Hintze Centre for Astrophysical Surveys

Abstract:

We present the discovery of AT 2024wpp (‘Whippet’), a fast and luminous 18cow-like transient. At a redshift of , revealed by Keck Cosmic Web Imager spectroscopy of its faint star-forming host, it is the fourth-nearest example of its class to date. Rapid identification of the source in the Zwicky Transient Facility data stream permitted ultraviolet-through-optical observations to be obtained prior to peak, allowing the first determination of the peak bolometric luminosity ( erg s), maximum photospheric radius ( cm), and total radiated energy ( erg) of an 18cow-like object. We present results from a comprehensive multiwavelength observing campaign, including a far-ultraviolet spectrum from the Cosmic Origins Spectrograph on the Hubble Space Telescope and deep imaging extending 100 d post-explosion from the Very Large Telescope, Hubble Space Telescope, Very Large Array, and Atacama Large Millimetre Array. We interpret the observations under a model in which a rapidly accreting central engine blows a fast ( 0.2 c) wind into the surrounding medium and irradiates it with X-rays. The high Doppler velocities and intense ionization within this wind prevent identifiable spectroscopic features from appearing in the ejecta or in the surrounding circumstellar material. Weak H and He signatures do emerge in the spectra after 35 d in the form of double-peaked narrow lines. Each peak is individually narrow (full width km s) but the two components are separated by km s, indicating stable structures of denser material, possibly representing streams of tidal ejecta or an ablated companion star.

Abstract:

Utilizing the optical imaging Fourier transform spectrograph SITELLE, the Star-formation, Ionized Gas and Nebular Abundances Legacy Survey (SIGNALS) is designed to study the connection between star-forming regions and their environments. Targeting 31 local star-forming galaxies, its data products also lend themselves to planetary nebula (PN) surveys. We present here a new pipeline to find PNe using automated emission-line diagnostics and morphology tests, that is able to distinguish PNe from contaminants with an accuracy similar to that of past visual methods. We also perform thorough completeness tests using mock PNe inserted into the data cubes with full spectra. We apply these tools to a pilot sample of two dwarf irregular galaxies from the SIGNALS survey, NGC 4214 and NGC 4449, with other galaxies to follow. For these two galaxies, we identify 25 PNe (including six new discoveries) and 23 PNe (including 13 new discoveries), respectively, and calculate PN luminosity function distances of and Mpc, respectively, the latter consistent with previous estimates. We also calculate the bolometric PN specific frequency of our galaxies (), as well as a newly defined V-band PN specific frequency () based solely on the galaxies’ total luminosities in that band.

Abstract:

Dark matter haloes are typically characterized by radial density profiles with fixed forms motivated by simulations (e.g. Navarro-Frenk-White [NFW]). However, simulation predictions depend on uncertain dark matter physics and baryonic modelling. Here, we present a method to constrain halo density profiles directly from observations using Exhaustive Symbolic Regression (ESR), a technique that searches the space of analytic expressions for the function that best balances accuracy and simplicity for a given dataset. We test the approach on mock weak lensing excess surface density (ESD) data of synthetic clusters with NFW profiles. Motivated by real data, we assign each ESD data point a constant fractional uncertainty and vary this uncertainty and the number of clusters to probe how data precision and sample size affect model selection. For fractional errors around 5%, ESR recovers the NFW profile even from samples as small as approximately 20 clusters. At higher uncertainties representative of current surveys, simpler functions are favoured over NFW, though it remains competitive. This preference arises because weak lensing errors are smallest in the outskirts, causing the fits to be dominated by the outer profile. ESR therefore provides a robust, simulation-independent framework both for testing mass models and determining which features of a halo's density profile are genuinely constrained by the data. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'.

Abstract:

Physics seeks to uncover the laws of Nature and express them through mathematical equations . Despite the vast diversity of natural phenomena, physical equations exhibit structural regularities that set them apart from arbitrary mathematical expressions. While principles such as dimensional analysis have long guided the formulation of physical models, the exploration of more subtle statistical patterns within the equations of physics remains an open question. Here, by analysing four corpora of physics equations and applying advanced implicit-likelihood techniques, we find that the frequency of mathematical operators follows an exponential decay law, in contrast to Zipf's power law for word frequencies in natural languages. This reveals a statistical meta-law of physics, possibly reflecting a combination of communication efficiency and constraints imposed by Nature itself. The meta-law offers practical benefits for symbolic regression by drastically narrowing down the space of physically plausible expressions. More broadly, it may inform the development of language models that can generate coherent mathematical representations, advancing the automation of physical law discovery. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'.