arXiv subjects

M. Lochner

Publications and source records attributed to M. Lochner.

9 recordsLinked to original sources

arXiv2025

Discovering Cataclysmic Variables from the Rubin Observatory LSST

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will provide a windfall of new transients and variable sources. Here we have performed mock observation simulations to understand LSST's expected detection rates for cataclysmic variables (CVs) with known large amplitude variations. Under the thin-disk approximation for the distribution of CVs in our Galaxy, we found that only 20% of WZ Sge-type dwarf novae systems, representing the most energetic disk-driven outbursts in CVs, will be detected during outbursts by the LSST. Given their large amplitude (7-9 mag), {only those} brighter than $17.5$ mag at outburst maximum are expected to have an r-band quiescence counterpart in individual scans. Thanks to the planned cadence of the LSST towards the Galactic center, ~70% of the simulated outbursts will be detected twice or more on the discovery night, and two-thirds will be observed in different bands. CVs of the Polar class, which display luminosity changes up to 4 mag, can be unbiasedly recovered to 22.5 mag with more than 100 detections over 10 years of the LSST operation. Finally, we attempt to characterize the detection rate of micronovae bursts, and find that about 2.6%} of the simulated sample will be observed as a >= 0.4 mag-amplitude and <= 1-d duration spike in the long-term light curve. Overall, our results consolidate LSST's capability to studying time-domain phenomena in CVs, and inform on how to plan and organize follow-up observation strategies on transients discovered by LSST.

astro-ph.SR↗

arXiv2023

Euclid Preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events

Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates. In this context, deep learning techniques are particularly suitable for the finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong lensing systems on the basis of their morphological characteristics. We train and test our models on different subsamples of a data set of forty thousand mock images, having characteristics similar to those expected in the wide survey planned with the ESA mission \Euclid, gradually including larger fractions of faint lenses. We also evaluate the importance of adding information about the colour difference between the lens and source galaxies by repeating the same training on single-band and multi-band images. Our models find samples of clear lenses with $\gtrsim 90\%$ precision and completeness, without significant differences in the performance of the three architectures. Nevertheless, when including lenses with fainter arcs in the training set, the three models' performance deteriorates with accuracy values of $\sim 0.87$ to $\sim 0.75$ depending on the model. Our analysis confirms the potential of the application of CNNs to the identification of galaxy-scale strong lenses. We suggest that specific training with separate classes of lenses might be needed for detecting the faint lenses since the addition of the colour information does not yield a significant improvement in the current analysis, with the accuracy ranging from $\sim 0.89$ to $\sim 0.78$ for the different models.

astro-ph.GA↗

arXiv2021

The MeerKAT Galaxy Cluster Legacy Survey I. Survey Overview and Highlights

MeerKAT's large number of antennas, spanning 8 km with a densely packed 1 km core, create a powerful instrument for wide-area surveys, with high sensitivity over a wide range of angular scales. The MeerKAT Galaxy Cluster Legacy Survey (MGCLS) is a programme of long-track MeerKAT L-band (900-1670 MHz) observations of 115 galaxy clusters, observed for $\sim$6-10 hours each in full polarisation. The first legacy product data release (DR1), made available with this paper, includes the MeerKAT visibilities, basic image cubes at $\sim$8" resolution, and enhanced spectral and polarisation image cubes at $\sim$8" and 15" resolutions. Typical sensitivities for the full-resolution MGCLS image products are $\sim$3-5 {\mu}Jy/beam. The basic cubes are full-field and span 4 deg^2. The enhanced products consist of the inner 1.44 deg^2 field of view, corrected for the primary beam. The survey is fully sensitive to structures up to $\sim$10' scales and the wide bandwidth allows spectral and Faraday rotation mapping. HI mapping at 209 kHz resolution can be done at $0<z<0.09$ and $0.19<z<0.48$. In this paper, we provide an overview of the survey and DR1 products, including caveats for usage. We present some initial results from the survey, both for their intrinsic scientific value and to highlight the capabilities for further exploration with these data. These include a primary beam-corrected compact source catalogue of $\sim$626,000 sources for the full survey, and an optical/infrared cross-matched catalogue for compact sources in Abell 209 and Abell S295. We examine dust unbiased star-formation rates as a function of clustercentric radius in Abell 209 and present a catalogue of 99 diffuse cluster sources (56 are new), some of which have no suitable characterisation. We also highlight some of the radio galaxies which challenge current paradigms and present first results from HI studies of four targets.

astro-ph.GA↗

arXiv2020

Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition which aimed to catalyze the development of robust classifiers under LSST-like conditions of a non-representative training set for a large photometric test set of imbalanced classes. Over 1,000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between Sep 28, 2018 and Dec 17, 2018, ultimately identifying three winners in February 2019. Participants produced classifiers employing a diverse set of machine learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multi-layer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state-of-the-art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next generation PLAsTiCC data set.

astro-ph.IM↗

arXiv2019

Mini-survey of the northern sky to Dec <+30

We propose an extension of the LSST survey to cover the northern sky to DEC < +30 (accessible at airmass <1.8). This survey will increase the LSST sky coverage by ~9,600 square degrees from 18,900 to 28,500 square degrees (a 50% increase) but use only 0.6-2.5% of the time depending on the synergies with other surveys. This increased area addresses a wide range of science cases that enhance all of the primary LSST science goals by significant amounts. The science enabled includes: increasing the area of the sky accessible for follow-up of multi-messenger transients including gravitational waves, mapping the milky way halo and halo dwarfs including discovery of RR Lyrae stars in the outer galactic halo, discovery of z>7 quasars in combination Euclid, enabling a second generation DESI and other spectroscopic surveys, and enhancing all areas of science by improving synergies with Euclid, WFIRST, and unique northern survey facilities. This white paper is the result of the Tri-Agency Working Group (TAG) appointed to develop synergies between missions and presents a unified plan for northern coverage. The range of time estimates reflects synergies with other surveys. If the modified DESC WFD survey, the ecliptic plane mini survey, and the north galactic spur mini survey are executed this plan would only need 0.6% of the LSST time, however if none of these are included the overall request is 2.5% of the 10 year survey life. In other words, the majority of these observations are already suggested as part of these other surveys and the intent of this white paper is to propose a unified baseline plan to carry out a broad range of objectives to facilitate a combination of multiple science objectives. A companion white paper gives Euclid specific science goals, and we support the white papers for southern extensions of the LSST survey.

astro-ph.IM↗

arXiv2019

Enhancing LSST Science with Euclid Synergy

This white paper is the result of the Tri-Agency Working Group (TAG) appointed to develop synergies between missions and is intended to clarify what LSST observations are needed in order to maximally enhance the combined science output of LSST and Euclid. To facilitate LSST planning we provide a range of possible LSST surveys with clear metrics based on the improvement in the Dark Energy figure of merit (FOM). To provide a quantifiable metric we present five survey options using only between 0.3 and 3.8% of the LSST 10 year survey. We also provide information so that the LSST DDF cadence can possibly be matched to those of \emph{Euclid} in common deep fields, SXDS, COSMOS, CDFS, and a proposed new LSST deep field (near the Akari Deep Field South). Co-coordination of observations from the Large Synoptic Survey Telescope (LSST) and Euclid will lead to a significant number of synergies. The combination of optical multi-band imaging from LSST with high resolution optical and near-infrared photometry and spectroscopy from \emph{Euclid} will not only improve constraints on Dark Energy, but provide a wealth of science on the Milky Way, local group, local large scale structure, and even on first galaxies during the epoch of reionization. A detailed paper has been published on the Dark Energy science case (Rhodes et al.) by a joint LSST/Euclid working group as well as a white paper describing LSST/Euclid/WFIRST synergies (Jain et al.), and we will briefly describe other science cases here. A companion white paper argues the general science case for an extension of the LSST footprint to the north at airmass < 1.8, and we support the white papers for southern extensions of the LSST survey.

astro-ph.IM↗

arXiv2018

Classification of Multiwavelength Transients with Machine Learning

With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine learning techniques are well suited to address this data challenge and rapidly classify newly detected transients. We present a multiwavelength classification algorithm consisting of three steps: (1) interpolation and augmentation of the data using Gaussian processes; (2) feature extraction using wavelets; and (3) classification with random forests. Augmentation provides improved performance at test time by balancing the classes and adding diversity into the training set. In the first application of machine learning to the classification of real radio transient data, we apply our technique to the Green Bank Interferometer and other radio light curves. We find we are able to accurately classify most of the 11 classes of radio variables and transients after just eight hours of observations, achieving an overall test accuracy of 78 percent. We fully investigate the impact of the small sample size of 82 publicly available light curves and use data augmentation techniques to mitigate the effect. We also show that on a significantly larger simulated representative training set that the algorithm achieves an overall accuracy of 97 percent, illustrating that the method is likely to provide excellent performance on future surveys. Finally, we demonstrate the effectiveness of simultaneous multiwavelength observations by showing how incorporating just one optical data point into the analysis improves the accuracy of the worst performing class by 19 percent.

astro-ph.IM↗

arXiv2018

Radio Galaxy Shape Measurement with Hamiltonian Monte Carlo in the Visibility Domain

Radio weak lensing, while a highly promising complementary probe to optical weak lensing, will require incredible precision in the measurement of galaxy shape parameters. In this paper, we extend the Bayesian Inference for Radio Observations model fitting approach to measure galaxy shapes directly from visibility data of radio continuum surveys, instead of from image data. We apply a Hamiltonian Monte Carlo (HMC) technique for sampling the posterior, which is more efficient than the standard Monte Carlo Markov Chain method when dealing with a large dimensional parameter space. Adopting the exponential profile for galaxy model fitting allows us to analytically calculate the likelihood gradient required by HMC, allowing a faster and more accurate sampling. The method is tested on SKA1-MID simulated observations at 1.4 GHz of a field containing up to 1000 star-forming galaxies. It is also applied to a simulated observation of the weak lensing precursor survey SuperCLASS. In both cases we obtain reliable measurements of the galaxies' ellipticity and size for all sources with SNR $\ge 10$, and we also find relationships between the convergence properties of the HMC technique and some source parameters. Direct shape measurement in the visibility domain achieves high accuracy at the expected source number densities of the current and next SKA precursor continuum surveys. The proposed method can be easily extended for the fitting of other galaxy and scientific parameters, as well as simultaneously marginalising over systematic and instrumental effects.

astro-ph.IM↗

arXiv2018

The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals

Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of their underlying physical processes. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional labeling procedures are inappropriate. Probabilistic classification is more appropriate for the data but are incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations intend to use these classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks probabilistic classifications and must serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) is an open competition aiming to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community both within and outside astronomy. Using mock classification probability submissions emulating archetypes of those anticipated of PLAsTiCC, we compare the sensitivity of metrics of classification probabilities under various weighting schemes, finding that they yield qualitatively consistent results. We choose as a metric for PLAsTiCC a weighted modification of the cross-entropy because it can be meaningfully interpreted. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic classifications.

astro-ph.IM↗