Publications

2024

• Gensior J, Feldmann R, Reina-Campos M, Trujillo-Gomez S, Mayer L, Keller BW, Wetzel A, Kruijssen JMD, Hopkins PF, Moreno J (2024). H  i discs of L* galaxies as probes of the baryonic physics of galaxy evolution, Monthly Notices of the Royal Astronomical Society 531(1):1158-1178 1883
• De Lucia G, Kruijssen JMD, Trujillo-Gomez S, Hirschmann M, Xie L (2024). On the origin of globular clusters in a hierarchical universe, Monthly Notices of the Royal Astronomical Society 530(3):2760-2777 1884
• Gianniotis N, Polsterer KL, Pérez IIC (2024). Positive and Scale Invariant Gaussian Process Latent Variable Model for Astronomical Spectra, ESANN 2024 proceesdings,pp.315-320,Ciaco – i6doc.com 1867

2023

• Trujillo-Gomez S, Kruijssen JMD, Pfeffer J, Reina-Campos M, Crain RA, Bastian N, Cabrera-Ziri I (2023). In situ or accreted? Using deep learning to infer the origin of extragalactic globular clusters from observables, Monthly Notices of the Royal Astronomical Society 526(4):5735-5755 1888
• Nunez FP, Gianniotis N, Polsterer K (2023). A Gaussian process cross-correlation approach to time-delay estimation in active galactic nuclei, A&A 1657
• Djorgovski S, Mahabal A, Graham M, Polsterer K, Krone-Martins A (2023). Applications of AI in Astronomy 1622
• Nuñez FP, Bruckmann C, Desamutara S, Czerny B, Panda S, Lobban AP, Pietrzyński G, Polsterer KL (2023). Modeling photometric reverberation mapping data for the next generation of big data surveys. Quasar accretion disks sizes with the LSST, Monthly Notices of the Royal Astronomical Society,stad286 1630
• Czerny B, Cao S, Jaiswal VK, Karas V, Khadka N, Martínez-Aldama ML, Naddaf MH, Panda S, Nuñez FP, Prince R, Ratra B, Sniegowska M, Yu Z, Zajaček M (2023). Accretion disks, quasars and cosmology: meandering towards understanding, Astrophys Space Sci 368(2),8 1628

2022

• Vaduvescu O, Macias AA, Wilson TG, Zegmott T, Pérez Toledo FM, Predatu M, Gherase R, Pinter V, Nunez FP, Ulaczyk K, Soszyński I, Mróz P, Wrona M, Iwanek P, Szymanski M, Udalski A, Char F, Olave HS, Aravena-Rojas G, Vergara AC, Saez C, Unda-Sanzana E, Alcalde B, Burgos Ad, Nespral D, Galera-Rosillo R, Amos NJ, Hibbert J, López-Comazzi A, Oey J, Serra-Ricart M, Licandro J, Popescu M (2022). The EURONEAR Lightcurve Survey of Near Earth Asteroids 2017–2020, Earth Moon Planets 126(2),6 1629
• Boch T, Allen M, Bot C, Fernique P, Baumann M, Buga M, Bonnarel F, Durand D, Polsterer K (2022). Innovative Tools Fostered by the HiPS Ecosystem 1623
• Kollasch F, Polsterer K (2022). Interactive Exploration Framework for Big Data Sets 1624
• Lerch S, Polsterer K (2022). Convolutional autoencoders for spatially-informed ensemble post-processing 1483
• Wielgórski P, Pietrzyński G, Pilecki B, Gieren W, Zgirski B, Górski M, Hajdu G, Narloch W, Karczmarek P, Smolec R, Kervella P, Storm J, Gallenne A, Breuval L, Lewis M, Kałuszyński M, Graczyk D, Pych W, Suchomska K, Taormina M, Garcia GR, Kotek A, Chini R, Nũnez FP, Noroozi S, Figaredo CS, Haas M, Hodapp K, Mikołajczyk P, Kotysz K, Moździerski D, Kołaczek-Szymański P (2022). An Absolute Calibration of the Near-infrared Period–Luminosity Relations of Type II Cepheids in the Milky Way and in the Large Magellanic Cloud, ApJ 927(1):89 1627
• Plier J, Zisler M, Furkel J, Knoll M, Marx A, Fischer A, Polsterer K, Konstandin MH, Petra S (2022). Learning Features via Transformer Networks for Cardiomyocyte Profiling, Bildverarbeitung für die Medizin 2022,pp.167-172,Springer Fachmedien Wiesbaden 1481

2021

• Gianniotis N, Nuñez FP, Polsterer KL (2021). Disentangling the optical AGN and host-galaxy luminosity with a probabilistic flux variation gradient, A&A 1303
• Polsterer K, Lerch S, D’Isanto A (2021). From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work 1473
• Mostert RIJ, Duncan KJ, Röttgering HJA, Polsterer KL, Best PN, Brienza M, Brüggen M, Hardcastle MJ, Jurlin N, Mingo B, Morganti R, Shimwell T, Smith D, Williams WL (2021). Unveiling the rarest morphologies of the LOFAR Two-metre Sky Survey radio source population with self-organised maps, A&A 645:A89 1225
• Plier J, Savarino F, Kočvara M, Petra S (2021). First-Order Geometric Multilevel Optimization for Discrete Tomography, Scale Space and Variational Methods in Computer Vision 12679:191-203,Springer International Publishing 1304

2020

• Polsterer KL (2020). Birds of a Feather Session on Machine Learning in Astronomy 1227
• Galvin TJ, Huynh MT, Norris RP, Wang XR, Hopkins E, Polsterer K, Ralph NO, O’Brien AN, Heald GH (2020). Cataloguing the radio-sky with unsupervised machine learning: a new approach for the SKA era, Monthly Notices of the Royal Astronomical Society 497(3):2730-2758 1164
• Doorenbos L, Cavuoti S, Brescia M, D’Isanto A, Longo G (2020). Comparison of outlier detection methods on astronomical image data 1259
• Shcherbakov O, Polsterer K, Svyatnyy VA (2020). Integration of Distributed Parallel Simulation Environment with Cloud-Infrastructures 1482

2019

• Nuñez FP, Gianniotis N, Blex J, Lisow T, Chini R, Polsterer KL, Pott J, Esser J, Pietrzyński G (2019). Optical continuum photometric reverberation mapping of the Seyfert-1 galaxy Mrk509, Monthly Notices of the Royal Astronomical Society 490(3):3936-3951 474
• Galvin TJ, Huynh M, Norris RP, Wang XR, Hopkins E, Wong OI, Shabala S, Rudnick L, Alger MJ, Polsterer KL (2019). Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-organizing Maps, PASP 131(1004):108009 1035
• Gianniotis N (2019). Mixed Variational Inference, 2019 International Joint Conference on Neural Networks (IJCNN),pp.1-8,IEEE 487

2018

• Wagner J (2018). Generalised model-independent characterisation of strong gravitational lenses. III. Perturbed axisymmetric lenses, A&A 615:A102 316
• Maat JR, Gianniotis N, Protopapas P (2018). Efficient Optimization of Echo State Networks for Time Series Datasets, 2018 International Joint Conference on Neural Networks (IJCNN),pp.1-7,IEEE 475
• Wagner J, Tessore N (2018). Generalised model-independent characterisation of strong gravitational lenses. II. Transformation matrix between multiple images, A&A 613:A6 317
• Wagner J, Liesenborgs J, Tessore N (2018). Model-independent and model-based local lensing properties of CL0024+1654 from multiply imaged galaxies, A&A 612:A17 318
• D’Isanto A, Polsterer KL (2018). Photometric redshift estimation via deep learning. Generalized and pre-classification-less, image based, fully probabilistic redshifts, A&A 609:A111 205
• D’Isanto A, Cavuoti S, Gieseke F, Polsterer KL (2018). Return of the features – Efficient feature selection and interpretation for photometric redshifts, Astronomy & Astrophysics, 616:A97 315

2017

• Gieseke F, Polsterer KL, Mahabal A, Igel C, Heskes T (2017). Massively-parallel best subset selection for ordinary least-squares regression, 2017 IEEE Symposium Series on Computational Intelligence (SSCI),pp.1-8,IEEE 207
• Polsterer K (2017). Astroinformatics; a new discipline or business as usual?, In Astronomical Data Analysis Software an Systems XXVII (ADASS XXVII), vol. 496 of Astronomical Society of the Pacific Conference Series, Eds: Taylor, A. R. and Rosolowsky, E., Sep 2017 209
• Wagner J (2017). Generalised model-independent characterisation of strong gravitational lenses. I. Theoretical foundations, A&A 601:A131 203
• Polsterer K (2017). Photometric redshift estimations, Proceedings of the International Astronomical Union Symposia and Colloquia, Cambridge University Press 444
• Gianniotis N (2017). Linear Dimensionality Reduction for Time Series, Neural Information Processing 10634:375-383,Springer International Publishing 451
• D’Isanto A, Polsterer KL (2017). Uncertain photometric redshifts via combining deep convolutional and mixture density networks, In 25th European Symposium on Artificial Neural Networks, ESANN 2015, Bruges, Belgium, April 26-28, 2017 1133

2016

• Kugler SD, Gianniotis N, Polsterer KL (2016). A spectral model for multimodal redshift estimation, 2016 IEEE Symposium Series on Computational Intelligence (SSCI),pp.1-8,IEEE 473
• Polsterer K (2016). Dealing with Uncertain Multimodal Photometric Redshift Estimations, Proc. IAU 12(S325):156-165. 2016 106
• Disanto A (2016). Uncertain Photometric Redshifts with Deep Learning Methods, IAU Symposium, Astroinformatics, vol. 325, Eds: Brescia, M. and Djorgovski, G. and Feigelson, E. and Longo, G. and S. Cavuoti, eds., Oct 2016 107
• Polsterer KL (2016). Dealing with Uncertain Multimodal Photometric Redshift Estimations., Proc. IAU 12(S325):156-165 208
• Crawford E, Norris R, Polsterer K (2016). WTF? Discovering the Unexpected in next-generation radio continuum surveys, In Astronomical Data Analysis Software an Systems XXV (ADASS XXV), vol. 496 of Astronomical Society of the Pacific Conference Series, Eds: Taylor, A. R. and Rosolowsky, E., Sep 2016 105
• Polsterer K, Gieseke F (2016). Probability Density Functions for Astronomy, In Astronomical Data Analysis Software an Systems XXV (ADASS XXV), vol. 496 of Astronomical Society of the Pacific Conference Series, Eds: Taylor, A. R. and Rosolowsky, E., Sep 2016 108
• Gianniotis N, Kügler SD, Tiňo P, Polsterer KL (2016). Model-coupled autoencoder for time series visualisation, Neurocomputing 192:139-146 472
• Gianniotis N, Schnörr C, Molkenthin C, Bora SS (2016). Approximate variational inference based on a finite sample of Gaussian latent variables, Pattern Anal Applic 19(2):475-485 471
• Disanto A, Cavuoti S, Brescia M, Donalek C, Longo G, Riccio G, Djorgovski S (2016). An analysis of feature relevance in the classification of astronomical transients with machine learning methods, Monthly Notices of the Royal Astronomical Society, 457:3119–3132 99
• Kügler S, Gianniotis N, Polsterer K (2016). An explorative approach for inspecting Kepler data, Monthly Notices of the Royal Astronomical Society, 455(4):4399–4405 102
• Polsterer K, Gieseke F, Igel C, Doser B, Gianniotis N (2016). Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs, In European Symposium on Artificial Neural Networks (ESANN), pp. 405–410 103

2015

• Hoecker M, Polsterer KL, Kugler SD, Heuveline V (2015). Clustering of Complex Data-Sets Using Fractal Similarity Measures and Uncertainties, 2015 IEEE 18th International Conference on Computational Science and Engineering,pp.82-91,IEEE 36
• López-Sánchez AR, Maksym WP, Polsterer KL, Borden K, Hollow RP, Whyte L (2015). Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection, Mon. Not. R. Astron. Soc. 453(3):2327-2341 35
• Polsterer KL, Gieseke F, Igel C (2015). Automatic Galaxy Classification via Machine Learning Techniques: Parallelized Rotation/Flipping INvariant Kohonen Maps (PINK), In Astronomical Data Analysis Software an Systems XXIV (ADASS XXIV), vol. 495 of Astronomical Society of the Pacific Conference Series, p. 81, Eds: Taylor, A. R. and Rosolowsky, E., Sep 2015 34
• Polsterer KL, Gieseke F, Gianniotis N, Kuegler SD (2015). Analyzing Complex and Structured Data via Unsupervised Learning Techniques, IAU General Assembly, 22:2258115, Aug 2015 32
• Kügler SD, Gianniotis N, Polsterer KL (2015). Featureless classification of light curves, Mon. Not. R. Astron. Soc. 451(4):3385-3392 33
• Gianniotis N, Kügler SD, Tino P, Polsterer KL, Misra R (2015). Autoencoding Time Series for Visualisation, In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 495-500, May 2015 31
• Kügler SD, Polsterer K, Hoecker M (2015). Determining spectroscopic redshifts by using k nearest neighbor regression. I. Description of method and analysis, A&A 576:A132 30

2014

• Kügler SD, Nilsson K, Heidt J, Esser J, Schultz T (2014). Properties of optically selected BL Lacertae candidates from the SDSS, A&A 569:A95 445
• Polsterer KL, Gieseke F, Igel C, Goto T (2014). Improving the performance of photometric regression models via massive parallel feature selection, Astronomical Data Analysis Software and Systems XXIII, 485:425 447
• Gieseke F, Polsterer K, Oancea CE, Igel C (2014). Speedy Greedy Feature Selection: Better Redshift Estimation via Massive Parallelism, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 23-25 April 2014 446

2013

• Kramer O, Gieseke F, Polsterer KL (2013). Learning morphological maps of galaxies with unsupervised regression, Expert Systems with Applications 40(8):2841-2844 449
• Heinermann J, Kramer O, Polsterer KL, Gieseke F (2013). On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy, In KI 2013: Advances in Artificial Intelligence, pp. 86–97, Springer Berlin Heidelberg, Berlin, Heidelberg 448
• Polsterer KL, Zinn P, Gieseke F (2013). Finding new high-redshift quasars by asking the neighbours, Monthly Notices of the Royal Astronomical Society 428(1):226-235 450

2012

• Gieseke F, Polsterer K, Zinn P (2012). Photometric Redshift Estimation of Quasars: Local versus Global Regression, Astronomical Data Analysis Software and Systems XXI. Proceedings of a Conference held at Marriott Rive Gauche Conference Center, Paris, France, 6-10 November 470

2011

• Assef RJ, Denney KD, Kochanek CS, Peterson BM, Kozłowski S, Ageorges N, Barrows RS, Buschkamp P, Dietrich M, Falco E, Feiz C, Gemperlein H, Germeroth A, Grier CJ, Hofmann R, Juette M, Khan R, Kilic M, Knierim V, Laun W, Lederer R, Lehmitz M, Lenzen R, Mall U, Madsen KK, Mandel H, Martini P, Mathur S, Mogren K, Mueller P, Naranjo V, Pasquali A, Polsterer K, Pogge RW, Quirrenbach A, Seifert W, Stern D, Shappee B, Storz C, Saders JV, Weiser P, Zhang D (2011). Black Hole Mass Estimates Based on C IV are Consistent with Those Based on the Balmer Lines, ApJ 742(2):93 453
• Pasquali A, Bik A, Zibetti S, Ageorges N, Seifert W, Brandner W, Rix H, Jütte M, Knierim V, Buschkamp P, Feiz C, Gemperlein H, Germeroth A, Hofmann R, Laun W, Lederer R, Lehmitz M, Lenzen R, Mall U, Mandel H, Müller P, Naranjo V, Polsterer K, Quirrenbach A, Schäffner L, Storz C, Weiser P (2011). Infrared Narrowband Tomography of the Local Starburst NGC 1569 with the Large Binocular Telescope/LUCIFER., The Astronomical Journal 141(4):132 454

2010

• Gieseke F, Polsterer KL, Thom A, Zinn P, Bomanns D, Dettmar R, Kramer O, Vahrenhold J (2010). Detecting Quasars in Large-Scale Astronomical Surveys, 2010 Ninth International Conference on Machine Learning and Applications,pp.352-357,IEEE 452
• Jütte M, Knierim V, Polsterer K, Lehmitz M, Storz C, Seifert W, Ageorges N (2010). The LUCIFER control software, Software and Cyberinfrastructure for Astronomy,p.774004,SPIE 469
• Ageorges N, Seifert W, Jütte M, Knierim V, Lehmitz M, Germeroth A, Buschkamp P, Polsterer K, Pasquali A, Naranjo V, Gemperlein H, Hill J, Feiz C, Hofmann R, Laun W, Lederer R, Lenzen R, Mall U, Mandel H, Müller P, Quirrenbach A, Schäffner L, Storz C, Weiser P (2010). LUCIFER1 commissioning at the LBT, Ground-based and Airborne Instrumentation for Astronomy III,p.77351L,SPIE 466
• Buschkamp P, Hofmann R, Gemperlein H, Polsterer K, Ageorges N, Eisenhauer F, Lederer R, Honsberg M, Haug M, Eibl J, Seifert W, Genzel R (2010). The LUCIFER MOS: a full cryogenic mask handling unit for a near-infrared multi-object spectrograph, Ground-based and Airborne Instrumentation for Astronomy III,p.773579,SPIE 467
• Seifert W, Ageorges N, Lehmitz M, Buschkamp P, Knierim V, Polsterer K, Germeroth A, Pasquali A, Naranjo V, Jütte M, Feiz C, Gemperlein H, Hofmann R, Laun W, Lederer R, Lenzen R, Mall U, Mandel H, Müller P, Quirrenbach A, Schäffner L, Storz C, Weiser P (2010). LUCIFER1: performance results, Ground-based and Airborne Instrumentation for Astronomy III,p.77357W,SPIE 468

2008

• Mandel H, Seifert W, Hofmann R, Jütte M, Lenzen R, Ageorges N, Bomans D, Buschkamp P, Dettmar R, Feiz C, Gemperlein H, Germeroth A, Geuer L, Heidt J, Knierim V, Laun W, Lehmitz M, Mall U, Müller P, Naranjo V, Polsterer K, Quirrenbach A, Schäffner L, Schwind F, Weiser P, Weisz H (2008). LUCIFER status report: summer 2008, Ground-based and Airborne Instrumentation for Astronomy II,p.70143S,SPIE 465

2006

• Polsterer KL, Jütte M, Knierim V, Lehmitz M, Mandel H (2006). Lucifer VR: a virtual instrument for the LBT, Advanced Software and Control for Astronomy,p.62740M,SPIE 462
• Jütte M, Polsterer K, Knierim V, Luks T, Schimmelmann J, Muhlack T, Mandel H, Lehmitz M (2006). The Java based control software of the LUCIFER instrument, Advanced Software and Control for Astronomy,p.62741H,SPIE 463
• Knierim V, Jütte M, Polsterer K, Schimmelmann J (2006). User interaction with the LUCIFER control software, Advanced Software and Control for Astronomy,p.62741N,SPIE 464
• Mandel HG, Appenzeller I, Seifert W, Baumeister H, Dettmar R, Feiz C, Gemperlein H, Germeroth A, Grimm B, Heidt J, Herbst T, Hofmann R, Jütte M, Knierim V, Laun W, Luks T, Lehmitz M, Lenzen R, Polsterer K, Quirrenbach A, Rohloff R, Rosenberger J, Weiser P, Weisz H (2006). LUCIFER status report: Summer 2006, Ground-based and Airborne Instrumentation for Astronomy,p.62693F,SPIE 461

2004

• Mandel H, Appenzeller I, Seifert W, Baumeister H, Bizenberger P, Dettmar R, Gemperlein H, Grimm B, Herbst TM, Hofmann R, Jutte M, Laun W, Lehmitz M, Ligori S, Lenzen R, Polsterer K, Rohloff R, Schuetze A, Seltmann A, Weiser P, Weisz H, Xu W (2004). LUCIFER status report, summer 2004, Ground-based Instrumentation for Astronomy,p.1208,SPIE 458
• Hofmann R, Gemperlein H, Grimm B, Jutte M, Mandel H, Polsterer K, Weisz H (2004). The cryogenic MOS unit for LUCIFER, Ground-based Instrumentation for Astronomy,p.1243,SPIE 459
• Juette M, Polsterer KL, Lehmitz M, Knierim V (2004). The development process of the LUCIFER control software, Advanced Software, Control, and Communication Systems for Astronomy,p.469,SPIE 460

2002

• Jütte M, Polsterer K, Lehmitz M, Dettmar R (2002). LUCIFER control software: an OO approach using CORBA technology, SPIE Proceedings 4848 : 387 486
• Seifert W, Appenzeller I, Baumeister H, Bizenberger P, Bomans D, Dettmar R, Grimm B, Herbst T, Hofmann R, Juette M, Laun W, Lehmitz M, Lemke R, Lenzen R, Mandel H, Polsterer K, Rohloff R, Schuetze A, Seltmann A, Thatte NA, Weiser P, Xu W (2002). LUCIFER: a Multi-Mode NIR Instrument for the LBT, Instrument Design and Performance for Optical/Infrared Ground-based Telescopes,p.962,SPIE 457

2023

• Schweder F (2023). Probabilistic Modelling of Time Series for explorative Big Data Analysis in Astronomy (Master’s Thesis) 1626

2019

• Hopkins E (2019). Semi-Automated Morphological Classification of All Radio Galaxies in the FIRST Survey Catalog Including Near-IR Counterparts in UKIDSS (Master’s Thesis) 1640
• D’Isanto A (2019). Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning (Doctoral Thesis) 1480

2015

• Kugler SD (2015). On the application of machine learning approaches in astronomy: Exploring novel representations of high-dimensional and complex astronomical data (Doctoral Thesis) 1625