Publications and Talks

Publications list on ADS

Journal Articles

Gupta, R., Muthukrishna, D., Lochner, M., A Classifier‑Based Approach to Multi‑Class Anomaly Detection for Astronomical Transients. 2024, in review. https://arxiv.org/abs/2403.14742

Huang, H., Muthukrishna, D., Nair, P., Zhang, Z., Fausnaugh, M., Majumder, T., Foley, R., Ricker, G. Predicting the Age of Astronomical Transients from Real‑Time Multivariate Time Series. 2023, Machine Learning and the Physical Sciences Workshop, Neural Information Processing Systems (NeurIPS) 2023. https://arxiv.org/abs/2311.1714

Fausnaugh, M., et al. (incl Muthukrishna, D.), Four years of Type Ia Supernovae Observed by TESS. 2022, ApJ, 956, 108. https://arxiv.org/abs/2307.11815

Agarwal, M., et al. (incl Muthukrishna, D.) Applications of Deep Learning to physics workflows. 2023. https://arxiv.org/abs/2306.08106

Tey, E., et al. (incl Muthukrishna, D.), Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations. 2022, AJ, 165, 95. https://arxiv.org/abs/2301.01371

Biswas, E., Ishida, E., et al. (incl Muthukrishna, D.), Enabling the discovery of fast transients: A science module for the Fink broker. 2022, A&A, 677, A77. https://arxiv.org/abs/2210.17433

D. Muthukrishna, K. Mandel, M. Lochner, S. Webb, G. Narayan, Real-time detection of anomalies in large-scale transient surveys, MNRAS. (2021). https://arxiv.org/abs/2111.00036

D. Muthukrishna, K. Mandel, M. Lochner, S. Webb, G. Narayan, Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data, Neurips workshop on Machine Learning and the Physical Sciences. (2021). https://arxiv.org/abs/2112.08415

D. Chatterjee, G. Narayan, P. Aleo, K. Malanchev, D. Muthukrishna, Electromagnetic Counterpart Identification of Gravitational-wave candidates using deep-learning, Neurips workshop on Machine Learning and the Physical Sciences. (2021). Link to paper

D. Chatterjee, G. Narayan, P. Aleo, K. Malanchev, D. Muthukrishna, El-CID: A filter for Gravitational-wave Electromagnetic Counterpart Identification, MNRAS. (2021). https://arxiv.org/abs/2108.04166

R. Hložek, et al., Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC). (2021). https://arxiv.org/abs/2012.12392

D. O. Jones, et al., The Young Supernova Experiment: Survey Goals, Overview, and Operations, ApJ. (2020). https://arxiv.org/abs/2010.09724

S. Webb, M. Lochner, D. Muthukrishna, et al., Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves, MNRAS. (2020). https://arxiv.org/abs/2008.04666

C. Stachie et al., Using machine learning for transient classification in searchers for gravitational-wave counterparts, MNRAS. (2020). https://arxiv.org/abs/1912.06383

D. Muthukrishna, G. Narayan, K. Mandel, R. Biswas, R. Hložek, RAPID: Early classification of explosive transients using Recurrent Neural Networks, PASP. (2019). https://arxiv.org/abs/1904.00014

D. Muthukrishna, D. Parkinson and B. Tucker, DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts, ApJ (2019). https://arxiv.org/abs/1903.02557

R. S. de Jong, et al., 4MOST: Project overview and information for the First Call for Proposals, The Messenger (2019). https://doi.eso.org/10.18727/0722-6691/5117

R. Kessler et al., Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC), PASP. (2019). https://arxiv.org/abs/1903.11756

DES Collaboration et al., First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters, ApJ. (2018). https://arxiv.org/abs/1811.02374.

D. Brout, et al., First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Analysis, Systematic Uncertainties, and Validation, ApJ. (2018). https://arxiv.org/abs/1811.02377.

R. Kessler, et al., First Cosmology Results using Type Ia Supernova from the Dark Energy Survey: Simulations to Correct Supernova Distance Biases, ApJ. (2018). https://arxiv.org/abs/1811.02379.

C. Andrea, et al., First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Survey Overview and Supernova Spectroscopy, ApJ. (2018). https://arxiv.org/abs/1811.09565.

A. Malz, et al., The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals, ApJ (submitted), https://arxiv.org/abs/1809.11145.

F. Campuzano, G. Bosch, G. Hägele, V.Firpo, D. Muthukrishna, M. Cardaci, Chemodynamics in Blue Compact Dwarf galaxies: II Zw 33 and Mrk 600, Boletín de la Asociación Argentina de Astronomía, 60, p.148-150 (2018). http://adsabs.harvard.edu/abs/2018BAAA…60..148C.

M. J. Childress, et al., OzDES multi-object spectroscopy for the Dark Energy Survey: 3-yr results and first data release, MNRAS. 472 (2017). https://doi.org/10.1093/mnras/stx1872.

D. Muthukrishna and D. Parkinson, A cosmographic analysis of the transition to acceleration using SN-Ia and BAO, J. Cosmol. Astropart. Phys. (2016). http://iopscience.iop.org/article/10.1088/1475-7516/2016/11/052.

V. Firpo, D. Muthukrishna, F. Campuzano-Castro, S. Torres-Flores, G. Bosch and G. Hagele, Violent star-forming processes in interacting galaxies, MNRAS. (2018). (in preparation).

White Papers and Notes

G. Allen, et al., Multi-Messenger Astrophysics: Harnessing the Data Revolution [White Paper], ArXiv e-prints (2018). http://adsabs.harvard.edu/abs/2018arXiv180704780A.

The PLAsTiCC Team, et al., The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set (2018). http://adsabs.harvard.edu/abs/2018arXiv181000001T.

Astronomical Telegrams

E. Shaya, et al. TNS Astronomical Transient Report No. 20724, (2018). https://wis-tns.weizmann.ac.il/object/2018dzv/discovery-cert.

L. Davies, et al., Classification of 3 DES supernova with OzDES and DEVILS, The Astronomer’s Telegram 11717 (2018). http://adsabs.harvard.edu/abs/2018ATel11717….1D.

E. Swann, et al., Classification of 18 DES supernova with OzDES The Astronomer’s Telegram 11679 (2018). http://adsabs.harvard.edu/abs/2018ATel11679….1S.

C. Angus, et al., Classification of 2 DES supernova by NTT, The Astronomer’s Telegram 11151 (2018). http://adsabs.harvard.edu/abs/2018ATel11151….1A.

L. Galbany, et al., Classification of 5 DES supernova by Magellan, The Astronomer’s Telegram 11150 (2018). http://adsabs.harvard.edu/abs/2018ATel11150….1G.

E. Macaulay, et al., Classification of 13 DES supernova with OzDES, The Astronomer’s Telegram 11148 (2018). http://adsabs.harvard.edu/abs/2018ATel11148….1M.

J. Calcino, et al., Classification of 26 DES supernova with OzDES, The Astronomer’s Telegram 11147 (2018). http://adsabs.harvard.edu/abs/2018ATel11147….1C.

J. Calcino, et al., Classification of 25 DES supernova with OzDES, The Astronomer’s Telegram 11146 (2018). http://adsabs.harvard.edu/abs/2018ATel11146….1C.

D. Muthukrishna, et al., Classification of 17 DES supernova with OzDES, The Astronomer’s Telegram 10759 (2017). http://adsabs.harvard.edu/abs/2017ATel10759….1M.

L. Galbany, et al., Classification of 10 DES supernova by Magellan, The Astronomer’s Telegram 10913 (2017). http://adsabs.harvard.edu/abs/2017ATel10913….1G.

B. E. Tucker, et al., WiFeS Classification of SMT17kdl/SN2017edm as a Type Ia Supernova, The Astronomer’s Telegram 10444 (2017). http://www.astronomerstelegram.org/?read=10444.

B. E. Tucker, et al., First Confirmed Supernova with the SkyMapper/Zooniverse Supernova Sighting Project, The Astronomer’s Telegram 10426 (2017). http://adsabs.harvard.edu/abs/2017ATel10426….1T.

R. Sharp, et al., Classification of 11 DES supernova with OzDES, The Astronomer’s Telegram 9961 (2017). http://adsabs.harvard.edu/abs/2017ATel.9961….1S.

M. Sullivan, et al., Classification of DES16C3dmp as a SLSN-I by VLT, The Astronomer’s Telegram 9953 (2017). http://adsabs.harvard.edu/abs/2017ATel.9953….1S.

J. K. Hoormann, et al., Classification of 17 DES Supernova with OzDES, The Astronomer’s Telegram 9855 (2016). http://adsabs.harvard.edu/abs/2016ATel.9855….1H.

D. Mudd, et al., Classification of 17 DES supernova with OzDES, The Astronomer’s Telegram 9742 (2016). http://adsabs.harvard.edu/abs/2016ATel.9742….1M.

Invited Talks & Conference Presentations

Korea Astronomy and Space Science Institute, Invited speaker at the Early Career Researcher Cosmology Seminar Series, (March 2022).

Rubin Observatory’s Dark Energy Science Collaboration, Invited speaker at the Machine Learning Topical Team Seminar Series, (June 2021).

Telstra (top Australian Telecommunications company), Invited speaker at the AI ML Forum, (March 2021).

University of Sheffield, UK, Invited speaker at the Astronomy Seminar (December 2020).

SLAC National Accelerator Laboratory at Stanford University, Guest speaker at the AI Seminar Series (February 2020).

MIT, Cambridge, MA, USA, Guest speaker at the MIT Kavli Institute’s Brown Bach Lunch Talk Series (February 2020).

University of Berkeley, San Francisco, California, USA, Guest speaker at the weekly SETI (Search of Extraterrestrial Intelligence) Meeting (February 2020).

Texas A&M University, College Station, Texas USA, Invited speaker at the Astronomical Data Science Workshop (February 2020).

DESY Zeuthen, Germany, Invited Astroparticle seminar speaker (November 2019).

ESO Garching, Germany, Contributed talk to the ESO conference on The extragalactic explosive Universe: the new era of transient surveys and data-driven discovery, (September 2019), “Classifying transients and detecting anomalies in upcoming large-scale surveys”, Garching near Munich, Germany.

Northwestern University, Illinois, USA, Invited speaker at the Hotwiring the transient universe workshop, (August 2019), “RAPID: Early Classification of Explosive Transients using Deep Learning”, Evanston, Illinois, USA.

Joint Statistical Meeting (JSM), Colorado, USA, Invited to the conference for being nominated for the Best Student Paper in Astrostatistics Award, (July 2019), “Time-series classification with Recurrent Neural Networks”, Denver, Colorado.

EWASS, Lyon, France, Contributed talk to the Time-domain symposium at the annual European Week of Astronomy and Space Science, (June 2019), “Early classification of transients”, Lyon, France.

Institute of Astronomy, University of Cambridge, Wednesday seminar, (June 2019), “Real-Time Classification of Explosive Transients Using Deep “, Cambridge, UK.

Space Telescope Science Institute, Baltimore, USA, Contributed talk at the Enabling Multi-messenger Astrophysics in the Big Data Era, (April 2019), “RAPID: Early Classification of Explosive Transients using Deep Learning”, Baltimore, USA.

Royal Astronomical Society, London, UK, Contributed talk at the Machine learning and Artificial Intelligence applied to astronomy workshop (March 2019), “RAPID: Real-time classification of explosive transients using Deep learning”, London, UK.

University of California, Santa Cruz, Transient Talk (March 2019), “RAPID: Real-time classification of explosive transients using Deep learning”, UC Santa Cruz, USA.

Harvard University (January 2019), “Real-time classification of explosive transients using deep recurrent neural networks”, Talk to CHASC Topics in Astrostatistics group.

Clare College, Cambridge, Invited talk to non-scientific audience (October 2018), “What is “Dark Energy, and how can exploding stars and machine learning help us find out if Einstein was right?”, Cambridge, United Kingdom.

University of Oxford, Contributed talk at the Wetton Workshop: Planning for Surprises in the era of Data-Driven Astronomy (June 2018), “Transient Classification with machine learning”, Oxford, United Kingdom.

CAASTRO Local Area Meeting (July 2017), “DASH: Deep learning for supernova classification in OzDES“, Sydney, Australia.

Astronomical Society of Australia Annual Meeting, Contributed talk at ANU (July 2017), “DASH: Deep learning for supernova classification“, Canberra, Australia.

University of Chicago, Contributed talk at the DES Collaboration Meeting, (June 2017), “Spectral classification of the Type-Ia supernova sample“, University of Chicago, USA.

Machine Learning in Astronomy Meetings, Invited talk (April 2017), “DASH: Deep Learning or the Spectral Classification of Supernovae“.

Gemini Observatory, Chile (Feb 2017), “Modelling emission line profiles in Giant HII Regions“. La Serena, Chile.

OzDES Science Workshop (Feb 2017), “DASH Tutorial to OzDES classifiers“, Canberra, Australia.

University of Chile, Invited talk to ML and transient group (Jan 2017), “DASH: Deep Learning for the Spectral Classification of Supernovae“, Santiago, Chile.

Australian National University, Feast of Facts talk (Oct 2016), “Model-independent Cosmology“, Canberra, Australia.

Conference Posters

Daniel Muthukrishna, Kaisey Mandel, Michelle Lochner, Sara Webb, Gautham Narayan, Early Classification of Explosive Transients, Poster presented at the 35th Conference on Neural Information Processing Systems (NeurIPS), USA (Dec 2021).
Daniel Muthukrishna, Gautham Narayan, Kaisey Mandel, Rahul Biswas and Renee Hlozek, Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data, Poster presented at the 233rd American Astronomical Society Meeting in Seattle, USA (Jan 2019). http://adsabs.harvard.edu/abs/2019AAS…23325824N

D. Muthukrishna, K. Mandel, and G. Narayan, Deep Learning for Real-Time Classification of Transient Time Series from Massive Astronomical Data Streams, Poster presented at the Cantab Capital Institute for the Mathematics of Information – connecting with industry workshop in Cambridge, UK (Nov 2018).

N. Lowson, D. Muthukrishna, B. Tucker, Determining the progenitor of the peculiar transient DES16X3bdj, Poster presented at the Astronomical Society of Australia Annual Meeting in Canberra, Australia (Jul 2017).

V. Firpo, D. Muthukrishna, F. Campuzano-Castro, S. Torres-Flores, G. Bosch and G. Hägele, Violent star-forming processes in interacting galaxies, Poster presented at the European Week of Astronomy and Space Science (EWASS) in Prague, Czech Republic (Jun 2017).

D. Muthukrishna, D. Parkinson and B. Tucker, DASH: Deep Learning for the Spectral Classification of Supernovae, Poster presented at the 6th Annual CAASTRO Conference in Perth, Australia (Nov 2016). http://danielmuthukrishna.com/wp-content/uploads/2016/12/CAASTROConferencePoster_v3.pdf

Theses

D. Muthukrishna, Data-driven Discovery of Transients in the New Era of Time-Domain Astronomy (Doctoral thesis). https://doi.org/10.17863/CAM.77709

D. Muthukrishna, Deep Learning for the Spectral Classification of Supernovae, Unpublished Honours Thesis, University of Queensland, Brisbane, Australia, (2016). http://danielmuthukrishna.com/projects/dash-thesis/