StarDICE II: Calibration of an Uncooled Infrared Thermal Camera for Atmospheric Gray Extinction Characterization

The StarDICE experiment strives to establish an instrumental metrology chain with a targeted accuracy of 1 mmag in griz bandpasses to meet the calibration requirements of next-generation cosmological surveys. Atmospheric transmission is a significant source of systematic uncertainty. We propose a solution relying on an uncooled infrared thermal camera to evaluate gray extinction variations. However, achieving accurate measurements with thermal imaging systems necessitates prior calibration due to temperature-induced effects, compromising their spatial and temporal precision. Moreover, these systems cannot provide scene radiance in physical units by default. This study introduces a new calibration process utilizing a tailored forward modeling approach. The method incorporates sensor, housing, flat-field support, and ambient temperatures, along with raw digital response, as input data. Experimental measurements were conducted inside a climatic chamber, with a FLIR Tau2 camera imaging a thermoregulated blackbody source. The results demonstrate the calibration effectiveness, achieving precise radiance measurements with a temporal pixel dispersion of 0.09 W m-2 sr-1 and residual spatial noise of 0.03 W m-2 sr-1. We emphasize that the accuracy of scene radiance retrieval can be systematically affected by the camera's close thermal environment, especially when the ambient temperature exceeds that of the scene.

Mitigating the Counterpart Selection Effect for Standard Sirens

The disagreement in the Hubble constant measured by different cosmological probes highlights the need for a better understanding of the observations or new physics. The standard siren method, a novel approach using gravitational-wave observations to determine the distance to binary mergers, has great potential to provide an independent measurement of the Hubble constant and shed light on the tension in the next few years. To realize this goal, we must thoroughly understand the sources of potential systematic bias of standard sirens. Among the known sources of systematic uncertainties, selection effects originating from electromagnetic counterpart observations of gravitational-wave sources may dominate the measurements with percent-level bias and no method to mitigate this effect is currently established. In this Letter, we develop a new formalism to mitigate the counterpart selection effect. We show that our formalism can reduce the systematic uncertainty of standard siren Hubble constant measurement to less than the statistical uncertainty with a simulated population of 200 observations (≲1%) for a realistic electromagnetic emission model. We conclude with how to apply our formalism to different electromagnetic emissions and observing scenarios.

Two-Dimensional (2D) Hybrid Method: Expanding 2D Correlation Spectroscopy (2D-COS) for Time Series Analysis

We present a concise report on the two-dimensional (2D) hybrid method, an innovative extension of 2D correlation spectroscopy, tailored for quasar light curve analysis. Addressing the challenge of discerning periodic variations against the background of intrinsic "red" noise fluctuations, this method employs cross-correlation of wavelet transform matrices to reveal distinct correlation patterns between underlying oscillations, offering new insights into quasar dynamics.

Multiscale Flow for robust and optimal cosmological analysis

We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing. Multiscale Flow uses hierarchical decomposition of cosmological fields via a wavelet basis and then models different wavelet components separately as Normalizing Flows. The log-likelihood of the original cosmological field can be recovered by summing over the log-likelihood of each wavelet term. This decomposition allows us to separate the information from different scales and identify distribution shifts in the data such as unknown scale-dependent systematics. The resulting likelihood analysis can not only identify these types of systematics, but can also be made optimal, in the sense that the Multiscale Flow can learn the full likelihood at the field without any dimensionality reduction. We apply Multiscale Flow to weak lensing mock datasets for cosmological inference and show that it significantly outperforms traditional summary statistics such as power spectrum and peak counts, as well as machine learning-based summary statistics such as scattering transform and convolutional neural networks. We further show that Multiscale Flow is able to identify distribution shifts not in the training data such as baryonic effects. Finally, we demonstrate that Multiscale Flow can be used to generate realistic samples of weak lensing data.

Explaining Dark Matter Halo Density Profiles with Neural Networks

We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation, which we physically interpret using mutual information. Without any prior knowledge of the halos' evolution, the network recovers the known relation between the early time assembly and the inner profile and discovers that the profile beyond the virial radius is described by a single parameter capturing the most recent mass accretion rate. The results illustrate the potential for machine-assisted scientific discovery in complicated astrophysical datasets.

Improved Hot Dark Matter Bound on the QCD Axion

We obtain a reliable cosmological bound on the axion mass m_{a} by (1) deriving the production rate directly from pion-pion scattering data, which overcomes the breakdown of chiral perturbation theory and results in ∼30% differences from previous estimates; (2) including momentum dependence in the Boltzmann equations for axion-pion scatterings, which enhances the relic abundance by ∼40%. Using present cosmological datasets we obtain m_{a}≤0.24  eV, at 95% C.L. We also constrain the sum of neutrino masses, ∑m_{ν}≤0.14  eV at 95% C.L., in the presence of relic axions and neutrinos. Finally, we show that reliable nonperturbative calculations above the QCD crossover are needed to exploit the reach of upcoming cosmological surveys for axion detection.

Astronomia ex machina: a history, primer and outlook on neural networks in astronomy

In this review, we explore the historical development and future prospects of artificial intelligence (AI) and deep learning in astronomy. We trace the evolution of connectionism in astronomy through its three waves, from the early use of multilayer perceptrons, to the rise of convolutional and recurrent neural networks, and finally to the current era of unsupervised and generative deep learning methods. With the exponential growth of astronomical data, deep learning techniques offer an unprecedented opportunity to uncover valuable insights and tackle previously intractable problems. As we enter the anticipated fourth wave of astronomical connectionism, we argue for the adoption of GPT-like foundation models fine-tuned for astronomical applications. Such models could harness the wealth of high-quality, multimodal astronomical data to serve state-of-the-art downstream tasks. To keep pace with advancements driven by Big Tech, we propose a collaborative, open-source approach within the astronomy community to develop and maintain these foundation models, fostering a symbiotic relationship between AI and astronomy that capitalizes on the unique strengths of both fields.

Massive Galaxy Clusters Like El Gordo Hint at Primordial Quantum Diffusion

It is generally assumed within the standard cosmological model that initial density perturbations are Gaussian at all scales. However, primordial quantum diffusion unavoidably generates non-Gaussian, exponential tails in the distribution of inflationary perturbations. These exponential tails have direct consequences for the formation of collapsed structures in the Universe, as has been studied in the context of primordial black holes. We show that these tails also affect the very-large-scale structures, making heavy clusters like "El Gordo," or large voids like the one associated with the cosmic microwave background cold spot, more probable. We compute the halo mass function and cluster abundance as a function of redshift in the presence of exponential tails. We find that quantum diffusion generically enlarges the number of heavy clusters and depletes subhalos, an effect that cannot be captured by the famed f_{NL} corrections. These late-Universe signatures could, thus, be fingerprints of quantum dynamics during inflation that should be incorporated in N-body simulations and checked against astrophysical data.

Discordances in Cosmology and the Violation of Slow-Roll Inflationary Dynamics

We identify examples of single field inflationary trajectories beyond the slow-roll regime that improve the fit to Planck 2018 data compared to a baseline Λ cold dark matter model with power law form of primordial spectrum and at the same time alleviate existing tensions between different datasets in the estimate of cosmological parameters such as H_{0} and S_{8}. A damped oscillation in the first Hubble flow function-or equivalently a feature in the potential-and the corresponding localized oscillations in the primordial power spectrum partially mimic the improvement in the fit of Planck data due to A_{L} or Ω_{K}. Compared to the baseline model, this model can lead simultaneously to a larger value of H_{0} and a smaller value of S_{8}, a trend that can be enhanced when the most recent SH0ES measurement for H_{0} is combined with Planck and BICEP-Keck 2018 data. Large scale structure data and more precise cosmic microwave background polarization measurements will further provide critical tests of this intermediate fast-roll phase.

Teleparallel gravity: from theory to cosmology

Teleparallel gravity (TG) has significantly increased in popularity in recent decades, bringing attention to Einstein's other theory of gravity. In this Review, we give a comprehensive introduction to how teleparallel geometry is developed as a gauge theory of translations together with all the other properties of gauge field theory. This relates the geometry to the broader metric-affine approach to forming gravitational theories where we describe a systematic way of constructing consistent teleparallel theories that respect certain physical conditions such as local Lorentz invariance. We first use TG to formulate a teleparallel equivalent of general relativity (GR) which is dynamically equivalent to GR but which may have different behaviors for other scenarios, such as quantum gravity. After setting this foundation, we describe the plethora of modified teleparallel theories of gravity that have been proposed in the literature. We attempt to connect them together into general classes of covariant gravitational theories. Of particular interest, we highlight the recent proposal of a teleparallel analogue of Horndeski gravity which offers the possibility of reviving all of the regular Horndeski contributions. In the second part of the Review, we first survey works in teleparallel astrophysics literature where we focus on the open questions in this regime of physics. We then discuss the cosmological consequences for the various formulations of TG. We do this at background level by exploring works using various approaches ranging from dynamical systems to Noether symmetries, and more. Naturally, we then discuss perturbation theory, firstly by giving a concise approach in which this can be applied in TG theories and then apply it to a number of important theories in the literature. Finally, we examine works in observational and precision cosmology across the plethora of proposal theories. This is done using some of the latest observations and is used to tackle cosmological tensions which may be alleviated in teleparallel cosmology. We also introduce a number of recent works in the application of machine learning to gravity, we do this through deep learning and Gaussian processes, together with discussions about other approaches in the literature.

Diffuse neutrino background from past core collapse supernovae

Core collapse supernovae are among the most powerful explosions in the Universe, which emit thermal neutrinos that carry away most of the gravitational binding energy released. These neutrinos produce a diffuse supernova neutrino background (DSNB), which is one of the largest energy budgets among all radiation backgrounds. Detecting the DSNB is an important goal of modern high-energy astrophysics and particle physics, which provides valuable insights into core collapse modeling, neutrino physics, and cosmic supernova rate history. In this review, the key ingredients of DSNB calculation and what can be learned from future detections, including black hole formation and non-standard neutrino interactions are discussed. Moreover, an overview of the latest updates in neutrino experiments, which could lead to the detection of the DSNB in the next decade, is provided. With the promise of this breakthrough discovery on the horizon, the study of DSNB has great potential to further our understanding of the Universe.

A very luminous jet from the disruption of a star by a massive black hole

Tidal disruption events (TDEs) are bursts of electromagnetic energy that are released when supermassive black holes at the centres of galaxies violently disrupt a star that passes too close¹. TDEs provide a window through which to study accretion onto supermassive black holes; in some rare cases, this accretion leads to launching of a relativistic jet^2-9, but the necessary conditions are not fully understood. The best-studied jetted TDE so far is Swift J1644+57, which was discovered in γ-rays, but was too obscured by dust to be seen at optical wavelengths. Here we report the optical detection of AT2022cmc, a rapidly fading source at cosmological distance (redshift z = 1.19325) the unique light curve of which transitioned into a luminous plateau within days. Observations of a bright counterpart at other wavelengths, including X-ray, submillimetre and radio, supports the interpretation of AT2022cmc as a jetted TDE containing a synchrotron 'afterglow', probably launched by a supermassive black hole with spin greater than approximately 0.3. Using four years of Zwicky Transient Facility¹⁰ survey data, we calculate a rate of [Formula: see text] per gigapascals cubed per year for on-axis jetted TDEs on the basis of the luminous, fast-fading red component, thus providing a measurement complementary to the rates derived from X-ray and radio observations¹¹. Correcting for the beaming angle effects, this rate confirms that approximately 1 per cent of TDEs have relativistic jets. Optical surveys can use AT2022cmc as a prototype to unveil a population of jetted TDEs.

sconce: a cosmic web finder for spherical and conic geometries

The latticework structure known as the cosmic web provides a valuable insight into the assembly history of large-scale structures. Despite the variety of methods to identify the cosmic web structures, they mostly rely on the assumption that galaxies are embedded in a Euclidean geometric space. Here, we present a novel cosmic web identifier called sconce (Spherical and CONic Cosmic wEb finder) that inherently considers the 2D (RA, DEC) spherical or the 3D (RA, DEC, z) conic geometry. The proposed algorithms in sconce generalize the well-known subspace constrained mean shift (scms) method and primarily address the predominant filament detection problem. They are intrinsic to the spherical/conic geometry and invariant to data rotations. We further test the efficacy of our method with an artificial cross-shaped filament example and apply it to the SDSS galaxy catalogue, revealing that the 2D spherical version of our algorithms is robust even in regions of high declination. Finally, using N-body simulations from Illustris, we show that the 3D conic version of our algorithms is more robust in detecting filaments than the standard scms method under the redshift distortions caused by the peculiar velocities of haloes. Our cosmic web finder is packaged in python as sconce-scms and has been made publicly available.

An integrated imaging sensor for aberration-corrected 3D photography

Planar digital image sensors facilitate broad applications in a wide range of areas^1-5, and the number of pixels has scaled up rapidly in recent years^2,6. However, the practical performance of imaging systems is fundamentally limited by spatially nonuniform optical aberrations originating from imperfect lenses or environmental disturbances^7,8. Here we propose an integrated scanning light-field imaging sensor, termed a meta-imaging sensor, to achieve high-speed aberration-corrected three-dimensional photography for universal applications without additional hardware modifications. Instead of directly detecting a two-dimensional intensity projection, the meta-imaging sensor captures extra-fine four-dimensional light-field distributions through a vibrating coded microlens array, enabling flexible and precise synthesis of complex-field-modulated images in post-processing. Using the sensor, we achieve high-performance photography up to a gigapixel with a single spherical lens without a data prior, leading to orders-of-magnitude reductions in system capacity and costs for optical imaging. Even in the presence of dynamic atmosphere turbulence, the meta-imaging sensor enables multisite aberration correction across 1,000 arcseconds on an 80-centimetre ground-based telescope without reducing the acquisition speed, paving the way for high-resolution synoptic sky surveys. Moreover, high-density accurate depth maps can be retrieved simultaneously, facilitating diverse applications from autonomous driving to industrial inspections.

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of ≈ 2.3 the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.

Computational challenges for multimodal astrophysics

In the coming decades, we will face major computational challenges, when the improved sensitivity of third-generation gravitational wave detectors will be such that they will be able to detect a high number (of the order of 7 × 104 per year) of multi-messenger events from binary neutron star mergers, similar to GW 170817. In this Perspective, we discuss the application of multimodal artificial intelligence techniques for multi-messenger astrophysics, fusing the information from different signal emissions.

Two Classes of Gamma-ray Bursts Distinguished within the First Second of Their Prompt Emission

Studies of Gamma-Ray Burst (GRB) properties, such as duration and spectral hardness, have found evidence for additional classes, beyond the short/hard and long/soft prototypes, using model-dependent methods. In this paper, a model-independent approach was used to analyse the gamma-ray light curves of large samples of GRBs detected by BATSE, Swift/BAT and Fermi/GBM. All the features were extracted from the GRB time profiles in four energy bands using the Stationary Wavelet Transform and Principal Component Analysis. t-distributed Stochastic Neighbourhood Embedding (t-SNE) visualisation of the features revealed two distinct groups of Swift/BAT bursts using the T100 interval with 64 ms resolution data. When the same analysis was applied to 4 ms resolution data, two groups were seen to emerge within the first second (T1) post-trigger. These two groups primarily consisted of short/hard (Group 1) and long/soft (Group 2) bursts, and were 95% consistent with the groups identified using the T100 64 ms resolution data. Kilonova candidates, arising from compact object mergers, were found to belong to Group 1, while those events with associated supernovae fell into Group 2. Differences in cumulative counts between the two groups in the first second, and in the minimum variability timescale, identifiable only with the 4 ms resolution data, may account for this result. Short GRBs have particular significance for multi-messenger science as a distinctive EM signature of a binary merger, which may be discovered by its gravitational wave emissions. Incorporating the T1 interval into classification algorithms may support the rapid classification of GRBs, allowing for an improved prioritisation of targets for follow-up observations.

Cluster Membership of Galaxies Using Multi-Layer Perceptron Neural Network

In this study, we report systematic investigations of the membership of galaxies inside a cluster using a machine learning (ML) neural network. By directly assigning the membership, rather than estimating the galaxy redshift as an intermediate step, we optimize the network structure to determine the membership classification. The cluster membership is determined by the Multi-Layer Perceptron (MLP) neural network trained using various observed photometric and morphological parameters of galaxies measured from I and V band images taken with the Subaru Suprime-Cam of 16 clusters at redshift ∼0.15–0.3. This dataset enables MLP to be applied to cluster galaxies in a wide range of cluster-centric distances, well into a field, and a wide range of galaxy magnitudes, into a regime of dwarf galaxies. We find: (1) With only two bands, our MLP model can achieve relatively high overall performance, obtaining high scores simultaneously in both the purity and the completeness of the classification; (2) The performance of MLP can be improved by including non-SED (Spectral Energy Distribution) parameters; (3) Faint galaxies are harder to assign their memberships even using our MLP model, though the performance is more robust than other photometric methods. ML can effectively combine various conventional methods of finding cluster membership, making it inherit advantages of each method. The overall good performance of the ML membership is vital to cluster studies in the era of faint and data-intensive galaxy survey in which the complete spectroscopic observation is out of reach.

Ultra Long Period Cepheids: Observation and Theory

Ultra Long Period Cepheids are becoming a very interesting and important topic thanks to the contribution that they can give to understanding the current tension existing between the early-universe and local Hubble constant measurements. These bright pulsating variables are observable up to cosmological distances (larger than 100 Mpc) allowing us, in principle, to measure the Hubble constant without the need for secondary indicators, thus reducing the possible systematic errors in the calibration of the extragalactic distance scale. The Ultra Long Period Cepheids also represent a useful tool for obtaining information on the star formation history of the host galaxy and a challenge for the evolutionary and pulsational models, particularly in the very metal poor regime. In this paper, the largest known ULP sample, consisting of 72 objects, including 10 new candidates, is analyzed to give an observational and theoretical overview of their role as distance indicators and of their evolutionary properties.

Scattering Searches for Dark Matter in Subhalos: Neutron Stars, Cosmic Rays, and Old Rocks

In many cosmologies dark matter clusters on subkiloparsec scales and forms compact subhalos, in which the majority of Galactic dark matter could reside. Null results in direct detection experiments since their advent four decades ago could then be the result of extremely rare encounters between the Earth and these subhalos. We investigate alternative and promising means to identify subhalo dark matter interacting with standard model particles: (1) subhalo collisions with old neutron stars can transfer kinetic energy and brighten the latter to luminosities within the reach of imminent infrared, optical, and ultraviolet telescopes; we identify new detection strategies involving single-star measurements and Galactic disk surveys, and obtain the first bounds on self-interacting dark matter in subhalos from the coldest known pulsar, PSR J2144-3933; (2) subhalo dark matter scattering with cosmic rays results in detectable effects; (3) historic Earth-subhalo encounters can leave dark matter tracks in Paleolithic minerals deep underground. These searches could discover dark matter subhalos weighing between gigaton and solar masses, with corresponding dark matter cross sections and masses spanning tens of orders of magnitude.

Multi-Messenger Constraints on the Hubble Constant Through Combination of Gravitational Waves, Gamma-Ray Bursts and Kilonovae from Neutron Star Mergers

The simultaneous detection of gravitational waves and light from the binary neutron star merger GW170817 led to independent measurements of distance and redshift, providing a direct estimate of the Hubble constant H0 that does not rely on a cosmic distance ladder, nor assumes a specific cosmological model. By using gravitational waves as “standard sirens”, this approach holds promise to arbitrate the existing tension between the H0 value inferred from the cosmic microwave background and those obtained from local measurements. However, the known degeneracy in the gravitational-wave analysis between distance and inclination of the source led to a H0 value from GW170817 that was not precise enough to resolve the existing tension. In this review, we summarize recent works exploiting the viewing-angle dependence of the electromagnetic signal, namely the associated short gamma-ray burst and kilonova, to constrain the system inclination and improve on H0. We outline the key ingredients of the different methods, summarize the results obtained in the aftermath of GW170817 and discuss the possible systematics introduced by each of these methods.

Cosmological Parameter Estimation Using Current and Future Observations of Strong Gravitational Lensing

The remarkable development of cosmology benefits from the increasingly improved measurements of cosmic distances, including absolute distances and relative distances. In recent years, however, the emerged cosmological tensions have motivated us to explore independent and precise late-universe probes. The two observational effects of strong gravitational lensing (SGL), the velocity dispersions of lens galaxies and the time delays between multiple images can provide measurements of relative and absolute distances, respectively, and their combination makes it possible to break the degeneracies between cosmological parameters and enable tight constraints on them. In this paper, we combine the observed 130 SGL systems with velocity-dispersion measurements and 7 SGL systems with time-delay measurements to constrain dark-energy cosmological models. It is found that the combination of the two effects does not significantly break the degeneracies between cosmological parameters as expected. However, with the simulations of 8000 SGL systems with well-measured velocity dispersions and 55 SGL systems with well-measured time delays based on the forthcoming LSST survey, we find that the combination of two effects can significantly break the parameter degeneracies, and make the constraint precision of cosmological parameters meet the standard of precision cosmology. We conclude that the observations of SGL will become a useful late-universe probe for precisely measuring cosmological parameters.

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

Comet fading begins beyond Saturn

The discovery probability of long-period comets (LPCs) passing near the Sun is highest during their first passage and then declines, or fades, during subsequent return passages. Comet fading is largely attributed to devolatilization and fragmentation via thermal processing within 2 to 3 astronomical unit (au) of the Sun (1 au being the Earth-Sun distance). Here, our numerical simulations show that comet-observing campaigns miss vast numbers of LPCs making returning passages through the Saturn region (near 10 au) because these comets fade during prior, even more distant passages exterior to Saturn and thus elude detection. Consequently, comet properties substantially evolve at solar distances much larger than previously considered, and this offers new insights into the physical and dynamical properties of LPCs, both near and far from Earth.

RR Lyrae Stars and Anomalous Cepheids as Population Tracers in Local Group Galaxies

We discuss the use and importance of pulsating variable stars as population tracers in Local Group galaxies. Among bright variable crossing the classical instability strip, we mostly focus on RR Lyrae stars and Anomalous Cepheids. We discuss their pulsational properties and how it is possible to use them to constrain the evolution and star formation history of the host galaxy. We discuss RR Lyrae stars as tracers of the old population, and how they can be used to trace the accretion history of large galaxies such as the Milky Way and M31, and also the early chemical evolution. Moreover, we show that the frequency of Anomalous Cepheids follows different relations, and therefore trace the intermediate-age star formation. Finally, we discuss the different methods to derive distances and the impact of the Gaia mission.

Milky Way Star Clusters and Gaia: A Review of the Ongoing Revolution

The unprecedented quality of the astrometric measurements obtained with the ESA Gaia spacecraft have initiated a revolution in Milky Way astronomy. Studies of star clusters in particular have been transformed by the precise proper motions and parallaxes measured by Gaia over the entire sky as well as Gaia’s deep all-sky photometry. This paper presents an overview of the many topics of cluster science that have been impacted by the Gaia DR1, DR2, and EDR3 catalogues from their release to the end of the year 2021. These topics include the identification of known clusters and the discovery of new objects, the formation of young clusters and associations, and the long-term evolution of clusters and their stellar content. In addition to the abundance of scientific results, Gaia is changing the way astronomers work with high-volume and high-dimensionality datasets and is teaching us precious lessons to deal with its upcoming data releases and with the large-scale astronomical surveys of the future.

Mapping the Galactic Metallicity Gradient with Open Clusters: The State-of-the-Art and Future Challenges

In this paper, we make use of data collected for open cluster members by high-resolution spectroscopic surveys and programmes (i.e., APOGEE, Gaia-ESO, GALAH, OCCASO, and SPA). These data have been homogenised and then analysed as a whole. The resulting catalogue contains [Fe/H] and orbital parameters for 251 Galactic open clusters. The slope of the radial metallicity gradient obtained through 175 open clusters with high-quality metallicity determinations is −0.064 ± 0.007 dex kpc−1. The radial metallicity distribution traced by open clusters flattens beyond RGal = 12.1 ± 1.1 kpc. The slope traced by open clusters in the [Fe/H]-Lz diagram is −0.31 ± 0.02 × 103 dex km−1 kpc−1 s, but it flattens beyond Lz = 2769 ± 177 km kpc s−1. In this paper, we also review some high-priority practical challenges around the study of open clusters that will significantly push our understanding beyond the state-of-the-art. Finally, we compare the shape of the galactic radial metallicity gradient to those of other spiral galaxies.

One-Point Statistics Matter in Extended Cosmologies

The late universe contains a wealth of information about fundamental physics and gravity, wrapped up in non-Gaussian fields. To make use of as much information as possible, it is necessary to go beyond two-point statistics. Rather than going to higher-order N-point correlation functions, we demonstrate that the probability distribution function (PDF) of spheres in the matter field (a one-point function) already contains a significant amount of this non-Gaussian information. The matter PDF dissects different density environments which are lumped together in two-point statistics, making it particularly useful for probing modifications of gravity or expansion history. Our approach in Cataneo et al. 2021 extends the success of Large Deviation Theory for predicting the matter PDF in ΛCDM in these “extended” cosmologies. A Fisher forecast demonstrates the information content in the matter PDF via constraints for a Euclid-like survey volume combining the 3D matter PDF with the 3D matter power spectrum. Adding the matter PDF halves the uncertainties on parameters in an evolving dark energy model, relative to the power spectrum alone. Additionally, the matter PDF contains enough non-linear information to substantially increase the detection significance of departures from General Relativity, with improvements up to six times the power spectrum alone. This analysis demonstrates that the matter PDF is a promising non-Gaussian statistic for extracting cosmological information, particularly for beyond ΛCDM models.

Stellar Shocks from Dark Matter Asteroid Impacts

Macroscopic dark matter is almost unconstrained over a wide "asteroidlike" mass range, where it could scatter on baryonic matter with geometric cross section. We show that when such an object travels through a star, it produces shock waves that reach the stellar surface, leading to a distinctive transient optical, UV, and x-ray emission. This signature can be searched for on a variety of stellar types and locations. In a dense globular cluster, such events occur far more often than flare backgrounds, and an existing UV telescope could probe orders of magnitude in dark matter mass in one week of dedicated observation.

Simulation and Evaluation of Cloud Storage Caching for Data Intensive Science

A common task in scientific computing is the data reduction. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for further analysis. Typically, these workflows use distributed storage and computing resources. A straightforward setup of storage media would be low-cost tape storage and higher-cost disk storage. The large, infrequently accessed input data are stored on tape storage. The smaller, frequently accessed derived data is stored on disk storage. In a best-case scenario, the large input data is only accessed very infrequently and in a well-planned pattern. However, practice shows that often the data has to be processed continuously and unpredictably. This can significantly reduce tape storage performance. A common approach to counter this is storing copies of the large input data on disk storage. This contribution evaluates an approach that uses cloud storage resources to serve as a flexible cache or buffer, depending on the computational workflow. The proposed model is explored for the case of continuously processed data. For the evaluation, a simulation tool was developed, which can be used to analyse models related to storage and network resources. We show that using commercial cloud storage can reduce on-premises disk storage requirements, while maintaining an equal throughput of jobs. Moreover, the key metrics of the model are discussed, and an approach is described, which uses the simulation to assist with the decision process of using commercial cloud storage. The goal is to investigate approaches and propose new evaluation methods to overcome future data challenges.

Cosmological Tests of Gravity: A Future Perspective

In this review, we outline the expected tests of gravity that will be achieved at cosmological scales in the upcoming decades. We focus mainly on constraints on phenomenologically parameterized deviations from general relativity, which allow to test gravity in a model-independent way, but also review some of the expected constraints obtained with more physically motivated approaches. After reviewing the state-of-the-art for such constraints, we outline the expected improvement that future cosmological surveys will achieve, focusing mainly on future large-scale structures and cosmic microwave background surveys but also looking into novel probes on the nature of gravity. We will also highlight the necessity of overcoming accuracy issues in our theoretical predictions, issues that become relevant due to the expected sensitivity of future experiments.

Linking solar minimum, space weather, and night sky brightness

This paper presents time-series observations and analysis of broadband night sky airglow intensity 4 September 2018 through 30 April 2020. Data were obtained at 5 sites spanning more than 8500 km during the historically deep minimum of Solar Cycle 24 into the beginning of Solar Cycle 25. New time-series observations indicate previously unrecognized significant sources of broadband night sky brightness variations, not involving corresponding changes in the Sun's 10.7 cm solar flux, occur during deep solar minimum. New data show; (1) Even during a deep solar minimum the natural night sky is rarely, if ever, constant in brightness. Changes with time-scales of minutes, hours, days, and months are observed. (2) Semi-annual night sky brightness variations are coincident with changes in the orientation of Earth's magnetic field relative to the interplanetary magnetic field. (3) Solar wind plasma streams from solar coronal holes arriving at Earth's bow shock nose are coincident with major night sky brightness increase events. (4) Sites more than 8500 km along the Earth's surface experience nights in common with either very bright or very faint night sky airglow emissions. The reason for this observational fact remains an open question. (5) It is plausible, terrestrial night airglow and geomagnetic indices have similar responses to the solar energy input into Earth's magnetosphere. Our empirical results contribute to a quantitative basis for understanding and predicting broadband night sky brightness variations. They are applicable in astronomical, planetary science, space weather, light pollution, biological, and recreational studies.

Gaseous atomic nickel in the coma of interstellar comet 2I/Borisov

On 31 August 2019, an interstellar comet was discovered as it passed through the Solar System (2I/Borisov). On the basis of initial imaging observations, 2I/Borisov seemed to be similar to ordinary Solar System comets^1,2-an unexpected characteristic given the multiple peculiarities of the only known previous interstellar visitor, 1I/'Oumuamua^3-6. Spectroscopic investigations of 2I/Borisov identified the familiar cometary emissions from CN (refs. ^7-9), C₂ (ref. ¹⁰), O I (ref. ¹¹), NH₂ (ref. ¹²), OH (ref. ¹³), HCN (ref. ¹⁴) and CO (refs. ^14,15), revealing a composition similar to that of carbon monoxide-rich Solar System comets. At temperatures greater than 700 kelvin, comets also show metallic vapours that are produced by the sublimation of metal-rich dust grains¹⁶. Observation of gaseous metals had until very recently¹⁷ been limited to bright sunskirting and sungrazing comets^18-20 and giant star-plunging exocomets²¹. Here we report spectroscopic observations of atomic nickel vapour in the cold coma of 2I/Borisov at a heliocentric distance of 2.322 astronomical units-equivalent to an equilibrium temperature of 180 kelvin. Nickel in 2I/Borisov seems to originate from a short-lived nickel-containing molecule with a lifetime of [Formula: see text] seconds at 1 astronomical unit and is produced at a rate of 0.9 ± 0.3 × 10²² atoms per second, or 0.002 per cent relative to OH and 0.3 per cent relative to CN. The detection of gas-phase nickel in the coma of 2I/Borisov is in line with the recent identification of this atom-as well as iron-in the cold comae of Solar System comets¹⁷.

The SiTian Project

SiTian is an ambitious ground-based all-sky optical monitoring project, developed by the Chinese Academy of Sciences. The concept is an integrated network of dozens of 1-m-class telescopes deployed partly in China and partly at various other sites around the world. The main science goals are the detection, identification and monitoring of optical transients (such as gravitational wave events, fast radio bursts, supernovae) on the largely unknown timescales of less than 1 day; SiTian will also provide a treasure trove of data for studies of AGN, quasars, variable stars, planets, asteroids, and microlensing events. To achieve those goals, SiTian will scan at least 10,000 square deg of sky every 30 min, down to a detection limit of $V \approx 21$ mag. The scans will produce simultaneous light-curves in 3 optical bands. In addition, SiTian will include at least three 4-m telescopes specifically allocated for follow-up spectroscopy of the most interesting targets. We plan to complete the installation of 72 telescopes by 2030 and start full scientific operations in 2032.

An intelligent Data Delivery Service for and beyond the ATLAS experiment

The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. iDDS has been designed to intelligently orchestrate workflow and data management systems, decoupling data pre-processing, delivery, and main processing in various workflows. It is an experiment-agnostic service around a workflow-oriented structure to work with existing and emerging use cases in ATLAS and other experiments. Here we will present the motivation for iDDS, its design schema and architecture, use cases and current status, and plans for the future.

Machine Learning Classification of Kuiper Belt Populations

In the outer solar system, the Kuiper Belt contains dynamical sub-populations sculpted by a combination of planet formation and migration and gravitational perturbations from the present-day giant planet configuration. The subdivision of observed Kuiper Belt objects (KBOs) into Different dynamical classes is based on their current orbital evolution in numerical integrations of their orbits. Here we demonstrate that machine learning algorithms are a promising tool for reducing both the computational time and human effort required for this classification. Using a Gradient Boosting Classifier, a type of machine learning regression tree classifier trained on features derived from short numerical simulations, we sort observed KBOs into four broad, dynamically distinct populations-classical, resonant, detached, and scattering- with a >97 per cent accuracy for the testing set of 542 securely classified KBOs. Over 80 per cent of these objects have a > 3σ probability of class membership, indicating that the machine learning method is classifying based on the fundamental dynamical features of each population. We also demonstrate how, by using computational savings over traditional methods, we can quickly derive a distribution of class membership by examining an ensemble of object clones drawn from the observational errors. We find two major reasons for misclassification: inherent ambiguity in the orbit of the object-for instance, an object that is on the edge of resonance-and a lack of representative examples in the training set. This work provides a promising avenue to explore for fast and accurate classification of the thousands of new KBOs expected to be found by surveys in the coming decade.

An SU(2) Gauge Principle for the Cosmic Microwave Background: Perspectives on the Dark Sector of the Cosmological Model

We review consequences for the radiation and dark sectors of the cosmological model arising from the postulate that the Cosmic Microwave Background (CMB) is governed by an SU(2) rather than a U(1) gauge principle. We also speculate on the possibility of actively assisted structure formation due to the de-percolation of lump-like configurations of condensed ultralight axions with a Peccei–Quinn scale comparable to the Planck mass. The chiral-anomaly induced potential of the axion condensate receives contributions from SU(2)/SU(3) Yang–Mills factors of hierarchically separated scales which act in a screened (reduced) way in confining phases.

Image Simulations for Strong and Weak Gravitational Lensing

Gravitational lensing has been identified as a powerful tool to address fundamental problems in astrophysics at different scales, ranging from exoplanet identification to dark energy and dark matter characterization in cosmology. Image simulations have played a fundamental role in the realization of the full potential of gravitational lensing by providing a means to address needs such as systematic error characterization, pipeline testing, calibration analyses, code validation, and model development. We present a general overview of the generation and applications of image simulations in strong and weak gravitational lensing.

The European Space Agency's Comet Interceptor lies in wait

The European Space Agency (ESA) recently selected Comet Interceptor as its first ‘fast’ (F-class) mission. It will be developed rapidly to share a launch with another mission and is unique, as it will wait in space for a yet-to-be-discovered comet.