Nikki Arendse

Postdoctoral researcher at Stockholm University

Photo by Karoline Hill

About me


I am fascinated by open questions in cosmology and how observations of astrophysical objects can help us answer them.

As a postdoctoral researcher at Stockholm University, I am searching for gravitationally lensed supernovae to constrain the present-day expansion rate of the Universe (the Hubble constant) and get insights into galaxy substructures.

During my PhD at the University of Copenhagen, I investigated the tension in the Hubble constant using observations from gravitationally lensed quasars, type Ia supernovae, and Baryon Acoustic Oscillations.


I like to work at the intersections of observations, theory, and simulations, and to explore statistical inference and machine learning techniques to obtain new insights about the Universe.

Additionally, I am passionate about finding creative ways to present research.

In my free time I enjoy bouldering, painting, ice bathing and hiking in the beautiful nature reserves around Stockholm.


My research focusses on the following fundamental open questions in cosmology: What is the expansion rate and geometry of the Universe? Do we need new physics beyond our standard cosmological model to describe the Universe? What is the nature of dark matter and dark energy, and how does dark energy evolve over time?

I aim to answer these questions using astrophysical observations, statistical methods and machine learning techniques. I am especially interested in the use of gravitationally lensed supernova to constrain the present-day expansion rate of the Universe (the Hubble constant) and learn more about dark matter and stars in lens galaxies.

Strongly lensed supernovae are extremely rare and powerful probes that can give insights into high-redshift supernova physics, substructures in massive galaxies, and the expansion rate of the Universe. The lensed supernova field is at a turning point, as we will go from a handful of current discoveries to orders of magnitudes more with the advance of the Legacy Survey of Space and Time (LSST) at the Vera Rubin Observatory.

In Arendse et al (2023), we investigated the cosmological prospects of lensed type Ia supernovae in LSST by quantifying how many we expect to find each year, the impact of stellar microlensing, the feasibility of conducting follow-up observations, and how to best separate lensed and unlensed type Ia supernovae. The simulation code used in the paper, Lensed Supernova Simulator Tool (LensedSST) is publicly available at github.


I am a member of the Zwicky Transient Facility (ZTF), a survey telescope which scans the sky each night looking for newly appearing objects. On August 2022, we discovered a strongly lensed type Ia supernova: SN Zwicky. In Goobar, Johansson, Schulze, Arendse et al. (2023), we documented the discovery of SN Zwicky, a highly magnified supernova with very short time delays (less than a day) which is part of a new, undiscovered population of low mass lens systems. In Pierel, Arendse et al. (2023), we analysed observations of SN Zwicky with the Hubble Space Telescope and explored a set of different lens models. I made a promo video of the discovery story of SN Zwicky using water colours.


As upcoming survey telescopes will collect unprecedented volumes of data, machine learning techniques are emerging as promising tools to extract information from these large data sets. I’m interested in using machine learning to find rare objects, such as lensed supernovae, and to infer cosmological parameters from them.

In Kodi Ramanah, Arendse & Wojtak (2022), we used a spatio-temporal neural network to distinguish between lensed and unlensed type Ia supernovae. Our classifier is a convolutional long short-term memory network (ConvLSTM) which is able to pick up spatial and temporal correlations in time-series of lensed supernova images. By adopting variational inference, we quantified approximate Bayesian uncertainties on the network predictions.


A machine learning regression problem I worked on is constraining cosmological parameters from galaxy clusters, the most massive gravitationally bound systems in the Universe. Kodi Ramanah, Wojtak & Arendse (2020) presents a 3D convolutional neural network that infers dynamical mass estimates of galaxy clusters, from their observed line-of-sight velocities and positions in the sky. We derived reliable uncertainties on the cluster mass estimates using a simulation-based inference framework. The method was applied to 800 galaxy clusters from the SDSS Legacy Survey, from which we constructed a cluster mass function that is consistent with the standard cosmological model.

The exact value of the present-day expansion rate of the Universe, the Hubble constant, is a matter of ongoing debate. Local measurements using Cepheids and type Ia supernovae predict a higher Hubble constant than observations from the Cosmic Microwave Background (CMB) radiation.

This ‘Hubble tension’ might suggest hidden systematics in either of the measurements, or it can point to new physics beyond our standard cosmological model. Many new cosmological models have been proposed to resolve the tension by increasing the value of the Hubble constant from the CMB. Intimately linked to the Hubble constant is the sound horizon, a distance scale in the early Universe which is also imprinted in the clustering of galaxies at later times, the Baryon Acoustic Oscillations (BAO).

In Arendse, Agnello & Wojtak (2019), we used BAO observations and type Ia supernovae calibrated by gravitationally lensed quasars to constrain the Hubble constant, the sound horizon and the curvature of the Universe. Since the BAO signal constrains the product of the Hubble constant and the sound horizon, models that solve the tension by increasing the Hubble constant should simultaneously decrease the sound horizon. Arendse et al. (2020) investigated four proposed solutions to the Hubble tension and found that modifications to our standard model in the late Universe are disfavoured by BAO data.


Research talks

Infering the Hubble constant from strongly lensed supernovae with the Rubin Observatory

BOOM! Conference
University of Illinois
Urbana-Champaign, USA

New physics or systematics behind the Hubble tension?
(full-dome planetarium show)

Information Universe Conference
DOT Planetarium
Groningen, Netherlands

Cosmic dissonance

Cosmology from home
Online conference

Outreach videos

Explaining Supernova Zwicky with watercolours

SN Zwicky press release
Oskar Klein Centre
Stockholm University

Finding rare gravitationally lensed supernovae (interview)

Physics Chat
University of Portsmouth

Our Dark Universe

Astronomy on tap Halloween event
HUSET Copenhagen

Publications (selected)

To see my full publication list, see

  • Detecting strongly-lensed type Ia supernovae with LSST

    N. Arendse, S. Dhawan, A. Sagués Carracedo, H. Peiris, A. Goobar, R. Wojtak, C. Alves, R. Biswas, S. Huber, S. Birrer, The LSST Dark Energy Science Collaboration.

    ArXiv: 2312.04621

  • GausSN: Bayesian Time-Delay Estimation for Strongly Lensed Supernovae

    E. Hayes, S. Thorp, K. Mandel, N. Arendse, M. Grayling, S. Dhawan.

    ArXiv: 2311.17997

  • LensWatch: I. Resolved HST Observations and Constraints on the Strongly-Lensed Type Ia Supernova 2022qmx (‘SN Zwicky’)

    J. Pierel, N. Arendse et al.

    ApJ (2023) | ArXiv: 2211.03772

  • Uncovering a population of gravitational lens galaxies with magnified standard candle SN Zwicky

    A. Goobar, J. Johansson, S. Schulze, N. Arendse et al.

    Nature Astronomy (2023) | ArXiv: 2211.00656

  • AI-driven spatio-temporal engine for finding gravitationally lensed supernovae

    D.K. Ramanah, N. Arendse, R. Wojtak.

    MNRAS (2022) | ArXiv: 2107.12399

  • Cosmic dissonance: new physics or systematics behind a short sound horizon?

    N. Arendse, R. Wojtak, A. Agnello, G. Chen, C. Fassnacht, D. Sluse, S. Hilbert, M. Millon, V. Bonvin, K. Wong, F. Courbin, S. Suyu, S. Birrer, T. Treu, L. Koopmans.

    A&A (2020) | ArXiv: 1909.07986

  • Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks

    D. K. Ramanah, R. Wojtak, N. Arendse.

    MNRAS (2020) | ArXiv: 2009.03340

  • Low-redshift measurement of the sound horizon through gravitational time-delays

    N. Arendse, A. Agnello, R. Wojtak.

    A&A (2019) | ArXiv: 1905.12000

Curriculum Vitae

Education and Research Positions

Postdoctoral Researcher

2021 - present

Oskar Klein Centre, Physics Department, Stockholm University, Sweden

Preparing for detecting gravitationally lensed supernovae with the Vera C. Rubin Observatory.

PhD in Astronomy

2018 - 2021

DARK, Niels Bohr Institute, University of Copenhagen, Denmark

Thesis: ‘Cosmic Dissonance: Addressing tensions in modern cosmology’. Advisors: Radek Wojtak & Jens Hjorth.

MSc Astronomy & Data Science

2016 - 2018

Kapteyn Institute, University of Groningen, the Netherlands

Master thesis: ‘Investigating the build-up of the Local Group galaxies with the CLUES simulation’. Advisor: Pratika Dayal.

BSc Astronomy

2012 - 2016

Kapteyn Institute, University of Groningen, the Netherlands

Bachelor thesis (at the Netherlands Institute for Radio Astronomy): ‘Directional calibration of LOFAR Using Statistically Efficient and Fast Calibration’. Advisors: Tammo Jan Dijkema & John McKean.


  • Lightning talk award, EuCAPT symposium 2022
  • Best PhD thesis in Danish astronomy 2021, Instrument Center for Danish Astrophysics (IDA)
  • Best talk prize, Annual Danish Astronomy Meeting 2021
  • Poster prize, Annual Danish Astronomy Meeting 2019

Commissions & Teaching

  • Co-convener of the Strong Lensing Topical Team at the LSST Dark Energy Science Collaboration (DESC): coordinating efforts for lensed supernova science with the Rubin Observatory (2023 – present)
  • Organiser of the weekly colloquium series ScientiFika at the physics department of Stockholm University (2022 – present)
  • Founding member and social media manager of the outreach series Astronomy on Tap Copenhagen (2020 – 2021)
  • Supervised 2 master students
  • Teaching Assistant for the masters courses General Relativity and Applied Statistics at the University of Copenhagen and for 5 bachelors courses at the University of Groningen (2014 – 2021)

Selected invited talks

  • Rubin Observatory LSST@Europe5: Towards LSST science, together! Poreč, Croatia, September, 2023
  • KICP Workshop: Lensing at different scales – strong, weak and synergies between the two, Chicago, US, July 2023
  • CERN Cosmology Seminar, Geneva, Switzerland, June 2023
  • Kavli Focus Meeting: Next generation surveys in the Rubin-LSST era, Cambridge, UK, March 2023
  • Rubin Observatory LSST@Europe4: Shaping the European Contribution to LSST, Rome, Italy,
  • October 2022
  • Boom! A Workshop on Explosive Transients with LSST, Urbana-Champaign, US, July 2022
  • European Astronomical Society Annual Meeting (EAS), Valencia, Spain, June 2022
  • Information Universe Conference, Groningen, The Netherlands, June 2022
  • Annual Danish Astronomy Meeting, Fredericia, Denmark, May 2022
  • Helsinki Astrophysics Seminar, University of Helsinki, November 2021
  • CosKASI Early Career Seminar, Korea Astronomy and Space Science Institute, May 2021

Get in touch


AlbaNova University Center,
Roslagstullsbacken 21,
11421 Stockholm, Sweden.



Albanova building
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