Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We investigate the role of baryonic physics in shaping the population, structure, and internal dynamics of galactic subhalos using the Mochima suite of cosmological zoom-in simulations. A refined method is developed to identify bound subhalo material by isolating the local gravitational potential and applying multi-criteria phase-space selection. This approach enables a robust characterisation of subhalo properties across five baryonic runs with varying prescriptions for star formation, and supernova and protostellar feedback, as well as a dark matter-only baseline. At the population level, we find that the concentration of the central massive host halo, modulated by baryonic physics in the central disc galaxy, is a key predictor of subhalo survival. Subhalos with more massive stellar components exhibit deeper internal potentials and enhanced resilience to tidal disruption. At the structural level, we identify a broad diversity in inner dark matter profiles, consistent with observations of dwarf galaxies. We show that this diversity correlates with both star formation history and environmental interaction. In particular, galaxies that form most of their stars early tend to retain steep cusps, while those with extended or recent star formation exhibit oscillating inner slopes shaped by bursty feedback and tidal perturbations. These findings suggest that the so-called “diversity problem” may reflect the complex interplay between feedback history and gravitational environment, rather than a breakdown of cold dark matter predictions.

Abstract:

We introduce a new kinematic disturbance parameter, (pronounced ‘DVSG’), which takes advantage of integral field spectroscopy (IFS) to quantify differences between a galaxy’s stellar and gas velocity maps. The motivation behind is to capture disturbances in the kinematics of a galaxy that might be missed by alternative methods, while also attempting to minimize bias towards galaxy properties or features of the IFS data. We first detail the reasons for introducing this parameter and explain how the value of a galaxy can be calculated. We then present initial results using to quantify the kinematic disturbance of obscured active galactic nuclei (AGNs) found in the MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) survey. We find that there is no statistically significant difference between the distributions of AGN and a control sample (matched in mass and redshift) of inactive galaxies. This suggests that AGN triggering may not be preferentially caused by any distinct kinematic disturbance process, or combination of processes, beyond those observed in inactive galaxies.

Abstract:

We present updated forecasts for the scientific performance of the degree-scale (0.5 deg FWHM at 93 GHz), deep-field survey to be conducted by the Simons Observatory (SO). By 2027, the SO Small Aperture Telescope (SAT) complement will be doubled from three to six telescopes, including a doubling of the detector count in the 93 GHz and 145 GHz channels to 48,160 detectors. Combined with a planned extension of the survey duration to 2035, this expansion will significantly enhance SO's search for a B-mode signal in the polarisation of the cosmic microwave background, a potential signature of gravitational waves produced in the very early Universe. Assuming a 1/f noise model with knee multipole ℓknee = 50 and a moderately complex model for Galactic foregrounds, we forecast a 1σ (or 68% confidence level) constraint on the tensor-to-scalar ratio r of σr = 1.2 × 10-3, assuming no primordial B-modes are present. This forecast assumes that 70% of the B-mode lensing signal can ultimately be removed using high resolution observations from the SO Large Aperture Telescope (LAT) and overlapping large-scale structure surveys. For more optimistic assumptions regarding foregrounds and noise, and assuming the same level of delensing, this forecast constraint improves to σr = 7 × 10-4. These forecasts represent a major improvement in SO's constraining power, being a factor of around 2.5 times better than what could be achieved with the originally planned campaign, which assumed the existing three SATs would conduct a five-year survey.

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'.