arXiv subjects

Nikolina Šarčević

Publications and source records attributed to Nikolina Šarčević.

10 recordsLinked to original sources

arXiv2026

Balancing bias, baryons, and scale cuts in LSST 3x2pt analysis

Stage IV surveys such as LSST will probe deeply into the nonlinear regime, where systematic effects from galaxy bias and baryonic feedback become dominant and poorly constrained nuisance parameters can lead to degeneracies. In this work we present a 3x2pt analysis for LSST Y1 and Y10 data using the BACCO emulator for modelling both the hybrid-effective field theory (HEFT) for nonlinear galaxy bias and the baryonic feedback using the baryonification mechanism. We aim to find a balance between model complexity and scale cuts, with particular attention to parameter degeneracies and baryonic feedback effects on the galaxy-matter and galaxy-galaxy power spectra. First, we find that a linear bias model delivers percent-level unbiased constraints on $Ω_{\rm m}$ and $σ_8$ only up to $k_{\rm max}=0.1 h/$Mpc, but pushing to smaller scales requires a perturbative approach. Second, we compare HEFT with a minimal bias variant with fixed higher-order terms, and find that the latter is unbiased in $Λ$CDM even at $k_{\rm max}=0.7 h/$Mpc. We show that higher-order bias can mimic baryonic suppression, but baryons cannot reproduce the full range of higher-order bias behaviour within the parameter range allowed by the BACCO baryonification model. Third, we find that a detection of the total neutrino mass $M_ν$ is possible for both Y1 and Y10 for $k \geq 0.5 h/$Mpc, at least when photo-$z$ uncertainties and related nuisance parameters are precisely known. However, the specific measured value is not robust across equally plausible mock scenarios: the inferred $M_ν$ can be significantly biased by adopting the minimal bias model. The entire analysis is conducted with a new independent, open source pipeline MGL that we present for the first time in this work.

astro-ph.CO↗

arXiv2026

Non-Gaussian Parameter Bias: Formalism and Validation

We present a framework for propagating systematic data mismatches into parameter biases and posterior deformations beyond the Gaussian Fisher approximation. Using the Derivative Approximation for Likelihoods (DALI), we derive analytic expressions for the maximum a posteriori (MAP) point and posterior mean, including a semi-analytic response expansion in systematic amplitude. We validate the framework against direct numerical calculations and Markov Chain Monte Carlo (MCMC) sampling in a controlled nonlinear two-parameter benchmark up to a covariance-weighted data-space mismatch of $d_{data}=5$. At $d_{data}=5$, Fisher predicts the wrong direction of the shift in one parameter and differs from the numerical result by a Fisher-metric distance of 23.1, whereas the analytic DALI MAP prediction remains within 0.091, with componentwise errors of 0.04% and 0.46%. The response expansion reproduces the posterior mean with errors of 0.02% and 0.18%, while the DALI posterior closely reproduces the displacement and deformation of the fully sampled nonlinear posterior. Mismatches of equal significance but different directions produce markedly different parameter shifts and posterior deformations, while multiple additive mismatches can produce nonadditive parameter responses through nonlinear inference. By reconstructing the posterior under systematic mismatch, the framework captures the nonlinear response and non-Gaussian geometry of parameter bias: how mismatches move and reshape the posterior, why their direction matters, and how their combined effects can be dissected to understand the interplay between multiple systematics.

astro-ph.CO↗

arXiv2026

Environmental sustainability in basic research: a perspective from HECAP+

The climate crisis and the degradation of the world's ecosystems require humanity to take immediate action. The international scientific community has a responsibility to limit the negative environmental impacts of basic research. The HECAP+ communities (High Energy Physics, Cosmology, Astroparticle Physics, and Hadron and Nuclear Physics) make use of common and similar experimental infrastructure, such as accelerators and observatories, and rely similarly on the processing of big data. Our communities therefore face similar challenges to improving the sustainability of our research. This document aims to reflect on the environmental impacts of our work practices and research infrastructure, to highlight best practice, to make recommendations for positive changes, and to identify the opportunities and challenges that such changes present for wider aspects of social responsibility.

physics.soc-ph↗

arXiv2026

Probabilistic characterization of blending with LSST and application to cluster lensing cosmology

Next-generation galaxy surveys, like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), will deliver unprecedented depth and sky coverage, enabling precise measurements of cosmic probes such as weak lensing and galaxy clustering. However, increased imaging depth leads to significant blending of galaxy images, particularly in dense fields like galaxy clusters. This blending, exacerbated by atmospheric blurring in ground-based observations, contaminates galaxy property measurements and causes source confusion. To simultaneously capture these effects, we develop a probabilistic framework introducing the blending entropy, a metric quantifying the ambiguity in matching detected objects to true galaxies or external reference sources. Using simulated data from the DESC Data Challenge 2 (DC2), we characterize blending in LSST data and quantify its impact on cluster lensing cosmology around cosmoDC2 halos. We demonstrate that imposing a blending entropy threshold of $S_b<0.2$ effectively filters out highly blended objects (around 25%), which are especially prevalent near the survey's magnitude limit and are associated with higher errors in shape measurements and photometric redshifts. Applying this cut substantially reduces blending-induced biases in cluster lensing profiles and mass estimates, thereby mitigating systematic errors in cosmological parameters---most notably reducing tension in $σ_8$ estimates. Our method is readily generalizable to other static probes and offers a practical path forward for real data analyses, particularly when leveraging overlapping high-resolution datasets from spaced-based missions such as Euclid or the Roman Space Telescope, where these external datasets can act as reference catalogs to improve the identification of blended sources in LSST data.

astro-ph.CO↗

arXiv2026

Probing Physics Beyond the Standard Model through Combined Analyses of Next-Generation Type Ia Supernova, CMB, and BAO Surveys

Observations of Type Ia supernovae (\sne), which probe the late Universe, together with baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB), which probe the intermediate and early epochs, provide complementary constraints on the expansion history of the Universe. In this work, we forecast constraints on dark energy and other extensions to the standard cosmological model by combining the SNIa sample expected from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), data from current and forthcoming CMB surveys, and BAO measurements from the Dark Energy Spectroscopic Instrument (DESI). For the CMB, we use temperature, polarization, and lensing power spectra ($TT/EE/TE/ϕϕ$) from South Pole Telescope, the planned Advanced Simons Observatory, and a CMB-S4-like experiment. We derive constraints on $Λ{\rm CDM}$ and its extensions involving the dark energy equation of state parameters $(w_{0}, w_{a})$ and the sum of neutrino masses $\sum m_ν$, using a Markov Chain Monte Carlo (MCMC) sampling framework. We find that the LSST Year-3 SNIa sample can improve upon the DES Year-5 dark energy constraints by a factor of $\times2-\times2.5$, with the gains driven primarily by the significantly higher SNIa density in the LSST sample. Similarly, DESI-DR3 shows up to a $\times1.8$ improvement on dark energy parameters over DR2, driven largely by the substantial increase in low-redshift sample. Combining CMB with LSST-Y3-SNIa and DESI-DR3-BAO yields $σ(w_{0}) = 0.028$ and $σ(w_{a}) = 0.11$ for $w_{0} w_{a} {\rm CDM}$ cosmology with the results being largely independent of the CMB dataset. The constraints weaken by 10%-30% when freeing $\sum m_ν$ and spatial curvature. Moreover, the joint analysis of the three datasets can enable a $2-3σ$ detection of $\sum m_ν$.

astro-ph.CO↗

arXiv2026

Smokescreen: A Python package for data vector blinding and encryption in cosmological analyses

Smokescreen is an open-source Python library for data-vector concealment (blinding) in cosmological analyses. Data-vector blinding works by applying cosmology-dependent shifts to the observed data vector, moving it away from the true cosmological signal without affecting its statistical properties, so that analysts cannot infer the true result until the analysis is frozen and the blinding is lifted. The package computes these shifts using Firecrown likelihoods applied to data vectors stored in the SACC format, ensuring that the theoretical model used for blinding is identical to that used for inference whilst remaining agnostic to the specific observable being blinded. To prevent accidental unblinding, the original SACC file, containing the true cosmology, is encrypted. Although developed for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Smokescreen is applicable to any experiment using Firecrown likelihoods and the SACC data format.

astro-ph.IM↗

arXiv2026

DerivKit: stable numerical derivatives bridging Fisher forecasts and MCMC

DerivKit is a Python package for derivative-based statistical inference. It implements stable numerical differentiation and derivative assembly utilities for Fisher-matrix forecasting and higher-order likelihood approximations in scientific applications, supporting scalar- and vector-valued models including black-box or tabulated functions where automatic differentiation is impractical or unavailable. These derivatives are used to construct Fisher forecasts, Fisher bias estimates, and non-Gaussian likelihood expansions based on the Derivative Approximation for Likelihoods (DALI). By extending derivative-based inference beyond the Gaussian approximation, DerivKit forms a practical bridge between fast Fisher forecasts and more computationally intensive sampling-based methods such as Markov chain Monte Carlo (MCMC).

astro-ph.IM↗

arXiv2025

Forecasting the Impact of Source Galaxy Photometric Redshift Uncertainties on the LSST $3\times2$pt Analysis

Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)'s galaxy clustering and weak lensing analysis, i.e., the $3\times 2$pt analysis. This paper introduces a Fisher forecast code FisherA2Z for the LSST Year 10 (Y10) $3 \times 2$pt and cosmic shear analyses, utilizing a 15-parameter redshift distribution model, with one redshift bias, variance, and outlier rate per tomographic bin. FisherA2Z employs the Core Cosmology Library CCL to compute the large-scale structure power spectrum and incorporates a four-parameter nonlinear alignment model for intrinsic alignments. We evaluate the impact of marginalizing over redshift distribution parameters on weak lensing, forecast biases in cosmological parameters due to redshift errors, and assess cosmological parameter sensitivity to redshift systematic parameters using decision trees. The sensitivity study reveals that for LSST $3\times2$pt analysis, $S_8$ is most sensitive to the mean redshift of the fourth out of the five source tomographic bins, while other cosmological parameters possess different sensitivities. Additionally, we provide cosmological analysis forecasts based on different scenarios of spectroscopic training datasets. We find that the figures-of-merit for the cosmological results increase with the number of spectroscopic training galaxies, and with the completeness of the training set above $z=1.6$, assuming the redshift information comes solely from the training set galaxies without other external constraints.

astro-ph.CO↗

arXiv2025

Joint Modelling of Astrophysical Systematics for Cosmology with LSST Cosmic Shear

We present a novel framework for jointly modelling the weak lensing source galaxy redshift distribution and the intrinsic alignment of galaxies via a shared luminosity function (LF). Considering this framework within the context of a Rubin Observatory's Legacy Survey of Space and Time (LSST) Year 1 and Year 10 cosmic shear analysis, we first demonstrate the substantial impact of the LF on both source galaxy redshift distributions and the intrinsic alignment contamination. We establish how the individual parameters of a Schechter LF model impact the redshift distribution of a magnitude-limited sample, and we demonstrate the effect of marginalising over the LF parameters as incorporated in the intrinsic alignment modelling of a standard cosmic shear analysis set-up. We forecast the impact of our joint modelling framework on cosmological parameter constraints. Our preliminary results are promising, indicating that this framework can yield cosmological constraints consistent with those expected from standard analyses, enhanced by the flexibility of not fixing LF parameters. We plan to further validate these findings with comprehensive Markov chain Monte Carlo simulations to robustly quantify bias avoidance and underscore the framework's efficacy. Taking advantage of our forecasting results and the parameter degeneracies, we identify the specific impact of the shape of the LF of source galaxies on the cosmic shear data vector. We also discuss the potential of this method in providing a way to model generic selection functions in redshift distribution estimation, as well as its possibilities for extension to a 3x2pt analysis, particularly with respect to incorporating galaxy bias in this luminosity-function-based framework. Although we consider the context of LSST cosmic shear in this work, the proposed joint modelling framework is generically applicable to weak lensing surveys.

astro-ph.CO↗

arXiv2024

AtLAST Science Overview Report

Submillimeter and millimeter wavelengths provide a unique view of the Universe, from the gas and dust that fills and surrounds galaxies to the chromosphere of our own Sun. Current single-dish facilities have presented a tantalising view of the brightest (sub-)mm sources, and interferometers have provided the exquisite resolution necessary to analyse the details in small fields, but there are still many open questions that cannot be answered with current facilities. In this report we summarise the science that is guiding the design of the Atacama Large Aperture Submillimeter Telescope (AtLAST). We demonstrate how tranformational advances in topics including star formation in high redshift galaxies, the diffuse circumgalactic medium, Galactic ecology, cometary compositions and solar flares motivate the need for a 50m, single-dish telescope with a 1-2 degree field of view and a new generation of highly multiplexed continuum and spectral cameras. AtLAST will have the resolution to drastically lower the confusion limit compared to current single-dish facilities, whilst also being able to rapidly map large areas of the sky and detect extended, diffuse structures. Its high sensitivity and large field of view will open up the field of submillimeter transient science by increasing the probability of serendipitous detections. Finally, the science cases listed here motivate the need for a highly flexible operations model capable of short observations of individual targets, large surveys, monitoring programmes, target of opportunity observations and coordinated observations with other observatories. AtLAST aims to be a sustainable, upgradeable, multipurpose facility that will deliver orders of magnitude increases in sensitivity and mapping speeds over current and planned submillimeter observatories.

astro-ph.IM↗