arXiv subjects

Camilla T. G. Sørensen

Publications and source records attributed to Camilla T. G. Sørensen.

3 recordsLinked to original sources

arXiv2025

The ANDICAM-SOFI Near-infrared and Optical type Ia Supernova (ASNOS) sample: Description and data release

Type Ia supernovae (SNe Ia) provide the most robust means of measuring extragalactic distances. While most of the effort has focused on increasing the number of SNe Ia observed in the optical, near-infrared (NIR) observations remain scarce despite their advantages, that is, reduced dust extinction and a more intrinsic standard candle behavior, requiring little to no empirical corrections. Here, we present ASNOS (ANDICAM-SOFI Near-infrared and Optical type Ia Supernova), a dataset with sample size of 1,482 epochs in the $BVRIYJH$ filters from the ANDICAM instrument on the 1.3-meter SMARTS telescope at Cerro Tololo Inter-American Observatory, along with 125 $JHK$ epochs from the SOFI instrument on the 3.58-meter New Technology Telescope on the La Silla Observatory. Additionally, we incorporate optical forced photometry from the Zwicky Transient Facility and the Asteroid Terrestrial-impact Last Alert System. The sample comprises 41 SNe Ia in total, including 29 normal events, eight 1991T-like objects, and four peculiar subtypes, all located at redshifts $z < 0.085$. This paper provides a detailed overview of the ASNOS sample selection, data reduction, SN photometry, host-galaxy spectral energy distribution construction, both global and local, and SN light-curve fitting using three methods: SALT3-NIR, SNooPy, and BayeSN. A companion paper will present the cosmological analysis.

astro-ph.CO↗

arXiv2025

Cosmic curl -- Features and convergence of the vorticity power spectrum in $N$-body simulations

Observations of the cosmic velocity field could become an important cosmological probe in the near future. To take advantage of future velocity-flow surveys we must however have the theoretical predictions under control. In many respects, the velocity field is easier to simulate than the density field because it is less severely affected by small-scale clustering. Therefore, as we also show in this paper, a particle-mesh (PM) based simulation approach is usually sufficient, yielding results within a few percent of a corresponding P$^3$M simulation in which short-range forces are properly accounted for, but which also carry a much larger computational cost. However, in other respects the velocity field is much more challenging to deal with than the density field: Interpolating the velocity field onto a grid is significantly more complicated, and the vorticity field (the curl-part of the velocity field) is severely affected by both sample variance and discretisation effects. While the former can be dealt with using fixed amplitude initial conditions, the former makes it infeasible to run fully converged simulations in a cosmological volume. However, using the $N$-body code CONCEPT we show that one can robustly extrapolate the cosmic vorticity power spectrum from just 4 simulations with different number of particles. We expect our extrapolated vorticity power spectra to be correct within 5\% of the fully converged result across three orders of magnitude in $k$. Finally, we have also investigated the time dependence of the vorticity as well as the ratio of vorticity to divergence.

astro-ph.CO↗

arXiv2025

Calculating Bayesian evidence for inflationary models using CONNECT

Bayesian evidence is a standard tool used for comparing the ability of different models to fit available data and is used extensively in cosmology. However, since the evidence calculation involves performing an integral of the likelihood function over the entire space of model parameters this can be prohibitively expensive in terms of both CPU and time consumption. For example, in the simplest $Λ$CDM model and using CMB data from the Planck satellite, the dimensionality of the model space is over 30 (typically 6 cosmological parameters and 28 nuisance parameters). Even the simplest possible model requires $\mathcal{O}(10^6)$ calls to an Einstein--Boltzmann solver such as CLASS or CAMB and takes several days. Here we present calculations of Bayesian evidence using the CONNECT framework to calculate cosmological observables. We demonstrate that we can achieve results comparable to those obtained using Einstein--Boltzmann solvers, but at a minute fraction of the computational cost. As a test case, we then go on to compute Bayesian evidence ratios for a selection of slow-roll inflationary models. In the setup presented here, the total computation time is completely dominated by the likelihood function calculation which now becomes the main bottleneck for increasing computation speed.

astro-ph.CO↗