arXiv subjects

Manodeep Sinha

Publications and source records attributed to Manodeep Sinha.

At least 19 recordsLinked to original sources

arXiv2024

Reversing Arrested Development: A New Method to Address Halo Assembly Bias

Halo assembly bias is a phenomenon whereby the clustering of dark matter halos is dependent on halo properties, such as age, at fixed mass. Understanding the origin of assembly bias is important for interpreting the clustering of galaxies and constraining cosmological models. One proposed explanation for the origin of assembly bias is the truncation of mass accretion in low-mass halos in the presence of more massive halos, called 'arrested development.' Halos undergoing arrested development would have older measured ages and exhibit stronger clustering than equal mass halos that have not undergone arrested development. We propose a new method to test the validity of this explanation for assembly bias, and correct for it in cosmological N-body simulations. The method is based on the idea that the early mass accretion history of a halo, before arrested development takes effect, can be used to predict the late-time evolution of the halo in the absence of arrested development. We implement this idea by fitting a model to the early portion of halo accretion histories and extrapolating to late times. We then calculate 'corrected' masses and ages for halos based on this extrapolation and investigate how this impacts the assembly bias signal. We find that correcting for arrested development this way leads to a factor of two reduction in the strength of the assembly bias signal across a range of low halo masses. This result provides new evidence that arrested development is a cause of assembly bias and validates our approach to mitigating the effect.

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arXiv2024

Identification and Quenching of Nugget Galaxies in the RESOLVE Survey at z = 0

We present a complete census of candidate nuggets, i.e., dense galaxies likely formed by compaction with intense gas influx, within the volume-limited $z \sim 0$ REsolved Spectroscopy Of a Local VolumE (RESOLVE) survey. These nuggets span all evolutionary stages and 3 orders of magnitude in stellar mass ($M_{*} \sim 10^{8} M_\odot$ to $10^{11} M_\odot$) from the dwarf to the giant regime. We develop selection criteria for our $z\sim0$ nugget candidates based on structure and introduce the use of environmental criteria to eliminate nugget-like objects with suspected non-compaction origins. The resulting $z\sim0$ nuggets follow expectations with respect to structure (i.e., density, size), population frequency, and likely origins. We show that the properties of our nugget census are consistent with permanent quenching above the gas-richness threshold scale (halo mass $M_{halo} \sim 10^{11.4} M_\odot$), cyclic temporary quenching below the threshold scale, and feedback from active galactic nuclei (AGN) assisting in permanent quenching. As predicted in simulations, most nuggets quench within the halo mass range $M_{halo} \sim 10^{11.45} M_\odot$ to $10^{11.9} M_\odot$. We find $\sim 0.29$ dex scatter around the star-forming main sequence for candidate blue nuggets below the threshold scale, which is consistent with temporary quenching as seen in simulations. A transitional population of green nuggets appears above the threshold scale. AGN also become more common in nuggets above this scale, and we see a likely AGN excess in nuggets vs. comparably selected non-nuggets. Our results provide the first observational confirmation of the mass-dependent, AGN-mediated shift from cyclic quenching to halo quenching in nuggets.

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arXiv2023

Dark Sage: Next-generation semi-analytic galaxy evolution with multidimensional structure and minimal free parameters

After more than five years of development, we present a new version of Dark Sage, a semi-analytic model (SAM) of galaxy formation that breaks the mould for models of its kind. Included among the major changes is an overhauled treatment of stellar feedback that is derived from energy conservation, operates on local scales, affects gas gradually over time rather than instantaneously, and predicts a mass-loading factor for every galaxy. Building on the model's resolved angular-momentum structure of galaxies, we now consider the heating of stellar discs, delivering predictions for disc structure both radially and vertically. We add a further dimension to stellar discs by tracking the distribution of stellar ages in each annulus. Each annulus--age bin has its own velocity dispersion and metallicity evolved in the model. This allows Dark Sage to make structural predictions for galaxies that previously only hydrodynamic simulations could. We present the model as run on the merger trees of the highest-resolution gravity-only simulation of the MillenniumTNG suite. Despite its additional complexity relative to other SAMs, Dark Sage only has three free parameters, the least of any SAM, which we calibrate exclusively against the cosmic star formation history and the $z=0$ stellar and HI mass functions using a particle-swarm optimisation method. The Dark Sage codebase, written in C and Python, is publicly available at https://github.com/arhstevens/DarkSage

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arXiv2023

Using the Star Forming Main Sequence To Explore Quiescent Galaxies Across Cosmic Time

In this letter, we explore the quiescent lives of central galaxies using the SAGE galaxy model and Uchuu dark matter simulation. We ask three questions: (1) How much of a galaxy's life is spent in quiescence? (2) How often do galaxies transition off the main sequence? (3) What is the typical duration of a quiescent phase? We find low and high-mass galaxies spend the highest fraction of their lives in quiescence: 45 \pm 19% for log10(Mstar) < 9.0 (3.68 \pm 1.80 Gyr) and 26 \pm 25% for log10(Mstar) > 11.5 (3.46 \pm 3.30 Gyr), falling to 7 \pm 13% for galaxies in-between (0.82 \pm 1.57 Gyr). Low mass galaxies move in and out of quiescence frequently, 2.8 \pm 1.3 times on average, though only for short periods, 1.49 \pm 1.04 Gyr. This can be traced to the influence of supernova feedback on their quite stochastic evolution. Galaxies of higher mass have fewer quiescent periods, ~0.7 \pm 0.9, and their length increases with mass, peaking at 1.97 \pm 2.27 Gyr. However, our high-mass population comprises star-forming and quiescent galaxies with diverging evolutionary paths, so the actual time may be even longer. These high-mass trends are driven by radio mode feedback from supermassive black holes, which, once active, tend to remain active for extended periods.

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arXiv2022

Dust contribution to the panchromatic galaxy emission

We have developed a pipeline called \mentari to generate the far-ultraviolet to far-infrared spectral energy distribution (SED) of galaxies from the \dustysage semi-analytic galaxy formation model (SAM). \dustysage incorporates dust-related processes directly on top of the basic ingredients of galaxy formation like gas infall, cooling, star formation, feedback, and mergers. We derive a physically motivated attenuation model from the computed dust properties in \dustysage, so each galaxy has a self-consistent set of attenuation parameters based on the complicated dust physics that occurred across the galaxy's assembly history. Then, we explore several dust emission templates to produce infrared spectra. Our results show that a physically-motivated attenuation model is better for obtaining a consistent multi-wavelength description of galaxy formation and evolution, compared to using a constant attenuation. We compare our predictions with a compilation of observations and find that the fiducial model is in reasonable agreement with: (i) the observed $z=0$ luminosity functions from the far-ultraviolet to far-infrared simultaneously, and hence (ii) the local cosmic SED in the same range, (iii) the rest-frame K-band luminosity function across $0 < z < 3$, and (iv) the rest-frame far-ultraviolet luminosity function across $0 < z < 1$. Our model underproduces the far-ultraviolet emission at $z=2$ and $z=3$, which can be improved by altering the AGN feedback and dust processes in \dustysage. However, this combination thus worses the agreement at $z=0$, which suggests that more detailed treatment of such processes is required.

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arXiv2022

Toward Accurate Modeling of Galaxy Clustering on Small Scales: Halo Model Extensions and Lingering Tension

This paper represents an effort to provide robust constraints on the galaxy-halo connection and simultaneously test the Planck LCDM cosmology using a fully numerical model of small-scale galaxy clustering. We explore two extensions to the standard Halo Occupation Distribution model: assembly bias, whereby halo occupation depends on both halo mass and the larger environment, and velocity bias, whereby galaxy velocities do not perfectly trace the velocity of the dark matter within the halo. Moreover, we incorporate halo mass corrections to account for the impact of baryonic physics on the halo population. We identify an optimal set of clustering measurements to constrain this "decorated" HOD model for both low- and high-luminosity galaxies in SDSS DR7. We find that, for low-luminosity galaxies, a model with both assembly bias and velocity bias provides the best fit to the clustering measurements, with no tension remaining in the fit. In this model we find evidence for both central and satellite galaxy assembly bias at the 99% and 95% confidence levels, respectively. In addition, we find evidence for satellite galaxy velocity bias at the 99.9% confidence level. For high luminosity galaxies, we find no evidence for either assembly bias or velocity bias, but our model exhibits significant tension with SDSS measurements. We find that all of these conclusions still stand when we include the effects of baryonic physics on the halo mass function, suggesting that the tension we find for high luminosity galaxies may be due to a problem with our assumed cosmological model.

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arXiv2022

Uchuu-$\nu^2$GC galaxies and AGN: Cosmic variance forecasts of high-redshift AGN for JWST, Euclid, and LSST

Measurements of the luminosity function of active galactic nuclei (AGN) at high redshift ($z\gtrsim 6$) are expected to suffer from field-to-field variance, including cosmic and Poisson variances. Future surveys, such as those from the Euclid telescope and James Webb Space Telescope (JWST), will also be affected by field variance. We use the Uchuu simulation, a state-of-the-art cosmological $N$-body simulation with 2.1 trillion particles in a volume of $25.7~\mathrm{Gpc}^3$, combined with a semi-analytic galaxy and AGN formation model, to generate the Uchuu-$\nu^2$GC catalog, publicly available, that allows us to investigate the field-to-field variance of the luminosity function of AGN. With this Uchuu-$\nu^2$GC model, we quantify the cosmic variance as a function of survey area, AGN luminosity, and redshift. In general, cosmic variance decreases with increasing survey area and decreasing redshift. We find that at $z\sim6-7$, the cosmic variance depends weakly on AGN luminosity. This is because the typical mass of dark matter haloes in which AGN reside does not significantly depend on luminosity. Due to the rarity of AGN, Poisson variance dominates the total field-to-field variance, especially for bright AGN. We also examine the effect of parameters related to galaxy formation physics on the field variance. We discuss uncertainties present in the estimation of the faint-end of the AGN luminosity function from recent observations, and extend this to make predictions for the expected number of AGN and their variance for upcoming observations with Euclid, JWST, and the Legacy Survey of Space and Time (LSST).

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arXiv2021

Toward Accurate Modeling of Galaxy Clustering on Small Scales: Constraining the Galaxy-Halo Connection with Optimal Statistics

Applying halo models to analyze the small-scale clustering of galaxies is a proven method for characterizing the connection between galaxies and their host halos. Such works are often plagued by systematic errors or are limited to clustering statistics which can be predicted analytically. In this work, we employ a numerical mock-based modeling procedure to examine the clustering of SDSS DR7 galaxies. We apply a standard halo occupation distribution (HOD) model to dark-matter-only simulations with a LCDM cosmology. To constrain the theoretical models, we utilize a combination of galaxy number density and selected scales of the projected correlation function, redshift-space correlation function, group multiplicity function, average group velocity dispersion, mark correlation function, and counts-in-cells statistics. We design an algorithm to choose an optimal combination of measurements that yields tight and accurate constraints on our model parameters. Compared to previous work using fewer clustering statistics, we find significant improvement in the constraints on all parameters of our halo model for two different luminosity-threshold galaxy samples. Most interestingly, we obtain unprecedented high-precision constraints on the scatter in the relationship between galaxy luminosity and halo mass. However, our best-fit model results in significant tension (>4 sigma) for both samples, indicating the need to add second-order features to the standard HOD model. To guarantee the robustness of these results, we perform an extensive analysis of the systematic and statistical errors in our modeling procedure, including a first-of-its-kind study of the sensitivity of our constraints to changes in the halo mass function due to baryonic physics.

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arXiv2021

Exploring the relation between dust mass and galaxy properties using Dusty SAGE

We explore the relation between dust and several fundamental properties of simulated galaxies using the Dusty SAGE semi-analytic model. In addition to tracing the standard galaxy properties, Dusty SAGE also tracks cold dust mass in the interstellar medium (ISM), hot dust mass in the halo and dust mass ejected by feedback activity. Based on their ISM dust content, we divide our galaxies into two categories: ISM dust-poor and ISM dust-rich. We split the ISM dust-poor group into two subgroups: halo dust-rich and dust-poor (the latter contains galaxies that lack dust in both the ISM and halo). Halo dust-rich galaxies have high outflow rates of heated gas and dust and are more massive. We divide ISM dust-rich galaxies based on their specific star formation rate (sSFR) into star-forming and quenched subgroups. At redshift z=0, we find that ISM dust-rich galaxies have a relatively high sSFR, low bulge-to-total (BTT) mass ratio, and high gas metallicity. The high sSFR of ISM dust-rich galaxies allows them to produce dust in the stellar ejecta. Their metal-rich ISM enables dust growth via grain accretion. The opposite is seen in the ISM dust-poor group. Furthermore, ISM dust-rich galaxies are typically late-types, while ISM dust-poor galaxies resemble the early-type population, and we show how their ISM content evolves from being dust-rich to dust-poor. Finally, we investigate dust production from z=3 to z=0 and find that all groups evolve similarly, except for the quenched ISM dust-rich group.

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arXiv2020

Void Galaxies Follow a Distinct Evolutionary Path in the Environmental COntext Catalog

We measure the environmental dependence, where environment is defined by the distance to the third nearest neighbor, of multiple galaxy properties inside the Environmental COntext (ECO) catalog. We focus primarily on void galaxies, which we define as the $10 \%$ of galaxies having the lowest local density. We compare the properties of void and non-void galaxies: baryonic mass, color, fractional stellar mass growth rate (FSMGR), morphology, and gas-to-stellar-mass ratio (estimated from a combination of HI data and photometric gas fractions calibrated with the RESOLVE survey). Our void galaxies typically have lower baryonic masses than galaxies in denser environments, and they display the properties expected of a lower mass population: they have more late-types, are bluer, have higher FSMGR, and are more gas rich. We control for baryonic mass and investigate the extent to which void galaxies are different at fixed mass. Void galaxies are bluer, more gas-rich, and more star forming at fixed mass than non-void galaxies, which is a possible signature of galaxy assembly bias. Furthermore, we show that these trends persist even at fixed mass and morphology, and we find that voids host a distinct population of early-types that are bluer and more star-forming than the typical red and quenched early-types. In addition to these empirical observational results, we also present theoretical results from mock catalogs with built-in galaxy assembly bias. We show that a simple matching of galaxy properties to (sub)halo properties, such as mass and age, can recover the observed environmental trends in ECO galaxies.

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arXiv2020

Machine Learning the Fates of Dark Matter Subhalos: A Fuzzy Crystal Ball

The evolution of a dark matter halo in a dark matter only simulation is governed purely byNewtonian gravity, making a clean testbed to determine what halo properties drive its fate.Using machine learning, we predict the survival, mass loss, final position, and merging time of subhalos within a cosmological N-body simulation, focusing on what instantaneous initial features of the halo, interaction, and environment matter most. Survival is well predicted, with our model achieving 96.5% accuracy using only 3 model inputs from the initial interaction.However, the mass loss, final location, and merging times are much more stochastic processes, with significant margins of error between the true and predicted quantities for much of our sample. The redshift, impact angle, relative velocity, and the masses of the host and subhalo are the only relevant initial inputs for determining subhalo evolution. In general, subhalos that enter their hosts at a mid-range of redshifts (typically z = 0.67-0.43) are the most challenging to make predictions for, across all of our final outcomes. Subhalo orbits that come in more perpendicular to the host are also easier to predict, except for in the case of predicting disruption, where the opposite appears to be true. We conclude that the detailed evolution of individual subhalos within N-body simulations is quite difficult to predict, pointing to a stochasticity in the merging process. We discuss implications for both simulations and observations

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arXiv2020

The Uchuu Simulations: Data Release 1 and Dark Matter Halo Concentrations

We introduce the Uchuu suite of large high-resolution cosmological $N$-body simulations. The largest simulation, named Uchuu, consists of 2.1 trillion ($12800^3$) dark matter particles in a box of side-length 2.0 Gpc/h, with particle mass $3.27 \times 10^{8}$ Msun/h. The highest resolution simulation, Shin-Uchuu, consists of 262 billion ($6400^3$) particles in a box of side-length 140 Mpc/h, with particle mass $8.97 \times 10^{5}$ Msun/h. Combining these simulations we can follow the evolution of dark matter halos and subhalos spanning those hosting dwarf galaxies to massive galaxy clusters across an unprecedented volume. In this first paper, we present basic statistics, dark matter power spectra, and the halo and subhalo mass functions, which demonstrate the wide dynamic range and superb statistics of the Uchuu suite. From an analysis of the evolution of the power spectra we conclude that our simulations remain accurate from the Baryon Acoustic Oscillation scale down to the very small. We also provide parameters of a mass-concentration model, which describes the evolution of halo concentration and reproduces our simulation data to within 5 per cent for halos with masses spanning nearly eight orders of magnitude at redshift 0 0.5, whereas no upturn is detected at z<0.5. We make publicly available various $N$-body products as part of Uchuu Data Release 1 on the Skies & Universes site. Future releases will include gravitational lensing maps and mock galaxy, X-ray cluster, and active galactic nuclei catalogues.

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arXiv2020

Mentari: A pipeline to model the galaxy SED using semi analytic models

We build a theoretical picture of how the light from galaxies evolves across cosmic time. In particular, we predict the evolution of the galaxy spectral energy distribution (SED) by carefully integrating the star formation and metal enrichment histories of semi-analytic model (SAM) galaxies and combining these with stellar population synthesis models which we call mentari. Our SAM combines prescriptions to model the interplay between gas accretion, star formation, feedback process, and chemical enrichment in galaxy evolution. From this, the SED of any simulated galaxy at any point in its history can be constructed and compared with telescope data to reverse engineer the various physical processes that may have led to a particular set of observations. The synthetic SEDs of millions of simulated galaxies from mentari can cover wavelengths from the far UV to infrared, and thus can tell a near complete story of the history of galaxy evolution. \keywords{galaxies: evolution - galaxies: stellar content - galaxies.}

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arXiv2020

The origin of dust in galaxies across cosmic time

We study the dust evolution in galaxies by implementing a detailed dust prescription in the SAGE semi-analytical model for galaxy formation. The new model, called Dusty SAGE, follows the condensation of dust in the ejecta of type II supernovae and asymptotic giant branch (AGB) stars, grain growth in the dense molecular clouds, destruction by supernovae shocks, and the removal of dust from the ISM by star formation, reheating, inflows and outflows. Our model successfully reproduces the observed dust mass function at redshift z = 0 and the observed scaling relations for dust across a wide range of redshifts. We find that the dust mass content in the present Universe is mainly produced via grain growth in the interstellar medium (ISM). By contrast, in the early Universe, the primary production mechanism for dust is the condensation in stellar ejecta. The shift of the significant production channel for dust characterises the scaling relations of dust-to-gas (DTG) and dust-to-metal (DTM) ratios. In galaxies where the grain growth dominates, we find positive correlations for DTG and DTM ratios with both metallicity and stellar mass. On the other hand, in galaxies where dust is produced primarily via condensation, we find negative or no correlation for DTM and DTG ratios with either metallicity or stellar mass. In agreement with observation showing that the circumgalactic medium (CGM) contains more dust than the ISM, our model also shows the same trend for z < 4. Our semi-analytic model is publicly available at https: //github.com/dptriani/dusty-sage.

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arXiv2019

emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC

emcee is a Python library implementing a class of affine-invariant ensemble samplers for Markov chain Monte Carlo (MCMC). This package has been widely applied to probabilistic modeling problems in astrophysics where it was originally published, with some applications in other fields. When it was first released in 2012, the interface implemented in emcee was fundamentally different from the MCMC libraries that were popular at the time, such as PyMC, because it was specifically designed to work with "black box" models instead of structured graphical models. This has been a popular interface for applications in astrophysics because it is often non-trivial to implement realistic physics within the modeling frameworks required by other libraries. Since emcee's release, other libraries have been developed with similar interfaces, such as dynesty (Speagle 2019). The version 3.0 release of emcee is the first major release of the library in about 6 years and it includes a full re-write of the computational backend, several commonly requested features, and a set of new "move" implementations.

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arXiv2019

Corrfunc: Blazing fast correlation functions with AVX512F SIMD Intrinsics

Correlation functions are widely used in extra-galactic astrophysics to extract insights into how galaxies occupy dark matter halos and in cosmology to place stringent constraints on cosmological parameters. A correlation function fundamentally requires computing pair-wise separations between two sets of points and then computing a histogram of the separations. Corrfunc is an existing open-source, high-performance software package for efficiently computing a multitude of correlation functions. In this paper, we will discuss the SIMD AVX512F kernels within Corrfunc, capable of processing 16 floats or 8 doubles at a time. The latest manually implemented Corrfunc AVX512F kernels show a speedup of up to $\sim 4\times$ relative to compiler-generated code for double-precision calculations. The AVX512F kernels show $\sim 1.6\times$ speedup relative to the AVX kernels and compare favorably to a theoretical maximum of $2\times$. In addition, by pruning pairs with too large of a minimum possible separation, we achieve a $\sim 5-10\%$ speedup across all the SIMD kernels. Such speedups highlight the importance of programming explicitly with SIMD vector intrinsics for complex calculations that can not be efficiently vectorized by compilers. Corrfunc is publicly available at https://github.com/manodeep/Corrfunc/.

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arXiv2019

Corrfunc --- A Suite of Blazing Fast Correlation Functions on the CPU

The two-point correlation function (2PCF) is the most widely used tool for quantifying the spatial distribution of galaxies. Since the distribution of galaxies is determined by galaxy formation physics as well as the underlying cosmology, fitting an observed correlation function yields valuable insights into both. The calculation for a 2PCF involves computing pair-wise separations and consequently, the computing time scales quadratically with the number of galaxies. The next-generation galaxy surveys are slated to observe many millions of galaxies, and computing the 2PCF for such surveys would be prohibitively time-consuming. Additionally, modern modelling techniques require the 2PCF to be calculated thousands of times on simulated galaxy catalogues of {\em at least} equal size to the data and would be completely unfeasible for the next generation surveys. Thus, calculating the 2PCF forms a substantial bottleneck in improving our understanding of the fundamental physics of the universe, and we need high-performance software to compute the correlation function. In this paper, we present Corrfunc --- a suite of highly optimised, OpenMP parallel clustering codes. The improved performance of Corrfunc arises from both efficient algorithms as well as software design that suits the underlying hardware of modern CPUs. Corrfunc can compute a wide range of 2-D and 3-D correlation functions in either simulation (Cartesian) space or on-sky coordinates. Corrfunc runs efficiently in both single- and multi-threaded modes and can compute a typical 2-point projected correlation function ($w_p(r_p)$) for ~1 million galaxies within a few seconds on a single thread. Corrfunc is designed to be both user-friendly and fast and is publicly available at https://github.com/manodeep/Corrfunc.

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arXiv2019

The Growing Importance of a Tech Savvy Astronomy and Astrophysics Workforce

Fundamental coding and software development skills are increasingly necessary for success in nearly every aspect of astronomical and astrophysical research as large surveys and high resolution simulations become the norm. However, professional training in these skills is inaccessible or impractical for many members of our community. Students and professionals alike have been expected to acquire these skills on their own, apart from formal classroom curriculum or on-the-job training. Despite the recognized importance of these skills, there is little opportunity to develop them - even for interested researchers. To ensure a workforce capable of taking advantage of the computational resources and the large volumes of data coming in the next decade, we must identify and support ways to make software development training widely accessible to community members, regardless of affiliation or career level. To develop and sustain a technology capable astronomical and astrophysical workforce, we recommend that agencies make funding and other resources available in order to encourage, support and, in some cases, require progress on necessary training, infrastructure and policies. In this white paper, we focus on recommendations for how funding agencies can lead in the promotion of activities to support the astronomy and astrophysical workforce in the 2020s.

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