirds of the critical energy density exists in a dark-energy component with negative pressure. Distance measurements to SNe Ia can…
We propose a novel strategy for extracting features in supervised learning that can be used to construct a classifier which is more robust to small perturbations in the input space. Our method builds upon the idea of the information…
We use the radial baryon acoustic oscillation (BAO) measurements of Gaztanaga et al. (2008) to constrain parameters of dark energy models. These constraints are comparable with constraints from other "non-radial" BAO data. The radial BAO…
The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial…
The coming decade will be an exciting period for dark energy research, during which astronomers will address the question of what drives the accelerated cosmic expansion as first revealed by type Ia supernova (SN) distances, and confirmed…
Numerical studies indicate that uncertainties in the treatment of baryonic physics can affect predictions for shear power spectra at a level that is significant for forthcoming surveys such as DES, SNAP, and LSST. Correspondingly, we show…
The last five years have seen remarkable progress in our quest to determine the equation of state of neutron rich matter. Recent advances across the theoretical, experimental, and observational landscape have been incorporated in a Bayesian…
Future detectors such as LISA promise signal-to-noise ratios potentially in the thousands and data containing simultaneous signals. Accurate numerical relativity waveforms will be essential to maximize the science return. A question of…
We present the predictions with standard sirens at Gravitational Waves detectors, such as the Laser Interferometer Space Antenna (LISA) and the Einstein Telescope (ET), for interacting dark energy theories. We focus on four models…
We carry out an independent re-analysis of the Dark Energy Spectroscopic Instrument (DESI) public dataset, focusing on extensions to the standard cosmological model, $\Lambda$CDM. Utilizing the dataset and Effective Field Theory (EFT)-based…
We study the utility of a large sample of type Ia supernovae that might be observed in an imaging survey that rapidly scans a large fraction of the sky for constraining dark energy. We consider information from the traditional luminosity…
In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings. To mitigate the limitations of robustness, we introduce a new measure called…
Optical phase measurement is a simple example of a quantum--limited measurement problem with important applications in metrology such as gravitational wave detection. The formulation of optimal strategies for such measurements is an…
Forecasting cosmological constraints from halo-based statistics often suffers from instability in derivative estimates, especially when the number of simulations is limited. This instability reduces the reliability of Fisher forecasts and…