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Petr Škoda

Publications and source records attributed to Petr Škoda.

At least 19 recordsLinked to original sources

PLATOSpec's first results: Three new transiting warm Jupiters from the WINE survey TIC 147027702, TIC 245076932 and TIC 87422071

We report the discovery and characterisation of three transiting warm Jupiters: TIC 147027702b, TIC 245076932b and TIC 87422071b. These systems were initially identified as transiting candidates using light curves generated from the full-frame images of the TESS mission. We confirmed the planetary nature of these objects with ground-based spectroscopic follow-up observations using FEROS and the new PLATOSpec spectrograph attached to the ESO 1.52 m telescope at the La Silla Observatory, and with ground-based photometric observations of the Observatoire Moana, Las Cumbres Observatory Global Telescope and ASTEP. From a global fit to the photometry and radial velocities, we determine that the planet TIC 147027702b has a low-eccentric orbit ($e = 0.13 \pm 0.05$) with a period of 44.4 days and has a mass of $1.09^{+0.07}_{-0.13}$ M$_J$ and a radius of $0.98 \pm 0.06$ R$_J$. TIC 245076932b has a moderately low mass of $0.51 \pm 0.05$ M$_J$, a radius of $0.97 \pm 0.05$ R$_J$, and an eccentric orbit ($e = 0.43 \pm 0.02$) with a period of 21.6 days. TIC 87422071b has a mass of $1.29 \pm 0.10$ M$_J$, a radius of $0.97 \pm 0.08$ R$_J$, and has a slightly eccentric orbit ($e = 0.12 \pm 0.07$) with a period of 11.3 days. These well-characterised warm Jupiters expand the currently limited sample of similar gas giants and provide valuable benchmarks for testing models of giant-planet formation, migration, and tidal evolution.

astro-ph.EP↗

TIC65910228b: A single-transit discovery of a massive long-period warm Jupiter with TESS

Context. Warm Jupiters are excellent case studies for the investigation of giant planet internal structures and formation theories. However, the sample of long-period transiting giants is still small today for a better understanding of this population. Aims. Starting from a single transit found in the Transiting Exoplanet Survey Satellite (TESS) data, we confirm the planetary nature of the signal and measure its orbital parameters, mass, and radius. We put this system in the context of long-period giant transiting planets and analyzed the viability to sustain atmospheric or dynamical follow-up. Methods. We carried out a spectroscopic follow-up using FEROS and PLATOSpec to obtain precise radial velocities. We added a photometric follow-up with HATPI and Observatoire Moana to obtain a more precise estimate of the orbital period. We derived the orbital and physical parameters through a joint analysis of this data. Results. We report the discovery and characterization of TIC65910228b, a transiting warm Jupiter with a mass of $4.554 \pm 0.255$ $M_J$ and a radius of $1.088 \pm 0.061$ $R_J$, orbiting an evolved F-type star every $\sim 180.52$ days in an eccentric orbit ($e = 0.25 \pm 0.04$). Conclusions. This planet joins a still under-explored population of long-period ($P > 100$) massive ($M_p > 4$ $M_J$) transiting giant planets, being one of the few with a mild eccentricity. This target is a nice example of the potential of single-transit events to populate this region of the parameter space.

astro-ph.EP↗

From Knots to Knobs: Towards Steerable Collaborative Filtering Using Sparse Autoencoders

Sparse autoencoders (SAEs) have recently emerged as pivotal tools for introspection into large language models. SAEs can uncover high-quality, interpretable features at different levels of granularity and enable targeted steering of the generation process by selectively activating specific neurons in their latent activations. Our paper is the first to apply this approach to collaborative filtering, aiming to extract similarly interpretable features from representations learned purely from interaction signals. In particular, we focus on a widely adopted class of collaborative autoencoders (CFAEs) and augment them by inserting an SAE between their encoder and decoder networks. We demonstrate that such representation is largely monosemantic and propose suitable mapping functions between semantic concepts and individual neurons. We also evaluate a simple yet effective method that utilizes this representation to steer the recommendations in a desired direction.

cs.IR↗

Semantic Web: Past, Present, and Future (with Machine Learning on Knowledge Graphs and Language Models on Knowledge Graphs)

Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on the Web, enhance them with semantics to give them meaning, and enable inference and reasoning on them. Throughout the years, semantic technologies, and in particular knowledge graphs, have been used in search engines, data integration, enterprise settings, and machine learning. In this paper, we recap the classical concepts and foundations of the Semantic Web as well as modern and recent concepts and applications, building upon these foundations. The classical topics we cover include knowledge representation, creating and validating knowledge on the Web, reasoning and linking, and distributed querying. We enhance this classical view of the so-called ``Semantic Web Layer Cake'' with an update of recent concepts. These include provenance, security and trust, as well as a discussion of practical impacts from industry-led contributions. We also provide an overiew of shallow and deep machine learning methods for knowledge graphs and discuss the relation of language models and knowledge graphs. We conclude with an outlook on the future directions of the Semantic Web.

cs.AI↗

Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.

cs.AI↗

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Current archives of the LAMOST telescope contain millions of pipeline-processed spectra that have probably never been seen by human eyes. Most of the rare objects with interesting physical properties, however, can only be identified by visual analysis of their characteristic spectral features. A proper combination of interactive visualisation with modern machine learning techniques opens new ways to discover such objects. We apply active learning classification supported by deep convolutional networks to automatically identify complex emission-line shapes in multi-million spectra archives. We used the pool-based uncertainty sampling active learning driven by a custom-designed deep convolutional neural network with 12 layers inspired by VGGNet, AlexNet, and ZFNet, but adapted for one-dimensional feature vectors. The unlabelled pool set is represented by 4.1 million spectra from the LAMOST DR2 survey. The initial training of the network was performed on a labelled set of about 13000 spectra obtained in the region around H$α$ by the 2m Perek telescope of the Ondřejov observatory, which mostly contains spectra of Be and related early-type stars. The differences between the Ondřejov intermediate-resolution and the LAMOST low-resolution spectrographs were compensated for by Gaussian blurring. After several iterations, the network was able to successfully identify emission-line stars with an error smaller than 6.5%. Using the technology of the Virtual Observatory to visualise the results, we discovered 1013 spectra of 948 new candidates of emission-line objects in addition to 664 spectra of 549 objects that are listed in SIMBAD and 2644 spectra of 2291 objects identified in an earlier paper of a Chinese group led by Wen Hou. The most interesting objects with unusual spectral properties are discussed in detail.

astro-ph.IM↗

Excluded minors for the Klein Bottle I. Low connectivity case

Graphs that are critical (minimal excluded minors) for embeddability in surfaces are studied. In Part I we consider the structure of graphs with a 2-vertex-cut that are critical with respect to the Euler genus. A general theorem describing the building blocks is presented. These constituents, called hoppers and cascades, are classified for the case when Euler genus is small. As a consequence, the complete list of obstructions of connectivity 2 for embedding graphs into the Klein bottle is obtained. This is the first complete result about obstructions for embeddability of graphs in the Klein bottle, and the outcome is somewhat surprising in the sense that there are considerably fewer excluded minors than expected.

math.CO↗

Excluded minors for the Klein Bottle II. Cascades

Graphs that are critical (minimal excluded minors) for embeddability in surfaces are studied. In Part I, it was shown that graphs that are critical for embeddings into surfaces of Euler genus $k$ or for embeddings into nonorientable surface of genus $k$ are built from 3-connected components, called hoppers and cascades. In Part II, all cascades for Euler genus 2 are classified. As a consequence, the complete list of obstructions of connectivity 2 for embedding graphs into the Klein bottle is obtained.

math.CO↗

Identification of Artifacts and Interesting Celestial Objects in LAMOST Spectral Survey

The LAMOST DR1 survey contains about two million of spectra labelled by its pipeline as stellar objects of common spectral classes. There is, however, a lot of spectra corrupted in some way by both instrumental and processing artifacts, which may mimic spectral properties of interesting celestial objects, namely emission lines of Be stars and quasars. We have tested several clustering methods as well as outliers analysis on a sample of one hundred thousand spectra using Spark scripts running on Hadoop cluster consisting of twenty-four sixteen-core nodes. This experiment was motivated by an attempt to find rare objects with interesting spectra as outliers most dissimilar from all common spectra. The result of this time-consuming procedure is a list of several hundred candidates where different artifacts are prominent, but also tens of very interesting emission-line spectra requiring further detailed examination. Many of them may be quasars or even blazars as well as yet unknown Be-stars. It deserves mentioning that most of the work benefitted considerably from technologies of Virtual Observatory.

astro-ph.IM↗

Identification of Interesting Objects in Large Spectral Surveys Using Highly Parallelized Machine Learning

The current archives of LAMOST multi-object spectrograph contain millions of fully reduced spectra, from which the automatic pipelines have produced catalogues of many parameters of individual objects, including their approximate spectral classification. This is, however, mostly based on the global shape of the whole spectrum and on integral properties of spectra in given bandpasses, namely presence and equivalent width of prominent spectral lines, while for identification of some interesting object types (e.g. Be stars or quasars) the detailed shape of only a few lines is crucial. Here the machine learning is bringing a new methodology capable of improving the reliability of classification of such objects even in boundary cases. We present results of Spark-based semi-supervised machine learning of LAMOST spectra attempting to automatically identify the single and double-peak emission of H alpha line typical for Be and B[e] stars. The labelled sample was obtained from archive of 2m Perek telescope at Ondřejov observatory. A simple physical model of spectrograph resolution was used in domain adaptation to LAMOST training domain. The resulting list of candidates contains dozens of Be stars (some are likely yet unknown), but also a bunch of interesting objects resembling spectra of quasars and even blazars, as well as many instrumental artefacts. The verification of a nature of interesting candidates benefited considerably from cross-matching and visualisation in the Virtual Observatory environment.

astro-ph.IM↗

Cascades and Obstructions of Low Connectivity for Embedding Graphs into the Klein Bottle

The structure of graphs with a 2-vertex-cut that are critical with respect to the Euler genus is studied. A general theorem describing the building blocks is presented. These constituents, called hoppers and cascades, are classified for the case when Euler genus is small. As a consequence, the complete list of obstructions of connectivity 2 for embedding graphs into the Klein bottle is obtained.

math.CO↗

Obstructions of Connectivity 2 for Embedding Graphs into the Torus

The complete set of minimal obstructions for embedding graphs into the torus is still not determined. In this paper, we present all obstructions for the torus of connectivity 2. Furthermore, we describe the building blocks of obstructions of connectivity 2 for any orientable surface.

math.CO↗

Calcium Emission in Interacting Binary Be Stars

Polidan (1976) suggested that Be stars showing the CaII IR triplet in emission are interacting binaries. With the advent of the Gaia satellite, which will host a spectrometer to observe stars in the range 8470--8750 Å, we carried out a spectroscopic survey of 150 Be stars, including Be binaries. We show that the Ca II triplet in emission, often connected with emission in Paschen lines, is an indicator of a peculiar environment in a Be star disc rather than a signature of an interacting binary Be star. However, Ca II emission without visible emission in Paschen lines is observed in interacting binary stars, as well as in peculiar objects. During the survey, a new interacting Be binary - HD 81357 - was discovered.

astro-ph.SR↗

The Stagnation of Contemporary Stellar Astronomy

The stellar astronomy has always been considered the fundamental source of knowledge about the basic building blocks of the universe - the stars. It has proved correctness of many physical theories - like e.g. the idea of nuclear fusion in stellar cores, the exchange of mass in interacting binaries or models of stellar evolution towards white dwarfs or neutron stars. Despite its well acknowledged importance it seems to be loosing its interestingness for students, for telescope allocation committees at large observatories, as well as for granting agencies. In the domain of big telescopes it has been gradually overtaken by the extra-galactic research and cosmology, surviving however at smaller observatories and among most advanced amateur astronomers. We try to analyse the main obstacles lowering the efficiency of research in contemporary stellar astronomy. We will shortly tackle several problems induced by paradigmatic changes in handling the extraordinary amount of data provided by current instruments as well as by introduction of economical criteria and factory-like management into the modern astronomy. Finally we speculate the reasons of a marginal role of Virtual observatory in contemporary stellar research and give some ideas of possible improvements.

astro-ph.IM↗

The Virtual Observatory and its Benefits for Amateur Astronomers

The contemporary astronomical instruments have been producing the unprecedented amount of data. The largest part of this "data avalanche" is being produced by deep all-sky surveys yielding terabytes of raw data per night. Such a great data volumes can hardly even been reduced by automatic pipelines running on supercomputer grids but it is impossible to exploit fully their content by a small group of professional astronomers in the interested research teams. New tools for collaborative work with heterogeneous data sets spread over distant servers are being developed in the framework of the Virtual Observatory (VO). As many VO resources are freely available on the Internet, a new opportunity opens for the amateur astronomers to do professional research using these tools in an Internet browser on a moderately fast connection. We give short overview of current and future sky surveys producing data on a millions of targets - hence the term Megasurveys, and we introduce the basic principles of Virtual Observatory and its current applications

astro-ph.IM↗

Investigation of Residual Blaze Functions in Slit-Based Echelle Spectrograph

We have studied the Residual Blaze Functions (RBF) resulting from division of individual echelle orders by extracted flat-field in spectra obtained by slit-fed OES spectrograph of 2m telescope of Ondřejov observatory, Czech Republic. We have eliminated the dependence on target and observation conditions by semiautomatic fitting of global response function, thus getting the instrument-only dependent part, which may be easily incorporated into data reduction pipeline. The improvement of reliability of estimation of continuum on spectra of targets with wide and shallow lines is noticeable and the merging of all orders into the one long spectrum gives much more reliable results.

astro-ph.IM↗

Searching of New Emission-Line Stars using the Astroinformatics Approach

Using data mining techniques applied on emission line characteristics of Be stars spectra we attempted to find new Be stars candidates in SDSS SEGUE survey. The mid-resolution spectra of confirmed Be stars obtained from VO-compatible archive of Ondřejov observatory 2m telescope were transformed to the spectral resolution of SDSS and important characteristics of emission line profiles were estimated, to be used as a training base of supervised learning methods. The obtained knowledge base of the characteristic shapes and sizes of Be emission lines was finally used to identify new potential candidates in SDSS spectral survey. The several newly found Be stars candidates justify our approach and approve Astroinformatics as a viable research methodology.

astro-ph.IM↗

Common Methods of Stellar Spectra Analysis and their Support in Virtual Observatory

The proper scientific analysis of a large amount of stellar spectra requires certain capabilities of the analysing tool (e.g. precise semi-automatic fitting for normalisation of the continuum or line-list assisted measurement of spectral lines) as well as flexible list-driven datafile handling. While most astronomical legacy packages comprise powerful analysing and data management features allowing rapid processing of quite complex data, current VO tools are lacking support of even basic capabilities commonly used in stellar spectroscopy. Especially the high resolution optical spectroscopy of stars with rapid line profile variations or of those with complicated emission profiles benefits from a number of specific methods unsupported in todays Virtual Observatory (VO) tools and even lacking definition in VO spectral protocols. In our contribution we identify these techniques, describe their possible implementations and finally give a short overview of several VO-compatible tools emphasising their deficiencies and comparing with the capabilities of common legacy packages.

astro-ph.IM↗