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Bayesian inference gets its name from Bayes's theorem , expressing posterior probabilities for hypotheses about a data generating process as the (normalized) product of prior probabilities and a likelihood function. But Bayesian inference uses all of probability theory, not just Bayes's theorem. Many hypotheses of scientific interest are composite hypotheses , with the strength of evidence for the hypothesis dependent on knowledge about auxiliary factors, such as the values of nuisance parameters (e.g., uncertain background rates or calibration factors). Many important capabilities of Bayesian methods arise from use of the law of total probability, which instructs analysts to compute probabilities for composite hypotheses by marginalization over auxiliary factors. This tutorial targets relative newcomers to Bayesian inference, aiming to complement tutorials that focus on Bayes's theorem and how priors modulate likelihoods. The emphasis here is on marginalization over parameter spaces—both how it is the foundation for important capabilities, and how it may motivate caution when parameter spaces are large. Topics covered include the difference between likelihood and probability, understanding the impact of priors beyond merely shifting the maximum likelihood estimate, and the role of marginalization in accounting for uncertainty in nuisance parameters, systematic error, and model misspecification. [ABSTRACT FROM AUTHOR]

We present a hierarchical Bayesian pipeline, BP3M, that measures positions, parallaxes, and proper motions (PMs) for cross-matched sources between Hubble Space Telescope (HST) images and Gaia—even for sparse fields (N * < 10 per image)—expanding from the recent GaiaHub tool. This technique uses Gaia-measured astrometry as priors to predict the locations of sources in HST images, and is therefore able to put the HST images onto a global reference frame without the use of background galaxies/QSOs. Testing our publicly available code in the Fornax and Draco dwarf spheroidal galaxies, we measure PMs that are a median of 8–13 times more precise than Gaia DR3 alone for 20.5 < G < 21 mag. We are able to explore the effect of observation strategies on BP3M astrometry using synthetic data, finding an optimal strategy to improve parallax and position precision at no cost to the PM uncertainty. Using 1619 HST images in the sparse COSMOS field (median nine Gaia sources per HST image), we measure BP3M PMs for 2640 unique sources in the 16 < G < 21.5 mag range, 25% of which have no Gaia PMs; the median BP3M PM uncertainty for 20.25 < G < 20.75 mag sources is 0.44 mas yr−1 compared to 1.03 mas yr−1 from Gaia, while the median BP3M PM uncertainty for sources without Gaia-measured PMs (20.75 < G < 21.5 mag) is 1.16 mas yr−1. The statistics that underpin the BP3M pipeline are a generalized way of combining position measurements from different images, epochs, and telescopes, which allows information to be shared between surveys and archives to achieve higher astrometric precision than that from each catalog alone. [ABSTRACT FROM AUTHOR]

We analyzed images of every northern hemisphere Sd galaxy listed in the Third Reference Catalogue of Bright Galaxies with a relatively face-on inclination (θ ≤ 30°). Specifically, we measured the spiral arms' winding angle, ϕ, in 85 galaxies. We applied a novel black hole mass planar scaling relation involving the rotational velocities (from the literature) and pitch angles of each galaxy to predict central black hole masses. This yielded 23 galaxies, each having at least a 50% chance of hosting a central intermediate-mass black hole (IMBH), 102 < M • ≤ 105 M ☉. These 23 nearby (≲50 Mpc) targets may be suitable for an array of follow-up observations to check for active nuclei. Based on our full sample of 85 Sd galaxies, we estimate that the typical Sd galaxy (which tends to be bulgeless) harbors a black hole with log (M • / M ☉) = 6.00 ± 0.14 , but with a 27.7% chance of hosting an IMBH, making this morphological type of galaxy fertile ground for hunting elusive IMBHs. Thus, we find that a ∼106 M ☉ black hole corresponds roughly to the onset of bulge development and serves as a conspicuous waypoint along the galaxy–supermassive black hole coevolution journey. Our survey suggests that >1.22% of bright galaxies (B T ≲ 15.5 mag) in the local Universe host an IMBH (i.e., the "occupation fraction"), which implies a number density >4.96 × 10−6 Mpc−3 for central IMBHs. Finally, we observe that Sd galaxies exhibit an unexpected diversity of properties that resemble the general population of spiral galaxies, albeit with an enhanced signature of the eponymous prototypical traits (i.e., low masses, loosely wound spiral arms, and smaller rotational velocities). [ABSTRACT FROM AUTHOR]

The aim of our paper is to investigate the properties of the classical phase-dispersion minimization (PDM), analysis of variance (AOV), string-length (SL), and Lomb–Scargle (LS) power statistics from a statistician's perspective. We confirm that when the data are perturbations of a constant function, i.e. under the null hypothesis of no period in the data, a scaled version of the PDM statistic follows a beta distribution, the AOV statistic follows an F distribution, and the LS power follows a chi-squared distribution with two degrees of freedom. However, the SL statistic does not have a closed-form distribution. We further verify these theoretical distributions through simulations and demonstrate that the extreme values of these statistics (over a range of trial periods), often used for period estimation and determination of the false alarm probability (FAP), follow different distributions than those derived for a single period. We emphasize that multiple-testing considerations are needed to correctly derive FAP bounds. Though, in fact, multiple-testing controls are built into the FAP bound for these extreme-value statistics, e.g. the FAP bound derived specifically for the maximum LS power statistic over a range of trial periods. Additionally, we find that all of these methods are robust to heteroscedastic noise aimed to mimic the degradation or miscalibration of an instrument over time. Finally, we examine the ability of these statistics to detect a non-constant periodic function via simulating data that mimics a well-detached binary system, and we find that the AOV statistic has the most power to detect the correct period, which agrees with what has been observed in practice. [ABSTRACT FROM AUTHOR]

Bayesian inference gets its name from Bayes’s theorem, expressing posterior probabilities for hypotheses about a data generating process as the (normalized) product of prior probabilities and a likelihood function. But Bayesian inference uses all of probability theory, not just Bayes’s theorem. Many hypotheses of scientific interest are composite hypotheses, with the strength of evidence for the hypothesis dependent on knowledge about auxiliary factors, such as the values of nuisance parameters (e.g., uncertain background rates or calibration factors). Many important capabilities of Bayesian methods arise from use of the law of total probability, which instructs analysts to compute probabilities for composite hypotheses by marginalization over auxiliary factors. This tutorial targets relative newcomers to Bayesian inference, aiming to complement tutorials that focus on Bayes’s theorem and how priors modulate likelihoods. The emphasis here is on marginalization over parameter spaces—both how it is the foundation for important capabilities, and how it may motivate caution when parameter spaces are large. Topics covered include the difference between likelihood and probability, understanding the impact of priors beyond merely shifting the maximum likelihood estimate, and the role of marginalization in accounting for uncertainty in nuisance parameters, systematic error, and model misspecification.

Galaxies play a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs, spanning cosmologically significant volumes. For such a daunting task, methods that can produce a direct mapping between dark matter halos from dark matter-only simulations and galaxies are strongly preferred, as producing mocks from full-fledged hydrodynamical simulations or semi-analytical models is too expensive. Here, we present a graph-neural-network-based model that is able to accurately predict key properties of galaxies such as stellar mass, g − r color, star formation rate, gas mass, stellar metallicity, and gas metallicity, purely from dark matter properties extracted from halos along the full assembly history of the galaxies. Tests based on the TNG300 simulation of the IllustrisTNG project show that our model can recover the baryonic properties of galaxies to high accuracy, over a wide redshift range (z = 0–5), for all galaxies with stellar masses more massive than 109 M ⊙ and their progenitors, with strong improvements over the state-of-the-art methods. We further show that our method makes substantial strides toward providing an understanding of the implications of the IllustrisTNG galaxy formation model. [ABSTRACT FROM AUTHOR]

We apply an imaging analysis technique to study the spatial distribution of the X-ray spectral hardness across the hot bubble of planetary nebula BD+30°3639. Hot bubble emission is typically photon starved, thus limiting the methods for spatial–spectral analysis; however, this technique uses the statistics of photon energies across the nebula to identify spatial variations in the X-ray spectral hardness. Using the median energy value of the X-ray photons, we identified a rise in median energy values toward the projected edge of the nebula, which we refer to as radial spectral hardening. We explored the origin of this radial spectral hardening with an X-ray spectral analysis of distinct regions of high and low median energy values. Given the results of the X-ray spectral fits and the fact that the hot bubble is embedded within a young, dense, planetary nebula, we conclude that the radial spectral hardening is due to an increased column density at the projected nebular edge. [ABSTRACT FROM AUTHOR]

We propose a simple, statistically principled, and theoretically justified method to improve supervised learning when the training set is not representative, a situation known as covariate shift. We build upon a well‐established methodology in causal inference and show that the effects of covariate shift can be reduced or eliminated by conditioning on propensity scores. In practice, this is achieved by fitting learners within strata constructed by partitioning the data based on the estimated propensity scores, leading to approximately balanced covariates and much‐improved target prediction. We refer to the overall method as Stratified Learning, or StratLearn. We demonstrate the effectiveness of this general‐purpose method on two contemporary research questions in cosmology, outperforming state‐of‐the‐art importance weighting methods. We obtain the best‐reported AUC (0.958) on the updated "Supernovae photometric classification challenge," and we improve upon existing conditional density estimation of galaxy redshift from Sloan Digital Sky Survey (SDSS) data. [ABSTRACT FROM AUTHOR]

We present the first measurement of the Ly α forest baryon acoustic oscillations (BAO) shift parameter from cosmological simulations. In particular, we generate a suite of 1000 accurate effective field-level bias-based Ly α forest simulations of volume $V={(1\,{h}^{-1}\,\mathrm{Gpc})}^{3}$ at z = 2, both in real and redshift space, calibrated upon two fixed-and-paired cosmological hydrodynamic simulations. To measure the BAO, we stack the 3D power spectra of the 1000 different realizations, compute the average, and use a model accounting for a proper smooth-peak component decomposition of the power spectrum, to fit it via an efficient Markov Chain Monte Carlo scheme estimating the covariance matrices directly from the simulations. We report the BAO shift parameters to be $\alpha ={0.9969}_{-0.0014}^{+0.0014}$ and $\alpha ={0.9905}_{-0.0027}^{+0.0027}$ in real and redshift space, respectively. We also measure the bias b _lya and the BAO broadening parameter Σ _nl , finding ${b}_{\mathrm{lya}}=-{0.1786}_{-0.0001}^{+0.0001}$ and ${{\rm{\Sigma }}}_{\mathrm{nl}}={3.87}_{-0.20}^{+0.20}$ in real space, and ${b}_{\mathrm{lya}}=-{0.073}_{-0.004}^{+0.005}$ and ${{\rm{\Sigma }}}_{\mathrm{nl}}={6.55}_{-0.22}^{+0.23}$ in redshift space. Moreover, we measure the linear Kaiser factor ${\beta }_{\mathrm{lya}}={1.39}_{-0.18}^{+0.24}$ from the isotropic redshift space fit. Overall, we find evidence for a negative shift of the BAO peak at the ∼2.2 σ and ∼3.5 σ levels in real and redshift space, respectively. This work sets new important theoretical constraints on the Ly α forest BAO scale and offers a potential solution to the tension emerging from previous observational analysis, in light of ongoing and upcoming Ly α forest spectroscopic surveys, such as DESI, the Prime Focus Spectrograph Survey, and WEAVE-QSO.

Understanding galaxy properties may be the key to unlocking some of the most intriguing mysteries of modern cosmology. Recent work relied on machine learning to extract cosmological constraints on Ω _m using only one galaxy. But if this is true, how should we select the galaxy to use for cosmology inference? In this Letter, we consider selecting a galaxy that lies in cosmic voids, the underdense regions of the cosmic web, and compare the constraints obtained with the ones obtained when randomly selecting a galaxy in the whole sample. We use the IllustrisTNG galaxy catalog from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project and the VIDE void finder to identify galaxies inside voids. We show that void galaxies provide stronger constraints on Ω _m compared to randomly selected galaxies. This result suggests that the distinctive characteristics of void galaxies may provide a cleaner and more effective environment for extracting cosmological information.

We present the first cosmological constraints using only the observed photometry of galaxies. Villaescusa-Navarro et al. recently demonstrated that the internal physical properties of a single simulated galaxy contain a significant amount of cosmological information. These physical properties, however, cannot be directly measured from observations. In this work, we present how we can go beyond theoretical demonstrations to infer cosmological constraints from actual galaxy observables (e.g., optical photometry) using neural density estimation and the CAMELS suite of hydrodynamical simulations. We find that the cosmological information in the photometry of a single galaxy is limited. However, we combine the constraining power of photometry from many galaxies using hierarchical population inference and place significant cosmological constraints. With the observed photometry of ∼20,000 NASA-Sloan Atlas galaxies, we constrain ${{\rm{\Omega }}}_{m}={0.323}_{-0.095}^{+0.075}$ and ${\sigma }_{8}={0.799}_{-0.085}^{+0.088}$ .

The apparent tension between the luminosity functions of red supergiant (RSG) stars and of RSG progenitors of Type II supernovae (SNe) is often referred to as the RSG problem and it motivated some to suggest that many RSGs end their life without an SN explosion. However, the luminosity functions of RSG SN progenitors presented so far were biased to high luminosities, because the sensitivity of the search was not considered. Here, we use limiting magnitudes to calculate a bias-corrected RSG progenitor luminosity function. We find that only (36 ± 11)% of all RSG progenitors are brighter than a bolometric magnitude of −7 mag, a significantly smaller fraction than (56 ± 5)% quoted by Davies & Beasor. The larger uncertainty is due to the relatively small progenitor sample, while uncertainties on measured quantities such as magnitudes, bolometric corrections, extinction, or SN distances, only have a minor impact, as long as they fluctuate randomly for different objects in the sample. The bias-corrected luminosity functions of RSG SN progenitors and Type M supergiants in the Large Magellanic Cloud are consistent with each other, as also found by Davies & Beasor for the uncorrected luminosity function. The RSG progenitor luminosity function, hence, does not imply the existence of failed SNe. The presented statistical method is not limited to progenitor searches, but applies to any situation in which a measurement is done for a sample of detected objects, but the probed quantity or property can only be determined for part of the sample.

I employ the Lucy rectification algorithm to recover the inclination-corrected distribution of local disk galaxies in the plane of absolute magnitude ( M _i ) and H i velocity width ( W _20 ). By considering the inclination angle as a random variable with a known probability distribution, the novel approach eliminates one major source of uncertainty in studies of the Tully–Fisher relation: inclination angle estimation from axial ratio. Leveraging the statistical strength derived from the entire sample of 28,264 H i -selected disk galaxies at z < 0.06 from the Arecibo Legacy Fast ALFA survey, I show that the restored distribution follows a sharp correlation that is approximately a power law between −16 > M _i > −22: ${M}_{i}={M}_{0}-2.5\beta \ [\mathrm{log}({W}_{20}/250\mathrm{km}/{\rm{s}})]$ , with M _0 = −19.77± 0.04 and β = 4.39 ± 0.06. At the brighter end ( M _i < −22), the slope of the correlation decreases to β ≈ 3.3, confirming previous results. Because the method accounts for measurement errors, the intrinsic dispersion of the correlation is directly measured: $\sigma (\mathrm{log}{W}_{20})\approx 0.06$ dex between −17 > M _i > −23, while σ ( M _i ) decreases from ∼0.8 in slow rotators to ∼0.4 in fast rotators. The statistical rectification method holds significant potential, especially in the studies of intermediate-to-high-redshift samples, where limited spatial resolution hinders precise measurements of inclination angles.

We present TRICERATOPS, a new Bayesian tool that can be used to vet and validate TESS Objects of Interest (TOIs). We test the tool on 68 TOIs that have been previously confirmed as planets or rejected as astrophysical false positives. By looking in the false-positive probability (FPP)-nearby false-positive probability (NFPP) plane, we define criteria that TOIs must meet to be classified as validated planets (FPP < 0.015 and NFPP < 10-3), likely planets (FPP < 0.5 and NFPP < 10-3), and likely nearby false positives (NFPP > 10-1). We apply this procedure on 384 unclassified TOIs and statistically validate 12, classify 125 as likely planets, and classify 52 as likely nearby false positives. Of the 12 statistically validated planets, 9 are newly validated. TRICERATOPS is currently the only TESS vetting and validation tool that models transits from nearby contaminant stars in addition to the target star. We therefore encourage use of this tool to prioritize follow-up observations that confirm bona fide planets and identify false positives originating from nearby stars.

Galaxy stellar mass is known to be monotonically related to the size of the galaxy's globular cluster (GC) population for Milky Way sized and larger galaxies. However, the relation becomes ambiguous for dwarf galaxies, where there is some evidence for a downturn in GC population size at low galaxy masses. Smaller dwarfs are increasingly likely to have no GCs, and these zeros cannot be easily incorporated into linear models. We introduce the Hierarchical Errors-in-variables ERrors-in-variables BAyesian Lognormal hurdle (HERBAL) model to represent the relationship between dwarf galaxies and their GC populations, and apply it to the sample of Local Group galaxies, where the luminosity range coverage is maximal. This bimodal model accurately represents the two populations of dwarf galaxies: those that have GCs and those that do not. Our model thoroughly accounts for all uncertainties, including measurement uncertainty, uncertainty in luminosity to stellar mass conversions, and intrinsic scatter. The hierarchical nature of our Bayesian model also allows us to estimate galaxy masses and individual mass-to-light ratios from luminosity data within the model. We find that 50% of galaxies are expected to host GC populations at a stellar mass of log 10 (M *) = 6.996 , and that the expected mass of GC populations remains linear down to the smallest galaxies. Our hierarchical model recovers an accurate estimate of the Milky Way stellar mass. Under our assumed error model, we find a nonzero intrinsic scatter of 0.59 − 0.21 + 0.3 (95% credible interval) that should be accounted for in future models. [ABSTRACT FROM AUTHOR]

We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain 3D positions and radial velocities of ∼1000 galaxies in tiny (25 h − 1 Mpc) 3 volumes our models can infer the value of Ωm with approximately 12% precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and active galactic nucleus feedback, run with five different codes and subgrid models—IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE—we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on 1024 simulations that cover a vast region in parameter space—variations in five cosmological and 23 astrophysical parameters—finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network has likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than ∼10 h −1 kpc. [ABSTRACT FROM AUTHOR]

The Halo Assembly in Lambda Cold Dark Matter: Observations in 7 Dimensions (HALO7D) survey measures the kinematics and chemical properties of stars in the Milky Way (MW) stellar halo to learn about the formation of our Galaxy. HALO7D consists of Keck II/DEIMOS spectroscopy and Hubble Space Telescope–measured proper motions of MW halo main-sequence turnoff stars in the four Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey fields. HALO7D consists of deep pencil beams, making it complementary to other contemporary wide-field surveys. We present the [Fe/H] and [ α /Fe] abundances for 113 HALO7D stars in the Galactocentric radial range of ∼10–40 kpc along four separate pointings. Using the full 7D chemodynamical data (3D positions, 3D velocities, and abundances) of HALO7D, we measure the velocity anisotropy, β, of the halo velocity ellipsoid for each field and for different metallicity-binned subsamples. We find that two of the four fields have stars on very radial orbits, while the remaining two have stars on more isotropic orbits. Separating the stars into high-, mid-, and low-[Fe/H] bins at −2.2 and −1.1 dex for each field separately, we find differences in the anisotropies between the fields and between the bins; some fields appear dominated by radial orbits in all bins, while other fields show variation between the [Fe/H] bins. These chemodynamical differences are evidence that the HALO7D fields have different fractional contributions from the progenitors that built up the MW stellar halo. Our results highlight the additional information available on smaller spatial scales compared to results from a spherical average of the stellar halo. [ABSTRACT FROM AUTHOR]

The presence of a population of point sources in a data set modifies the underlying neutrino-count statistics from the Poisson distribution. This deviation can be exactly quantified using the non-Poissonian template fitting technique, and in this work we present the first application of this approach to the IceCube high-energy neutrino data set. Using this method, we search in 7 yr of IceCube data for point-source populations correlated with the disk of the Milky Way, the Fermi bubbles, the Schlegel, Finkbeiner, and Davis dust map, or with the isotropic extragalactic sky. No evidence for such a population is found in the data using this technique, and in the absence of a signal, we establish constraints on population models with source-count distribution functions that can be described by a power law with a single break. The derived limits can be interpreted in the context of many possible source classes. In order to enhance the flexibility of the results, we publish the full posterior from our analysis, which can be used to establish limits on specific population models that would contribute to the observed IceCube neutrino flux.

Solar flares result from the sudden release of energy deposited by subphotospheric motions into the magnetic field of the corona. The deposited energy accumulates secularly between events. One may interpret the observed event statistics as resulting from a state-dependent Poisson process in which the instantaneous flare rate is a function of the stress in the system and a flare becomes certain as the stress approaches a threshold set by the microphysics of the flare trigger. If the system is driven fast, and if the threshold is static and uniform globally, a cross-correlation is predicted between the size of a flare and the forward waiting time to the next flare. This cross-correlation is broadly absent from the Geostationary Operational Environmental Satellite (GOES) soft X-ray flare database. One also predicts higher cross-correlations in active regions where the shapes of the waiting time and size distributions match. Again, there is no evidence for such an association in the GOES data. The data imply at least one of the following: (i) the threshold at which a flare is triggered varies in time; (ii) the rate at which energy is driven into active regions varies in time; (iii) historical flare catalogs are incomplete; or (iv) the description of solar flares as resulting from a buildup and release of energy, once a threshold is reached, is incomplete. [ABSTRACT FROM AUTHOR]

We present new mass estimates for Andromeda (M31) using the orbital angular momenta of four satellite galaxies (M33, NGC 185, NGC 147, and IC 10) derived from existing proper motions, distances, and line-of-sight velocities. We infer two masses for M31: M vir = 2.85 − 0.77 + 1.47 × 10 12 M ⊙ using satellite galaxy phase-space information derived with Hubble Space Telescope (HST)-based M31 proper motions and M vir = 3.02 − 0.69 + 1.30 × 10 12 M ⊙ using phase-space information derived with the weighted average of HST+Gaia-based M31 proper motions. The precision of our new M31 mass estimates (23%–50%) improves by a factor of two compared to previous mass estimates using a similar methodology with just one satellite galaxy, and places our results among the highest-precision M31 estimates in recent literature. Furthermore, our results are consistent with recently revised estimates for the total mass of the Local Group (LG), with the stellar mass–halo mass relation, and with observed kinematic data for both M31 and its entire population of satellites. An M31 mass >2.5 × 1012 M ⊙ could have major implications for our understanding of LG dynamics, M31's merger and accretion history, and our understanding of LG galaxies in a cosmological context. [ABSTRACT FROM AUTHOR]

Deep learning has increasingly been applied to supervised learning tasks in astronomy, such as classifying images of galaxies based on their apparent shape (i.e., galaxy morphology classification) to gain insight regarding the evolution of galaxies. In this work, we examine the effect of pretraining on the performance of the classical AlexNet convolutional neural network (CNN) in classifying images of 14,034 galaxies from the Sloan Digital Sky Survey Data Release 4. Pretraining involves designing and training CNNs on large labeled image datasets unrelated to astronomy, which takes advantage of the vast amounts of such data available compared to the relatively small amount of labeled galaxy images. We show a statistically significant benefit of using pretraining, both in terms of improved overall classification success and reduced computational cost to achieve such performance.

We review the iid2022 workshop on statistical methods for X-ray and γ-ray astronomy and high–energy astrophysics event data in astronomy, held in Guntersville, AL, on Nov. 15–18 2022. New methods for faint source detection, spatial point processes, variability and spectral analysis, and machine learning are discussed. Ideas for future developments of advanced methodology are shared.

We constrain the angular momentum architecture of VHS J125601.92-125723.9, a 140 ± 20 Myr old hierarchical triple system composed of a low-mass binary and a widely separated planetary-mass companion, VHS 1256 b. VHS 1256 b has been a prime target for multiple characterization efforts, revealing the highest measured substellar photometric variability to date and the presence of silicate clouds and disequilibrium chemistry. Here we add a key piece to the characterization of this super-Jupiter on a Tatooine-like orbit: we measure its spin-axis tilt relative to its orbit, i.e., the obliquity of VHS 1256 b. We accomplish this by combining three measurements. We find a projected rotation rate $v\sin {i}_{p}=8.7\pm 0.1\,\mathrm{km}\,{{\rm{s}}}^{-1}$ for VHS 1256 b using near-infrared high-resolution spectra from Gemini/IGRINS. Combining this with a published photometric rotation period indicates that the companion is viewed edge on, with a line-of-sight spin axis inclination of i _p = 90° ± 18°. We refit available astrometry measurements to confirm an orbital inclination of ${i}_{{\rm{o}}}={23}_{-13}^{+10}$ degrees. Taken together, VHS 1256 b has a large planetary obliquity of ψ = 90° ± 25°. In total, we have three measured angular momentum vectors for the system: the binary orbit normal, companion orbit normal, and companion spin axis. All three are misaligned with respect to each other. Although VHS 1256 b is tilted like Uranus, their origins are distinct. We rule out planet-like scenarios including collisions and spin–orbit resonances, and suggest that top-down formation via core/filament fragmentation is promising.

Understanding the relationship between close-in small (CS) planets and distant giants (DGs) is central to understanding the formation of planetary systems like our own. Most studies of this connection have found evidence for a positive correlation, though significant statistical and systematic uncertainties remain due to differences in sample size, target selection bias, and even the definitions of CS and DG planets. Recently, M. L. Bryan & E. J. Lee conducted a study of 184 stars hosting super-Earths ( $M\sin i=1-20\,{M}_{\oplus }$ or R = 1 − 4 R _⊕ ) to determine the effect of stellar metallicity on the prevalence of DG companions ( $M\sin i=0.5-20\,\,{M}_{{\rm{J}}}$ , a = 1−10 au). They found that such giants are twice as common in the presence of inner planets, but only in metal-rich systems: P(DG∣CS, [Fe/H] > 0) = ${28.0}_{-4.6}^{+4.9} \% $ versus P(DG∣[Fe/H] > 0) = ${14.3}_{-1.8}^{+2.0} \% $ . Further, they found that this correlation disappears for metal-poor stars: P(DG∣CS, [Fe/H] ≤ 0) = ${4.5}_{-1.9}^{+2.6} \% $ versus P(DG∣[Fe/H] ≤ 0) = ${5.0}_{-1.3}^{+1.6} \% $ . We conducted an analogous study to test whether this effect was present in the California Legacy Survey sample. Using the same planet definitions, we did not find evidence for an enhancement in metal-rich systems: P(DG∣CS, [Fe/H] > 0) = ${14}_{-8}^{+11} \% $ versus P(DG∣[Fe/H] > 0) = ${17.9}_{-2.4}^{+2.5} \% $ . We also found a 2 σ tension between our conditional rate and a 2x enhancement. We brought our results into closer agreement with M. L. Bryan & E. J. Lee by repeating our analysis with different planet definitions. For example, considering only CS planets with $1\leqslant M\sin i\leqslant 10\,{M}_{\oplus }$ and DG planets with $0.5\leqslant M\sin i\leqslant 20\,\,{M}_{{\rm{J}}}$ gives P(DG∣CS, [Fe/H] > 0) = ${48}_{-17}^{+17} \% $ and P(DG∣[Fe/H] > 0) = ${21.8}_{-2.7}^{+2.9} \% $ , consistent with a 2x enhancement. However, we find that this 2x enhancement is also present when we apply the same planet definitions to the overall sample, challenging the idea that metallicity is responsible. Our sample, though only ∼1/6 the size of M. L. Bryan & E. J. Lee, was assembled and analyzed self-consistently. The discrepancy between our findings and theirs may arise from a variety of sources, underscoring the need for large exoplanet surveys to overcome both small number statistics and sample inhomogeneities.

The acquisition of complex astronomical data is accelerating, especially with newer telescopes producing ever more large-scale surveys. The increased quantity, complexity, and variety of astronomical data demand a parallel increase in skill and sophistication in developing, deciding, and deploying statistical methods. Understanding limitations and appreciating nuances in statistical and machine learning methods and the reasoning behind them is essential for improving data-analytic proficiency and acumen. Aiming to facilitate such improvement in astronomy, we delineate cautionary tales in statistics via six maxims, with examples drawn from the astronomical literature. Inspired by the significant quality improvement in business and manufacturing processes by the routine adoption of Six Sigma, we hope the routine reflection on these six maxims will improve the quality of both data analysis and scientific findings in astronomy.

We propose a novel approach to the detection of point-like sources of high-energy neutrinos. Motivated by evidence for emerging sources in existing data, we focus on the characterization and interpretation of these sources rather than the rejection of the background-only hypothesis. The hierarchical Bayesian model is implemented in the Stan platform, enabling computation of the posterior distribution with a Hamiltonian Monte Carlo algorithm. We simulate a population of weak neutrino sources detected by the IceCube experiment and use the resulting data set to demonstrate and validate our framework. We show that even for the challenging case of sources at the threshold of detection and using limited prior information, it is possible to correctly infer the source properties. Additionally, we demonstrate how modeling flexible connections between similar sources can be used to recover the contribution of sources that would not be detectable individually. While a direct comparison of our method to existing approaches is challenged by the fundamental differences in frequentist and Bayesian frameworks, we draw parallels where possible. In particular, we highlight how including more complexity into the source modeling can increase the sensitivity to sources and their populations.

Estimating photometric redshifts (photo- z ) of quasars is crucial for measuring cosmic distances and monitoring cosmic evolution. While numerous point estimation methods have successfully determined photo- z , they often struggle with the inherently ill-posed nature of the problem and frequently overlook significant morphological features in the probability density functions (pdfs) of photo- z , such as calibration and sharpness. To address these challenges, we introduce a cross-modal contrastive learning probabilistic model that employs adversarial training, contrastive loss functions, and a mixture density network to estimate the pdf of photo- z . This method facilitates the conversion between multiband photometric data attributes, such as magnitude and color, and photometric image features, while extracting features invariant across modalities. We utilize the continuous ranked probability score (CRPS) and the probability integral transform (PIT) as metrics to assess the quality of the pdf. Our approach demonstrates robust performance across various survey bands, image qualities, and redshift distributions. Specifically, in a comprehensive data set from the Sloan Digital Sky Survey and the Wide-field Infrared Survey Explorer (WISE) survey, our probabilistic model achieved a CRPS of 0.1187. Additionally, in a combined data set from SkyMapper and WISE, it reached a CRPS of 0.0035. Our probabilistic model also produced well-calibrated PIT histograms for both data sets, indicating nearly uniform distributions. We further tested our approach in classification tasks within the SkyMapper data set. Despite the absence of u , v , and g bands, it effectively distinguished between quasars, galaxies, and stars with an accuracy of 98.96%. This versatile method can be extended to other scenarios, such as analyzing extended sources like galaxies, across different surveys and varying redshift distributions.

The large-scale measurement of galaxy redshifts holds significant importance for cosmological research, as well as for understanding the formation and evolution of galaxies. This study utilizes a known sample obtained by cross-correlating the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys DR10 galaxy catalog with various galaxy catalogs from different spectroscopic surveys. The methods Easy and Accurate Photometric Redshifts from Yale (EAZY) and CatBoost are employed to estimate redshifts. In the case of EAZY, the known sample is used solely for testing, while CatBoost utilizes it for both training and testing purposes. The known sample is categorized into different subsamples based on various classification methods. Several CatBoost regression models are trained and optimized using these subsamples. By comparing the performance of different methods and models, it is observed that the two-step and two-part models outperform the one-step model, with further enhancements achieved through the combination of the two-step and two-part models. Based on the findings from all experiments, we propose a photometric redshift estimation workflow designed to facilitate the photometric redshift measurement of all galaxies within the DESI Legacy Imaging Surveys DR10. Consequently, a photometric redshift catalog has been released, comprising a total of 1,533,107,988 galaxies. Among these, 312,960,837 galaxies have reliable redshift estimates, determined using the CatBoost algorithm, with magnitude limits set at g > 24.0, r > 23.4, and z > 22.5. For galaxies with g , r , and z magnitudes exceeding these thresholds, the photometric redshifts estimated by EAZY can be employed as a reference.

We investigate the evolutionary dynamics with archival continuum and line data of 27 giant molecular clouds (GMCs) in the Milky Way, focusing on their influence on star formation. Examining the dense gas mass fraction (DGMF) among the GMCs, we categorize them into low-DGMF (DGMF < 20%), medium-DGMF (20% < DGMF < 60%), and high-DGMF (60% < DGMF) groups. The analysis uncovers systematic trends in the free-fall time, virial parameter, surface density, star formation rate (SFR), SFR per unit area (Σ _SFR ), and star formation efficiency for dense gas as the DGMF increases within GMCs. We identified 362 filaments and 3623 clumps within the GMCs. Increasing DGMF correlates with higher proportions of star-forming clumps and clumps capable of forming massive stars. Clump properties such as hydrogen number density and surface density increase with DGMF, while the mass and radius decrease. The dust temperature and virial parameters show no significant variation with DGMF. We also observe convergence in the hydrogen number density and dust temperature between star-forming and starless clumps with rising DGMF. Filaments are found to be spatially associated with clumps capable of forming high-mass stars, with those on filaments exhibiting greater mass, radius, hydrogen number density, surface density, and velocity dispersion. Moreover, filaments hosting clumps capable of forming high-mass stars demonstrate larger mass, length, and line mass. In summary, this comprehensive analysis of GMCs, filaments, and clumps supports the notion of a multiscale coevolution process. From GMCs to filaments and subsequently to clumps, the DGMF emerges as a valuable tracer for understanding the evolutionary trajectory of GMCs and the processes governing their development.

Blue horizontal branch stars (BHBs), excellent distant tracers for probing the Milky Way’s halo density profile, are distinguished in the ${\left(g-r\right)}_{0}$ versus ( i − z ) _0 color space from another class of stars, blue straggler stars. We develop a Bayesian mixture model to classify BHBs using high-precision photometry data from the Dark Energy Survey Data Release 2 (DES DR2). We select ∼2100 highly probable BHBs based on their griz photometry and the associated uncertainties, and we use these stars to map the stellar halo over the Galactocentric radial range 20 kpc ≲ R ≲ 70 kpc. After excluding known stellar overdensities, we find that the number density n _⋆ of BHBs can be represented by a power-law density profile n _⋆ ∝ R ^− ^α with an index of $\alpha ={4.34}_{-0.12}^{+0.13}\pm 0.52$ , consistent with existing literature values. In addition, we examine the impact of systematic errors and the spatial inhomogeneity on the fitted density profile. Our work demonstrates the effectiveness of high-precision griz photometry in selecting BHBs. The upcoming photometric survey from the Rubin Observatory, expected to reach depths 2–3 mag greater than DES during its 10 yr mission, will enable us to investigate the density profile of the Milky Way’s halo out to the virial radius, unraveling the complex processes of formation and evolution in our Galaxy.

Asteroseismic time series data have imprints of stellar oscillation modes, whose detection and characterization through time series analysis allows us to probe stellar interior physics. Such analyses usually occur in the Fourier domain by computing the Lomb–Scargle (LS) periodogram, an estimator of the power spectrum underlying unevenly sampled time series data. However, the LS periodogram suffers from the statistical problems of (1) inconsistency (or noise) and (2) bias due to high spectral leakage. Here, we develop a multitaper power spectrum estimator using the nonuniform fast Fourier transform ( mtNUFFT ) to tackle the inconsistency and bias problems of the LS periodogram. Using a simulated light curve, we show that the mtNUFFT power spectrum estimate of solar-like oscillations has lower variance and bias than the LS estimate. We also apply our method to the Kepler-91 red giant, and combine it with PBjam peakbagging to obtain mode parameters and a derived age estimate of 3.97 ± 0.52 Gyr. PBjam allows the improvement of age precision relative to the 4.27 ± 0.75 Gyr APOKASC-2 (uncorrected) estimate, whereas partnering mtNUFFT with PBjam speeds up peakbagging thrice as much as LS. This increase in efficiency has promising implications for Galactic archaeology, in addition to stellar structure and evolution studies. Our new method generally applies to time-domain astronomy and is implemented in the public Python package tapify , available at https://github.com/aaryapatil/tapify .

JWST is uncovering the properties of ever-increasing numbers of galaxies at z > 6, during the epoch of reionization. Connecting these observed populations to the process of reionization requires understanding how efficiently they produce Lyman continuum (LyC) photons and what fraction ( f _esc ) of these photons escape into the intergalactic medium. By applying the Cox proportional hazards model, a survival analysis technique, to the Low-redshift Lyman Continuum Survey (LzLCS), we develop new, empirical, multivariate predictions for f _esc . The models developed from the LzLCS reproduce the observed f _esc for z ∼ 3 samples, which suggests that LyC emitters may share similar properties at low and high redshift. Our best-performing models for the z ∼ 3 galaxies include information about dust attenuation, ionization, and/or morphology. We then apply these models to z ≳ 6 galaxies. For large photometric samples, we find a median predicted f _esc = 0.047–0.14. For smaller spectroscopic samples, which may include stronger emission-line galaxies, we find that ≥33% of the galaxies have f _esc > 0.2, and we identify several candidate extreme leakers with f _esc ≥ 0.5. The current samples show no strong trend between predicted f _esc and UV magnitude, but limited spectroscopic information makes this result uncertain. Multivariate predictions can give significantly different results from single-variable predictions, and the predicted f _esc for high-redshift galaxies can differ significantly depending on whether star formation rate surface density or radius is used as a measure of galaxy morphology. We provide all parameters necessary to predict f _esc for additional samples of high-redshift galaxies using these models.

The Period–Luminosity (PL) relation for Cepheid variable stars in the LMC is crucial for distance measurements in astronomy. This study analyzes the impact of using the median rather than the mean of the PL relation’s slope and zero-point. It also examines the persistence of the break at approximately 10 days and addresses specification issues in the PL relation model. Using VI -band median and mean magnitudes from the OGLE-IV survey, corrected for extinction, we fit the PL relation employing robust MM -regression, which features a high breakdown point and robust standard errors. Statistical tests and residual analysis are conducted to identify and correct model deficiencies. Our findings indicate a significant change in the PL relation for Cepheids with periods of 10 days or longer, regardless of whether median or mean magnitudes are used. A bias in the zero-point and slope estimators is observed when using median magnitudes instead of mean magnitudes, especially in the V band. By identifying and correcting regression issues and considering the period break, our estimators for the slope and zero-point are more accurate for distance calculations. Comparative analysis of the models for each band quantifies the bias introduced by using median magnitudes, highlighting the importance of considering the Cepheids’ periods for accurate location measure results, similar to those obtained using mean magnitudes.

The information regarding how the intergalactic medium is reionized by astrophysical sources is contained in the tomographic three-dimensional 21 cm images from the epoch of reionization. In Zhao et al. (“Paper I”), we demonstrated for the first time that density estimation likelihood-free inference (DELFI) can be applied efficiently to perform a Bayesian inference of the reionization parameters from the 21 cm images. Nevertheless, the 3D image data needs to be compressed into informative summaries as the input of DELFI by, e.g., a trained 3D convolutional neural network (CNN) as in Paper I ( DELFI-3D CNN ). Here in this paper, we introduce an alternative data compressor, the solid harmonic wavelet scattering transform (WST), which has a similar, yet fixed (i.e., no training), architecture to CNN, but we show that this approach (i.e., solid harmonic WST with DELFI) outperforms earlier analyses based on 3D 21 cm images using DELFI-3D CNN in terms of credible regions of parameters. Realistic effects, including thermal noise and residual foreground after removal, are also applied to the mock observations from the Square Kilometre Array. We show that under the same inference strategy using DELFI, the 21 cm image analysis with solid harmonic WST outperforms the 21 cm power spectrum analysis. This research serves as a proof of concept, demonstrating the potential to harness the strengths of WST and simulation-based inference to derive insights from future 21 cm light-cone image data.

We present a hierarchical Bayesian pipeline, BP3M , that measures positions, parallaxes, and proper motions (PMs) for cross-matched sources between Hubble Space Telescope (HST) images and Gaia—even for sparse fields ( N _* < 10 per image)—expanding from the recent GaiaHub tool. This technique uses Gaia-measured astrometry as priors to predict the locations of sources in HST images, and is therefore able to put the HST images onto a global reference frame without the use of background galaxies/QSOs. Testing our publicly available code in the Fornax and Draco dwarf spheroidal galaxies, we measure PMs that are a median of 8–13 times more precise than Gaia DR3 alone for 20.5 < G < 21 mag. We are able to explore the effect of observation strategies on BP3M astrometry using synthetic data, finding an optimal strategy to improve parallax and position precision at no cost to the PM uncertainty. Using 1619 HST images in the sparse COSMOS field (median nine Gaia sources per HST image), we measure BP3M PMs for 2640 unique sources in the 16 < G < 21.5 mag range, 25% of which have no Gaia PMs; the median BP3M PM uncertainty for 20.25 < G < 20.75 mag sources is 0.44 mas yr ^−1 compared to 1.03 mas yr ^−1 from Gaia, while the median BP3M PM uncertainty for sources without Gaia-measured PMs (20.75 < G < 21.5 mag) is 1.16 mas yr ^−1 . The statistics that underpin the BP3M pipeline are a generalized way of combining position measurements from different images, epochs, and telescopes, which allows information to be shared between surveys and archives to achieve higher astrometric precision than that from each catalog alone.

Due to the high dimensionality or multimodality that is common in modern astronomy, sampling Bayesian posteriors can be challenging. Several publicly available codes based on different sampling algorithms can solve these complex models, but the execution of the code is not always efficient or fast enough. The article introduces a C language general-purpose code, Nii-C, that implements a framework of automatic parallel tempering Markov Chain Monte Carlo. Automatic in this context means that the parameters that ensure an efficient parallel tempering process can be set by a control system during the inital stages of a sampling process. The autotuned parameters consist of two parts, the temperature ladders of all parallel tempering Markov Chains and the proposal distributions for all model parameters across all parallel tempering chains. In order to reduce dependencies in the compilation process and increase the code’s execution speed, Nii-C code is constructed entirely in the C language and parallelized using the message-passing interface protocol to optimize the efficiency of parallel sampling. These implementations facilitate rapid convergence in the sampling of high-dimensional and multimodal distributions, as well as the expeditious code execution time. The Nii-C code can be used in various research areas to trace complex distributions due to its high sampling efficiency and quick execution speed. This article presents a few applications of the Nii-C code.

To understand how galaxies reionized the Universe, we must determine how the escape fraction of Lyman continuum (LyC) photons ( f _esc ) depends on galaxy properties. Using the z ∼ 0.3 Low-redshift Lyman Continuum Survey (LzLCS), we develop and analyze new multivariate predictors of f _esc . These predictions use the Cox proportional hazards model, a survival analysis technique that incorporates both detections and upper limits. Our best model predicts the LzLCS f _esc detections with an rms scatter of 0.31 dex, better than single-variable correlations. According to ranking techniques, the most important predictors of f _esc are the equivalent width (EW) of Lyman-series absorption lines and the UV dust attenuation, which track line-of-sight absorption due to H i and dust. The H i absorption EW is uniquely crucial for predicting f _esc for the strongest LyC emitters, which show properties similar to weaker LyC emitters and whose high f _esc may therefore result from favorable orientation. In the absence of H i information, star formation rate surface density (Σ _SFR ) and [O iii ]/[O ii ] ratio are the most predictive variables and highlight the connection between feedback and f _esc . We generate a model suitable for z > 6, which uses only the UV slope, Σ _SFR , and [O iii ]/[O ii ]. We find that Σ _SFR is more important in predicting f _esc at higher stellar masses, whereas [O iii ]/[O ii ] plays a greater role at lower masses. We also analyze predictions for other parameters, such as the ionizing-to-nonionizing flux ratio and Ly α escape fraction. These multivariate models represent a promising tool for predicting f _esc at high redshift.

The information gathered from observing planetary systems is not limited to the discovery of planets, but also includes the observational upper limits constraining the presence of any additional planets. Incorporating these upper limits into statistical analyses of individual systems can significantly improve our ability to find hidden planets in these systems by narrowing the parameter space in which to search. Here, I include radial velocity (RV), transit, and transit timing variation (TTV) upper limits on additional planets in known multiplanet systems into the Dynamite software package and test their impact on the predicted planets for these systems. The tests are run on systems with previous Dynamite analysis and with updated known planet parameters in the 2–3 yr since the original predictions. I find that the RV limits provide the strongest constraints on additional planets, lowering the likelihood of finding them within orbital periods of ∼10–100 days in the inner planetary systems, as well as truncating the likely planet size (radius and/or mass) distributions toward planets smaller than those currently observed. Transit and TTV limits also provide information on the size and inclination distributions of both the known and predicted planets in the system. Utilizing these limits on a wider range of systems in the near future will help determine which systems might be able to host temperate terrestrial planets and contribute to the search for extraterrestrial biosignatures.

We analyzed images of every northern hemisphere Sd galaxy listed in the Third Reference Catalogue of Bright Galaxies with a relatively face-on inclination ( θ ≤ 30°). Specifically, we measured the spiral arms’ winding angle, ϕ , in 85 galaxies. We applied a novel black hole mass planar scaling relation involving the rotational velocities (from the literature) and pitch angles of each galaxy to predict central black hole masses. This yielded 23 galaxies, each having at least a 50% chance of hosting a central intermediate-mass black hole (IMBH), 10 ^2 < M _• ≤ 10 ^5 M _☉ . These 23 nearby (≲50 Mpc) targets may be suitable for an array of follow-up observations to check for active nuclei. Based on our full sample of 85 Sd galaxies, we estimate that the typical Sd galaxy (which tends to be bulgeless) harbors a black hole with $\mathrm{log}({M}_{\bullet }/{M}_{\odot })=6.00\pm 0.14$ , but with a 27.7% chance of hosting an IMBH, making this morphological type of galaxy fertile ground for hunting elusive IMBHs. Thus, we find that a ∼10 ^6 M _☉ black hole corresponds roughly to the onset of bulge development and serves as a conspicuous waypoint along the galaxy–supermassive black hole coevolution journey. Our survey suggests that >1.22% of bright galaxies ( B _T ≲ 15.5 mag) in the local Universe host an IMBH (i.e., the “occupation fraction”), which implies a number density >4.96 × 10 ^−6 Mpc ^−3 for central IMBHs. Finally, we observe that Sd galaxies exhibit an unexpected diversity of properties that resemble the general population of spiral galaxies, albeit with an enhanced signature of the eponymous prototypical traits (i.e., low masses, loosely wound spiral arms, and smaller rotational velocities).

Previous work has established the enhanced occurrence of compact systems of multiple small exoplanets around metal-poor stars. Understanding the origin of this effect in the planet formation process is a topic of ongoing research. Here we consider the radii of planets residing in systems of multiple transiting planets, compared to those residing in single-transiting systems, with a particular focus on late-type host stars. We investigate whether the two radius distributions are consistent with being drawn from the same underlying planetary population. We construct a planetary sample of 290 planets around late K and M dwarfs containing 149 planets from single-transiting planetary systems and 141 planets from multi-transiting compact multiple planetary systems (54 compact multiples). We performed a two-sample Kolmogorov–Smirnov test, Mann–Whitney U test, and Anderson–Darling k-sampling test on the radius distributions of our two samples. We find statistical evidence ( p < 0.0026) that planets in compact multiple systems are larger, on average, than their single-transiting counterparts for planets with R _p < 6 R _⊕ . We determine that the offset cannot be explained by detection bias. We investigate whether this effect could be explained via more efficient outgassing of a secondary atmosphere in compact multiple systems due to the stress and strain forces of interplanetary tides on planetary interiors. We find that this effect is insufficient to explain our observations without significant enrichment in H _2 O compared to Earth-like bulk composition.

Space scientists often face the question of whether data collected by different instruments are measurements of the same source population. This paper proposes a statistical validation method for evaluating the agreement between such related data sets. It offers a detailed case study focused on validating a new data set from the Interstellar Boundary Explorer (IBEX) mission, which serves as a practical how-to guide for similar analyses. Since 2008, the IBEX satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data were in fact susceptible to contamination, having relatively low ENA counts and high background rates. To mitigate this issue, the mission team recently considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to be measuring the same source population as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics provide additional confirmation of source rate coherence across data products. The framework introduced here is general and can be applied to other validation problems both within and outside the field of space physics.

We investigate the long-term persistence of solar energetic particle (SEP) time series by means of four different methods: Hurst rescaled range R / S analysis, detrended fluctuation analysis, centered moving average analysis, and the fluctuation of natural time under the time reversal method. For these analyses, we use data sets from the Integrated Science Investigation of the Sun instrument suite on board NASA's Parker Solar Probe. Background systematic noise is modeled using cross-correlation analysis between different SEP energy channels and subtracted from the original data. The use of these four methods for deriving the time-series persistence allows us to (i) differentiate between quiet- and active-Sun periods based on the values of the corresponding self-similarity exponents alone; (ii) identify the onset of an ongoing activity well before it reaches its maximum SEP flux; (iii) reveal an interesting fine structure when activity is observed; and (iv) provide, for the first time, an estimate of the maximum SEP flux of a future storm based on the entropy change of natural time under time reversal.

We estimate the coronal density of Capella using the O vii and Fe xvii line systems in the soft X-ray regime that have been observed over the course of the Chandra mission. Our analysis combines measures of error due to uncertainty in the underlying atomic data with statistical errors in the Chandra data to derive meaningful overall uncertainties on the plasma density of the coronae of Capella. We consider two Bayesian frameworks. First, the so-called pragmatic Bayesian approach considers the atomic data and their uncertainties as fully specified and uncorrectable. The fully Bayesian approach, on the other hand, allows the observed spectral data to update the atomic data and their uncertainties, thereby reducing the overall errors on the inferred parameters. To incorporate atomic data uncertainties, we obtain a set of atomic data replicates, the distribution of which captures their uncertainty. A principal component analysis of these replicates allows us to represent the atomic uncertainty with a lower-dimensional multivariate Gaussian distribution. A t -distribution approximation of the uncertainties of a subset of plasma parameters including a priori temperature information, obtained from the temperature-sensitive-only Fe xvii spectral line analysis, is carried forward into the density- and temperature-sensitive O vii spectral line analysis. Markov Chain Monte Carlo based model fitting is implemented including Multi-step Monte Carlo Gibbs Sampler and Hamiltonian Monte Carlo. Our analysis recovers an isothermally approximated coronal plasma temperature of ≈5 MK and a coronal plasma density of ≈10 ^10 cm ^−3 , with uncertainties of 0.1 and 0.2 dex, respectively.

We propose a second-order statistic parameter ε , the relative occurrence rate between hot Jupiters (HJs) and cold Jupiters (CJs) ( ε = η _HJ / η _CJ ), to probe the migration of gas giants. Since the planet occurrence rate is the combined outcome of the formation and migration processes, a joint analysis of HJ and CJ frequency may shed light on the dynamical evolution of giant planet systems. We first investigate the behavior of ε as the stellar mass changes observationally. Based on the occurrence rate measurements of HJs ( η _HJ ) from the Transiting Exoplanet Survey Satellite survey and CJs ( η _CJ ) from the California Legacy Survey, we find a tentative trend (97% confidence) that ε drops when the stellar mass rises from 0.8 to 1.4 M _⊙ , which can be explained by different giant planet growth and disk migration timescales around different stars. We carry out planetesimal and pebble accretion simulations, both of which can reproduce the results of η _HJ , η _CJ , and ε . Our findings indicate that the classical core accretion + disk migration model can explain the observed decreasing trend of ε . We propose two ways to increase the significance of the trend and verify the anticorrelation. Future works are required to better constrain ε , especially for M dwarfs and for more massive stars.

Conventional studies of galaxy clustering within the framework of halo models typically assume that the density profile of all dark matter halos can be approximated by the Navarro–Frenk–White (NFW) spherically symmetric profile. However, both modern N -body simulations and observational data suggest that most halos are either oblate or prolate, and almost never spherical. In this paper we present a modified model of the galaxy correlation function. In addition to the five “classical” halo occupation distribution (HOD) parameters proposed by Zheng et al., it includes an additional free parameter ϕ in the modified NFW density profile describing the asymmetry of the host dark matter halo. Using a subhalo abundance matching model, we populate galaxies within BolshoiP N -body simulations. We compute the projected two-point correlation function w _p ( r _p ) for six stellar-mass volume-limited galaxy samples. We fit our model to the results and then compare the best-fit asymmetry parameter ϕ (and other halo parameters) to the asymmetry of dark matter halos measured directly from the simulations and find that they agree within 1 σ . We then fit our model to the w _p ( r _p ) results from Zehavi et al. and compare halo parameters. We show that our model accurately retrieves the halo asymmetry and other halo parameters. Additionally, we find 2%–6% differences between the halo masses ( $\mathrm{log}{M}_{\min }$ and log M _1 ) estimated by our model and those estimated by “classical” HOD models. The model proposed in this paper can serve as an alternative to multiparameter HOD models, since it can be used for relatively small samples of galaxies.

We present a method to quantify the convergence rate of the fast estimators of the covariance matrices in the large-scale structure analysis. Our method is based on the Kullback–Leibler (KL) divergence, which describes the relative entropy of two probability distributions. As a case study, we analyze the delete- d jackknife estimator for the covariance matrix of the galaxy correlation function. We introduce the information factor or the normalized KL divergence with the help of a set of baseline covariance matrices to diagnose the information contained in the jackknife covariance matrix. Using a set of quick particle mesh mock catalogs designed for the Baryon Oscillation Spectroscopic Survey DR11 CMASS galaxy survey, we find that the jackknife resampling method succeeds in recovering the covariance matrix with 10 times fewer simulation mocks than that of the baseline method at small scales ( s ≤ 40 h ^−1 Mpc). However, the ability to reduce the number of mock catalogs is degraded at larger scales due to the increasing bias on the jackknife covariance matrix. Note that the analysis in this paper can be applied to any fast estimator of the covariance matrix for galaxy clustering measurements.

Data-driven models of stellar spectra are useful tools to study nonstellar information, such as the diffuse interstellar bands (DIBs) caused by intervening interstellar material. Using ∼55,000 spectra of ∼17,000 red clump stars from the APOGEE DR16 data set, we create second-order polynomial models of the continuum-normalized flux as a function of stellar parameters ( T _eff , $\mathrm{log}g$ , [Fe/H], [ α /Fe], and age). The model and data show good agreement within uncertainties across the APOGEE wavelength range, although many regions reveal residuals that are not in the stellar rest-frame. We show that many of these residual features—having average extrema at the level of ∼3% in stellar flux on average—can be attributed to incompletely removed spectral lines from the Earth’s atmosphere and DIBs from the interstellar medium (ISM). After removing most of the remaining contamination from Earth’s sky, we identify 84 absorption features not seen in unreddened sightlights that have

Galaxies play a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs, spanning cosmologically significant volumes. For such a daunting task, methods that can produce a direct mapping between dark matter halos from dark matter-only simulations and galaxies are strongly preferred, as producing mocks from full-fledged hydrodynamical simulations or semi-analytical models is too expensive. Here, we present a graph-neural-network-based model that is able to accurately predict key properties of galaxies such as stellar mass, g − r color, star formation rate, gas mass, stellar metallicity, and gas metallicity, purely from dark matter properties extracted from halos along the full assembly history of the galaxies. Tests based on the TNG300 simulation of the IllustrisTNG project show that our model can recover the baryonic properties of galaxies to high accuracy, over a wide redshift range ( z = 0–5), for all galaxies with stellar masses more massive than 10 ^9 M _⊙ and their progenitors, with strong improvements over the state-of-the-art methods. We further show that our method makes substantial strides toward providing an understanding of the implications of the IllustrisTNG galaxy formation model.