Machine learning cosmological structure formation

We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matter haloes, without the need to introduce approximate halo collapse models. We gain insights into the physics driving halo formation by evaluating the predictive performance of the algorithm when provided with different types of information about the local environment around dark matter particles. The algorithm learns to predict whether or not dark matter particles will end up in haloes of a given mass range, based on spherical overdensities. We show that the resulting predictions match those of spherical collapse approximations such as extended Press-Schechter theory. Additional information on the shape of the local gravitational potential is not able to improve halo collapse predictions; the linear density field contains sufficient information for the algorithm to also reproduce ellipsoidal collapse predictions based on the Sheth-Tormen model. We investigate the algorithm's performance in terms of halo mass and radial position and perform blind analyses on independent initial conditions realisations to demonstrate the generality of our results.Comment: 10 pages, 7 figures. Minor changes to match version published in MNRAS. Accepted on 22/06/201

Insights into cosmological structure formation with machine learning

Our modern understanding of cosmological structure formation posits that small matter density fluctuations present in the early Universe, as traced by the cosmic microwave background, grow via gravitational instability to form extended haloes of dark matter. A theoretical understanding of the structure, evolution and formation of dark matter haloes is an essential step towards unravelling the intricate connection between halo and galaxy formation, needed to test our cosmological model against data from upcoming galaxy surveys. Physical understanding of the process of dark matter halo formation is made difficult by the highly non-linear nature of the haloes' evolution. I describe a new approach to gain physical insight into cosmological structure formation based on machine learning. This approach combines the ability of machine learning algorithms to learn non-linear relationships, with techniques that enable us to physically interpret the learnt mapping. I describe applications of the method, with the aim of investigating which aspects of the early universe density field impact the later formation of dark matter haloes. First I present a case where the process of halo formation is turned into a binary classification problem; the algorithm predicts whether or not dark matter `particles' in the initial conditions of a simulation will collapse into haloes of a given mass range. Second, I present its generalization to regression, where the algorithm infers the final mass of the halo to which each particle will later belong. I show that the initial tidal shear does not play a significant role compared to the initial density field in establishing final halo masses. Finally, I demonstrate that extending the framework to deep learning algorithms such as convolutional neural networks allows us to explore connections between the early universe and late time haloes beyond those studied by existing analytic approximations of halo collapse

Halo assembly bias from a deep learning model of halo formation

We build a deep learning framework that connects the local formation process of dark matter halos to the halo bias. We train a convolutional neural network (CNN) to predict the final mass and concentration of dark matter halos from the initial conditions. The CNN is then used as a surrogate model to derive the response of the halos' mass and concentration to long-wavelength perturbations in the initial conditions, and consequently the halo bias parameters following the "response bias" definition. The CNN correctly predicts how the local properties of dark matter halos respond to changes in the large-scale environment, despite no explicit knowledge of halo bias being provided during training. We show that the CNN recovers the known trends for the linear and second-order density bias parameters $b_1$ and $b_2$, as well as for the local primordial non-Gaussianity linear bias parameter $b_\phi$. The expected secondary assembly bias dependence on halo concentration is also recovered by the CNN: at fixed mass, halo concentration has only a mild impact on $b_1$, but a strong impact on $b_\phi$. Our framework opens a new window for discovering which physical aspects of the halo's Lagrangian patch determine assembly bias, which in turn can inform physical models of halo formation and bias.Comment: 11 pages, 5 figures, to be submitted to MNRAS, comments welcom

An interpretable machine learning framework for dark matter halo formation

We present a generalization of our recently proposed machine-learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the formation of haloes over the mass range 11.4 ≤ log (M/M⊙) ≤ 13.4. The algorithm is trained on an N-body simulation to infer the final mass of the halo to which each dark matter particle will later belong. We then quantify the difference in the predictive accuracy between machine-learning models using a metric based on the Kullback–Leibler divergence. We first train the algorithm with information about the density contrast in the particles’ local environment. The addition of tidal shear information does not yield an improved halo collapse model over one based on density information alone; the difference in their predictive performance is consistent with the statistical uncertainty of the density-only based model. This result is confirmed as we verify the ability of the initial conditions-to-halo mass mapping learnt from one simulation to generalize to independent simulations. Our work illustrates the broader potential of developing interpretable machine-learning frameworks to gain physical understanding of non-linear large-scale structure formation

Trypanosoma brucei PRMT1 Is a Nucleic Acid Binding Protein with a Role in Energy Metabolism and the Starvation Stress Response.

In Trypanosoma brucei and related kinetoplastid parasites, transcription of protein coding genes is largely unregulated. Rather, mRNA binding proteins, which impact processes such as transcript stability and translation efficiency, are the predominant regulators of gene expression. Arginine methylation is a posttranslational modification that preferentially targets RNA binding proteins and is, therefore, likely to have a substantial impact on T. brucei biology. The data presented here demonstrate that cells depleted of T. brucei PRMT1 (TbPRMT1), a major type I protein arginine methyltransferase, exhibit decreased virulence in an animal model. To understand the basis of this phenotype, quantitative global proteomics was employed to measure protein steady-state levels in cells lacking TbPRMT1. The approach revealed striking changes in proteins involved in energy metabolism. Most prominent were a decrease in glycolytic enzyme abundance and an increase in proline degradation pathway components, changes that resemble the metabolic remodeling that occurs during T. brucei life cycle progression. The work describes several RNA binding proteins whose association with mRNA was altered in TbPRMT1-depleted cells, and a large number of TbPRMT1-interacting proteins, thereby highlighting potential TbPRMT1 substrates. Many proteins involved in the T. brucei starvation stress response were found to interact with TbPRMT1, prompting analysis of the response of TbPRMT1-depleted cells to nutrient deprivation. Indeed, depletion of TbPRMT1 strongly hinders the ability of T. brucei to form cytoplasmic mRNA granules under starvation conditions. Finally, this work shows that TbPRMT1 itself binds nucleic acids in vitro and in vivo, a feature completely novel to protein arginine methyltransferases.IMPORTANCETrypanosoma brucei infection causes human African trypanosomiasis, also known as sleeping sickness, a disease with a nearly 100% fatality rate when untreated. Current drugs are expensive, toxic, and highly impractical to administer, prompting the community to explore various unique aspects of T. brucei biology in search of better treatments. In this study, we identified the protein arginine methyltransferase (PRMT), TbPRMT1, as a factor that modulates numerous aspects of T. brucei biology. These include glycolysis and life cycle progression signaling, both of which are being intensely researched toward identification of potential drug targets. Our data will aid research in those fields. Furthermore, we demonstrate for the first time a direct association of a PRMT with nucleic acids, a finding we believe could translate to other organisms, including humans, thereby impacting research in fields as distant as human cancer biology and immune response modulation. Copyright © 2018 Kafková et al

The causal effect of environment on halo mass and concentration

Understanding the impact of environment on the formation and evolution of dark matter halos and galaxies is a crucial open problem. Studying statistical correlations in large simulated populations sheds some light on these impacts, but the causal effect of an environment on individual objects is harder to pinpoint. Addressing this, we present a new method for resimulating a single dark matter halo in multiple large-scale environments. In the initial conditions, we 'splice' (i.e. insert) the Lagrangian region of a halo into different Gaussian random fields, while enforcing consistency with the statistical properties of $\Lambda$CDM. Applying this technique, we demonstrate that the mass of halos is primarily determined by the density structure inside their Lagrangian patches, while the halos' concentration is more strongly affected by environment. The splicing approach will also allow us to study, for example, the impact of the cosmic web on accretion processes and galaxy quenching.Comment: 6 pages, 5 figures. Accepted 2021 September 10. Received 2021 September 9; in original form 2021 July

Exploring anhedonia in adolescents with Chronic Fatigue Syndrome (CFS):A mixed-methods study

BACKGROUND: Chronic Fatigue Syndrome (CFS/ME) may get in the way of enjoying activities. A substantial minority of adolescents with CFS/ME are depressed. Anhedonia is a core symptom of depression. Anhedonia in adolescents with CFS/ME has not been previously investigated. METHOD: One hundred and sixty-four adolescents, age 12 to 18, with CFS/ME completed a diagnostic interview (K-SADS) and questionnaires (HADS, RCADS). We used a mixed-methods approach to explore the experience of anhedonia and examine how common it is, comparing those with clinically significant anhedonia to those without. RESULTS: Forty-two percent of adolescents with CFS/ME reported subclinical or clinical levels of anhedonia. Fifteen percent had clinically significant anhedonia. Thematic analysis generated two themes: (1) stopping activities that they previously enjoyed and (2) CFS/ME obstructs enjoyment. Most (72%) of those who reported clinically significant anhedonia met the depression diagnostic criteria. Those who were depressed used more negative language to describe their experience of activities than in those who were not depressed, although the themes were broadly similar. CONCLUSIONS: Experiencing pleasure from activities may be affected in CFS/ME, particularly in those who are depressed. Anhedonia may get in the way of behavioural strategies used within CFS/ME treatments

Optical Design of a Miniaturised Solar Magnetograph for Space Applications

Measuring the Sun’s magnetic field is a key component of monitoring solar activity and forecasting space weather. The main goal of the research presented in this paper is to investigate the possibility of reducing the dimensions and weight of a solar magnetograph while preserving its optical quality. This article presents a range of different designs, along with their advantages and disadvantages, and an analysis of the optical performance of each. All proposed designs are based on the magneto-optical filter (MOF) technique. As a result of the design study, a miniaturised solar magnetograph is proposed with an ultra-compact layout. The dimensions are 345 mm × 54 mm × 54 mm, and the optical quality is almost at the diffraction limit. The design has an entrance focal ratio of F/17.65, with a plate scale of 83.58 arcsec/mm at the telescope image focal plane, and produces a magnification of 0.79. The field of view is 1920 arcsec in diameter, equivalent to ±0.27 degrees, sufficient to cover the entire solar disk