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Evaluating the Kirchhoff-Fresnel diffraction integral is essential in studying wave effects in astrophysical lensing, but is often intractable because of the highly oscillatory integrated. A recent breakthrough was made by exploiting the Picard-Lefschetz theory: the integral can be performed along the `Lefschetz thimbles' in the complex domain where the integrand is not oscillatory but rapidly converging. The application of this method, however, has been limited by both the unfamiliar concepts involved and the low numerical efficiency of the method used to find the Lefschetz thimbles. In this paper, we give simple examples of the Lefschetz thimbles and define the `flow lines' that facilitate the understanding of the concepts. Based on this, we propose new ways to obtain the Lefschetz thimbles with high numerical efficiency, which provide an effective tool for studying wave effects in astrophysical lensing., Comment: Published in MNRAS, comments are welcome. Code is available at https://github.com/shixun22/Lefschetz_thimble

Deviations of the cosmic microwave background (CMB) energy spectrum from a perfect blackbody uniquely probe a wide range of physics, ranging from fundamental physics in the primordial Universe ($\mu$-distortion) to late-time baryonic feedback processes (y-distortion). While the y-distortion can be detected with a moderate increase in sensitivity over that of COBE/FIRAS, the $\Lambda$CDM-predicted $\mu$-distortion is roughly two orders of magnitude smaller and requires substantial improvements, with foregrounds presenting a serious obstacle. Within the standard model, the dominant contribution to $\mu$ arises from energy injected via Silk damping, yielding sensitivity to the primordial power spectrum at wavenumbers $k \approx 1-10^{4}$ Mpc$^{-1}$. Here, we present a new instrument concept, SPECTER, with the goal of robustly detecting $\mu$. The instrument technology is similar to that of LiteBIRD, but with an absolute temperature calibration system. Using a Fisher approach, we optimize the instrument's configuration to target $\mu$ while robustly marginalizing over foreground contaminants. Unlike Fourier-transform-spectrometer-based designs, the specific bands and their individual sensitivities can be independently set in this instrument, allowing significant flexibility. We forecast SPECTER to observe the $\Lambda$CDM-predicted $\mu$-distortion at $\approx 5\sigma$ (10$\sigma$) assuming an observation time of 1 (4) year(s) (corresponding to mission duration of 2 (8) years), after foreground marginalization. Our optimized configuration includes 16 bands spanning 1-2000 GHz with degree-scale angular resolution at 150 GHz and 1046 total detectors. SPECTER will additionally measure the y-distortion at sub-percent precision and its relativistic correction at percent-level precision, yielding tight constraints on the total thermal energy and mean temperature of ionized gas., Comment: 26 pages, 12 figures

Twenty-one-centimetre signals from the Epoch of Reionization (EoR) are expected to be detected in the low-frequency radio window by the next-generation interferometers, particularly the Square Kilometre Array (SKA). However, precision data analysis pipelines are required to minimize the systematics within an infinitesimal error budget. Consequently, there is a growing need to characterize the sources of errors in EoR analysis. In this study, we identify one such error origin, namely source blending, which is introduced by the overlap of objects in the densely populated observing sky under SKA1-Low's unprecedented sensitivity and resolution, and evaluate its two-fold impact in both the spatial and frequency domains using a novel hybrid evaluation (HEVAL) pipeline combining end-to-end simulation with an analytic method to mimic EoR analysis pipelines. Sky models corrupted by source blending induce small but severe frequency-dependent calibration errors when coupled with astronomical foregrounds, impeding EoR parameter inference with strong additive residuals in the two-dimensional power spectrum space. We report that additive residuals from poor calibration against sky models with blending ratios of 5 and 0.5 per cent significantly contaminate the EoR window. In contrast, the sky model with a 0.05 per cent blending ratio leaves little residual imprint within the EoR window, therefore identifying a blending tolerance at approximately 0.05 per cent. Given that the SKA observing sky is estimated to suffer from an extended level of blending, strategies involving de-blending, frequency-dependent error mitigation, or a combination of both, are required to effectively attenuate the calibration impact of source-blending defects., Comment: 25 pages, 15 figures, 5 tables, accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS), Fg21Sim+ github repository: https://github.com/Fg21Sim/Fg21SimPlus

We develop a transformer-based conditional generative model for discrete point objects and their properties. We use it to build a model for populating cosmological simulations with gravitationally collapsed structures called dark matter halos. Specifically, we condition our model with dark matter distribution obtained from fast, approximate simulations to recover the correct three-dimensional positions and masses of individual halos. This leads to a first model that can recover the statistical properties of the halos at small scales to better than 3% level using an accelerated dark matter simulation. This trained model can then be applied to simulations with significantly larger volumes which would otherwise be computationally prohibitive with traditional simulations, and also provides a crucial missing link in making end-to-end differentiable cosmological simulations. The code, named GOTHAM (Generative cOnditional Transformer for Halo's Auto-regressive Modeling) is publicly available at \url{https://github.com/shivampcosmo/GOTHAM}., Comment: 6 pages, 2 figures. Accepted by the Structured Probabilistic Inference & Generative Modeling workshop of ICML 2024

We explore full-shape analysis with simulation-based priors, which is the simplest approach to galaxy clustering data analysis that combines effective field theory (EFT) on large scales and numerical simulations on small scales. The core ingredient of our approach is the prior density of EFT parameters which we extract from a suite of 10500 galaxy simulations based on the halo occupation distribution (HOD) model. We measure the EFT parameters with the field-level forward model, which enables us to cancel cosmic variance. On the theory side, we develop a new efficient approach to calculate field-level transfer functions using time-sliced perturbation theory and the logarithmic fast Fourier transform. We study cosmology dependence of EFT parameters of dark matter halos and HOD galaxies and find that it can be ignored for the purpose of prior generation. We use neural density estimation to model the measured distribution of EFT parameters. Our distribution model is then used as a prior in a reanalysis of the BOSS full-shape galaxy power spectrum data. Assuming the $\Lambda$CDM model, we find significant ($\approx 30\%$ and $\approx 60\%$) improvements for the matter density fraction and the mass fluctuation amplitude, which are constrained to $\Omega_m= 0.315 \pm 0.010$ and $\sigma_8 = 0.671 \pm 0.027$. The value of the Hubble constant does not change, $H_0= 68.7\pm 1.1$ km/s/Mpc. This reaffirms earlier reports of the structure growth tension from the BOSS data. Finally, we use the measured EFT parameters to constrain galaxy formation physics., Comment: 40 pages, 14 figs

The absolute flux calibration of the Mid-Infrared Instrument Imaging and Coronagraphy is based on observations of multiple stars taken during the first 2.5 years of JWST operations. The observations were designed to ensure that the flux calibration is valid for a range of flux densities, different subarrays, and different types of stars. The flux calibration was measured by combining observed aperture photometry corrected to infinite aperture with predictions based on previous observations and models of stellar atmospheres. A subset of these observations were combined with model point-spread-functions to measure the corrections to infinite aperture. Variations in the calibration factor with time, flux density, background level, type of star, subarray, integration time, rate, and well depth were investigated, and the only significant variations were with time and subarray. Observations of the same star taken approximately every month revealed a modest time-dependent response loss seen mainly at the longest wavelengths. This loss is well characterized by a decaying exponential with a time constant of ~200 days. After correcting for the response loss, the band-dependent scatter around the corrected average (aka repeatability) was found to range from 0.1 to 1.2%. Signals in observations taken with different subarrays can be lower by up to 3.4% compared to FULL frame. After correcting for the time and subarray dependencies, the scatter in the calibration factors measured for individual stars ranges from 1 to 4% depending on the band. The formal uncertainties on the flux calibration averaged for all observations are 0.3 to 1.0%, with longer-wavelength bands generally having larger uncertainties., Comment: 26 pages, 17 figures, submitted to AJ

ANNZ is a fast and simple algorithm which utilises artificial neural networks (ANNs), it was known as one of the pioneers of machine learning approaches to photometric redshift estimation decades ago. We enhanced the algorithm by introducing new activation functions like tanh, softplus, SiLU, Mish and ReLU variants; its new performance is then vigorously tested on legacy samples like the Luminous Red Galaxy (LRG) and Stripe-82 samples from SDSS, as well as modern galaxy samples like the Physics of the Accelerating Universe Survey (PAUS). This work focuses on testing the robustness of activation functions with respect to the choice of ANN architectures, particularly on its depth and width, in the context of galaxy photometric redshift estimation. Our upgraded algorithm, which we named ANNZ+, shows that the tanh and Leaky ReLU activation functions provide more consistent and stable results across deeper and wider architectures with > 1 per cent improvement in root-mean-square error ($\sigma_{\textrm{RMS}}$) and 68th percentile error ($\sigma_{68}$) when tested on SDSS data sets. While assessing its capabilities in handling high dimensional inputs, we achieved an improvement of 11 per cent in $\sigma_{\textrm{RMS}}$ and 6 per cent in $\sigma_{68}$ with the tanh activation function when tested on the 40-narrowband PAUS dataset; it even outperformed ANNZ2, its supposed successor, by 44 per cent in $\sigma_{\textrm{RMS}}$. This justifies the effort to upgrade the 20-year-old ANNZ, allowing it to remain viable and competitive within the photo-z community today. The updated algorithm ANNZ+ is publicly available at https://github.com/imdadmpt/ANNzPlus., Comment: 37 pages, 9 figures, submitted to JCAP

Many modern applications of Bayesian inference, such as in cosmology, are based on complicated forward models with high-dimensional parameter spaces. This considerably limits the sampling of posterior distributions conditioned on observed data. In turn, this reduces the interpretability of posteriors to their one- and two-dimensional marginal distributions, when more information is available in the full dimensional distributions. We show how to learn smooth and differentiable representations of posterior distributions from their samples using normalizing flows, which we train with an added evidence error loss term, to improve accuracy in multiple ways. Motivated by problems from cosmology, we implement a robust method to obtain one and two-dimensional posterior profiles. These are obtained by optimizing, instead of integrating, over other parameters, and are thus less prone than marginals to so-called projection effects. We also demonstrate how this representation provides an accurate estimator of the Bayesian evidence, with log error at the 0.2 level, allowing accurate model comparison. We test our method on multi-modal mixtures of Gaussians up to dimension 32 before applying it to simulated cosmology examples. Our code is publicly available at https://github.com/mraveri/tensiometer., Comment: 10 pages, 6 figures Tutorial available at https://tensiometer.readthedocs.io/en/latest/example_posterior_profiles.html

Cosmological models are often motivated and formulated in the language of particle physics, using quantities such as the axion decay constant, but tested against data using ostensibly physical quantities, such as energy density ratios, assuming uniform priors on the latter. This approach neglects priors on the model from fundamental theory, including from particle physics and string theory, such as the preference for sub-Planckian axion decay constants. We introduce a novel approach to learning theory-informed priors for Bayesian inference using normalizing flows (NF), a flexible generative machine learning technique that generates priors on model parameters when analytic expressions are unavailable or difficult to compute. As a test case, we focus on early dark energy (EDE), a model designed to address the Hubble tension. Rather than using uniform priors on the $\textit{phenomenological}$ EDE parameters $f_{\rm EDE}$ and $z_c$, we train a NF on EDE cosmologies informed by theory expectations for axion masses and decay constants. Our method recovers known constraints in this representation while being $\sim 300,000$ times more efficient in terms of total CPU compute time. Applying our NF to $\textit{Planck}$ and BOSS data, we obtain the first theory-informed constraints on EDE, finding $f_{\rm EDE} \lesssim 0.02$ at $95\%$ confidence with an $H_0$ consistent with $\textit{Planck}$, but in $\sim 6\sigma$ tension with SH0ES. This yields the strongest constraints on EDE to date, additionally challenging its role in resolving the Hubble tension., Comment: 12 pages, 6 figures, 2 tables

Observations of quasars show that the polarization position angle of the emission coming from them varies greatly over time, including periods called rotations during which the angle changes in an orderly manner. The study proposes a method for identifying such events and assessing their statistical significance. The operation of the method is demonstrated using the example of long-term polarimetric observations of the blazars CTA 102, 3C 454.3, and OT 081. During the analysis of light curves, 51 rotations of the polarization position angle were found and it was shown that for CTA 102 and 3C 454.3 the rotations are predominantly oriented in one direction., Comment: 21 pages, 12 figures

We are merging a large participatory science effort with machine learning to enhance the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). Our overall goal is to remove false positives, allowing us to use lower signal-to-noise data and sources with low goodness-of-fit. With six million classifications through Dark Energy Explorers, we can confidently determine if a source is not real at over 94% confidence level when classified by at least ten individuals; this confidence level increases for higher signal-to-noise sources. To date, we have only been able to apply this direct analysis to 190,000 sources. The full sample of HETDEX will contain around 2-3M sources, including nearby galaxies ([O II] emitters), distant galaxies (Lyman-alpha emitters or LAEs), false positives, and contamination from instrument issues. We can accommodate this tenfold increase by using machine learning with visually-vetted samples from Dark Energy Explorers. We have already increased by over ten-fold in number of sources that have been visually vetted from our previous pilot study where we only had 14,000 visually vetted LAE candidates. This paper expands on the previous work increasing the visually-vetted sample from 14,000 to 190,000. In addition, using our currently visually-vetted sample, we generate a real or false positive classification for the full candidate sample of 1.2 million LAEs. We currently have approximately 17,000 volunteers from 159 countries around the world. Thus, we are applying participatory or citizen scientist analysis to our full HETDEX dataset, creating a free educational opportunity that requires no prior technical knowledge., Comment: 11 pages, 5 figures

We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit (BTK), serves as a modular, flexible, easy-to-install, and simple-to-use interface for exploring and analyzing systematic effects related to blended galaxies in cosmological surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). BTK has three main components: (1) a set of modules that perform fast image simulations of blended galaxies, using the open source image simulation package GalSim; (2) a module that standardizes the inputs and outputs of existing deblending algorithms; (3) a library of deblending metrics commonly defined in the galaxy deblending literature. In combination, these modules allow researchers to explore the impacts of galaxy blending in cosmological surveys. Additionally, BTK provides researchers who are developing a new deblending algorithm a framework to evaluate algorithm performance and make principled comparisons with existing deblenders. BTK includes a suite of tutorials and comprehensive documentation. The source code is publicly available on GitHub at https://github.com/LSSTDESC/BlendingToolKit., Comment: 15 pages, 9 figures, 2 tables

The gravitational lensing wave effect generated by a microlensing field embedded in a lens galaxy is an inevitable phenomenon in strong lensed gravitational waves (SLGWs). This effect presents both challenges and opportunities for the detection and application of SLGWs. However, investigating this wave effect requires computing a complete diffraction integral over each microlens in the field. This is extremely time-consuming due to the large number of microlenses. Therefore, simply adding all the microlenses is impractical. Additionally, the complexity of the time delay surface makes the lens plane resolution a crucial factor in controlling numerical errors. In this paper, we propose a trapezoid approximation-based adaptive hierarchical tree algorithm to meet the challenges of calculation speed and precision. We find that this algorithm accelerates the calculation by four orders of magnitude compared to the simple adding method and is one order of magnitude faster than the fixed hierarchical tree algorithm proposed for electromagnetic microlensing. More importantly, our algorithm ensures controllable numerical errors, increasing confidence in the results. Together with our previous work, this paper addresses all numerical issues, including integral convergence, precision, and computational time. Finally, we conducted a population study on the microlensing wave effect of SLGWs using this algorithm and found that the microlensing wave effect cannot be ignored, especially for Type II SLGWs due to their intrinsic geometric structures and their typical intersection with a denser microlensing field. Statistically, more than 33% (11%) of SLGWs have a mismatch larger than 1% (3%) compared to the unlensed waveform. Additionally, we found that the mismatch between signal pairs in a doubly imaged GW is generally larger than 10^{-3}, and 61% (25%) of signal pairs have a mismatch larger than 1% (3%)., Comment: 19 pages, 11 figures, minor revision before publication

The Epoch of Reionization Spectrometer (EoR-Spec) is an upcoming Line Intensity Mapping (LIM) instrument designed to study the evolution of the early universe (z = 3.5 to 8) by probing the redshifted [CII] 158 $\mu$m fine-structure line from aggregates of galaxies. The [CII] emission is an excellent tracer of star formation since it is the dominant cooling line from neutral gas heated by OB star light and thus can be used to probe the reionization of the early Universe due to star formation. EoR-Spec will be deployed on Prime-Cam, a modular direct-detection receiver for the 6-meter Fred Young Submillimeter Telescope (FYST), currently under construction by CPI Vertex Antennentechnik GmbH and to be installed near the summit of Cerro Chajnantor in the Atacama Desert. This instrument features an image plane populated with more than 6500 Microwave Kinetic Inductance Detectors (MKIDs) that are illuminated by a 4-lens optical design with a cryogenic, scanning Fabry-Perot Interferometer (FPI) at the pupil of the optical system. The FPI is designed to provide a spectral resolving power of $R\sim100$ over the full spectral range of 210--420 GHz. EoR-Spec will tomographically survey the E-COSMOS and E-CDFS fields with a depth of about 4000 hours over a 5 year period. Here we give an update on EoR-Spec's final mechanical/optical design and the current status of fabrication, characterization and testing towards first light in 2026., Comment: Presented at SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII

Wave-optics phenomena in gravitational lensing occur when the signal's wavelength is commensurate to the gravitational radius of the lens. Although potentially detectable in lensed gravitational waves, fast radio bursts and pulsars, accurate numerical predictions are challenging to compute. Here we present novel methods for wave-optics lensing that allow the treatment of general lenses. In addition to a general algorithm, specialized methods optimize symmetric lenses (arbitrary number of images) and generic lenses in the single-image regime. We also develop approximations for simple lenses (point-like and singular isothermal sphere) that drastically outperform known solutions without compromising accuracy. These algorithms are implemented in Gravitational Lensing of Waves (GLoW): an accurate, flexible, and fast code. GLoW efficiently computes the frequency-dependent amplification factor for generic lens models and arbitrary impact parameters in O(1 ms) to O(10 ms) depending on the lens configuration and complexity. GLoW is readily applicable to model lensing diffraction on gravitational-wave signals, offering new means to investigate the distribution of dark-matter and large-scale structure with signals from ground and space detectors., Comment: 21 pages, 10 figures. GLoW is publicly available at https://github.com/miguelzuma/GLoW_public . All the scripts needed to reproduce the figures in this paper can also be found in the repository

Orbiting space objects have become in the last decade a major nuisance impacting ground astronomy and orbiting space assets, from observatories to satellites and space stations. In particular with the rise of the satellite population in Low Earth Orbits (LEOs), space objects are becoming an even bigger threat and a strong problem to astronomical observations. To tackle these threats several coordinated surveillance networks composed of dedicated sensors (telescopes, radars and laser ranging facilities) track and survey space objects, from debris to active satellites. As part of the European Space Surveillance \& Tracking (EU-SST) network, Portugal is developing the PAmpilhosa da Serra Space Observatory (PASO), with both radio and optical telescopes dedicated to the Space Situational Awareness (SSA) domain, deployed at a Dark Sky destination. To optimize telescope survey time, we developed {\tt{CLOWN}} (CLOud Watcher at Night), an application interface that automatically monitors clouds in real time. This software can correctly trace clouds positions in the sky, provides accurate pointing information to the observation planning of the optical telescope to avoid cloudy areas. {\tt{CLOWN}} only requires the use of an all-sky camera, which is already a norm in observatories with optical telescopes and can be used with any camera, including those for which no information about its model specification do exist. {\tt{CLOWN}} does not require great computing power and it does not require the installation of additional equipment. {\tt{CLOWN}} results are very promising and confirm that the app can correctly identify clouds in a variety of different conditions and cloud types., Comment: 15 pages, 22 figures. This is the Accepted Manuscript version of an article accepted for publication in Astromomical Journal. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record will be available online at https://iopscience.iop.org/journal/1538-3881

Accounting for selection effects in supernova type Ia (SN Ia) cosmology is crucial for unbiased cosmological parameter inference -- even more so for the next generation of large, mostly photometric-only surveys. The conventional "bias correction" procedure has a built-in systematic bias towards the fiducial model used to derive it and fails to account for the additional Eddington bias that arises in the presence of significant redshift uncertainty. On the other hand, Bayesian hierarchical models scale poorly with the data set size and require explicit assumptions for the selection function that may be inaccurate or contrived. To address these limitations, we introduce STAR NRE, a simulation-based approach that makes use of a conditioned deep set neural network and combines efficient high-dimensional global inference with subsampling-based truncation in order to scale to very large survey sizes while training on sets with varying cardinality. Applying it to a simplified SN Ia model consisting of standardised brightnesses and redshifts with Gaussian uncertainties and a selection procedure based on the expected LSST sensitivity, we demonstrate precise and unbiased inference of cosmological parameters and the redshift evolution of the volumetric SN Ia rate from ~100 000 mock SNae Ia. Our inference procedure can incorporate arbitrarily complex selection criteria, including transient classification, in the forward simulator and be applied to complex data like light curves. We outline these and other steps aimed at integrating STAR NRE into an end-to-end simulation-based pipeline for the analysis of future photometric-only SN Ia data., Comment: 13 pages, 6 figures + appendices

The radio interferometric closure phases can be a valuable tool for studying cosmological {H\scriptsize{I}}~from the early Universe. Closure phases have the advantage of being immune to element-based gains and associated calibration errors. Thus, calibration and errors therein, which are often sources of systematics limiting standard visibility-based approaches, can be avoided altogether in closure phase analysis. In this work, we present the first results of the closure phase power spectrum of {H\scriptsize{I}}~21-cm fluctuations using the Murchison Widefield Array (MWA), with $\sim 12$ hours of MWA-phase II observations centered around redshift, $z\approx 6.79$, during the Epoch of Reionisation. On analysing three redundant classes of baselines -- 14~m, 24~m, and 28~m equilateral triads, our estimates of the $2\sigma$ ($95\%$ confidence interval) 21-cm power spectra are $\lesssim (184)^2 pseudo \rm ~mK^2$ at ${k}_{||} = 0.36 $ $pseudo~h {\rm Mpc^{-1}}$ in the EoR1 field for the 14~m baseline triads, and $\lesssim (188)^2 pseudo \rm ~mK^2$ at $k_{||} = 0.18 $ $pseudo~h {\rm Mpc^{-1}}$ in the EoR0 field for the 24~m baseline triads. The ``$pseudo$'' units denote that the length scale and brightness temperature should be interpreted as close approximations. Our best estimates are still 3-4 orders high compared to the fiducial 21-cm power spectrum; however, our approach provides promising estimates of the power spectra even with a small amount of data. These data-limited estimates can be further improved if more datasets are included into the analysis. The evidence for excess noise has a possible origin in baseline-dependent systematics in the MWA data that will require careful baseline-based strategies to mitigate, even in standard visibility-based approaches., Comment: 24 pages, 21 figures, 4 tables (accepted for publication in PASA)

Observations of the 21 cm signal face significant challenges due to bright astrophysical foregrounds that are several orders of magnitude higher than the brightness of the hydrogen line, along with various systematics. Successful 21 cm experiments require accurate calibration and foreground mitigation. Errors introduced during the calibration process such as systematics, can disrupt the intrinsic frequency smoothness of the foregrounds, leading to power leakage into the Epoch of Reionisation (EoR) window. Therefore, it is essential to develop strategies to effectively address these challenges. In this work, we adopt a stringent approach to identify and address suspected systematics, including malfunctioning antennas, frequency channels corrupted by radio frequency interference (RFI), and other dominant effects. We implement a statistical framework that utilises various data products from the data processing pipeline to derive specific criteria and filters. These criteria and filters are applied at intermediate stages to mitigate systematic propagation from the early stages of data processing. Our analysis focuses on observations from the Murchison Widefield Array (MWA) Phase I configuration. Out of the observations processed by the pipeline, our approach selects 18%, totalling 58 hours, that exhibit fewer systematic effects. The successful selection of observations with reduced systematic dominance enhances our confidence in achieving 21 cm measurements., Comment: 18 pages, 17 figures, 2 tables (accepted for publication in PASA)

$N$-body simulations are computationally expensive, so machine-learning (ML)-based emulation techniques have emerged as a way to increase their speed. Although fast, surrogate models have limited trustworthiness due to potentially substantial emulation errors that current approaches cannot correct for. To alleviate this problem, we introduce COmoving Computer Acceleration (COCA), a hybrid framework interfacing ML with an $N$-body simulator. The correct physical equations of motion are solved in an emulated frame of reference, so that any emulation error is corrected by design. This approach corresponds to solving for the perturbation of particle trajectories around the machine-learnt solution, which is computationally cheaper than obtaining the full solution, yet is guaranteed to converge to the truth as one increases the number of force evaluations. Although applicable to any ML algorithm and $N$-body simulator, this approach is assessed in the particular case of particle-mesh cosmological simulations in a frame of reference predicted by a convolutional neural network, where the time dependence is encoded as an additional input parameter to the network. COCA efficiently reduces emulation errors in particle trajectories, requiring far fewer force evaluations than running the corresponding simulation without ML. We obtain accurate final density and velocity fields for a reduced computational budget. We demonstrate that this method shows robustness when applied to examples outside the range of the training data. When compared to the direct emulation of the Lagrangian displacement field using the same training resources, COCA's ability to correct emulation errors results in more accurate predictions. COCA makes $N$-body simulations cheaper by skipping unnecessary force evaluations, while still solving the correct equations of motion and correcting for emulation errors made by ML., Comment: 23 pages, 13 figures

In 21-cm experimental cosmology, accurate characterization of a radio telescope's antenna beam response is essential to measure the 21-cm signal. Computational electromagnetic (CEM) simulations estimate the antenna beam pattern and frequency response by subjecting the EM model to different dependencies, or beam hyper-parameters, such as soil dielectric constant or orientation with the environment. However, it is computationally expensive to search all possible parameter spaces to optimize the antenna design or accurately represent the beam to the level required for use as a systematic model in 21-cm cosmology. We therefore present $\texttt{MEDEA}$, an emulator which rapidly and accurately generates farfield radiation patterns over a large hyper-parameter space. $\texttt{MEDEA}$ takes a subset of beams simulated by CEM software, spatially decomposes them into coefficients in a complete, linear basis, and then interpolates them to form new beams at arbitrary hyper-parameters. We test $\texttt{MEDEA}$ on an analytical dipole and two numerical beams motivated by upcoming lunar lander missions, and then employ $\texttt{MEDEA}$ as a model to fit mock radio spectrometer data to extract covariances on the input beam hyper-parameters. We find that the interpolated beams have RMS relative errors of at most $10^{-2}$ using 20 input beams or less, and that fits to mock data are able to recover the input beam hyper-parameters when the model and mock derive from the same set of beams. When a systematic bias is introduced into the mock data, extracted beam hyper-parameters exhibit bias, as expected. We propose several future extensions to $\texttt{MEDEA}$ to potentially account for such bias., Comment: 23 pages, 12 figures, accepted for publication in ApJ

Due to the unprecedented depth of the upcoming ground-based Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, approximately two-thirds of the galaxies are likely to be affected by blending - the overlap of physically separated galaxies in images. Thus, extracting reliable shapes and photometry from individual objects will be limited by our ability to correct blending and control any residual systematic effect. Deblending algorithms tackle this issue by reconstructing the isolated components from a blended scene, but the most commonly used algorithms often fail to model complex realistic galaxy morphologies. As part of an effort to address this major challenge, we present MADNESS, which takes a data-driven approach and combines pixel-level multi-band information to learn complex priors for obtaining the maximum a posteriori solution of deblending. MADNESS is based on deep neural network architectures such as variational auto-encoders and normalizing flows. The variational auto-encoder reduces the high-dimensional pixel space into a lower-dimensional space, while the normalizing flow models a data-driven prior in this latent space. Using a simulated test dataset with galaxy models for a 10-year LSST survey and a galaxy density ranging from 48 to 80 galaxies per arcmin2 we characterize the aperture-photometry g-r color, structural similarity index, and pixel cosine similarity of the galaxies reconstructed by MADNESS. We compare our results against state-of-the-art deblenders including scarlet. With the r-band of LSST as an example, we show that MADNESS performs better than in all the metrics. For instance, the average absolute value of relative flux residual in the r-band for MADNESS is approximately 29% lower than that of scarlet. The code is publicly available on GitHub., Comment: 20 pages, 19 figures, submitted to Astronomy & Astrophysics

We construct two new summary statistics, the scale-dependent peak height function (scale-PKHF) and the scale-dependent valley depth function (scale-VLYDF), and forecast their constraining power on PNG amplitudes $\{f_\mathrm{NL}^\mathrm{local}, f_\mathrm{NL}^\mathrm{equil},f_\mathrm{NL}^\mathrm{ortho}\}$ and standard cosmological parameters based on ten thousands of density fields drawn from \textsc{Quijote} and \textsc{Quijote-PNG} simulations at $z=0$. With the Fisher analysis, we find that the scale-PKHF and scale-VLYDF are capable of capturing a wealth of primordial information about the Universe. Specifically, the constraint on the scalar spectral index $n_s$ obtained from the scale-VLYDF (scale-PKHF) is 12.4 (8.6) times tighter than that from the power spectrum, and 3.9 (2.7) times tighter than that from the bispectrum. The combination of the two statistics yields constraints on $\{f_\mathrm{NL}^\mathrm{local}, f_\mathrm{NL}^\mathrm{equil}\}$ similar to those from the bispectrum and power spectrum combination, but provides a 1.4-fold improvement in the constraint on $f_\mathrm{NL}^\mathrm{ortho}$. After including the power spectrum, its constraining power well exceeds that of the bispectrum and power spectrum combination by factors of 1.1--2.9 for all parameters., Comment: 7 pages, 4 figures, 1 table. Comments welcome and appreciated

Although fast radio bursts (FRBs) were discovered more than a decade ago, and they have been one of the active fields in astronomy and cosmology, their origins are still unknown. An interesting topic closely related to the origins of FRBs is their classifications. On the other hand, FRBs are actually a promising probe to study cosmology. In the literature, some new classifications of FRBs different from repeaters and non-repeaters were suggested, and some tight empirical relations have been found for them. In particular, Guo and Wei suggested to classify FRBs into the ones associated with old or young populations, which have also some new empirical relations. They also proposed to use one of the empirical relations without dispersion measure (DM) to calibrate FRBs as standard candles for cosmology. This shows the potential of the new classification and the empirical relations for FRBs. Nowadays, more than 50 FRBs have been well localized, and hence their redshifts $z$ are observationally known. Thus, it is time to check the empirical relations with the current localized FRBs. We find that many empirical relations still hold, and in particular the one used to calibrate FRBs as standard candles for cosmology stands firm., Comment: 15 pages, 2 tables, 7 figures, revtex4

We present an improved zodiacal light model for COBE-DIRBE derived through global Bayesian analysis within the Cosmoglobe Data Release 2 framework. The parametric form of the ZL model is identical to that introduced by Kelsall et al. (1998), but the specific best-fit parameter values are re-derived using the combination of DIRBE Calibrated Individual Observations, Planck HFI sky maps, and WISE and Gaia compact object catalogs. Furthermore, the ZL parameters are fitted jointly with astrophysical parameters, such as thermal dust and starlight emission, and the new model takes into account excess radiation that appears stationary in solar-centric coordinates as reported in a companion paper. The relative differences between the predicted signals from K98 and our new model are $\lesssim 5\%$ in the 12 and 25 $\mu$m channels over the full sky. The zero-levels of the cleaned DR2 maps are lower than those of the K98 Zodiacal light Subtracted Mission Average maps by $\sim 10$ kJy/sr at 1.25-3.5 $\mu$m, which is larger than the entire predicted contribution from high-redshift galaxies to the Cosmic Infrared Background at the same wavelengths. The total RMS of each DR2 map at wavelengths up to and including 25 $\mu$m are $\sim 30$ $\%$ lower at high Galactic latitudes than the corresponding DIRBE ZSMA maps. Still, obvious ZL residuals can be seen in several of the DR2 maps, and further work is required to mitigate these. Joint analysis with existing and future high-resolution full-sky surveys such as AKARI, IRAS, Planck HFI, and SPHEREx will be essential both to break key degeneracies in the current model and to determine whether the reported solar-centric excess radiation has a ZL or instrumental origin. Thus, while the results presented in this paper do redefine the state-of-the-art for DIRBE modelling, it also only represents the first among many steps toward a future optimal Bayesian ZL model., Comment: 27 pages, 21 figures, submitted to A&A

The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual cataloging methodologies are anticipated to be insufficient to exploit the capabilities of new radio surveys. Radio interferometric images of diffuse sources present a challenge for image segmentation tasks due to noise, artifacts, and embedded radio sources. In response to these challenges, we introduce Radio U-Net, a fully convolutional neural network based on the U-Net architecture. Radio U-Net is designed to detect faint and extended sources in radio surveys, such as radio halos, relics, and cosmic web filaments. Radio U-Net was trained on synthetic radio observations built upon cosmological simulations and then tested on a sample of galaxy clusters, where the detection of cluster diffuse radio sources relied on customized data reduction and visual inspection of LOFAR Two Metre Sky Survey (LoTSS) data. The 83% of clusters exhibiting diffuse radio emission were accurately identified, and the segmentation successfully recovered the morphology of the sources even in low-quality images. In a test sample comprising 246 galaxy clusters, we achieved a 73% accuracy rate in distinguishing between clusters with and without diffuse radio emission. Our results establish the applicability of Radio U-Net to extensive radio survey datasets, probing its efficiency on cutting-edge high-performance computing systems. This approach represents an advancement in optimizing the exploitation of forthcoming large radio surveys for scientific exploration., Comment: Accepted by MNRAS, 16 pages, 9 figures, 2 tables

SPIDER is a balloon-borne instrument designed to map the cosmic microwave background at degree-angular scales in the presence of Galactic foregrounds. SPIDER has mapped a large sky area in the Southern Hemisphere using more than 2000 transition-edge sensors (TESs) during two NASA Long Duration Balloon flights above the Antarctic continent. During its first flight in January 2015, SPIDER observed in the 95 GHz and 150 GHz frequency bands, setting constraints on the B-mode signature of primordial gravitational waves. Its second flight in the 2022-23 season added new receivers at 280 GHz, each using an array of TESs coupled to the sky through feedhorns formed from stacks of silicon wafers. These receivers are optimized to produce deep maps of polarized Galactic dust emission over a large sky area, providing a unique data set with lasting value to the field. In this work, we describe the instrument's performance during SPIDER's second flight., Comment: Submitted to SPIE Astronomical Telescopes + Instrumentation 2024, JATIS

Measurements of the galaxy density and weak-lensing profiles of galaxy clusters typically rely on an assumed cluster center, which is taken to be the brightest cluster galaxy or other proxies for the true halo center. Departure of the assumed cluster center from the true halo center bias the resultant profile measurements, an effect known as miscentering bias. Currently, miscentering is typically modeled in stacked profiles of clusters with a two parameter model. We use an alternate approach in which the profiles of individual clusters are used with the corresponding likelihood computed using a Gaussian mixture model. We test the approach using halos and the corresponding subhalo profiles from the IllustrisTNG hydrodynamic simulations. We obtain significantly improved estimates of the miscentering parameters for both 3D and projected 2D profiles relevant for imaging surveys. We discuss applications to upcoming cosmological surveys., Comment: 7 pages, 6 figures, to be submitted to A&A The Python package used in this paper is publicly available at https://github.com/KyleMiller1/Halo-Miscentering-Mixture-Model

The main challenge of 21 cm cosmology experiments is astrophysical foregrounds which are difficult to separate from the signal due to telescope systematics. An earlier study has shown that foreground residuals induced by antenna gain errors can be estimated and subtracted using the hybrid foreground residual subtraction (HyFoReS) technique which relies on cross-correlating linearly filtered data. In this paper, we apply a similar technique to the CHIME stacking analysis to subtract beam-induced foreground contamination. Using a linear high-pass delay filter for foreground suppression, the CHIME collaboration reported a $11.1\sigma$ detection in the 21 cm signal stacked on eBOSS quasar locations, despite foreground residual contamination mostly due to the instrument chromatic transfer function. We cross-correlate the foreground-dominated data at low delay with the contaminated signal at high delay to estimate residual foregrounds and subtract them from the signal. We find foreground residual subtraction can improve the signal-to-noise ratio of the stacked 21 cm signal by $ 10 - 20\%$ after the delay foreground filter, although some of the improvement can also be achieved with an alternative flagging technique. We have shown that it is possible to use HyFoReS to reduce beam-induced foreground contamination, benefiting the analysis of the HI auto power spectrum with CHIME and enabling the recovery of large scale modes.

With the increasing precision of recent cosmological surveys and the discovery of important tensions within the $\Lambda$CDM paradigm, it is becoming more and more important to develop tools to quantify accurately the discordance between different probes. One such tool is the Surprise statistic, a measure based on the Kullback-Leibler divergence. The Surprise, however, has been up to now applied only under its Gaussian approximation, which can fail to properly capture discordance in cases that deviate significantly from Gaussianity. In this paper we developed the klsurprise code which computes the full numerical non-Gaussian Surprise, and analyse the Surprise for BAO + BBN and supernova data. We test different cosmological models, some of which the parameters deviate significantly from Gaussianity. We find that the non-Gaussianities, mainly present in the Supernova dataset, change the Surprise statistic significantly from its Gaussian approximation, and reveal a $2.7\sigma$ tension in the curved $w$CDM model (o$w$CDM) between the combined Pantheon+ and SH0ES (Pantheon+ & SH0ES) data and the dataset which combines SDSS, BOSS and eBOSS BAO. This tension is hidden in the Gaussian Surprise approximation. For DESI the tension with Pantheon+ & SH0ES is at the meager $1.9\sigma$ level for o$w$CDM, but a large $3.3\sigma$ for $\Lambda$CDM., Comment: 11 pages, 9 figures, comments are welcome

Foreground removal presents a significant obstacle in both current and forthcoming intensity mapping surveys. While numerous techniques have been developed that show promise in simulated datasets, their efficacy often diminishes when applied to real-world data. A primary issue is the frequency-dependent variations in the instrumental response. In this paper, we propose a novel approach utilizing the internal cross-correlation among different frequencies to calibrate the beam's frequency fluctuations. Using a simulated dataset that incorporates frequency-dependent random fluctuations into the beam model, we illustrate that our method can achieve considerable improvements over traditional techniques. Our results represent a step forward in enhancing the precision and reliability of foreground removal in intensity mapping surveys., Comment: Accepted for publication in ApJS

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the 'renoising' noise bias correction approach introduced by Metacalibration. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin LSST imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a percent, meeting LSST DESC requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond., Comment: 15 pages, 11 figures, submitted to MNRAS

Next-generation gravitational-wave detectors like the Einstein Telescope and Cosmic Explorer, currently in their preparatory phase, have the potential to significantly improve our understanding of astrophysics, cosmology and fundamental physics. We examine how the arm orientations of the proposed detectors influence the sensitivity of a combined Einstein Telescope - Cosmic Explorer network with respect to the sensitivity to the stochastic gravitational-wave background and compact binary coalescences, where measuring both gravitational-wave polarizations is favorable. We present a method to optimize the arm orientations in the network for these two targets, and also demonstrate how to achieve a balanced configuration for both stochastic background and compact binary coalescence searches. For five specific network configurations, we explicitly compare the sensitivity to the stochastic background and binary neutron star mergers. For the latter, we conduct Bayesian parameter estimation on the extrinsic parameters of a reference binary neutron star system to assess sky localization and distance estimation capabilities. These are illustrated through efficiency curves showing the fraction of events meeting sky localization and distance uncertainty criteria as a function of redshift. Our findings suggest that globally coordinating efforts towards the next-generation gravitational-wave detector network is advantageous., Comment: 18 pages, 14 figures, 3 tables

The Hubble tension is one of the most relevant unsolved problems in cosmology today. Strongly gravitationally lensed transient objects, such as strongly lensed supernovae, are an independent and competitive probe that can be used to determine the Hubble constant. In this context, the time delay between different images of lensed supernovae is a key ingredient. We present a method, to retrieve time delays and the amount of differential dust extinction between multiple images of lensed type IIP supernovae through their color curves, which display a kink in the time evolution. With multiple realistic mock color curves based on an observed unlensed supernova from the Carnegie Supernova Project, we demonstrate that we can retrieve the time delay with uncertainties of $\pm$1.0 days for light curves with 2-day cadence and 35% missing data due to weather losses. The differential dust extinction is more susceptible to uncertainties, because it depends on imposing the correct extinction law. Further we also investigate the kink structure in the color curves for different rest-frame wavelength bands, particularly rest-frame UV light curves from SWIFT, finding sufficiently strong kinks for our method to work for typical lensed SN redshifts that would redshift the kink feature to optical wavelengths. With the upcoming Rubin Observatory Legacy Survey of Space and Time, hundreds of strongly lensed supernovae will be detected and our new method for lensed SN IIP is readily applicable to provide delays., Comment: 16 pages, 19 figures; submitted to A&A

Large angular scale surveys in the absence of atmosphere are essential for measuring the primordial $B$-mode power spectrum of the Cosmic Microwave Background (CMB). Since this proposed measurement is about three to four orders of magnitude fainter than the temperature anisotropies of the CMB, in-flight calibration of the instruments and active suppression of systematic effects are crucial. We investigate the effect of changing the parameters of the scanning strategy on the in-flight calibration effectiveness, the suppression of the systematic effects themselves, and the ability to distinguish systematic effects by null-tests. Next-generation missions such as LiteBIRD, modulated by a Half-Wave Plate (HWP), will be able to observe polarisation using a single detector, eliminating the need to combine several detectors to measure polarisation, as done in many previous experiments and hence avoiding the consequent systematic effects. While the HWP is expected to suppress many systematic effects, some of them will remain. We use an analytical approach to comprehensively address the mitigation of these systematic effects and identify the characteristics of scanning strategies that are the most effective for implementing a variety of calibration strategies in the multi-dimensional space of common spacecraft scan parameters. We also present Falcons, a fast spacecraft scanning simulator that we developed to investigate this scanning parameter space.

SuperBIT was a 0.5-meter near-ultraviolet to near-infrared wide-field telescope that launched on a NASA superpressure balloon into the stratosphere from New Zealand for a 45-night flight. SuperBIT acquired multi-band images of galaxy clusters to study the properties of dark matter using weak gravitational lensing. We provide an overview of the instrument and its various subsystems. We then present the instrument performance from the flight, including the telescope and image stabilization system, the optical system, the power system, and the thermal system. SuperBIT successfully met the instrument's technical requirements, achieving a telescope pointing stability of 0.34 +/- 0.10 arcseconds, a focal plane image stability of 0.055 +/- 0.027 arcseconds, and a PSF FWHM of ~ 0.35 arcseconds over 5-minute exposures throughout the 45-night flight. The telescope achieved a near-diffraction limited point-spread function in all three science bands (u, b, and g). SuperBIT served as a pathfinder to the GigaBIT observatory, which will be a 1.34-meter near-ultraviolet to near-infrared balloon-borne telescope., Comment: 17 pages, 25 pages, published in the Astronomical Journal

Characterization of the redshift distribution of ensembles of galaxies is pivotal for large scale structure cosmological studies. In this work, we focus on improving the Self-Organizing Map (SOM) methodology for photometric redshift estimation (SOMPZ), specifically in anticipation of the Dark Energy Survey Year 6 (DES Y6) data. This data set, featuring deeper and fainter galaxies than DES Year 3 (DES Y3), demands adapted techniques to ensure accurate recovery of the underlying redshift distribution. We investigate three strategies for enhancing the existing SOM-based approach used in DES Y3: 1) Replacing the Y3 SOM algorithm with one tailored for redshift estimation challenges; 2) Incorporating $\textit{g}$-band flux information to refine redshift estimates (i.e. using $\textit{griz}$ fluxes as opposed to only $\textit{riz}$); 3) Augmenting redshift data for galaxies where available. These methods are applied to DES Y3 data, and results are compared to the Y3 fiducial ones. Our analysis indicates significant improvements with the first two strategies, notably reducing the overlap between redshift bins. By combining strategies 1 and 2, we have successfully managed to reduce redshift bin overlap in DES Y3 by up to 66$\%$. Conversely, the third strategy, involving the addition of redshift data for selected galaxies as an additional feature in the method, yields inferior results and is abandoned. Our findings contribute to the advancement of weak lensing redshift characterization and lay the groundwork for better redshift characterization in DES Year 6 and future stage IV surveys, like the Rubin Observatory.

Variations in the instrumental noise of the Laser Interferometer Space Antenna (LISA) over time are expected as a result of e.g. scheduled satellite operations or unscheduled glitches. We demonstrate that these fluctuations can be leveraged to improve the sensitivity to stochastic gravitational wave backgrounds (SGWBs) compared to the stationary noise scenario. This requires optimal use of data segments with downward noise fluctuations, and thus a data analysis pipeline capable of analysing and combining shorter time segments of mission data. We propose that simulation based inference is well suited for this challenge. In an approximate, but state-of-the-art, modeling setup, we show by comparison with Fisher Information Matrix estimates that the optimal information gain can be achieved in practice., Comment: 12 pages, 5 figures. saqqara available at https://github.com/peregrine-gw/saqqara, GW_response available at https://github.com/Mauropieroni/GW_response

We present a formalism to extract the angular power spectrum of fields sampled at a finite number of points with arbitrary positions -- a common situation for several catalog-based astrophysical probes -- through a simple extension of the standard pseudo-$C_\ell$ algorithm. A key complication in this case is the need to handle the shot noise component of the associated discrete angular mask which, for sparse catalogs, can lead to strong coupling between very different angular scales. We show that this problem can be solved easily by estimating this contribution analytically and subtracting it. The resulting estimator is immune to small-scale pixelization effects and aliasing, and, more interestingly, unbiased against the contribution from measurement noise uncorrelated between different sources. We demonstrate the validity of the method in the context of cosmic shear datasets, and showcase its usage in the case of other spin-0 and spin-1 astrophysical fields of interest. We incorporate the method in the public $\texttt{NaMaster}$ code (https://github.com/LSSTDESC/NaMaster)., Comment: 33 pages, 10 figures. Comments welcome!

The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k=0.5.

Prime-Cam is a first-generation science instrument for the CCAT Observatory's six-meter aperture Fred Young Submillimeter Telescope (FYST). FYST's crossed-Dragone design provides high optical throughput to take advantage of its unique site at 5600 m on Cerro Chajnantor in Chile's Atacama Desert to reach mapping speeds over ten times greater than current and near-term submillimeter experiments. Housing up to seven independent instrument modules in its 1.8-meter diameter cryostat, Prime-Cam will combine broadband polarization-sensitive modules and spectrometer modules designed for observations in several frequency windows between 210 GHz and 850 GHz to study a wide range of astrophysical questions from Big Bang cosmology to the formation of stars and galaxies in the Epoch of Reionization and beyond. In order to cover this range of frequencies and observation modes, each of the modules contains a set of cold reimaging optics that is optimized for the science goals of that module. These optical setups include several filters, three or four anti-reflection-coated silicon lenses, and a Lyot stop to control the field of view and illumination of the primary mirror, satisfy a series of mechanical constraints, and maximize optical performance within each passband. We summarize the design considerations and trade-offs for the optics in these modules and provide a status update on the fabrication of the Prime-Cam receiver and the design of its 1 K and 100 mK thermal BUSs., Comment: 10 pages, 6 figures, presented at SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII

We present the detection and characterization of fluctuations in linearly polarized emission from the atmosphere above the South Pole. These measurements make use of Austral winter survey data from the SPT-3G receiver on the South Pole Telescope in three frequency bands centered at 95, 150, and 220 GHz. We use the cross-correlation between detectors to produce an unbiased estimate of the power in Stokes I, Q, and U parameters on large angular scales. Our results are consistent with the polarized signal being produced by the combination of Rayleigh scattering of thermal radiation from the ground and thermal emission from a population of horizontally aligned ice crystals with an anisotropic distribution described by Kolmogorov turbulence. The signal is most significant at large angular scales, high observing frequency, and low elevation angle. Polarized atmospheric emission has the potential to significantly impact observations on the large angular scales being targeted by searches for inflationary B-mode CMB polarization. We present the distribution of measured angular power spectrum amplitudes in Stokes Q and I for 4 years of winter observations, which can be used to simulate the impact of atmospheric polarization and intensity fluctuations at the South Pole on a specified experiment and observation strategy. For the SPT-3G data, downweighting the small fraction of significantly contaminated observations is an effective mitigation strategy. In addition, we present a strategy for further improving sensitivity on large angular scales where maps made in the 220 GHz band are used to measure and subtract the polarized atmosphere signal from the 150 GHz band maps. In observations with the SPT-3G instrument at the South Pole, the polarized atmospheric signal is a well-understood and sub-dominant contribution to the measured noise after implementing the mitigation strategies described here., Comment: 32 pages, 28 figures

More than fifty years have been elapsed from the first discovery of a Gamma Ray Burst (GRB) with the American Vela satellites, and more than twenty-five years from the discovery with the BeppoSAX satellite of the first X-ray afterglow of a GRB. Thanks to the afterglow discovery and to the possibility given to the optical and radio astronomers to discover the GRB optical counterparts, the long-time mystery about the origin of these events was solved. Now we know that GRBs are huge explosions, mainly ultra relativistic jets, in galaxies at cosmological distances. Starting from the first GRB detection with the Vela satellites, I will review the story of these discoveries, those obtained with BeppoSAX, the contribution to GRBs by other satellites and ground experiments, among them Venera, Compton Gamma Ray Observatory, HETE2, Swift, Fermi, AGILE, MAGIC, H.E.S.S., which were, and some of them are still, very important for the study of GRB properties. Then I will review the main results obtained thus far and the still open problems and prospects of the GRB astronomy., Comment: 42 pages, 29 figures

The turbulent ionosphere causes phase shifts to incoming radio waves on a broad range of temporal and spatial scales. When an interferometer is not sufficiently calibrated for the direction-dependent ionospheric effects, the time-varying phase shifts can cause the signal to decorrelate. The ionosphere's influence over various spatiotemporal scales introduces a baseline-dependent effect on the interferometric array. We study the impact of baseline-dependent decorrelation on high-redshift observations with the Low Frequency Array (LOFAR). Datasets with a range of ionospheric corruptions are simulated using a thin-screen ionosphere model, and calibrated using the state-of-the-art LOFAR Epoch of Reionisation pipeline. For the first time ever, we show the ionospheric impact on various stages of the calibration process including an analysis of the transfer of gain errors from longer to shorter baselines using realistic end-to-end simulations. We find that direction-dependent calibration for source subtraction leaves excess power of up to two orders of magnitude above the thermal noise at the largest spectral scales in the cylindrically averaged auto-power spectrum under normal ionospheric conditions. However, we demonstrate that this excess power can be removed through Gaussian process regression, leaving no excess power above the ten per cent level for a $5~$km diffractive scale. We conclude that ionospheric errors, in the absence of interactions with other aggravating effects, do not constitute a dominant component in the excess power observed in LOFAR Epoch of Reionisation observations of the North Celestial Pole. Future work should therefore focus on less spectrally smooth effects, such as beam modelling errors., Comment: 22 pages, 20 figures. Accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS)

Reionization is one of the least understood processes in the evolution history of the Universe, mostly because of the numerous astrophysical processes occurring simultaneously about which we do not have a very clear idea so far. In this article, we use the Gaussian Process Regression (GPR) method to learn the reionization history and infer the astrophysical parameters. We reconstruct the UV luminosity density function using the HFF and early JWST data. From the reconstructed history of reionization, the global differential brightness temperature fluctuation during this epoch has been computed. We perform MCMC analysis of the global 21-cm signal using the instrumental specifications of SARAS, in combination with Lyman-$\alpha$ ionization fraction data, Planck optical depth measurements and UV luminosity data. Our analysis reveals that GPR can help infer the astrophysical parameters in a model-agnostic way than conventional methods. Additionally, we analyze the 21-cm power spectrum using the reconstructed history of reionization and demonstrate how the future 21-cm mission SKA, in combination with Planck and Lyman-$\alpha$ forest data, improves the bounds on the reionization astrophysical parameters by doing a joint MCMC analysis for the astrophysical parameters plus 6 cosmological parameters for $\Lambda$CDM model. The results make the GPR-based reconstruction technique a robust learning process and the inferences on the astrophysical parameters obtained therefrom are quite reliable that can be used for future analysis.

Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-level distributions can serve as effective priors for parameters of interest. With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. However, in many astrophysical applications, it is often difficult or even impossible to acquire independent and identically distributed samples from the underlying data-generating process of interest to train these models. In these cases, corrupted data or a surrogate, e.g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors. This, in turn, can bias the inferred posteriors in ways that are difficult to quantify, which limits the potential applicability of these models in real-world scenarios. In this work, we propose addressing this issue by iteratively updating the population-level distributions by retraining the model with posterior samples from different sets of observations and showcase the potential of this method on the problem of background image reconstruction in strong gravitational lensing when score-based models are used as data-driven priors. We show that starting from a misspecified prior distribution, the updated distribution becomes progressively closer to the underlying population-level distribution, and the resulting posterior samples exhibit reduced bias after several updates., Comment: 17 pages, 15 figures, Submitted to The Astrophysical Journal

The redshifted $21$\,cm signal of neutral hydrogen can be used as a direct probe of the intergalactic medium during Cosmic Dawn\,(CD) and Epoch of Reionization\,(EoR). However, detecting this inherently weak signal has numerous challenges. The major ones include accurate foreground removal from low-frequency radio observations and systematics arising from instrumental effects. The Earth's ionosphere poses a major obstacle at these low radio frequencies. Thus, a systematic study of ionospheric effects on these sensitive low-frequency observations is critical, given that the construction of the Square Kilometre Array (SKA1-Low) is in full progress. We use the end-to-end pipeline, called {\sc 21cme2e}, to study the effect of time-varying ionospheric corruption on the $21$\,cm power spectrum recovery. We use two models: a) a catalogue-based model focused on source position shift due to the refractive effect of the ionosphere and b) a realistic ionospheric condition generated using Kolmogorov's turbulence model. We assess the effect of the imperfections thus generated on the extraction of \HI\ $21$\,cm signal power spectrum. Our study shows that beyond ``median ionospheric offset" ($\theta_{\text{MIO}} \lesssim 0.1''$), the $21$\,cm signal from the EoR is unaffected by residual ionospheric effects. Our study emphasizes the need for the development of efficient ionospheric calibration algorithms for the upcoming SKA1-Low observations to extract the \HI\ $21$\,cm power spectra from the CD/EoR., Comment: 28 pages, 9 figures, To be submitted to JCAP

In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing the thousands of lenses anticipated from future cosmic surveys. New tools for inference, such as SBI using Neural Ratio Estimation (NRE), address this challenge effectively. By training a machine learning model on simulated data of strong lenses, we can learn the likelihood-to-evidence ratio for robust inference. Our scalable approach enables more constrained population-level inference of $w$ compared to individual lens analysis, constraining $w$ to within $1\sigma$. Our model can be used to provide cosmological constraints from forthcoming strong lens surveys, such as the 4MOST Strong Lensing Spectroscopic Legacy Survey (4SLSLS), which is expected to observe 10,000 strong lenses., Comment: Accepted to the workshop on AI for Science at the 41st International Conference on Machine Learning 2024 (ICML 2024)

Astrometric surveys offer us a method to search for elusive cosmic signatures, such as ultralight dark photon dark matter and gravitational waves, by observing the deflection to the apparent positions of the stars. The detection capabilities of such surveys rapidly decrease at low frequencies, because the signals become hardly distinguishable from the background motion of stars. In this work, we find that the background motion can be well described by a linear model over time, based on which we propose a linear background subtraction scheme. Compared to the conventional quadratic subtraction, the advantage of linear subtraction emerges within the frequency range below $6 \times 10^{-9}~{\rm Hz}$. Taking dark photons with purely gravitational interactions, dark photons with additional $U(1)_{B}$ or $U(1)_{B-L}$ gauge interactions, and low-frequency gravitational waves as examples, we illustrate that the linear subtraction scheme can result in an enhancement of more than one order of magnitude in the exclusion limits of Gaia-like experiments in the low-frequency range., Comment: 14 pages, 2 figures

When fits of the same physical model to two different datasets disagree, we call this tension. Several apparent tensions in cosmology have occupied researchers in recent years, and a number of different metrics have been proposed to quantify tension. Many of these metrics suffer from limiting assumptions, and correctly calibrating these is essential if we want to successfully determine whether discrepancies are significant. A commonly used metric of tension is the evidence ratio R. The statistic has been widely adopted by the community as a Bayesian way of quantifying tensions, however, it has a non-trivial dependence on the prior that is not always accounted for properly. We show that this can be calibrated out effectively with Neural Ratio Estimation. We demonstrate our proposed calibration technique with an analytic example, a toy example inspired by 21-cm cosmology, and with observations of the Baryon Acoustic Oscillations from the Dark Energy Spectroscopic Instrument~(DESI) and the Sloan Digital Sky Survey~(SDSS). We find no significant tension between DESI and SDSS.