Microlensing signatures of extended dark objects using machine learning

This paper presents a machine learning-based method for the detection of the unique gravitational microlensing signatures of extended dark objects, such as boson stars, axion miniclusters and subhalos. We adapt MicroLIA, a machine learning-based package tailored to handle the challenges posed by low-cadence data in microlensing surveys. Using realistic observational time stamps, our models are trained on simulated light curves to distinguish between microlensing by pointlike and extended lenses, as well as from other object classes which give a variable magnitude. We focus on boson stars and Navarro-Frenk-White (NFW) subhalos and show that the former, which are examples of objects with a relatively flat mass distribution, can be confidently identified for 0.8 ≲ r=rE ≲ 3. Intriguingly, we also find that more sharply peaked structures, such as NFW subhalos, can be distinctly recognized from point lenses under regular observation cadence. Our findings significantly advance the potential of microlensing data in uncovering the elusive nature of extended dark objects. The code and dataset used are also provided

Detection Limits and Planet Occurrence Rate in the CARMENES Sample

Màster Oficial d'Astrofísica, Física de Partícules i Cosmologia, Facultat de Física, Universitat de Barcelona, Curs: 2019-2020, Tutors: Juan Carlos Morales, Carme JordiThe CARMENES survey is monitoring more than 300 M-dwarf stars looking for exoplanets. Besides planet discoveries, the data it produces can also be used to estimate the statistics of planets around late-type stars. In this work, we aim at estimating the detection limits of the CARMENES survey, and the occurrence rate of Jupiter- and Neptune-like planets around M-dwarf stars. For this purpose, we use a sample with 324 stars for which values for the radial velocity as a function of time have been measured. We remove the signals produced by planets or intrinsic stellar variability to analyse the instrumental noise. In this noise we look for the minimum planetary mass that could be discovered, obtaining a lower detection limit. With this result we estimate the occurrence rate of M-dwarf planets at different minimum mass and orbital period ranges. For Jupiter- and Neptune-like planets we obtained an occurrence rate of ~ 1%

Microlensing signatures of extended dark objects using machine learning

This paper presents a machine learning-based method for the detection of the unique gravitational microlensing signatures of extended dark objects, such as boson stars, axion miniclusters and subhalos. We adapt MicroLIA, a machine learning-based package tailored to handle the challenges posed by low-cadence data in microlensing surveys. Using realistic observational timestamps, our models are trained on simulated light curves to distinguish between microlensing by point-like and extended lenses, as well as from other object classes which give a variable magnitude. We show that boson stars, examples of objects with a relatively flat mass distribution, can be confidently identified for $0.8 \lesssim r/r_E\lesssim 3$. Intriguingly, we also find that more sharply peaked structures, such as NFW-subhalos, can be distinctly recognized from point-lenses under regular observation cadence. Our findings significantly advance the potential of microlensing data in uncovering the elusive nature of extended dark objects. The code and dataset used are also provided.Comment: 12 pages, 13 figures. Code provided in https://gitlab.com/miguel.romao/microlensing-extended-objects-machine-learning . Data provided in https://zenodo.org/records/1056686

Bayesian estimates of astronomical time delays between gravitationally lensed stochastic light curves

The gravitational field of a galaxy can act as a lens and deflect the light emitted by a more distant object such as a quasar. Strong gravitational lensing causes multiple images of the same quasar to ap- pear in the sky. Since the light in each gravitationally lensed image traverses a different path length from the quasar to the Earth, fluc- tuations in the source brightness are observed in the several images at different times. The time delay between these fluctuations can be used to constrain cosmological parameters and can be inferred from the time series of brightness data or light curves of each image. To estimate the time delay, we construct a model based on a state- space representation for irregularly observed time series generated by a latent continuous-time Ornstein-Uhlenbeck process. We account for microlensing, an additional source of independent long-term ex- trinsic variability, via a polynomial regression. Our Bayesian strategy adopts a Metropolis-Hastings within Gibbs sampler. We improve the sampler by using an ancillarity-sufficiency interweaving strategy and adaptive Markov chain Monte Carlo. We introduce a profile likeli- hood of the time delay as an approximation of its marginal posterior distribution. The Bayesian and profile likelihood approaches comple- ment each other, producing almost identical results; the Bayesian method is more principled but the profile likelihood is simpler to implement. We demonstrate our estimation strategy using simulated data of doubly- and quadruply-lensed quasars, and observed data from quasars Q0957+561 and J1029+2623

Single-lens mass measurement in the high-magnification microlensing event Gaia19bld located in the Galactic disc

This work was supported from the Polish NCN grants: Preludium No. 2017/25/N/ST9/01253, Harmonia No. 2018/30/M/ST9/00311, MNiSW grant DIR/WK/2018/12, Daina No. 2017/27/L/ST9/03221, and by the Research Council of Lithuania, grant No. S-LL-19-2. The OGLE project has received funding from the NCN grant MAESTRO 2014/14/A/ST9/00121 to AU. We acknowledge the European Commission’s H2020 OPTICON grant No. 730890. YT acknowledges the support of DFG priority program SPP 1992 “Exploring the Diversity of Extrasolar Planets” (WA 1047/11-1). EB and RS gratefully acknowledge support from NASA grant 80NSSC19K0291. Work by AG was supported by JPL grant 1500811. Work by JCY was supported by JPL grant 1571564. SJF thanks Telescope Live for access to their telescope network. NN acknowledges the support of Data Science Research Center, Chiang Mai University. FOE acknowledges the support from the FONDECYT grant nr. 1201223. MK acknowledges the support from the NCN grant No. 2017/27/B/ST9/02727.Context. Microlensing provides a unique opportunity to detect non-luminous objects. In the rare cases that the Einstein radius θE and microlensing parallax πE can be measured, it is possible to determine the mass of the lens. With technological advances in both ground- and space-based observatories, astrometric and interferometric measurements are becoming viable, which can lead to the more routine determination of θE and, if the microlensing parallax is also measured, the mass of the lens.  Aims. We present the photometric analysis of Gaia19bld, a high-magnification (A approximate to 60) microlensing event located in the southern Galactic plane, which exhibited finite source and microlensing parallax effects. Due to a prompt detection by the Gaia satellite and the very high brightness of I = 9.05 mag at the peak, it was possible to collect a complete and unique set of multi-channel follow-up observations, which allowed us to determine all parameters vital for the characterisation of the lens and the source in the microlensing event.  Methods. Gaia19bld was discovered by the Gaia satellite and was subsequently intensively followed up with a network of ground-based observatories and the Spitzer Space Telescope. We collected multiple high-resolution spectra with Very Large Telescope (VLT)/X-shooter to characterise the source star. The event was also observed with VLT Interferometer (VLTI)/PIONIER during the peak. Here we focus on the photometric observations and model the light curve composed of data from Gaia, Spitzer, and multiple optical, ground-based observatories. We find the best-fitting solution with parallax and finite source effects. We derived the limit on the luminosity of the lens based on the blended light model and spectroscopic distance.  Results. We compute the mass of the lens to be 1.13 ± 0.03 M⊙ and derive its distance to be 5.52-0.64+0.35 kpc. The lens is likely a main sequence star, however its true nature has yet to be verified by future high-resolution observations. Our results are consistent with interferometric measurements of the angular Einstein radius, emphasising that interferometry can be a new channel for determining the masses of objects that would otherwise remain undetectable, including stellar-mass black holes.Publisher PDFPeer reviewe

The Impact of Gaia and LSST on Binaries and Exoplanets

Two upcoming large scale surveys, the ESA Gaia and LSST projects, will bring a new era in astronomy. The number of binary systems that will be observed and detected by these projects is enormous, estimations range from millions for Gaia to several tens of millions for LSST. We review some tools that should be developed and also what can be gained from these missions on the subject of binaries and exoplanets from the astrometry, photometry, radial velocity and their alert system

Deep Learning, Shallow Dips: Transit light curves have never been so trendy

At the crossroad between photometry and time-domain astronomy, light curves are invaluable data objects to study distant events and sources of light even when they can not be spatially resolved. In particular, the field of exoplanet sciences has tremendously benefited from acquired stellar light curves to detect and characterise a majority of the outer worlds that we know today. Yet, their analysis is challenged by the astrophysical and instrumental noise often diluting the signals of interest. For instance, the detection of shallow dips caused by transiting exoplanets in stellar light curves typically require a precision of the order of 1 ppm to 100 ppm in units of stellar flux, and their very study directly depends upon our capacity to correct for instrumental and stellar trends. The increasing number of light curves acquired from space and ground-based telescopes—of the order of billions—opens up the possibility for global, efficient, automated processing algorithms to replace individual, parametric and hard-coded ones. Luckily, the field of deep learning is also progressing fast, revolutionising time series problems and applications. This reinforces the incentive to develop data-driven approaches hand-in-hand with existing scientific models and expertise. With the study of exoplanetary transits in focus, I developed automated approaches to learn and correct for the time-correlated noise in and across light curves. In particular, I present (i) a deep recurrent model trained via a forecasting objective to detrend individual transit light curves (e.g. from the Spitzer space telescope); (ii) the power of a Transformer-based model leveraging whole datasets of light curves (e.g. from large transit surveys) to learn the trend via a masked objective; (iii) a hybrid and flexible framework to combine neural networks with transit physics

Single-lens mass measurement in the high-magnification microlensing event Gaia 19bld located in the Galactic disc

CONTEXT: Microlensing provides a unique opportunity to detect non-luminous objects. In the rare cases that the Einstein radius θ_{E} and microlensing parallax π_{E} can be measured, it is possible to determine the mass of the lens. With technological advances in both ground- and space-based observatories, astrometric and interferometric measurements are becoming viable, which can lead to the more routine determination of θ_{E} and, if the microlensing parallax is also measured, the mass of the lens. AIMS: We present the photometric analysis of Gaia19bld, a high-magnification (A ≈ 60) microlensing event located in the southern Galactic plane, which exhibited finite source and microlensing parallax effects. Due to a prompt detection by the Gaia satellite and the very high brightness of I = 9.05 mag at the peak, it was possible to collect a complete and unique set of multi-channel follow-up observations, which allowed us to determine all parameters vital for the characterisation of the lens and the source in the microlensing event. METHODS: Gaia19bld was discovered by the Gaia satellite and was subsequently intensively followed up with a network of ground-based observatories and the Spitzer Space Telescope. We collected multiple high-resolution spectra with Very Large Telescope (VLT)/X-shooter to characterise the source star. The event was also observed with VLT Interferometer (VLTI)/PIONIER during the peak. Here we focus on the photometric observations and model the light curve composed of data from Gaia, Spitzer, and multiple optical, ground-based observatories. We find the best-fitting solution with parallax and finite source effects. We derived the limit on the luminosity of the lens based on the blended light model and spectroscopic distance. RESULTS: We compute the mass of the lens to be 1.13 ± 0.03 M_{⊙} and derive its distance to be 5.52_{−0.64}^{+0.35} kpc. The lens is likely a main sequence star, however its true nature has yet to be verified by future high-resolution observations. Our results are consistent with interferometric measurements of the angular Einstein radius, emphasising that interferometry can be a new channel for determining the masses of objects that would otherwise remain undetectable, including stellar-mass black holes

Rubin Observatory LSST Transients and Variable Stars Roadmap

The Vera C. Rubin Legacy Survey of Space and Time holds the potential to revolutionize time domain astrophysics, reaching completely unexplored areas of the Universe and mapping variability time scales from minutes to a decade. To prepare to maximize the potential of the Rubin LSST data for the exploration of the transient and variable Universe, one of the four pillars of Rubin LSST science, the Transient and Variable Stars Science Collaboration, one of the eight Rubin LSST Science Collaborations, has identified research areas of interest and requirements, and paths to enable them. While our roadmap is ever-evolving, this document represents a snapshot of our plans and preparatory work in the final years and months leading up to the survey\u27s first light