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The Weizmann Fast Astronomical Survey Telescope (W-FAST) is a 55cm optical survey telescope with a high cadence (25Hz) monitoring of the sky over a wide field of view (~7deg^2). The high frame rate allows detection of sub-second transients over multiple images. We present a sample of ~0.1--0.3s duration flares detected in an un-targeted survey for such transients. We show that most, if not all of them, are glints of sunlight reflected off geosynchronous and graveyard orbit satellites. The flares we detect have a typical magnitude of 9--11, which translates to ~14--16th magnitude if diluted by a 30s exposure time. We estimate the rate of events brighter than ~11mag to be on the order of 30--40 events per day per deg^2, for declinations between -20 and +10^\circ, not including the declination corresponding to the geostationary belt directly above the equator, where the rate can be higher. We show that such glints are common in large area surveys (e.g., ZTF and LSST), and that some of them have a point-like appearance, confounding searches for fast transients such as Fast Radio Burst counterparts and Gamma-ray bursts. By observing in the direction of the Earth's shadow we are able to put an upper limit on the rate of fast astrophysical transients of 0.052deg^{-2}day^{-1} (95\% confidence limit) for events brighter than 11mag. We also suggest that the single image, high declination flare observed in coincidence with the GN-z11 galaxy and assumed to be a Gamma-ray burst, is also consistent with such a satellite glint., 8 pages, 5 figures. Submitted to MNRAS

We present an analysis of the long-term performance of the W. M. Keck observatory laser guide star adaptive optics (LGS-AO) system and explore factors that influence the overall AO performance most strongly. Astronomical surveys can take years or decades to finish, so it is worthwhile to characterize the AO performance on such timescales in order to better understand future results. The Keck telescopes have two of the longest-running LGS-AO systems in use today, and as such they represent an excellent test-bed for processing large amounts of AO data. We use a Keck-II near infrared camera 2 (NIRC2) LGSAO surve of the Galactic Center (GC) from 2005 to 2019 for our analysis, combining image metrics with AO telemetry files, multiaperture scintillation sense/differential imaging motion monitor turbulence profiles, seeing information, weather data, and temperature readings in a compiled dataset to highlight areas of potential performance improvement. We find that image quality trends downward over time, despite multiple improvements made to Keck-II and its AO system, resulting in a 9 mas increase in the average full width at half maximum (FWHM) and a 3% decrease in the average Strehl ratio over the course of the survey. Image quality also trends upward with ambient temperature, possibly indicating the presence of uncorrected turbulence in the beam path. Using nine basic features from our dataset, we train a simple machine learning (ML) algorithm to predict the delivered image quality of NIRC2 given current atmospheric conditions, which could eventually be used for real-time observation planning and exposure time adjustments. A random forest algorithm trained on this data can predict the Strehl ratio of an image to within 18% and the FWHM to within 7%, which is a solid baseline for future applications involving more advanced ML techniques. The assembled dataset and coding tools are released to the public as a resource for testing new predictive control and point spread function-reconstruction algorithms.

The ESA cornerstone mission Gaia was successfully launched in 2013, and is now scanning the sky to accurately measure the positions and motions of about two billion point-like sources of 3, Comment: 23 pages, 5 figures, invited review accepted for publication in ASR, based on the interdisciplinary lecture given at the COSPAR 2018 aseembly in Pasadena, USA

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract] We present an adaptive visualization tool for unsupervised classification of astronomical objects in a Big Data context such as the one found in the increasingly popular large spectrophotometric sky surveys. This tool is based on an artificial intelligence technique, Kohonen’s self-organizing maps, and our goal is to facilitate the analysis work of the experts by means of oriented domain visualizations, which is impossible to achieve by using a generic tool. We designed a client-server that handles the data treatment and computational tasks to give responses as quickly as possible, and we used JavaScript Object Notation to pack the data between server and client. We optimized, parallelized, and evenly distributed the necessary calculations in a cluster of machines. By applying our clustering tool to several databases, we demonstrated the main advantages of an unsupervised approach: the classification is not based on pre-established models, thus allowing the “natural classes” present in the sample to be discovered, and it is suited to isolate atypical cases, with the important potential for discovery that this entails. Gaia Utility for the Analysis of self-organizing maps is an analysis tool that has been developed in the context of the Data Processing and Analysis Consortium, which processes and analyzes the observations made by ESA’s Gaia satellite (European Space Agency) and prepares the mission archive that is presented to the international community in sequential periodic publications. Our tool is useful not only in the context of the Gaia mission, but also allows segmenting the information present in any other massive spectroscopic or spectrophotometric database. This work made use of the infrastructures acquired with grants provided by the State Research Agency (AEI) of the Spanish Government and the European Regional Development Fund (FEDER), RTI2018-095076-B-C22. We acknowledge support from CIGUS-CITIC, funded by Xunta de Galicia and the European Union (FEDER Galicia 2014-2020 Program) through grant ED431G 2019/01 and research consolidation grant ED431B 2021/36. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC), https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High Performance Computing at the University of Utah. The SDSS website is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration. We also want to acknowledge Alhambra survey funded by the Spanish Goverment under Grant AYA2006-14056. Open Access funding provided thanks to the Universidade da Coruña/CISUG agreement with Springer Nature Xunta de Galicia; ED431G 2019/01 Xunta de Galicia; ED431B 2021/36

Indra is a suite of large-volume cosmological $N$-body simulations with the goal of providing excellent statistics of the large-scale features of the distribution of dark matter. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, with 1024$^3$ dark matter particles in a box of length 1 Gpc/h, 64 snapshots of particle data and halo catalogs, and 505 time steps of the Fourier modes of the density field, amounting to almost a petabyte of data. All of the Indra data are immediately available for analysis via the SciServer science platform, which provides interactive and batch computing modes, personal data storage, and other hosted data sets such as the Millennium simulations and many astronomical surveys. We present the Indra simulations, describe the data products and how to access them, and measure ensemble averages, variances, and covariances of the matter power spectrum, the matter correlation function, and the halo mass function to demonstrate the types of computations that Indra enables. We hope that Indra will be both a resource for large-scale structure research and a demonstration of how to make very large datasets public and computationally accessible., matches published version. Indra is available on SciServer at http://sciserver.org

Measurements on astronomical survey data are the link between scientific questions and scientific findings that help to answer these questions. In acknowledgement of their importance, NASA requires proposers to specify what measurements their space mission concept can make and what physical properties or processes can be derived from those measurements. NASA is now requiring a plan for actually making these measurements and how they will be made available in useable form to astronomical community. We will explore the benefits and issues involved in having a NASA mission take responsibility for making and distributing astronomical measurements. We use as a case study the NASA Probe mission concept, CETUS (Cosmic Evolution Through UV Surveys) posted at arXiv:1909.10437.

Upcoming multi-wavelength astronomical surveys will soon discover all massive galaxy clusters and provide unprecedented constraints on cosmology and cluster astrophysics. In this paper, we investigate the constraining power of the multi-band cluster surveys, through a joint analysis of three observables associated with clusters of galaxies, including thermal Sunyaev-Zel'dovich (tSZ) effect in cosmic microwave background (CMB), X-ray emission of ionized gas, and gravitational weak lensing effect of background galaxies by the cluster's gravitational potential. We develop a theoretical framework to predict and interpret two-point correlation statistics among the three observables using a semi-analytic model of intracluster medium (ICM) and halo-based approach. In this work, we show that the auto- and cross-angular power spectra in tSZ, X-ray and lensing statistics from upcoming missions (eROSITA, CMB-S4, and LSST) can help break the degeneracy between cosmology and ICM physics. These correlation statistics are less sensitive to selection biases, and are able to probe ICM physics in distant, faint and small clusters that are otherwise difficult to be detected individually. We show that the correlation statistics are able to provide cosmological constraints comparable to the conventional cluster abundance measurements, while constraining cluster astrophysics at the same time. Our results indicate that the correlation statistics can significantly enhance the scientific returns of upcoming multi-wavelength cluster surveys., 21 pages, 12 figures, 4 tables, accepted for publication in MNRAS

Weak gravitational lensing is one of the most promising cosmological probes of the late universe. Several large ongoing (DES, KiDS, HSC) and planned (LSST, Euclid, WFIRST) astronomical surveys attempt to collect even deeper and larger scale data on weak lensing. Due to gravitational collapse, the distribution of dark matter is non-Gaussian on small scales. However, observations are typically evaluated through the two-point correlation function of galaxy shear, which does not capture non-Gaussian features of the lensing maps. Previous studies attempted to extract non-Gaussian information from weak lensing observations through several higher order statistics such as the three-point correlation function, peak counts, or Minkowski functionals. Deep convolutional neural networks (CNN) emerged in the field of computer vision with tremendous success, and they offer a new and very promising framework to extract information from 2D or 3D astronomical data sets, confirmed by recent studies on weak lensing. We show that a CNN is able to yield significantly stricter constraints of (σ8, Ωm) cosmological parameters than the power spectrum using convergence maps generated by full N-body simulations and ray-tracing, at angular scales and shape noise levels relevant for future observations. In a scenario mimicking LSST or Euclid, the CNN yields 2.4–2.8 times smaller credible contours than the power spectrum, and 3.5–4.2 times smaller at noise levels corresponding to a deep space survey such as WFIRST. We also show that at shape noise levels achievable in future space surveys the CNN yields 1.4–2.1 times smaller contours than peak counts, a higher order statistic capable of extracting non-Gaussian information from weak lensing maps.

Efficient automated detection of flux-transient, re-occurring flux-variable, and moving objects is increasingly important for large-scale astronomical surveys. We present braai, a convolutional-neural-network, deep-learning real/bogus classifier designed to separate genuine astrophysical events and objects from false positive, or bogus, detections in the data of the Zwicky Transient Facility (ZTF), a new robotic time-domain survey currently in operation at the Palomar Observatory in California, USA. Braai demonstrates a state-of-the-art performance as quantified by its low false negative and false positive rates. We describe the open-source software tools used internally at Caltech to archive and access ZTF’s alerts and light curves (kowalski ), and to label the data (zwickyverse). We also report the initial results of the classifier deployment on the Edge Tensor Processing Units that show comparable performance in terms of accuracy, but in a much more (cost-) efficient manner, which has significant implications for current and future surveys.

The field of galaxy formation and evolution synthesizes the physics of baryons and dark matter to describe the origin of systems such as the Milky Way and the enormous diversity of the galaxy population. The broad variation in possible formation histories and the wide range of cosmic environments make large statistical samples of galaxies essential for identifying the important physical mechanisms that govern their formation. Starting in the early 2020s, the Large Synoptic Survey Telescope (LSST) will provide an unmatched dataset for galaxy evolution studies by observing the entire southern sky in ultraviolet, optical and near-infrared wavelengths, producing multi-epoch digital images over a 10-year nominal mission that when summed will provide the deepest, wide-angle view of our Universe ever assembled. Here, we discuss the importance of LSST for deepening our understanding of galaxy formation and evolution over cosmic time. We present some outstanding problems in the field that LSST will address, and we present a roadmap of some preparatory research efforts required to make effective use of the LSST dataset for galaxy formation science. The Large Synoptic Survey Telescope (LSST), an upcoming astronomical survey, will deeply observe the entire southern sky in a broad range of colours. We present the LSST science opportunities and technical challenges in the field of galaxy formation and evolution.

Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of algorithms tailored to specific telescope arrays and science cases. We present DeepSource - a deep learning solution - that uses convolutional neural networks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio (SNR) of the original map and then uses dynamic blob detection to detect sources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT images with a total of 300,000 sources, DeepSource is essentially perfect in both purity and completeness down to SNR = 4 and outperforms PyBDSF in all metrics. For uniformly-weighted images it achieves a Purity x Completeness (PC) score at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For natural-weighting we find a smaller improvement of ~40% in the PC score at SNR = 3. If instead we ask where either of the purity or completeness first drop to 90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the 4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can learn to optimally trade off purity and completeness for any science case under consideration. Our results show that deep learning is a promising approach to point source detection in astronomical images., 15 pages, 13 figures, submitted to MNRAS

The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of $\sim$13,000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity HII region. We have released a catalog with the WGAN anomaly scores; the code and catalog are available at https://github.com/kstoreyf/anomalies-GAN-HSC, and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es., Published in MNRAS

We outline the challenges faced by the planetary science community in the era of next-generation large-scale astronomical surveys, and highlight needs that must be addressed in order for the community to maximize the quality and quantity of scientific output from archival, existing, and future surveys, while satisfying NASA's and NSF's goals.

Analysis of three astronomical surveys provides some of the best evidence yet against the existence of a giant planet at the fringes of the Solar System. Analysis of three astronomical surveys provides some of the best evidence yet against the existence of a giant planet at the fringes of the Solar System.

Full list of authors: Vaduvescu, O.; Gorgan, D.; Copandean, D.; Bacu, V.; Stefanut, T.; Sabou, A.; Nandra, C.; Boldea, C.; Pinter, V.; Popescu, M.; Petrescu, E.; Bertesteanu, D.; Davison, T.; Pérez Toledo, F. M.; Boldea, A.; Predatu, M.; Zegmott, T.; Wilson, T. G.; Hudin, L.; Stanescu, M.; Stanica, A.; Buhulea, A.; Stoica, A.; Timpea, A.; Anghel, S.; Ciobanu, D.; Toma, R.; Casanova, V.; Stecklum, B.; Choque-Challapa, N.; Short, P.; Tudorica, A., In 2015 we started a PhD thesis aiming to write a moving objects processing system (MOPS) aimed to detect near Earth asteroids (NEAs) in astronomical surveys planned within the EURONEAR project. Based on this MOPS experience, in 2017 we proposed the NEARBY project to the Romanian Space Agency, which awarded funding to the Technical University of Cluj-Napoca (UTCN) and the University of Craiovafor building a cloud-based online platform to reduce survey images, detect, validate and report in near real time asteroid detections and NEA candidates. The NEARBY platform was built and is available at UTCN since Feb 2018, being tested during 5 pilot surveys observed in 2017-2018 with the Isaac Newton Telescope in La Palma. Two NEAs were discovered in Nov 2018 (2018 VQ1 and 2018 VN3), being recovered and reported to MPC within 2 hours. Other 4 discovered NEAs were found from a few dozen possible NEA candidates promptly being followed, allowing us to discover 22 Hungarias and 7 Mars crossing asteroids using the NEARBY platform. Compared with other few available software, NEARBY could detect more asteroids (by 8-41%), but scores less than human detection (by about 10%). Using resulted data, the astrometric accurancy, photometric limits and an INT NEA survey case study are presented as guidelines for planning future surveys. © 2021., This research has been supported by ROSA (Romanian Space Agency) by the Contract CDI-STAR 192/2017, NEARBY - Visual Analysis of Multidimensional Astrophysics Data for Moving Objects Detection., With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709.

Modern astronomical surveys are observing spectral data for millions of stars. These spectra contain chemical information that can be used to trace the Galaxy's formation and chemical enrichment history. However, extracting the information from spectra, and making precise and accurate chemical abundance measurements are challenging. Here, we present a data-driven method for isolating the chemical factors of variation in stellar spectra from those of other parameters (i.e. \teff, \logg, \feh). This enables us to build a spectral projection for each star with these parameters removed. We do this with no ab initio knowledge of elemental abundances themselves, and hence bypass the uncertainties and systematics associated with modeling that rely on synthetic stellar spectra. To remove known non-chemical factors of variation, we develop and implement a neural network architecture that learns a disentangled spectral representation. We simulate our recovery of chemically identical stars using the disentangled spectra in a synthetic APOGEE-like dataset. We show that this recovery declines as a function of the signal to noise ratio, but that our neural network architecture outperforms simpler modeling choices. Our work demonstrates the feasibility of data-driven abundance-free chemical tagging., Comment: Accepted by ApJ. code available at github.com/drd13/tagging-package

With the advent of large astronomical surveys plus multi-messenger astronomy, both automatic detection and classification of Type Ia supernovae have been addressed by different machine learning techniques. In this article we present three solutions aimed at the future spectrometer of the KDUST project, within a scope of benchmark, considering three different methodologies. The systems presented here are the following: CINTIA (based on hierarchical neural network architecture), SUZAN (which incorporates the solution known as fuzzy systems) and DANI (based on Deep Learning with Convolutional Neural Networks). The characteristics of the systems are presented and the benchmark is performed considering a data set containing 15.134 spectra. The best performance is obtained by the DANI architecture which provides 96% accuracy in the classification of Type Ia supernovae in relation to other spectral types.

We present a detailed analysis of the new planetary nebula (PN) IPHASX J193718.6+202102 using deep imaging and intermediate- and high-resolution spectroscopy that are interpreted through morpho-kinematic and photoionization modelling. The physical structure of the nebula consists of a fragmented torus and an extremely faint orthogonal bipolar outflow, contrary to the pinched waist PN morphology suggested by its optical image. Our kinematic analysis indicates that the torus is expanding at 25 ± 5 km s-1 and is gradually breaking up. At an estimated distance of 7.1-0.3+0.8 kpc, the corresponding kinematic age of ∼26 000 yr is consistent with a faint and disintegrating PN. The intermediate-resolution spectra reveal an excited PN with chemical abundances typical of Type II PNe. Based on the latter, we also estimate an initial mass for the progenitor in the range 2-3 M⊙ and a central star (CSPN) mass MCSPN ∼0.61 M⊙. The Spitzer MIPS 24 μm emission that closely follows the fragmented torus could be attributed to the emission of [O iv] at 25.9 μm rather than to dust emission. All the results coherently point towards an evolved moderately massive bipolar Type II PN on the brink of dissolving into the interstellar medium. © 2020 The Author(s)., The authors are thankful to the referees for their comments that improved this paper. LS acknowledges support from UNAM DGAPA PAPIIT project IN101819 (Mexico). MAG acknowledges support from grant AYA PGC2018-102184-B-I00 co-funded with FEDER funds. SZ works under the collaboration agreement ‘UNAM-TecNM 43310-3020-30-IX-15’. JAT and MAG are funded by UNAM DGAPA PAPIIT project IA100720. GRL acknowledges support from CONACYT (grant 263373) and PRODEP (Mexico). This work is partially based on observations collected at the Observatorio Astronómico Nacional at San Pedro Mártir, B.C., Mexico and we thank the daytime and night support staff at the OAN-SPM for facilitating and helping obtain our observations. Based on observations made with the Nordic Optical Telescope, operated by the Nordic Optical Telescope Scientific Association, and the Gran Telescopio Canarias (GTC), both installed in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias in La Palma, Spain. This paper makes use of data obtained as part of the INT Photometric H α Survey of the Northern Galactic Plane (IPHAS, www.iphas.org) carried out at the Isaac Newton Telescope (INT). The INT is operated on the island of La Palma by the Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias. All IPHAS data are processed by the Cambridge Astronomical Survey Unit, at the Institute of Astronomy in Cambridge. The bandmerged DR2 catalogue was assembled at the Centre for Astrophysics Research, University of Hertfordshire, supported by STFC grant ST/J001333/1. The OSN director is acknowledged for awarding observations through a DDT programme and the telescope operator Alfredo Sota for conducting the observations. This work is based in part on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. In memoriam of Johannes Andersen, director of the NOT from 2002 to 2013.

XL-Calibur is a balloon-borne hard X-ray polarimetry mission, the first flight of which is currently foreseen for 2021. XL-Calibur carries an X-ray telescope consists of consists of 213 Wolter I grazing-incidence mirrors which are nested in a coaxial and cofocal configuration. The optics design is nearly identical to the Hard X-ray Telescope (HXT) on board the ASTRO-H satellite. The telescope was originally fabricated for the Formation Flying Astronomical Survey Telescope (FFAST) project. However, the telescope can be used for XL-Calibur, since the FFAST project was terminated before completion. The mirror surfaces are coated with Pt/C depth-graded multilayers to reflect hard X-rays above 10 keV by Bragg reflection. The effective area of the telescope is larger than 300 cm^2 at 30 keV. The mirrors are supported by alignment bars in the housing, and each of the bars has a series of 213 grooves to hold the mirrors. To obtain the best focus of the optics, the positions of the mirrors have to be adjusted by tuning the positions of the alignment bars. The tuning of the mirror positions is conducted using the X-ray beam at the synchrotron facility SPring-8 BL20B2, and this process is called optical tuning. First the positions of the second reflectors are tuned, and then those of the first reflectors are tuned. We did the first optical tuning in Jan 2020. The second tuning will be planned between April to July, 2020. This paper reports the current status of the hard X-ray telescope for XL-Calibur.

The addition of multiple “mini-trackers” (MTs) to the Southern African Large Telescope (SALT) would create in effect several four-to-six-meter class telescopes that take advantage of the SALT 10-meter diameter primary mirror’s 35 degree diameter uncorrected field-of-view. These devices, with a 100 square degree patrol area, would provide valuable follow up capability for the large astronomical surveys either in operation (e.g. MeerKAT, eROSITA, Gaia), or expected to begin operations soon (e.g. LSST, SKA, Euclid). A feasibility study was conducted to evaluate the technical practicality associated with the design, fabrication, integration, and testing of a prototype MT for SALT. The study determined that the development of a mini-tracker was indeed feasible, and work has begun on the concept design phase of the project.

The Cerro­ Chajnantor Atacama Telescope prime (CCAT-p) Observatory is a wide­field, 6­ meter aperture submillimeter telescope. Prime-­Cam ­will be a powerful, first­ light camera for CCAT-p with imagers working at several wavelengths and a spectroscopic instrument aimed at intensity mapping during the epoch of reionization. We present the design of an instrument module in Prime­-Cam, operating at 350 microns — the shortest wavelength on the instrument, and the most novel for astronomical surveys, taking full advantage of the atmospheric transparency at the high 5600 meter CCAT-p siting on Cerro Chajnantor. This instrument module will provide unprecedented broadband intensity and polarization measurement capabilities to address pressing astrophysical questions regarding galaxy formation, Big Bang cosmology, and star formation within our own Galaxy. We present the overall optical and mechanical design for the module, and laboratory characterization of the 860-GHz KID array.

The use of Type Ia Supernovae (SNe Ia) to measure cosmological parameters has grown significantly over the past two decades. However, there exists a significant diversity in the SN Ia population that is not well understood. Over-luminous SN 1991T-like and sub-luminous SN 1991bg-like objects are two characteristic examples of peculiar SNe. The identification and classification of such objects is an important step in studying what makes them unique from the remaining SN population. With the upcoming Vera C. Rubin Observatory promising on the order of a million new SNe over a ten-year survey, spectroscopic classifications will be possible for only a small subset of observed targets. As such, photometric classification has become an increasingly important concern in preparing for the next generation of astronomical surveys. Using observations from the Sloan Digital Sky Survey II (SDSS-II) SN Survey, we apply here an empirically based classification technique targeted at the identification of SN 1991bg-like SNe in photometric data sets. By performing dedicated fits to photometric data in the rest-frame redder and bluer bandpasses, we classify 16 previously unidentified 91bg-like SNe. Using SDSS-II host-galaxy measurements, we find that these SNe are preferentially found in host galaxies having an older average stellar age than the hosts of normal SNe Ia. We also find that these SNe are found at a further physical distance from the center of their host galaxies. We find no statistically significant bias in host galaxy mass or specific star formation rate for these targets.

Two projects – “Mapping the Sun” and “Reading the Hurlers” – have shone new light on the multiple stone circle complex, The Hurlers, on Bodmin Moor, Cornwall, in southwest Britain. In 2013, excavation revealed a stone “pavement” between the central and northern circles: this inter-circle link had first been discovered in 1938 but had then been re-covered. Work in 2016 discovered a solitary fallen (once standing) stone which lay 100 m to the north of The Hurlers complex. Geological studies of the standing stones accompanied by astronomical surveys have prompted new insights into the make-up of this monument, its landscape setting and astronomical significance. Astronomical observations of this major Bronze Age landscape reveal a design with significant alignments between key monuments and near and distant landmarks. Additional astronomical links suggest a number of interesting phenomena which would be experienced at the site, particularly surrounding the materiality of the inter-circle link.

The structure, kinematics, and chemical composition of the far side of the Milky Way disk, beyond the bulge, are still to be revealed. Classical Cepheids (CCs) are young and luminous standard candles. We aim to use a well-characterized sample of these variable stars to study the present time properties of the far side of the Galactic disk. A sample of 45 Cepheid variable star candidates were selected from near infrared time series photometry obtained by the VVV survey. We characterized this sample using high quality near infrared spectra obtained with VLT/X-Shooter, deriving radial velocities and iron abundances for all the sample Cepheids. This allowed us to separate the CCs, which are metal rich and with kinematics consistent with the disk rotation, from type II Cepheids (T2Cs), which are more metal poor and with different kinematics. We estimated individual distances and extinctions using VVV photometry and period-luminosity relations, reporting the characterization of 30 CCs located on the far side of the Galactic disk, plus 8 T2Cs mainly located in the bulge region, of which 10 CCs and 4 T2Cs are new discoveries. This is the first sizeable sample of CCs in this distant region of our Galaxy that has been spectroscopically confirmed. We use their positions, kinematics, and metallicities to confirm that the general properties of the far disk are similar to those of the well-studied disk on the solar side of the Galaxy. In addition, we derive for the first time the radial metallicity gradient on the disk's far side. Considering all the CCs with $R_{\mathrm{GC}} < 17\,\rm{kpc}$, we measure a gradient with a slope of $-0.062 \, \mathrm{dex\, kpc^{-1}}$ and an intercept of $+0.59 \, \rm{dex}$, which is in agreement with previous determinations based on CCs on the near side of the disk., 16 pages, 10 figures, accepted for publication in Astronomy & Astrophysics

Utilizing charge-coupled device (CCD) imaging technology in astronomical surveying and positioning facilitates high-precision measurements without manual observation. This eliminates the in...

This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan&ndash, Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today&rsquo, s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.

Camera lenses are increasingly used in wide-field astronomical surveys due to their high performance, wide field-of-view (FOV) unreachable from traditional telescope optics, and modest cost. The machining and assembly tolerances for commercially available optical systems cause a slight misalignment (tilt) between the lens and CCD, resulting in point spread function (PSF) degradation. We have built an automated alignment system (Robotilters) to solve this challenge, optimizing four degrees of freedom-two tilt axes, a separation axis (the distance between the CCD and lens), and the lens focus (the built-in focus of the lens by turning the lens focusing ring, which moves the optical elements relative to one another) in a compact and low-cost package. The Robotilters remove tilt and optimize focus at the sub-10-μm level, are completely automated, take ≈2 h to run, and remain stable for multiple years once aligned. The Robotilters were built for the Evryscope telescope (a 780-MPix 22-camera array with an 8150-sq. deg FOV and continuous 2-min cadence) designed to detect short-timescale events across extremely large sky areas simultaneously. Variance in quality across the image field, especially the corners and edges compared to the center, is a significant challenge in wide-field astronomical surveys like the Evryscope. The individual star PSFs (which typically extend only a few pixels) are highly susceptible to slight increases in optical aberrations in this situation. The Robotilter solution resulted in a limiting magnitude improvement of 0.5 mag in the center of the image and 1.0 mag in the corners for typical Evryscope cameras, with less distorted and smaller PSFs (half the extent in the corners and edges in many cases). We describe the Robotilter mechanical and software design, camera alignment results, long-term stability, and image improvement. The potential for general use in wide-field astronomical surveys is also explored., This research was supported by the NSF CAREER (Grant No. AST-1555175), NSF/ATI (Grant No. AST-1407589), and the Research Corporation Scialog (Grant Nos. 23782 and 23822). H. C. was supported by the NSF GRF (Grant No. DGE-1144081). O. F. and D. dS. acknowledged the support by the Spanish Ministerio de Economía y Competitividad (MINECO/FEDER, UE) under Grant Nos. AYA2013-47447-C3-1-P, AYA2016-76012-C3-1-P, and MDM-2014-0369 of ICCUB (Unidad de Excelencia ‘María de Maeztu’)

Quasars are ideal targets to use for astrometric calibration of large scale astronomical surveys as they have negligible proper motion and parallax. The forthcoming 4-m International Liquid Mirror Telescope (ILMT) will survey the sky that covers a width of about 27 arcminute. To carry out astrometric calibration of the ILMT observations, we aimed to compile a list of quasars with accurate equatorial coordinates and falling in the ILMT stripe. Towards this, we cross-correlated all the quasars that are known till the present date with the sources in the Gaia-DR2 catalogue, as the Gaia-DR2 sources have position uncertainties as small as a few milli arcsec (mas). We present here the results of this cross-correlation which is a catalogue of 6738 quasars that is suitable for astrometric calibration of the ILMT fields. In this work, we present this quasar catalogue. This catalogue of quasars can also be used to study quasar variability over diverse time scales when the ILMT starts its observations. While preparing this catalogue, we also confirmed that quasars in the ILMT stripe have proper motion and parallax lesser than 20 mas/yr and 10 mas, respectively., Comment: 9 pages, 9 figures, Accepted in Journal of Astrophysics & Astronomy

This chapter traces the development of astronomical observational methods from visual, for decades tracking the behavior of individual objects, to modern space missions that produce data simultaneously for several hundred thousand objects in different spectral ranges. Thanks to this, astronomy has moved from the study of individual objects to the study of the universe as a whole and has become the science of Big Data. The chapter describes briefly visual, photographic, and CCD surveys of stars, galaxies and the intergalactic medium, spectral photographic and spectral CCD surveys, and multiwavelength ground-based and space-born databases and archives, which made it possible to create a high-precision coordinate system, to discover new properties of celestial bodies, and, as a result, to construct three-dimensional models of the visible parts of the universe. We mention also the “conserved” data of photographic astroplates accumulated over the centuries, which have been actively digitized during the last decades and provide a new knowledge from the comparative analysis of old and new observational material. Astronomy research is changing from being hypothesis-driven to being data-driven to being data-intensive. To cope with the various challenges and opportunities offered by the exponential growth of astronomical data volumes, rates, and complexity, the new disciplines of astrostatistics and astroinformatics have emerged.

Developing methods for analyzing and extracting information from modern sky surveys is a challenging task in astrophysical studies and is important for many investigations of galactic and extragalactic objects. We have designed a method for determination of stellar parameters and interstellar extinctions from multicolor photometry. This method was applied to objects drawn from modern large photometric surveys. In this work, we give a review of the surveys and discuss problems of cross-identification, paying particular attention to the information flags contained in the surveys. Also we have determined new statistical relations to estimate the stellar atmospheric parameters using MK spectral classification.

The death of massive stars is shrouded in many mysteries. One of them is the mechanism that overturns the collapse of the degenerate iron core into an explosion, a process that determines the supernova explosion energy, properties of the surviving compact remnant, and the nucleosynthetic yields. The number of core-collapse supernova observations has been growing with an accelerating pace thanks to modern time-domain astronomical surveys and new tests of the explosion mechanism are becoming possible. We review predictions of parameterized supernova explosion models and compare them with explosion properties inferred from observed light curves, spectra, and neutron star masses., Comment: Reviews in Frontiers of Modern Astrophysics; From Space Debris to Cosmology, edited by Kab\'ath, Petr; Jones, David; Skarka, Marek. ISBN: 978-3-030-38509-5. Cham: Springer International Publishing, 2020, pp. 189-211

We present a novel intelligent quasar continuum neural network (iQNet), predicting the intrinsic continuum of any quasar in the rest-frame wavelength range 1020 Angstroms $\leq \lambda \leq$ 1600 Angstroms. We train this network using high-resolution Hubble Space Telescope/Cosmic Origin Spectrograph ultraviolet quasar spectra at low redshift ($z \sim 0.2$) from the Hubble Spectroscopic Legacy Archive, and apply it to predict quasar continua from different astronomical surveys. We utilize the HSLA quasar spectra that are well-defined in the rest-frame wavelength range [1020, 1600] Angstroms with an overall median signal-to-noise ratio of at least five. The iQNet achieves a median AFFE of 2.24% on the training quasar spectra, and 4.17% on the testing quasar spectra. We apply iQNet and predict the continua of $\sim$3200 SDSS-DR16 quasar spectra at higher redshift ($2< z \leq 5$) and measure the redshift evolution of mean transmitted flux ($< F >$) in the Ly-$\alpha$ forest region. We measure a gradual evolution of $< F >$ with redshift, which we characterize as a power-law fit to the effective optical depth of the Ly-$\alpha$ forest. Our measurements are broadly consistent with other estimates of $$ in the literature, but provide a more accurate measurement as we are directly measuring the quasar continuum where there is minimum contamination from the Ly-$\alpha$ forest. This work proves that the deep learning iQNet model can predict the quasar continuum with high accuracy and shows the viability of such methods for quasar continuum prediction., Comment: MNRAS Accepted 2021 January 18. 24 pages, 18 figures

International audience; The search for objects with unusual astronomical properties, or anomalies, is one of the most anticipated results to be delivered by the next generation of large scale astronomical surveys. Moreover, given the volume and complexity of current data sets, machine learning algorithms will undoubtedly play an important role in this endeavor. The SNAD team is specialized in the development, adaptation and improvement of such techniques with the goal of constructing optimal anomaly detection strategies for astronomy. We present here the preliminary results from the third annual SNAD workshop (https://snad.space/2020/) that was held on-line in 2020 July.

We give examples of data mining from the multiwavelength astronomical surveys to provide the automated morphological galaxy classification by multiparametric diagrams and machine learning methods; to reconstruct a large-scale galaxy distribution behind the Zone of Avoidance of the Milky Way; and to model the flux variability of galaxies with active nuclei.

Event processing is an umbrella term for technologies and that are conceptually centered around ingestion, manipulation, and dissemination of events. An event could be any discrete occurrence within a defined domain which is interesting enough to mark down. In most scenarios, events are organized in streams which can arrive from various sources and have a heterogeneous structure. Event processing concepts and technologies are proven in industries that demand high throughput of data and quick decision making and can be tailored for astronomical data stream analytics. They enable real-time pattern application, suitable for Big Data systems where automatized reaction: is crucial. Upcoming big astronomical surveys such as LSST (2023) and ZTF (already in operation), along with various simulated data streams, can be analyzed by applying event processing mechanism such as filtering, aggregation, pattern matching, etc.

We studied dust environment such as flux, temperature, mass and inclination angle of the cavity structure around the C-rich Asymptotic giant branch star in 60 μm and 100 μm wavelengths band using Infrared Astronomical Survey. We observed the data of AGB star having IRAS name 01142+6306 corresponding to R.A.(J2000)= 01h 17m 33.504s and Dec.(J2000)= 630 22’ 4.98’. Flexible image transport system image was downloaded from Sky View Observatory; we obtained the surrounding flux density using software Aladinv2.5. The dust color average temperature and mass are found to be 25.08 K and 4.731026 kg (0.00024 Mʘ) respectively. The dust color temperature ranges from 18.76 ± 3.16 K to 33.21 ± 4.07 K. The isolated cavity like structure around AGB star has extension 45.67 parsec and contraction 17.02 parsec was observed using detailed calculation. The core region is found to be edge-on having inclination angle 79.46o. BIBECHANA 16 (2019) 31-40

The matching of sources between photometric catalogues can lead to cases where objects of differing brightness are incorrectly assumed to be detections of the same source. The rejection of unphysical matches can be achieved through the inclusion of information about the sources' magnitudes. The method described here uses the additional photometric information from both catalogues in the process of accepting or rejecting counterparts, providing approximately a factor 10 improvement in Bayes' factor with its inclusion. When folding in the photometric information we avoid using prior astrophysical knowledge. Additionally, the method allows for the possibility of no counterparts to sources as well as the possibility that sources overlap multiple potential counterparts. We formally describe the probability of two sources being the same astrometric object, allowing systematic effects of astrometric perturbation (by, e.g., contaminant objects) to be accounted for. We apply the method to two cases. First, we test IPHAS-Gaia matches to compare the resulting matches in two catalogues of similar wavelength coverage but differing dynamical ranges. Second, we apply the method to matches between IPHAS and 2MASS and show that the method holds when considering two catalogues with approximately equal astrometric precision. We discuss the importance of including the magnitude information in each case. Additionally, we discuss extending the method to multiple catalogue matches through an iterative matching process. The method allows for the selection of high-quality matches by providing an overall probability for each pairing, giving the flexibility to choose stars known to be good matches., 19 pages, 8 figures, 3 tables; accepted for publication in MNRAS. Updated equations 26 and 27 to fix minor typographical errors

The Large Synoptic Survey Telescope (LSST), a next generation astronomical survey, sited on Cerro Pachon in Chile, will provide an unprecedented amount of imaging data for studies of the faint optical sky. The LSST system includes an 8.4m (6.7m effective) primary mirror and a 3.2 Gigapixel camera with a 9.6 sq. deg. field of view. This system will enable about 10,000 sq. deg. of sky to be covered twice per night, every three to four nights on average, with typical 5-sigma depth for point sources ofr= 24.5 (AB). With over 800 observations in theugrizybands over a 10-year period, these data will enable coadded images reachingr= 27.5 (about 5 magnitudes deeper than SDSS) as well as studies of faint time-domain astronomy. The measured properties of newly discovered and known astrometric and photometric transients will be publicly reported within 60 sec after closing the shutter. The resulting hundreds of petabytes of imaging data for about 40 billion objects will be used for scientific investigations ranging from the properties of near-Earth asteroids to characterizations of dark matter and dark energy. For example, simulations estimate that LSST will discover about 1,000 quasars at redshifts exceeding 7; this sample will place tight constraints on the cosmic environment at the end of the reionization epoch. In addition to a brief introduction to LSST, I review the value of LSST data in support of epoch of reionization experiments and discuss how international participants can join LSST.

The nature and physical properties of asteroids, in particular those orbiting in the near-Earth space, are of scientific interest and practical importance. Exoplanet surveys can be excellent resources to detect asteroids, both already known and new objects. This is due to their similar observing requirements: large fields of view, long sequences, and short cadence. If the targeted fields are not located far from the ecliptic, many asteroids will cross the field of view occasionally. We present two complementary methodologies to identify asteroids serendipitously observed in large-area astronomical surveys. One methodology focuses on detecting already known asteroids using the Virtual Observatory tool SkyBoT, which predicts their positions and motions in the sky at a specific epoch. The other methodology applies the ssos pipeline, which is able to identify known and new asteroids based on their apparent motion. The application of these methods to the 6.4 deg2 of the sky covered by the Wide-Field CAMera Transit Survey in the J-band is described. We identified 15 661 positions of 1821 different asteroids. Of them, 182 are potential new discoveries. A publicly accessible online, Virtual Observatory compliant catalogue was created. We obtained the shapes and periods for five of our asteroids from their light curves built with additional photometry taken from external archives. We demonstrated that our methodologies are robust and reliable approaches to find, at zero cost of observing time, asteroids observed by chance in astronomical surveys. Our future goal is to apply them to other surveys with adequate temporal coverage. This research has been financed by ASTERICS, a project supported by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement no. 653477. This research has made use of the Spanish Virtual Observatory (http://svo.cab.inta-csic.es) supported from the Spanish MINECO/FEDER through grant no. AYA2017-84089-P. MCC and FJE acknowledge financial support from the Tec2Space-CM project (P2018/NMT-4291). SBF acknowledge support by the Spanish State Research Agency (AEI) through project no. ESP2017-87676C5-1-R and no. MDM-2017-0737 Unidad de Excelencia 'Maria de Maeztu'-Centro de Astrobiolog'ia (CSIC-INTA). This research has made use of Topcat (Taylor 2005) and STILTS (Taylor 2006). This research has made use of 'Aladin sky atlas' developed at CDS, Strasbourg Observatory, France. This research has made use of IMCCE's SkyBoT VO tool (Berthier et al. 2006). The work of J. D was supported by the grant 18-04514J of the Czech Science Foundation. MM is funded by the European Space Agency under the research contract C4000122918; With funding from the Spanish government through the "María de Maeztu Unit of Excellence" accreditation (MDM-2017-0737). Peer review

Context. The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning. In these exploratory applications, it is important to evaluate potential existing tools and determine those that are optimal for extracting scientific knowledge from the available observations. Aims. We explore the possibility of using unsupervised clustering algorithms to separate stellar populations with distinct chemical patterns. Methods. Star clusters are likely the most chemically homogeneous populations in the Galaxy, and therefore any practical approach to identifying distinct stellar populations should at least be able to separate clusters from each other. We have applied eight clustering algorithms combined with four dimensionality reduction strategies to automatically distinguish stellar clusters using chemical abundances of 13 elements. Our test-bed sample includes 18 stellar clusters with a total of 453 stars. Results. We have applied statistical tests showing that some pairs of clusters (e.g., NGC 2458–NGC 2420) are indistinguishable from each other when chemical abundances from the Apache Point Galactic Evolution Experiment (APOGEE) are used. However, for most clusters we are able to automatically assign membership with metric scores similar to previous works. The confusion level of the automatically selected clusters is consistent with statistical tests that demonstrate the impossibility of perfectly distinguishing all the clusters from each other. These statistical tests and confusion levels establish a limit for the prospect of blindly identifying stars born in the same cluster based solely on chemical abundances. Conclusion. We find that some of the algorithms we explored are capable of blindly identify stellar populations with similar ages and chemical distributions in the APOGEE data. Even though we are not able to fully separate the clusters from each other, the main confusion arises from clusters with similar ages. Because some stellar clusters are chemically indistinguishable, our study supports the notion of extending weak chemical tagging that involves families of clusters instead of individual clusters.

Due to the increasing dimension, complexity and cost of the future astronomical surveys, new technologies enabling more compact and simpler systems are required. The development of curved detectors allows to enhance the performances of the optical system used (telescope or astronomical instrument), while keeping the system more compact. We describe here a set of five curved CMOS detectors developed within a collaboration between CEA-LETI and CNRS-LAM. These fully-functional detectors 20 Mpix (CMOSIS CMV20000) have been curved to different radii of curvature and spherical shapes (both convex and concave) over a size of 24x32 mm^2. Before being able to use them for astronomical observations, we assess the impact of the curving process on their performances. We perform a full electro-optical characterization of the curved detectors, by measuring the gain, the full well capacity, the dynamic-range and the noise properties, such as dark current, readout noise, pixel-relative-non-uniformity. We repeat the same process for the flat version of the same CMOS sensor, as a reference for comparison. We find no significant difference among most of the characterization values of the curved and flat samples. We obtain values of readout noise of 10e$^-$ for the curved samples compared to the 11e$^-$ of the flat sample, which provides slightly larger dynamic ranges for the curved detectors. Additionally we measure consistently smaller values of dark current compared to the flat CMOS sensor. The curving process for the prototypes shown in this paper does not significantly impact the performances of the detectors. These results represent the first step towards their astronomical implementation., Comment: 10 pages, 4 figures, accepted by Applied Optics

Accurate specification of a likelihood function is becoming increasingly difficult in many inference problems in astronomy. As sample sizes resulting from astronomical surveys continue to grow, deficiencies in the likelihood function lead to larger biases in key parameter estimates. These deficiencies result from the oversimplification of the physical processes that generated the data, and from the failure to account for observational limitations. Unfortunately, realistic models often do not yield an analytical form for the likelihood. The estimation of a stellar initial mass function (IMF) is an important example. The stellar IMF is the mass distribution of stars initially formed in a given cluster of stars, a population which is not directly observable due to stellar evolution and other disruptions and observational limitations of the cluster. There are several difficulties with specifying a likelihood in this setting since the physical processes and observational challenges result in measurable masses that cannot legitimately be considered independent draws from an IMF. This work improves inference of the IMF by using an approximate Bayesian computation approach that both accounts for observational and astrophysical effects and incorporates a physically-motivated model for star cluster formation. The methodology is illustrated via a simulation study, demonstrating that the proposed approach can recover the true posterior in realistic situations, and applied to observations from astrophysical simulation data.

We have extended the pure rotational investigation of the two isomers syn and anti vinyl mercaptan to the millimeter domain using a frequency-multiplication spectrometer. The species were produced by a radiofrequency discharge in 1,2-ethanedithiol. Additional transitions have been re-measured in the centimeter band using Fourier-transform microwave spectroscopy to better determine rest frequencies of transitions with low-$J$ and low-$K_a$ values. Experimental investigations were supported by quantum chemical calculations on the energetics of both the [C$_2$,H$_4$,S] and [C$_2$,H$_4$,O] isomeric families. Interstellar searches for both syn and anti vinyl mercaptan as well as vinyl alcohol were performed in the EMoCA (Exploring Molecular Complexity with ALMA) spectral line survey carried out toward Sagittarius (Sgr) B2(N2) with the Atacama Large Millimeter/submillimeter Array (ALMA). Highly accurate experimental frequencies (to better than 100 kHz accuracy) for both syn and anti isomers of vinyl mercaptan have been measured up to 250 GHz; these deviate considerably from predictions based on extrapolation of previous microwave measurements. Reliable frequency predictions of the astronomically most interesting millimeter-wave lines for these two species can now be derived from the best-fit spectroscopic constants. From the energetic investigations, the four lowest singlet isomers of the [C$_2$,H$_4$,S] family are calculated to be nearly isoenergetic, which makes this family a fairly unique test bed for assessing possible reaction pathways. Upper limits for the column density of syn and anti vinyl mercaptan are derived toward the extremely molecule-rich star-forming region Sgr B2(N2) enabling comparison with selected complex organic molecules., Comment: 13 pages, 5 figures, 2 tables, 2 appendices. Accepted for publication in A&A 13th of February, 2019

Spontaneous wavefunction collapse models, like the Continuous Spontaneous Localization, are designed to suppress macroscopic superpositions, while preserving microscopic quantum phenomena. An observable consequence of collapse models is spontaneous heating of massive objects. Here we calculate the collapse-induced heating rate of astrophysical objects, and the corresponding equilibrium temperature. We apply these results to neutron stars, the densest phase of baryonic matter in the universe. Stronger collapse model parameters imply greater heating, allowing us to derive competitive bounds on model parameters using neutron star observational data, and to propose speculative bounds based on the capabilities of current and future astronomical surveys., Comment: v3: minor modifications, close to published version v2: Thanks to a correspondence with Philip Pearle, we found an error in our previous calculation shortly after submission. This revision corrects the error, which significantly changes our conclusions and discussion

Since January 2017, the Commensal Real-time ASKAP Fast Transients survey (CRAFT) has been utilising commissioning antennas of the Australian SKA Pathfinder (ASKAP) to survey for fast radio bursts (FRBs) in fly's eye mode. This is the first extensive astronomical survey using phased array feeds (PAFs), and a total of 20 FRBs have been reported. Here we present a calculation of the sensitivity and total exposure of this survey, using the pulsars B1641-45 (J1644-4559) and B0833-45 (J0835-4510, i.e.\ Vela) as calibrators. The design of the survey allows us to benchmark effects due to PAF beamshape, antenna-dependent system noise, radio-frequency interference, and fluctuations during commissioning on timescales from one hour to a year. Observation time, solid-angle, and search efficiency are calculated as a function of FRB fluence threshold. Using this metric, effective survey exposures and sensitivities are calculated as a function of the source counts distribution. The implied FRB rate is significantly lower than the $37$\,sky$^{-1}$\,day$^{-1}$ calculated using nominal exposures and sensitivities for this same sample by \citet{craft_nature}. At the Euclidean power-law index of $-1.5$, the rate is $10.7_{-1.8}^{+2.7}\,{\rm (sys)} \, \pm \, 3\,{\rm (stat)}$\,sky$^{-1}$\,day$^{-1}$ above a threshold of $57\pm6\,{\rm (sys)}$\,Jy\,ms, while for the best-fit index for this sample of $-2.1$, it is $16.6_{-1.5}^{+1.9} \,{\rm (sys)}\, \pm 4.7\,{\rm (stat)}$\,sky$^{-1}$\,day$^{-1}$ above a threshold of $41.6\pm1.5\,{\rm (sys)}$\,Jy\,ms. This strongly suggests that these calculations be performed for other FRB-hunting experiments, allowing meaningful comparisons to be made between them., Comment: 21 pages, 15 figures, 2 tables, accepted for publication in PASA

The next generation of astronomical surveys will revolutionize our understanding of the Universe, raising unprecedented data challenges in the process. One of them is the impossibility to rely on human scanning for the identification of unusual/unpredicted astrophysical objects. Moreover, given that most of the available data will be in the form of photometric observations, such characterization cannot rely on the existence of high resolution spectroscopic observations. We introduce an analysis of anomaly detection in the Open Supernova Catalog (http://sne.space/) with use of machine learning. We developed a strategy and pipeline — where anomalous objects are identified and then submitted to careful individual analysis. This project represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire.

In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the discovered transients will belong to the known classes of astronomical objects. However, it is expected that some transients will be rare or completely new events of unknown physical nature. The task of finding them can be framed as an anomaly detection problem. In this work, we perform for the first time an automated anomaly detection analysis in the photometric data of the Open Supernova Catalog (OSC), which serves as a proof of concept for the applicability of these methods to future large scale surveys. The analysis consists of the following steps: 1) data selection from the OSC and approximation of the pre-processed data with Gaussian processes, 2) dimensionality reduction, 3) searching for outliers with the use of the isolation forest algorithm, 4) expert analysis of the identified outliers. The pipeline returned 81 candidate anomalies, 27 (33%) of which were confirmed to be from astrophysically peculiar objects. Found anomalies correspond to a selected sample of 1.4% of the initial automatically identified data sample of ~2000 objects. Among the identified outliers we recognised superluminous supernovae, non-classical Type Ia supernovae, unusual Type II supernovae, one active galactic nucleus and one binary microlensing event. We also found that 16 anomalies classified as supernovae in the literature are likely to be quasars or stars. Our proposed pipeline represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire. All code and products of this investigation are made publicly available., Comment: 18 pages, 19 figures, accepted in MNRAS

Gravitational lensing is a phenomenon that occurs when the light from a background galaxy is bent by a foreground galaxy before reaching the observer.Gravitational lenses are a unique tool to study the structure of galaxies and to measure fundamental cosmological parameters such as the expansion rate of the Universe.Thus, it is important to identify these occurences of aligned galaxies which are particularly rare, about one for each thousand galaxies.Traditionally, this search has heavily relied on the visual inspection of images by astronomers.However, it has becoming increasingly difficult to build complete samples of gravitational lenses in such a way because upcoming astronomical surveys are going to observe billions of galaxies. For this reason it is essential to develop automatic lens-finder algorithms.In this thesis, I have developed and trained machine learning algorithms based on artificial neural networks to identify gravitational lenses in the Kilo-Degree Survey (KiDS). KiDS is an optical survey that has observed part of the South sky with unprecedented image quality.The artificial neural networks have analyzed images of tens of thousands galaxies and have aided in discovering hundreds of new gravitational lens candidates for which the images are being collected at the website https://www.astro.rug.nl/lensesinkids.This way I have demonstrated that applying these kinds of automatic methods for finding gravitational lenses is feasible in large sets of imaging data. In fact, many millions of galaxies will be analyzed by a computer neural network instead of a human eye and its neural network. This way it will be possible to select thousands of lens candidates as input for astrophysical research with minimal human intervention.