Search

We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wide-field, twelve-band optical images obtained from the Southern Photometric Local Universe Survey. A comprehensive astrophysical analysis of the jellyfish (JClass > 0), non-jellyfish (JClass = 0), and independently organized control samples is undertaken. We develop a semi-automated pipeline using self-supervised learning and similarity search to detect jellyfish galaxies. The proposed framework is designed to assist visual classifiers by providing more reliable JClasses for galaxies. We find that jellyfish candidates exhibit a lower Gini coefficient, higher entropy, and a lower 2D S\'ersic index as the jellyfish features in these galaxies become more pronounced. Jellyfish candidates show elevated star formation rates (including contributions from the main body and tails) by $\sim$1.75 dex, suggesting a significant increase in the SFR caused by the ram-pressure stripping phenomenon. Galaxies in the Antlia and Fornax clusters preferentially fall towards the cluster's centre, whereas only a mild preference is observed for Hydra galaxies. Our self-supervised pipeline, applied in visually challenging cases, offers two main advantages: it reduces human visual biases and scales effectively for large datasets. This versatile framework promises substantial enhancements in morphology studies for future galaxy image surveys., Comment: 25 pages, 22 figures, accepted for publication in MNRAS

We introduce galmoss, a python-based, torch-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, galmoss meets the high computational demands of large-scale galaxy surveys, placing galaxy profile fitting in the LSST-era. It incorporates widely used profiles such as the S\'ersic, Exponential disk, Ferrer, King, Gaussian, and Moffat profiles, and allows for the easy integration of more complex models. Tested on 8,289 galaxies from the Sloan Digital Sky Survey (SDSS) g-band with a single NVIDIA A100 GPU, galmoss completed classical S\'ersic profile fitting in about 10 minutes. Benchmark tests show that galmoss achieves computational speeds that are 6 $\times$ faster than those of default implementations., Comment: 12 pages, 8 figures, Accepted for publication in A&C

Nuclear star clusters (NSCs) are dense star clusters located at the centre of galaxies spanning a wide range of masses and morphologies. Analysing NSC occupation statistics in different environments provides an invaluable window into investigating early conditions of high-density star formation and mass assembly in clusters and group galaxies. We use HST/ACS deep imaging to obtain a catalogue of dwarf galaxies in two galaxy clusters in the Shapley Supercluster: the central cluster Abell 3558 and the northern Abell 1736a. The Shapley region is an ideal laboratory to study nucleation as it stands as the highest mass concentration in the nearby Universe. We investigate the NSC occurrence in quiescent dwarf galaxies as faint as $M_{I} = -10$ mag and compare it with all other environments where nucleation data is available. We use galaxy cluster/group halo mass as a proxy for the environment and employ a Bayesian logistic regression framework to model the nucleation fraction ($f_{n}$) as a function of galaxy luminosity and environment. We find a notably high $f_n$ in Abell 3558: at $M_{I} \approx -13.1$ mag, half the galaxies in the cluster host NSCs. This is higher than in the Virgo and Fornax clusters but comparable to the Coma Cluster. On the other hand, the $f_n$ in Abell 1736a is relatively lower, comparable to groups in the Local Volume. We find that the probability of nucleation varies with galaxy luminosity remarkably similarly in galaxy clusters. These results reinforce previous findings of the important role of the environment in NSC formation/growth., Comment: 20 pages, 10 figures, accepted for publication in the MNRAS

We present a spatially resolved stellar population analysis of 61 jellyfish galaxies and 47 control galaxies observed with ESO/MUSE attempting to understand the general trends of the stellar populations as a function of the stripping intensity and mass. This is the public sample from the GASP programme, with $0.01 < z < 0.15$ and $8.9 <\log(M_{\star}/M_{\odot}) < 12.0$. We apply the spectral population synthesis code FADO to fit self-consistently both the stellar and nebular contributions to the spectra of the sources. We present 2D morphological maps for mean stellar ages, metallicities, gas-phase oxygen abundances, and star formation rates for the galaxies with Integrated Nested Laplace Approximation (INLA), which is efficient in reconstructing spatial data of extended sources. We find that "extreme stripping" and "stripping" galaxies are typically younger than the other types. Regarding stellar and nebular metallicities, the "stripping" and "control passive" galaxies are the most metal-poor. Based on the phase space for jellyfish cluster members we find trends in ages, metallicities, and abundances with different regions of the diagram. We also compute radial profiles for the same quantities. We find that both the stripping and the stellar masses seem to influence the profiles, and we see differences between various groups and distinct mass bins. The radial profiles for different mass bins present relations already shown in the literature for undisturbed galaxies, i.e., profiles of ages and metallicities tend to increase with mass. However, beyond $\sim0.75$ effective radius, the ages of the most massive galaxies become similar to or lower than the ages of the lower mass ones., Comment: Accepted by MNRAS. 71 pages (13 of main text and 58 of supplementay material). 123 figures

Context: When selecting a classifier to use for a supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, i.e. contamination within the photometrically classified SN Ia sample, rather than a measure of cosmological constraining power. If the former is an appropriate proxy for the latter, this practice would save those designing an analysis pipeline from the computational expense of a full cosmology forecast. Aims: This study tests the assumption that classification metrics are an appropriate proxy for cosmology metrics. Methods: We emulate photometric SN Ia cosmology samples with controlled contamination rates of individual contaminant classes and evaluate each of them under a set of classification metrics. We then derive cosmological parameter constraints from all samples under two common analysis approaches and quantify the impact of contamination by each contaminant class on the resulting cosmological parameter estimates. Results: We observe that cosmology metrics are sensitive to both the contamination rate and the class of the contaminating population, whereas the classification metrics are insensitive to the latter. Conclusions: We therefore discourage exclusive reliance on classification-based metrics for cosmological analysis design decisions, e.g. classifier choice, and instead recommend optimizing using a metric of cosmological parameter constraining power., Comment: 9 pages, 6 figures; submitted to A&A

The light curve of Gaia23bab (= SPICY 97589) shows two significant ($\Delta G>2$ mag) brightening events, one in 2017 and an ongoing event starting in 2022. The source's quiescent spectral energy distribution indicates an embedded ($A_V>5$ mag) pre-main-sequence star, with optical accretion emission and mid-infrared disk emission. This characterization is supported by the source's membership in an embedded cluster in the star-forming cloud DOBASHI 1604 at a distance of $900\pm45$~pc. Thus, the brightening events are probable accretion outbursts, likely of EX Lup-type., Comment: 4 pages and 1 figure. Submitted to Research Notes of the AAS

We present a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational auto-encoders (VAE) and domain adaptation (DA). We demonstrate the effectiveness of this approach using a sample of low redshift galaxies with detailed morphological type labels from the Galaxy-Zoo DECaLS project. We show that 40-dimensional latent variables can effectively reproduce most morphological features in galaxy images. To further validate the effectiveness of our approach, we utilised a classical random forest (RF) classifier on the 40-dimensional latent variables to make detailed morphology feature classifications. This approach performs similarly to a direct neural network application on galaxy images. We further enhance our model by tuning the VAE network via DA using galaxies in the overlapping footprint of DECaLS and BASS+MzLS, enabling the unbiased application of our model to galaxy images in both surveys. We observed that DA led to even better morphological feature extraction and classification performance. Overall, this combination of VAE and DA can be applied to achieve image dimensionality reduction, defect image identification, and morphology classification in large optical surveys., Comment: Accepted by MNRAS 2023 October 12. 10 pages, 8 figures

We examine the potential for using the LIGO-Virgo-KAGRA network of gravitational-wave detectors to provide constraints on the physical properties of core-collapse supernovae through the observation of their gravitational radiation. We use waveforms generated by 14 of the latest 3D hydrodynamic core-collapse supernova simulations, which are added to noise samples based on the predicted sensitivities of the GW detectors during the O5 observing run. Then we use the BayesWave algorithm to model-independently reconstruct the gravitational-wave waveforms, which are used as input for various machine learning algorithms. Our results demonstrate how these algorithms perform in terms of i) indicating the presence of specific features of the progenitor or the explosion, ii) predicting the explosion mechanism, and iii) estimating the mass and angular velocity of the progenitor, as a function of the signal-to-noise ratio of the observed supernova signal. The conclusions of our study highlight the potential for GW observations to complement electromagnetic detections of supernovae by providing unique information about the exact explosion mechanism and the dynamics of the collapse., Comment: 12 pages, 3 figures, submitted to ApJ

The $^{16}$O(p,$\gamma$)$^{17}$F reaction is the slowest hydrogen-burning process in the CNO mass region. Its thermonuclear rate sensitively impacts predictions of oxygen isotopic ratios in a number of astrophysical sites, including AGB stars. The reaction has been measured several times at low bombarding energies using a variety of techniques. The most recent evaluated experimental rates have a reported uncertainty of about 7.5\% below $1$~GK. However, the previous rate estimate represents a best guess only and was not based on rigorous statistical methods. We apply a Bayesian model to fit all reliable $^{16}$O(p,$\gamma$)$^{17}$F cross section data, and take into account independent contributions of statistical and systematic uncertainties. The nuclear reaction model employed is a single-particle potential model involving a Woods-Saxon potential for generating the radial bound state wave function. The model has three physical parameters, the radius and diffuseness of the Woods-Saxon potential, and the asymptotic normalization coefficients (ANCs) of the final bound state in $^{17}$F. We find that performing the Bayesian $S$ factor fit using ANCs as scaling parameters has a distinct advantage over adopting spectroscopic factors instead. Based on these results, we present the first statistically rigorous estimation of experimental $^{16}$O(p,$\gamma$)$^{17}$F reaction rates, with uncertainties ($\pm 4.2$\%) of about half the previously reported values., Comment: 3 figures

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 halos. Our cosmic web finder is packaged in python as SCONCE-SCMS and has been made publicly available., Comment: 20 pages, 9 figures, 2 tables

Given the ever-increasing number of time-domain astronomical surveys, employing robust, interpretative, and automated data-driven classification schemes is pivotal. Based on graph theory, we present new data-driven classification heuristics for spectral data. A spectral classification scheme of Type II supernovae (SNe II) is proposed based on the phase relative to the maximum light in the $V$ band and the end of the plateau phase. We utilize a compiled optical data set that comprises 145 SNe and 1595 optical spectra in 4000-9000 $\overset{\circ}{\mathrm {A}}$. Our classification method naturally identifies outliers and arranges the different SNe in terms of their major spectral features. We compare our approach to the off-the-shelf umap manifold learning and show that both strategies are consistent with a continuous variation of spectral types rather than discrete families. The automated classification naturally reflects the fast evolution of Type II SNe around the maximum light while showcasing their homogeneity close to the end of the plateau phase. The scheme we develop could be more widely applicable to unsupervised time series classification or characterisation of other functional data., Comment: Accepted for publication at Astronomy and Computing

We present qrpca, a fast and scalable QR-decomposition principal component analysis package. The software, written in both R and python languages, makes use of torch for internal matrix computations, and enables GPU acceleration, when available. qrpca provides similar functionalities to prcomp (R) and sklearn (python) packages respectively. A benchmark test shows that qrpca can achieve computational speeds 10-20 $\times$ faster for large dimensional matrices than default implementations, and is at least twice as fast for a standard decomposition of spectral data cubes. The qrpca source code is made freely available to the community.

Galaxy morphological classification is a fundamental aspect of galaxy formation and evolution studies. Various machine learning tools have been developed for automated pipeline analysis of large-scale surveys, enabling a fast search for objects of interest. However, crowded regions in the image may pose a challenge as they can lead to bias in the learning algorithm. In this Research Note, we present galmask, an open-source package for unsupervised galaxy masking to isolate the central object of interest in the image. galmask is written in Python and can be installed from PyPI via the pip command., Comment: Submitted to RNAAS

Wide-field searches for young stellar objects (YSOs) can place useful constraints on the prevalence of clustered versus distributed star formation. The Spitzer/IRAC Candidate YSO (SPICY) catalog is one of the largest compilations of such objects (~120,000 candidates in the Galactic midplane). Many SPICY candidates are spatially clustered, but, perhaps surprisingly, approximately half the candidates appear spatially distributed. To better characterize this unexpected population and confirm its nature, we obtained Palomar/DBSP spectroscopy for 26 of the optically-bright (G<15 mag) "isolated" YSO candidates. We confirm the YSO classifications of all 26 sources based on their positions on the Hertzsprung-Russell diagram, H and Ca II line-emission from over half the sample, and robust detection of infrared excesses. This implies a contamination rate of <10% for SPICY stars that meet our optical selection criteria. Spectral types range from B4 to K3, with A-type stars most common. Spectral energy distributions, diffuse interstellar bands, and Galactic extinction maps indicate moderate to high extinction. Stellar masses range from ~1 to 7 $M_\odot$, and the estimated accretion rates, ranging from $3\times10^{-8}$ to $3\times10^{-7}$ $M_\odot$ yr$^{-1}$, are typical for YSOs in this mass range. The 3D spatial distribution of these stars, based on Gaia astrometry, reveals that the "isolated" YSOs are not evenly distributed in the Solar neighborhood but are concentrated in kpc-scale dusty Galactic structures that also contain the majority of the SPICY YSO clusters. Thus, the processes that produce large Galactic star-forming structures may yield nearly as many distributed as clustered YSOs., Comment: Accepted for publication in AJ. 22 pages, 9 figures, and 4 tables. Figure sets are available from https://sites.astro.caltech.edu/~mkuhn/SPICY/PaperIII/

We present a citation pattern analysis between astronomical papers and 13 other disciplines, based on the arXiv database over the past decade ($2010 - 2020$). We analyze 12,600 astronomical papers citing over 14,531 unique publications outside astronomy. Two striking patterns are unraveled. First, general relativity recently became the most cited field by astronomers, a trend highly correlated with the discovery of gravitational waves. Secondly, the fast growth of referenced papers in computer science and statistics, the first with a notable 15-fold increase since 2015. Such findings confirm the critical role of interdisciplinary efforts involving astronomy, statistics, and computer science in recent astronomical research., Comment: Submitted to RNAAS

We present a standalone implementation of a data-deconvolution method based on singular value decomposition. The tool is written in python and packaged in the open-source yonder package. yonder receives as input two matrices, one for the data and another for the errors, and outputs a denoised version of the original dataset. In this Research Note, we briefly describe the methodology and show a demonstration of the yonder on a simulated dataset., Comment: Submitted to RNAAS

Globular clusters (GCs) are proxies of the formation assemblies of their host galaxies. However, few studies exist targeting GC systems of spiral galaxies up to several effective radii. Through 12-band Javalambre Photometric Local Universe Survey (J-PLUS) imaging, we study the point sources around the M81/M82/NGC3077 triplet in search of new GC candidates. We develop a tailored classification scheme to search for GC candidates based on their similarity to known GCs via a principal components analysis (PCA) projection. Our method accounts for missing data and photometric errors. We report 642 new GC candidates in a region of 3.5 deg$^2$ around the triplet, ranked according to their Gaia astrometric proper motions when available. We find tantalising evidence for an overdensity of GC candidate sources forming a bridge connecting M81 and M82. Finally, the spatial distribution of the GC candidates $(g-i)$ colours is consistent with halo/intra-cluster GCs, i.e. it gets bluer as they get further from the closest galaxy in the field. We further employ a regression-tree based model to estimate the metallicity distribution of the GC candidates based on their J-PLUS bands. The metallicity distribution of the sample candidates is broad and displays a bump towards the metal-rich end. Our list increases the population of GC candidates around the triplet by 3-fold, stresses the usefulness of multi-band surveys in finding these objects, and provides a testbed for further studies analysing their spatial distribution around nearby (spirals) galaxies., Comment: 20 pages, 18 figures, MNRAS accepted

Big bang nucleosynthesis (BBN) is the standard model theory for the production of the light nuclides during the early stages of the universe, taking place for a period of about 20 minutes after the big bang. Deuterium production, in particular, is highly sensitive to the primordial baryon density and the number of neutrino species, and its abundance serves as a sensitive test for the conditions in the early universe. The comparison of observed deuterium abundances with predicted ones requires reliable knowledge of the relevant thermonuclear reaction rates, and their corresponding uncertainties. Recent observations reported the primordial deuterium abundance with percent accuracy, but some theoretical predictions based on BBN are at tension with the measured values because of uncertainties in the cross section of the deuterium-burning reactions. In this work, we analyze the S-factor of the D(p,$\gamma$)$^3$He reaction using a hierarchical Bayesian model. We take into account the results of eleven experiments, spanning the period of 1955--2021; more than any other study. We also present results for two different fitting functions, a two-parameter function based on microscopic nuclear theory and a four-parameter polynomial. Our recommended reaction rates have a 2.2\% uncertainty at $0.8$~GK, which is the temperature most important for deuterium BBN. Differences between our rates and previous results are discussed.

The Gaia Data Release 2 provides precise astrometry for nearly 1.5 billion sources across the entire sky, including several thousand asteroids. In this work, we provide evidence that reasonably large asteroids (diameter $>$ 20 km) have high correlations with Gaia relative flux uncertainties and systematic right ascension errors. We further capture these correlations using a logistic Bayesian additive regression tree model. We compile a small list of probable large asteroids that can be targeted for direct diameter measurements and shape reconstruction., Comment: 3 pages, 1 figure, accepted for publication at Research Notes of the AAS

We use deep high resolution \textit{HST/ACS} imaging of two fields in the core of the Coma cluster to investigate the occurrence of nuclear star clusters (NSCs) in quiescent dwarf galaxies as faint as $M_{I} = -10$ mag. We employ a hierarchical Bayesian logistic regression framework to model the faint end of the nucleation fraction ($f_{n}$) as a function of both galaxy luminosity and environment. We find that $f_n$ is remarkably high in Coma: at $M_{I} \approx -13$ mag half of the cluster dwarfs still host prominent NSCs. Comparison with dwarf systems in nearby clusters and groups shows that, within the uncertainties, the rate at which the probability of nucleation varies with galaxy luminosity is nearly universal. On the other hand, the fraction of nucleated galaxies at fixed luminosity does exhibit an environmental dependence. More massive environments feature higher nucleation fractions and fainter values of the half-nucleation luminosity, which roughly scales with host halo virial mass as $L_{I,f_{n50}} \propto \mathcal{M}_{200}^{-0.2}$. Our results reinforce the role of galaxy luminosity/mass as a major driver of the efficiency of NSC formation and also indicate a clear secondary dependence on the environment, hence paving the way to more refined theoretical models., Comment: 15 pages, 11 figures, accepted to MNRAS

The recent paper by AlShebli et al. (2020) investigates the impact of mentorship in young scientists. Among their conclusions, they state that female prot\'eg\'es benefit more from male than female mentorship. We herein expose a critical flaw in their methodological design that is a common issue in Astronomy, namely "selection biases". An effect that if not treated properly may lead to unwarranted causality claims. In their analysis, selection biases seem to be present in the response rate of their survey (8.35%), the choice of database, success criterion, and the overlook of the numerous drawbacks female researchers face in academia. We discuss these issues and their implications -- one of them being the potential increase in obstacles for women in academia. Finally, we reinforce the dangers of not considering selection bias effects in studies aimed at retrieving causal relations., Comment: Submitted to Nature Communications on Nov. 27 -- comments are welcome

We present ~120,000 Spitzer/IRAC candidate young stellar objects (YSOs) based on surveys of the Galactic midplane between l~255 deg and 110 deg, including the GLIMPSE I, II, and 3D, Vela-Carina, Cygnus X, and SMOG surveys (613 square degrees), augmented by near-infrared catalogs. We employed a classification scheme that uses the flexibility of a tailored statistical learning method and curated YSO datasets to take full advantage of IRAC's spatial resolution and sensitivity in the mid-infrared ~3-9 micron range. Multi-wavelength color/magnitude distributions provide intuition about how the classifier separates YSOs from other red IRAC sources and validate that the sample is consistent with expectations for disk/envelope-bearing pre-main-sequence stars. We also identify areas of IRAC color space associated with objects with strong silicate absorption or polycyclic aromatic hydrocarbon emission. Spatial distributions and variability properties help corroborate the youthful nature of our sample. Most of the candidates are in regions with mid-IR nebulosity, associated with star-forming clouds, but others appear distributed in the field. Using Gaia DR2 distance estimates, we find groups of YSO candidates associated with the Local Arm, the Sagittarius-Carina Arm, and the Scutum-Centaurus Arm. Candidate YSOs visible to the Zwicky Transient Facility tend to exhibit higher variability amplitudes than randomly selected field stars of the same magnitude, with many high-amplitude variables having light-curve morphologies characteristic of YSOs. Given that no current or planned instruments will significantly exceed IRAC's spatial resolution while possessing its wide-area mapping capabilities, Spitzer-based catalogs such as ours will remain the main resources for mid-infrared YSOs in the Galactic midplane for the near future., Comment: Published in ApJS. 42 pages, 3 tables, and 28 figures (including 4 figure sets). Some column names in Table 1 have been modified to match the published version, but data remain unchanged. For convenience, copies of the tables can be accessed at https://sites.astro.caltech.edu/~mkuhn/SPICY/

The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic follow-up (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment. In this work, we test the robustness of active learning techniques in a realistic simulated astronomical data scenario. Our experiment takes into account the evolution of training and pool samples, different costs per object, and two different sources of budget. Results show that traditional active learning strategies significantly outperform random sampling. Nevertheless, more complex batch strategies are not able to significantly overcome simple uncertainty sampling techniques. Our findings illustrate three important points: 1) active learning strategies are a powerful tool to optimize the label-acquisition task in astronomy, 2) for upcoming large surveys like LSST, such techniques allow us to tailor the construction of the training sample for the first day of the survey, and 3) the peculiar data environment related to the detection of astronomical transients is a fertile ground that calls for the development of tailored machine learning algorithms., Comment: Accepted to the 2020 IEEE Symposium Series on Computational Intelligence

The Vanishing & Appearing Sources during a Century of Observations (VASCO) project investigates astronomical surveys spanning a time interval of 70 years, searching for unusual and exotic transients. We present herein the VASCO Citizen Science Project, which can identify unusual candidates driven by three different approaches: hypothesis, exploratory, and machine learning, which is particularly useful for SETI searches. To address the big data challenge, VASCO combines three methods: the Virtual Observatory, user-aided machine learning, and visual inspection through citizen science. Here we demonstrate the citizen science project and its improved candidate selection process, and we give a progress report. We also present the VASCO citizen science network led by amateur astronomy associations mainly located in Algeria, Cameroon, and Nigeria. At the moment of writing, the citizen science project has carefully examined 15,593 candidate image pairs in the data (ca. 10% of the candidates), and has so far identified 798 objects classified as "vanished". The most interesting candidates will be followed up with optical and infrared imaging, together with the observations by the most potent radio telescopes., Comment: 16 pages, 7 figures, contribution to IAC 2020 conference

Astrometric detection involves a precise measurement of stellar positions, and is widely regarded as the leading concept presently ready to find earth-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope[39] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary stars. In particular the mission will be optimised to search for habitable-zone planets around Alpha Centauri AB. If the separation between these two stars can be monitored with sufficient precision, tiny perturbations due to the gravitational tug from an unseen planet can be witnessed and, given the configuration of the optical system, the scale of the shifts in the image plane are about one millionth of a pixel. Image registration at this level of precision has never been demonstrated (to our knowledge) in any setting within science. In this paper we demonstrate that a Deep Convolutional Auto-Encoder is able to retrieve such a signal from simplified simulations of the TOLIMAN data and we present the full experimental pipeline to recreate out experiments from the simulations to the signal analysis. In future works, all the more realistic sources of noise and systematic effects present in the real-world system will be injected into the simulations., Comment: Preprint version of the manuscript to appear in the Volume "Intelligent Astrophysics" of the series "Emergence, Complexity and Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature Switzerland, ISSN: 2194-7287

Modern astronomy has been rapidly increasing our ability to see deeper into the universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from these datasets requires a wide range of sophisticated statistical and machine learning methods. Long-standing problems in cosmology include characterization of galaxy clustering and estimation of galaxy distances from photometric colors. Bayesian inference, central to linking astronomical data to nonlinear astrophysical models, addresses problems in solar physics, properties of star clusters, and exoplanet systems. Likelihood-free methods are growing in importance. Detection of faint signals in complicated noise is needed to find periodic behaviors in stars and detect explosive gravitational wave events. Open issues concern treatment of heteroscedastic measurement errors and understanding probability distributions characterizing astrophysical systems. The field of astrostatistics needs increased collaboration with statisticians in the design and analysis stages of research projects, and to jointly develop new statistical methodologies. Together, they will draw more astrophysical insights into astronomical populations and the cosmos itself., Comment: Accepted for publication in volume 8 of Annual Reviews of Statistics and Its Application. 26 pages, 7 figures

Cosmic voids and their corresponding redshift-projected mass densities, known as troughs, play an important role in our attempt to model the large-scale structure of the Universe. Understanding these structures enables us to compare the standard model with alternative cosmologies, constrain the dark energy equation of state, and distinguish between different gravitational theories. In this paper, we extend the subspace-constrained mean shift algorithm, a recently introduced method to estimate density ridges, and apply it to 2D weak lensing mass density maps from the Dark Energy Survey Y1 data release to identify curvilinear filamentary structures. We compare the obtained ridges with previous approaches to extract trough structure in the same data, and apply curvelets as an alternative wavelet-based method to constrain densities. We then invoke the Wasserstein distance between noisy and noiseless simulations to validate the denoising capabilities of our method. Our results demonstrate the viability of ridge estimation as a precursor for denoising weak lensing observables to recover the large-scale structure, paving the way for a more versatile and effective search for troughs., Comment: 12 pages, 5 figures, accepted for publication in MNRAS

Big bang nucleosynthesis provides the earliest probe of standard model physics, at a time when the universe was less than a thousand seconds old. It determines the abundances of the lightest nuclides, which give rise to the subsequent history of the visible matter in the Universe. This work derives new $^7$Be(n,p)$^7$Li thermonuclear reaction rates based on all available experimental information. This reaction sensitively impacts the primordial abundances of $^{7}$Be and $^7$Li during big bang nucleosynthesis. We critically evaluate all available data and disregard experimental results that are questionable. For the nuclear model, we adopt an incoherent sum of single-level, two-channel R-matrix approximation expressions, which are implemented into a hierarchical Bayesian model, to analyze the remaining six data sets we deem most reliable. In the fitting of the data, we consistently model all known sources of uncertainty, including discrepant absolute normalizations of different data sets, and also take the variation of the neutron and proton channel radii into account, hence providing less biased estimates of the $^7$Be(n,p)$^7$Li thermonuclear rates. From the resulting posteriors, we extract R-matrix parameters ($E_r$, $\gamma^2_n$, $\gamma^2_p$) and derive excitation energies, partial and total widths. Our fit is sensitive to the contributions of the first three levels above the neutron threshold. Reaction rates were computed by integrating 10,000 samples of the reduced cross section. Our $^7$Be(n,p)$^7$Li thermonuclear rates have uncertainties between 1.5% and 2.0% at temperatures of $\leq$1 GK. We compare our rates to previous results and find that the $^7$Be(n,p)$^7$Li rates most commonly used in big bang simulations have too optimistic uncertainties., Comment: 24 pages, 8 figures, ApJ in press

In this paper we report the current status of a new research program. The primary goal of the "Vanishing & Appearing Sources during a Century of Observations" (VASCO) project is to search for vanishing and appearing sources using existing survey data to find examples of exceptional astrophysical transients. The implications of finding such objects extend from traditional astrophysics fields to the more exotic searches for evidence of technologically advanced civilizations. In this first paper we present new, deeper observations of the tentative candidate discovered by Villarroel et al. (2016). We then perform the first searches for vanishing objects throughout the sky by comparing 600 million objects from the US Naval Observatory Catalogue (USNO) B1.0 down to a limiting magnitude of $\sim 20 - 21$ with the recent Pan-STARRS Data Release-1 (DR1) with a limiting magnitude of $\sim$ 23.4. We find about 150,000 preliminary candidates that do not have any Pan-STARRS counterpart within a 30 arcsec radius. We show that these objects are redder and have larger proper motions than typical USNO objects. We visually examine the images for a subset of about 24,000 candidates, superseding the 2016 study with a sample ten times larger. We find about $\sim$ 100 point sources visible in only one epoch in the red band of the USNO which may be of interest in searches for strong M dwarf flares, high-redshift supernovae or other catagories of unidentified red transients., Comment: SETI meets time domain astronomy. Accepted into the Astronomical Journal

The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At such depths, one major limiting factor is the blending of galaxies due to line-of-sight projection, with an expected fraction of blended galaxies of up to 50%. Current deblending approaches are in most cases either too slow or not accurate enough to reach the level of requirements. This work explores the use of deep neural networks to estimate the photometry of blended pairs of galaxies in monochrome space images, similar to the ones that will be delivered by the Euclid space telescope. Using a clean sample of isolated galaxies from the CANDELS survey, we artificially blend them and train two different network models to recover the photometry of the two galaxies. We show that our approach can recover the original photometry of the galaxies before being blended with $\sim$7% accuracy without any human intervention and without any assumption on the galaxy shape. This represents an improvement of at least a factor of 4 compared to the classical SExtractor approach. We also show that forcing the network to simultaneously estimate a binary segmentation map results in a slightly improved photometry. All data products and codes will be made public to ease the comparison with other approaches on a common data set., Comment: 16 pages, 12 figures, submitted to MNRAS, comments welcome

We conducted this study to determine whether fallopian tube anatomy can predict the likelihood of pregnancy and pregnancy outcomes after tubal sterilization reversal. We built a flexible, non-parametric, multivariate model via generalized additive models to assess the effects of the following tubal parameters observed during tubal reparative surgery: tubal lengths; differences in tubal segment location, and diameters at the anastomosis sites; and, fibrosis of the tubal muscularis. In this study population, age and tubal length - in that order - were the primary factors predicting the likelihood of pregnancy. For pregnancy outcomes, tubal length was the most influential predictor of birth and ectopic pregnancy, while age was the primary predictor of miscarriage. Segment location and diameters contributed slightly to the odds of miscarriage and ectopic pregnancy. Tubal muscularis fibrosis had a little apparent effect. This study is the first to show that a statistical learning predictive model based on fallopian tube anatomy can predict pregnancy and pregnancy outcome probabilities after tubal reversal surgery., Comment: Accepted for publication at Statistical Methods in Medical Research

The $^3$H(d,n)$^4$He reaction has a large low-energy cross section and will likely be utilized in future commercial fusion reactors. This reaction also takes place during big bang nucleosynthesis. Studies of both scenarios require accurate and precise fusion rates. To this end, we implement a one-level, two-channel R-matrix approximation into a Bayesian model. Our main goals are to predict reliable astrophysical S-factors and to estimate R-matrix parameters using the Bayesian approach. All relevant parameters are sampled in our study, including the channel radii, boundary condition parameters, and data set normalization factors. In addition, we take uncertainties in both measured bombarding energies and S-factors rigorously into account. Thermonuclear rates and reactivities of the $^3$H(d,n)$^4$He reaction are derived by numerically integrating the Bayesian S-factor samples. The present reaction rate uncertainties at temperatures between $1.0$ MK and $1.0$ GK are in the range of 0.2% to 0.6%. Our reaction rates differ from previous results by 2.9% near 1.0 GK. Our reactivities are smaller than previous results, with a maximum deviation of 2.9% near a thermal energy of $4$ keV. The present rate or reactivity uncertainties are more reliable compared to previous studies that did not include the channel radii, boundary condition parameters, and data set normalization factors in the fitting. Finally, we investigate previous claims of electron screening effects in the published $^3$H(d,n)$^4$He data. No such effects are evident and only an upper limit for the electron screening potential can be obtained., Comment: Phys. Rev. C, 2019, in press

This work determines the degree to which a standard Lambda-CDM analysis based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-dependent dark energy equation of state w(z). We introduce and apply a novel random curve generator to simulate instances of w(z) from constraint families with increasing distinction from a cosmological constant. After producing a series of mock catalogs of binned type Ia supernovae corresponding to each w(z) curve, we perform a standard Lambda-CDM analysis to estimate the corresponding posterior densities of the absolute magnitude of type Ia supernovae, the present-day matter density, and the equation of state parameter. Using the Kullback-Leibler divergence between posterior densities as a difference measure, we demonstrate that a standard type Ia supernova cosmology analysis has limited sensitivity to extensive redshift dependencies of the dark energy equation of state. In addition, we report that larger redshift-dependent departures from a cosmological constant do not necessarily manifest easier-detectable incompatibilities with the Lambda-CDM model. Our results suggest that physics beyond the standard model may simply be hidden in plain sight., Comment: 14 pages, 9 figures

We developed a hierarchical Bayesian framework to estimate S-factors and thermonuclear rates for the $^3$He(d,p)$^{4}$He reaction, which impacts the primordial abundances of $^3$He and $^7$Li. The available data are evaluated and all direct measurements are taken into account in our analysis for which we can estimate separate uncertainties for systematic and statistical effects. For the nuclear reaction model, we adopt a single-level, two-channel approximation of R-matrix theory, suitably modified to take the effects of electron screening at lower energies into account. Apart from the usual resonance parameters (resonance location and reduced widths for the incoming and outgoing reaction channel), we include for the first time the channel radii and boundary condition in the fitting process. Our new analysis of the $^3$He(d,p)$^{4}$He S-factor data results in improved estimates for the thermonuclear rates. This work represents the first nuclear rate evaluation using the R-matrix theory embedded into a hierarchical Bayesian framework, properly accounting for all known sources of uncertainty. Therefore, it provides a test bed for future studies of more complex reactions.

We introduce a value-added full-sky catalogue of galaxies, named as Galaxy List for the Advanced Detector Era, or GLADE. The purpose of this catalogue is to (i) help identifications of host candidates for gravitational-wave events, (ii) support target selections for electromagnetic follow-up observations of gravitational-wave candidates, (iii) provide input data on the matter distribution of the local universe for astrophysical or cosmological simulations, and (iv) help identifications of host candidates for poorly localised electromagnetic transients, such as gamma-ray bursts observed with the InterPlanetary Network. Both being potential hosts of astrophysical sources of gravitational waves, GLADE includes inactive and active galaxies as well. GLADE was constructed by cross-matching and combining data from five separate (but not independent) astronomical catalogues: GWGC, 2MPZ, 2MASS XSC, HyperLEDA and SDSS-DR12Q. GLADE is complete up to $d_L = 37^{+3}_{-4}$ Mpc in terms of the cumulative B-band luminosity of galaxies within luminosity distance $d_L$, and contains all of the brightest galaxies giving half of the total B-band luminosity up to $d_L = 91$ Mpc. As B-band luminosity is expected to be a tracer of binary neutron star mergers (currently the prime targets of joint GW+EM detections), our completeness measures can be used as estimations of completeness for containing all binary neutron star merger hosts in the local universe., Comment: 10 pages, 4 figures, published in MNRAS

We present a review of data types and statistical methods often encountered in astronomy. The aim is to provide an introduction to statistical applications in astronomy for statisticians and computer scientists. We highlight the complex, often hierarchical, nature of many astronomy inference problems and advocate for cross-disciplinary collaborations to address these challenges., Comment: 9 pages, 5 figures

We present AMADA, an interactive web application to analyse multidimensional datasets. The user uploads a simple ASCII file and AMADA performs a number of exploratory analysis together with contemporary visualizations diagnostics. The package performs a hierarchical clustering in the parameter space, and the user can choose among linear, monotonic or non-linear correlation analysis. AMADA provides a number of clustering visualization diagnostics such as heatmaps, dendrograms, chord diagrams, and graphs. In addition, AMADA has the option to run a standard or robust principal components analysis, displaying the results as polar bar plots. The code is written in R and the web interface was created using the Shiny framework. AMADA source-code is freely available at https://goo.gl/KeSPue, and the shiny-app at http://goo.gl/UTnU7I., Comment: Accepted for publication in Astronomy & Computing

Structures in Warm Dark Matter (WDM) models are exponentially suppressed below a certain scale, characterized by the dark matter particle mass, $m_{\rm x}$. Since structures form hierarchically, the presence of collapsed objects at high-redshifts can set strong lower limits on $m_{\rm x}$. We place robust constraints on $m_{\rm x}$ using recent results from the {\it Swift} database of high-redshift gamma-ray bursts (GRBs). We parameterize the redshift evolution of the ratio between the cosmic GRB rate and star formation rate (SFR) as $\propto (1+z)^\alpha$, thereby allowing astrophysical uncertainties to partially mimic the cosmological suppression of structures in WDM models. Using a maximum likelihood estimator on two different $z>4$ GRB subsamples (including two bursts at $z>8$), we constrain $m_{\rm x} \gtrsim 1.6$-1.8 keV at 95% C.L., when marginalized over a flat prior in $\alpha$. We further estimate that 5 years of a SVOM-like mission would tighten these constraints to $m_{\rm x} \gtrsim 2.3 $ keV. Our results show that GRBs are a powerful probe of high-redshift structures, providing robust and competitive constraints on $m_{\rm x}$., Comment: 11 pages, 7 figures, accepted for publication in MNRAS

We study the statistical properties (such as shape and spin) of high-z halos likely hosting the first (PopIII) stars with cosmological simulations including detailed gas physics. In the redshift range considered ($11 < z < 16$) the average sphericity is $ = 0.3 \pm 0.1$, and for more than 90% of halos the triaxiality parameter is $T \lesssim 0.4$, showing a clear preference for oblateness over prolateness. Larger halos in the simulation tend to be both more spherical and prolate: we find $s \propto M_h^{\alpha_s}$ and $T \propto M_h^{\alpha_T}$, with $\alpha_s \approx 0.128$ and $\alpha_T= 0.276$ at z = 11. The spin distributions of dark matter and gas are considerably different at $z=16$, with the baryons rotating slower than the dark matter. At lower redshift, instead, the spin distributions of dark matter and gas track each other almost perfectly, as a consequence of a longer time interval available for momentum redistribution between the two components. The spin of both the gas and dark matter follows a lognormal distribution, with a mean value at z=16 of $<\lambda> =0.0184$, virtually independent of halo mass. This is in good agreement with previous studies. Using the results of two feedback models (MT1 and MT2) by McKee & Tan (2008) and mapping our halo spin distribution into a PopIII IMF, we find that at high-$z$ the IMF closely tracks the spin lognormal distribution. Depending on the feedback model, though, the distribution can be centered at $\approx 65 M_\odot$ (MT1) or $\approx 140 M_\odot$ (MT2). At later times, model MT1 evolves into a bimodal distribution with a second prominent peak located at $35-40 M_\odot$ as a result of the non-linear relation between rotation and halo mass. We conclude that the dark matter halo properties might be a key factor shaping the IMF of the first stars., Comment: 10 pages, 6 figures, accepted for publication in MNRAS