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Subhabrata Majumdar

Subhabrata Majumdar appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
astro-ph.COastro-ph.GAMachine Learningastro-phcs.CYMethodologyApplicationsArtificial Intelligencegr-qchep-phmath.STStatistics Theoryastro-ph.HECryptography and Securityhep-thSocial and Information Networks

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Published work

30 published item(s)

preprint2026arXiv

Consistency as a Testable Property: Statistical Methods to Evaluate AI Agent Reliability

This paper establishes a rigorous measurement science for AI agent reliability, providing a foundational framework for quantifying consistency under semantically preserving perturbations. By leveraging $U$-statistics for output-level reliability and kernel-based metrics for trajectory-level stability, we offer a principled approach to evaluating agents across diverse operating conditions. Our proposal highlights the important distinction between the core capability and execution robustness of an agent, showing that minor task-level variations can induce complete strategy breakdowns despite the agent possessing the requisite knowledge for the task. We validate our framework through extensive experiments on three agentic benchmarks, demonstrating that trajectory-level consistency metrics provide far greater diagnostic sensitivity than traditional pass@1 rates. By providing the mathematical tools to isolate where and why agents deviate, we enable the identification and rectification of architectural concerns that hinder the deployment of agents in high-stakes, real-world environments.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Deriving accurate galaxy cluster masses using X-ray thermodynamic profiles and graph neural networks

Precise determination of galaxy cluster masses is crucial for establishing reliable mass-observable scaling relations in cluster cosmology. We employ graph neural networks (GNNs) to estimate galaxy cluster masses from radially sampled profiles of the intra-cluster medium (ICM) inferred from X-ray observations. GNNs naturally handle inputs of variable length and resolution by representing each ICM profile as a graph, enabling accurate and flexible modeling across diverse observational conditions. We trained and tested GNN model using state-of-the-art hydrodynamical simulations of galaxy clusters from The Three Hundred Project. The mass estimates using our method exhibit no systematic bias compared to the true cluster masses in the simulations. Additionally, we achieve a scatter in recovered mass versus true mass of about 6%, which is a factor of six smaller than obtained from a standard hydrostatic equilibrium approach. Our algorithm is robust to both data quality and cluster morphology and it is capable of incorporating model uncertainties alongside observational uncertainties. Finally, we apply our technique to XMM-Newton observed galaxy cluster samples and compare the GNN derived mass estimates with those obtained with $Y_{\rm SZ}$-M$_{500}$ scaling relations. Our results provide strong evidence, at 5$σ$ level, for a mass-dependent bias in SZ derived masses, with higher mass clusters exhibiting a greater degree of deviation. Furthermore, we find the median bias to be $(1-b)=0.85_{-0.14}^{+0.34}$, albeit with significant dispersion due to its mass dependence. This work takes a significant step towards establishing unbiased observable mass scaling relations by integrating X-ray, SZ and optical datasets using deep learning techniques, thereby enhancing the role of galaxy clusters in precision cosmology.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Detecting Bias in the Presence of Spatial Autocorrelation

In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or mitigating bias issues for spatial data. We initiate the study of bias in spatial applications in this paper, taking the first step towards formalizing this line of quantitative methods. Bias in spatial data applications often gets confounded by underlying spatial autocorrelation. We propose hypothesis testing methodology to detect the presence and strength of this effect, then account for it by using a spatial filtering-based approach -- in order to enable application of existing bias detection metrics. We evaluate our proposed methodology through numerical experiments on real and synthetic datasets, demonstrating that in the presence of several types of confounding effects due to the underlying spatial structure our testing methods perform well in maintaining low type-II errors and nominal type-I errors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Feature Selection using e-values

In the context of supervised parametric models, we introduce the concept of e-values. An e-value is a scalar quantity that represents the proximity of the sampling distribution of parameter estimates in a model trained on a subset of features to that of the model trained on all features (i.e. the full model). Under general conditions, a rank ordering of e-values separates models that contain all essential features from those that do not. The e-values are applicable to a wide range of parametric models. We use data depths and a fast resampling-based algorithm to implement a feature selection procedure using e-values, providing consistency results. For a $p$-dimensional feature space, this procedure requires fitting only the full model and evaluating $p+1$ models, as opposed to the traditional requirement of fitting and evaluating $2^p$ models. Through experiments across several model settings and synthetic and real datasets, we establish that the e-values method as a promising general alternative to existing model-specific methods of feature selection.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models

The rapid development of high-throughput technologies has enabled the generation of data from biological or disease processes that span multiple layers, like genomic, proteomic or metabolomic data, and further pertain to multiple sources, like disease subtypes or experimental conditions. In this work, we propose a general statistical framework based on Gaussian graphical models for horizontal (i.e. across conditions or subtypes) and vertical (i.e. across different layers containing data on molecular compartments) integration of information in such datasets. We start with decomposing the multi-layer problem into a series of two-layer problems. For each two-layer problem, we model the outcomes at a node in the lower layer as dependent on those of other nodes in that layer, as well as all nodes in the upper layer. We use a combination of neighborhood selection and group-penalized regression to obtain sparse estimates of all model parameters. Following this, we develop a debiasing technique and asymptotic distributions of inter-layer directed edge weights that utilize already computed neighborhood selection coefficients for nodes in the upper layer. Subsequently, we establish global and simultaneous testing procedures for these edge weights. Performance of the proposed methodology is evaluated on synthetic and real data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity

Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for releasing the output of numeric queries (e.g., the Laplace mechanism and smooth sensitivity mechanism). Those mechanisms guarantee differential privacy by adding noise to the true query's output. The amount of noise added is calibrated by the notions of global sensitivity and local sensitivity of the query that measure the impact of the addition or removal of an individual on the query's output. Mechanisms that use local sensitivity add less noise and, consequently, have a more accurate answer. However, although there has been some work on generic mechanisms for releasing the output of non-numeric queries using global sensitivity (e.g., the Exponential mechanism), the literature lacks generic mechanisms for releasing the output of non-numeric queries using local sensitivity to reduce the noise in the query's output. In this work, we remedy this shortcoming and present the local dampening mechanism. We adapt the notion of local sensitivity for the non-numeric setting and leverage it to design a generic non-numeric mechanism. We provide theoretical comparisons to the exponential mechanism and show under which conditions the local dampening mechanism is more accurate than the exponential mechanism. We illustrate the effectiveness of the local dampening mechanism by applying it to three diverse problems: (i) percentile selection problem. We report the p-th element in the database; (ii) Influential node analysis. Given an influence metric, we release the top-k most influential nodes while preserving the privacy of the relationship between nodes in the network; (iii) Decision tree induction. We provide a private adaptation to the ID3 algorithm to build decision trees from a given tabular dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Non-thermal Sunyaev-Zeldovich signal from radio galaxy lobes

Energetic electrons in the lobes of radio galaxies make them potential sources for not only radio and X-rays but also Sunyaev-Zeldovich (SZ) distortions in the cosmic microwave background (CMB) radiation. Previous works have discussed the energetics of radio galaxy lobes, but assuming thermal SZ effect, coming from the non-thermal electron population. We use an improved evolutionary model for radio galaxy lobes to estimate the observed parameters such as the radio luminosity and intensity of SZ-distortions at the redshifts of observation. We, further, quantify the effects of various relevant physical parameters of the radio galaxies, such as the jet power, the time scale over which the jet is active, the evolutionary time scale for the lobe, etc on the observed parameters. For current SZ observations towards galaxy clusters, we find that the non-thermal SZ distortions from radio lobes embedded in galaxy clusters can be non-negligible compared to the amount of thermal SZ distortion from the intra-cluster medium and, hence, can not be neglected. We show that small and young (and preferably residing in a cluster environment) radio galaxies offer better prospects for the detection of the non-thermal SZ signal from these sources. We further discuss the limits on different physical parameters for some sources for which SZ effect has been either detected or upper limits are available. The evolutionary models enable us to obtain limits, previously unavailable, on the low energy cut-off of electron spectrum ($p_{min} \sim 1\hbox{--}2$) in order to explain the recent non-thermal SZ detection. Finally, we discuss how future CMB experiments, which would cover higher frequency bands ($>$400 GHz), may provide clear signatures for non-thermal SZ effect.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

An Interpretable Graph-based Mapping of Trustworthy Machine Learning Research

There is an increasing interest in ensuring machine learning (ML) frameworks behave in a socially responsible manner and are deemed trustworthy. Although considerable progress has been made in the field of Trustworthy ML (TwML) in the recent past, much of the current characterization of this progress is qualitative. Consequently, decisions about how to address issues of trustworthiness and future research goals are often left to the interested researcher. In this paper, we present the first quantitative approach to characterize the comprehension of TwML research. We build a co-occurrence network of words using a web-scraped corpus of more than 7,000 peer-reviewed recent ML papers -- consisting of papers both related and unrelated to TwML. We use community detection to obtain semantic clusters of words in this network that can infer relative positions of TwML topics. We propose an innovative fingerprinting algorithm to obtain probabilistic similarity scores for individual words, then combine them to give a paper-level relevance score. The outcomes of our analysis inform a number of interesting insights on advancing the field of TwML research.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Towards Integrating Fairness Transparently in Industrial Applications

Numerous Machine Learning (ML) bias-related failures in recent years have led to scrutiny of how companies incorporate aspects of transparency and accountability in their ML lifecycles. Companies have a responsibility to monitor ML processes for bias and mitigate any bias detected, ensure business product integrity, preserve customer loyalty, and protect brand image. Challenges specific to industry ML projects can be broadly categorized into principled documentation, human oversight, and need for mechanisms that enable information reuse and improve cost efficiency. We highlight specific roadblocks and propose conceptual solutions on a per-category basis for ML practitioners and organizational subject matter experts. Our systematic approach tackles these challenges by integrating mechanized and human-in-the-loop components in bias detection, mitigation, and documentation of projects at various stages of the ML lifecycle. To motivate the implementation of our system -- SIFT (System to Integrate Fairness Transparently) -- we present its structural primitives with an example real-world use case on how it can be used to identify potential biases and determine appropriate mitigation strategies in a participatory manner.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On Weighted Multivariate Sign Functions

Multivariate sign functions are often used for robust estimation and inference. We propose using data dependent weights in association with such functions. The proposed weighted sign functions retain desirable robustness properties, while significantly improving efficiency in estimation and inference compared to unweighted multivariate sign-based methods. Using weighted signs, we demonstrate methods of robust location estimation and robust principal component analysis. We extend the scope of using robust multivariate methods to include robust sufficient dimension reduction and functional outlier detection. Several numerical studies and real data applications demonstrate the efficacy of the proposed methodology.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Little evidence for entropy and energy excess beyond $r_{500}$ - An end to ICM preheating?

Non-gravitational feedback affects the nature of the intra-cluster medium (ICM). X-ray cooling of the ICM and in situ energy feedback from AGN's and SNe as well as {\it preheating} of the gas at epochs preceding the formation of clusters are proposed mechanisms for such feedback. While cooling and AGN feedbacks are dominant in cluster cores, the signatures of a preheated ICM are expected to be present even at large radii. To estimate the degree of preheating, with minimum confusion from AGN feedback/cooling, we study the excess entropy and non-gravitational energy profiles upto $r_{200}$ for a sample of 17 galaxy clusters using joint data sets of {\it Planck} SZ pressure and {\it ROSAT/PSPC} gas density profiles. The canonical value of preheating entropy floor of $\gtrsim 300$ keV cm$^2$, needed in order to match cluster scalings, is ruled out at $\approx 3σ$. We also show that the feedback energy of 1 keV/particle is ruled out at 5.2$σ$ beyond $r_{500}$. Our analysis takes both non-thermal pressure and clumping into account which can be important in outer regions. Our results based on the direct probe of the ICM in the outermost regions do not support any significant preheating.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Robust estimation of principal components from depth-based multivariate rank covariance matrix

Analyzing principal components for multivariate data from its spatial sign covariance matrix (SCM) has been proposed as a computationally simple and robust alternative to normal PCA, but it suffers from poor efficiency properties and is actually inadmissible with respect to the maximum likelihood estimator. Here we use data depth-based spatial ranks in place of spatial signs to obtain the orthogonally equivariant Depth Covariance Matrix (DCM) and use its eigenvector estimates for PCA. We derive asymptotic properties of the sample DCM and influence functions of its eigenvectors. The shapes of these influence functions indicate robustness of estimated principal components, and good efficiency properties compared to the SCM. Finite sample simulation studies show that principal components of the sample DCM are robust with respect to deviations from normality, as well as are more efficient than the SCM and its affine equivariant version, Tyler's shape matrix. Through two real data examples, we also show the effectiveness of DCM-based PCA in analyzing high-dimensional data and outlier detection, and compare it with other methods of robust PCA.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

The many scales to cosmic homogeneity: Use of multiple tracers from the SDSS

We carry out multifractal analyses of multiple tracers namely the main galaxy sample, the LRG sample and the quasar sample from the SDSS to test the assumption of cosmic homogeneity and identify the scale of transition to homogeneity, if any. We consider the behaviour of the scaled number counts and the scaling relations of different moments of the galaxy number counts in spheres of varying radius $R$ to calculate the spectrum of the Minkowski-Bouligand general dimension $D_{q} (R)$ for $-4 \leq q \leq 4$. The present analysis provides us the opportunity to study the spectrum of the generalized dimension $D_{q}(R)$ for multiple tracers of the cosmic density field over a wide range of length scales and allows us to confidently test the validity of the assumption of cosmic homogeneity. Our analysis indicates that the SDSS main galaxy sample is homogeneous on a length scales of $80\, h^{-1}\, {\rm Mpc} $ and beyond whereas the SDSS quasar sample and the SDSS LRG sample show transition to homogeneity on an even larger length scales at $\sim 150\, h^{-1}\, {\rm Mpc}$ and $\sim 230\, h^{-1}\, {\rm Mpc}$ respectively. These differences in the scale of homogeneity arise due to the effective mass and redshift scales probed by the different tracers in a Universe where structures form hierarchically. Our results reaffirm the validity of cosmic homogeneity on large scales irrespective of the tracers used and strengthens the foundations of the Standard Model of Cosmology.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

CMB distortion from circumgalactic gas

We study the Sunyaev-Zel'dovich (SZ) distortion of the cosmic microwave background radiation (CMBR) from extensive circumgalactic gas (CGM) in massive galactic halos. Recent observations have shown that galactic halos contain a large amount of X-ray emitting gas at the virial temperature, as well as a significant amount of warm OVI absorbing gas. We consider the SZ distortion from the hot gas in those galactic halos in which the gas cooling time is longer than the halo destruction time scale. We show that the SZ distortion signal from the hot gas in these galactic halos at redshifts $z\approx 1\hbox{--}8$ can be significant at small angular scales ($\ell\sim 10^4$), and dominate over the signal from galaxy clusters. The estimated SZ signal for most massive galaxies (halo mass $\ge 10^{12.5}$ M$_\odot$) is consistent with the marginal detection by {\it Planck} at these mass scales. We also consider the SZ effect from warm circumgalactic gas. The integrated Compton distortion from the warm OVI absorbing gas is estimated to be $y\sim 10^{-8}$, which could potentially be detected by experiments planned for the near future. Finally, we study the detectability of the SZ signal from circumgalactic gas in two types of surveys, a simple extension of the SPT survey and a more futuristic cosmic variance-limited survey. We find that these surveys can easily detect the kSZ signal from CGM. With the help of a Fisher Matrix analysis, we find that it will be possible for these surveys to constrain the gas fraction in CGM, after marginalizing over cosmological parameters, to $\le 33$\%, in case of no redshift evolution of the gas fraction.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Probing the circumgalactic baryons through cross-correlations

We study the cross-correlation of distribution of galaxies, the Sunyaev-Zel'dovich (SZ) and X-ray power spectra of galaxies from current and upcoming surveys and show these to be excellent probes of the nature, i.e. extent, evolution and energetics, of the circumgalactic medium (CGM). The SZ-galaxy cross-power spectrum, especially at large multipoles, depends on the steepness of the pressure profile of the CGM. This property of the SZ signal can, thus, be used to constrain the pressure profile of the CGM. The X-ray cross power spectrum also has a similar shape. However, it is much more sensitive to the underlying density profile. We forecast the detectability of the cross-correlated galaxy distribution, SZ and X-ray signals by combining South Pole Telescope-Dark Energy Survey (SPT-DES) and eROSITA-DES/eROSITA-LSST (extended ROentgen Survey with an Imaging Telescope Array-Large Synoptic Survey Telescope) surveys, respectively. We find that, for the SPT-DES survey, the signal-to-noise ratio (SNR) peaks at high mass and redshift with SNR $\sim 9$ around $M_h\sim 10^{13} h^{-1} M_{\odot}$ and $z\sim 1.5\hbox{--} 2$ for flat density and temperature profiles. The SNR peaks at $\sim 6 (12 )$ for the eROSITA-DES (eROSITA-LSST) surveys. We also perform a Fisher matrix analysis to find the constraint on the gas fraction in the CGM in the presence or absence of an unknown redshift evolution of the gas fraction. Finally, we demonstrate that the cross-correlated SZ-galaxy and X-ray-galaxy power spectrum can be used as powerful probes of the CGM energetics and potentially discriminate between different feedback models recently proposed in the literature; for example, one can distinguish a `no active galactic nuclei feedback' scenario from a CGM energized by `fixed-velocity hot winds' at greater than $3σ$.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Post-Planck Dark Energy Constraints

We constrain plausible dark energy models, parametrized by multiple candidate equation of state, using the recently published Cosmic Microwave Background (CMB) temperature anisotropy data from Planck together with the WMAP-9 low-$\ell$ polarization data and data from low redshift surveys. To circumvent the limitations of any particular equation of state towards describing all existing dark energy models, we work with three different equation of state covering a broader class of dark energy models and, hence, provide more robust and generic constraints on the dark energy properties. We show that a clear tension exists between dark energy constraints from CMB and non-CMB observations when one allows for dark energy models having both phantom and non-phantom behavior; while CMB is more favorable to phantom models, the low-z data prefers model with behavior close to a Cosmological Constant. Further, we reconstruct the equation of state of dark energy as a function of redshift using the results from combined CMB and non-CMB data and find that Cosmological Constant lies outside the 1$σ$ band for multiple dark energy models allowing phantom behavior. A considerable fine tuning is needed to keep models with strict non-phantom history inside 2$σ$ allowed range. This result might motivate one to construct phantom models of dark energy,which is achievable in the presence of higher derivative operators as in string theory. However, disallowing phantom behavior, based only on strong theoretical prior, leads to both CMB and non-CMB datasets agree on the nature of dark energy, with the mean equation of state being very close to the Cosmological Constant. Finally, to illustrate the impact of additional dark energy parameters on other cosmological parameters, we provide the cosmological parameter constraints for different dark energy models.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Adjusting for Treatment Effects in Studies of Quantitative Traits

A population-based study of a quantitative trait, e.g. Blood Pressure(BP) may be seriously compromised when the trait is subject to the effects of a treatment. Without appropriate corrections this can lead to considerable reduction of statistical power. Here we demonestrate this in the scenario of QTL mapping through Single-Marker Analysis. The data are simulated from a normal mixtrure for different values of allele frequencies, separation between normal populations and Linkage Disequilibrium, and several methods of correction are compared to check which can best compensate for the loss of power if treatment effects are ignored. In one of these methods, underlying BPs are approximated by subtracting an estimate of mean value of medicine effect from obsereved BPs in treated subjects. We domonestrate the efficacy of this method throughout different choices of parameters. Finally to account for quantitative traits that follow non-normal distributions, data are simulated from lognormal mixtures similarly and Kruskal-Wallis test is used to obtain estimates of powers for different methods of analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

AGN feedback and entropy injection in galaxy cluster cores

The non-gravitational energy feedback is of crucial importance in modeling/simulating clusters to be used as cosmological probes. AGNs are, arguably, of primary importance in injecting energy in the cluster cores. We make the first estimate of non-gravitational energy {\it profiles} in galaxy cluster cores (and beyond) from observational data. Comparing the observed entropy profiles within $r_{500}$, from the Representative {\it XMM-Newton} Cluster Structure Survey (REXCESS), to simulated entropy profiles from both AMR and SPH non-radiative simulations, we estimate the amount of non-gravitational energy, $E_{\rm ICM}$, contained in the ICM. Adding the radiative losses we estimate the total energy feedback, $E_{\rm Feedback}$, into the clusters. The profiles for the energy deposition, $ΔE_{\rm ICM}(x)$, in the inner regions differ for Cool-Core (CC) and Non Cool-Core (NCC) clusters, decreasing after accounting for the radiative cooling. The total feedback energy scales with the mean spectroscopic temperature as $E_{\rm Feedback} \propto T_{\rm sp}^{2.52 \pm0.08}$ and $E_{\rm Feedback} \propto T_{\rm sp}^{2.17 \pm 0.11}$, when compared with the baseline SPH and AMR profiles respectively. The scatter in the two cases is 15% and 23%, respectively. The mean non-gravitational energy per particle within $r_{500}$, is $ε_{\rm ICM} = {2.8} \pm {0.8}$ keV for the SPH theoretical relation and $ε_{\rm ICM} = {1.7} \pm {0.9}$ keV for the AMR theoretical relation. We use the {\it NRAO/VLA Sky Survey} (NVSS) source catalog to determine the radio luminosity, $L_R$, at 1.4 GHz of the central source(s) of our sample. For $T_{\rm sp} > 3$ keV, the $E_{\rm Feedback}$ correlates with $L_R$. We show that AGNs could provide a significant component of the feedback. (Abridged)

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preprint2013arXiv

Cosmology with the largest galaxy cluster surveys: Going beyond Fisher matrix forecasts

We make the first detailed MCMC likelihood study of cosmological constraints that are expected from some of the largest, ongoing and proposed, cluster surveys in different wave-bands and compare the estimates to the prevalent Fisher matrix forecasts. Mock catalogs of cluster counts expected from the surveys -- eROSITA, WFXT, RCS2, DES and Planck, along with a mock dataset of follow-up mass calibrations are analyzed for this purpose. A fair agreement between MCMC and Fisher results is found only in the case of minimal models. However, for many cases, the marginalized constraints obtained from Fisher and MCMC methods can differ by factors of 30-100%. The discrepancy can be alarmingly large for a time dependent dark energy equation of state, w(a); the Fisher methods are seen to under-estimate the constraints by as much as a factor of 4--5. Typically, Fisher estimates become more and more inappropriate as we move away from LCDM, to a constant-w dark energy to varying-w dark energy cosmologies. Fisher analysis, also, predicts incorrect parameter degeneracies. From the point of mass-calibration uncertainties, a high value of unknown scatter about the mean mass-observable relation, and its redshift dependence, is seen to have large degeneracies with the cosmological parameters sigma_8 and w(a) and can degrade the cosmological constraints considerably. We find that the addition of mass-calibrated cluster datasets can improve dark energy and sigma_8 constraints by factors of 2--3 from what can be obtained compared to CMB+SNe+BAO only. Since, details of future cluster surveys are still being planned, we emphasize that optimal survey design must be done using MCMC analysis rather than Fisher forecasting. [abridged]

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preprint2013arXiv

Deriving the velocity distribution of Galactic Dark Matter particles from rotation curve data

The velocity distribution function (VDF) of the hypothetical Weakly Interacting Massive Particles (WIMPs), currently the most favored candidate for the Dark Matter (DM) in the Galaxy, is determined directly from the circular speed ("rotation") curve data of the Galaxy assuming isotropic VDF. This is done by "inverting" --- using Eddington's method --- the Navarro-Frenk-White universal density profile of the DM halo of the Galaxy, the parameters of which are determined, by using Markov Chain Monte Carlo (MCMC) technique, from a recently compiled set of observational data on the Galaxy's rotation curve extended to distances well beyond the visible edge of the disk of the Galaxy. The derived most-likely local isotropic VDF strongly differs from the Maxwellian form assumed in the "Standard Halo Model" (SHM) customarily used in the analysis of the results of WIMP direct-detection experiments. A parametrized (non-Maxwellian) form of the derived most-likely local VDF is given. The astrophysical "g-factor" that determines the effect of the WIMP VDF on the expected event rate in a direct-detection experiment can be lower for the derived most-likely VDF than that for the best Maxwellian fit to it by as much two orders of magnitude at the lowest WIMP mass threshold of a typical experiment.

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preprint2013arXiv

Searching for systematics in SNIa and galaxy cluster data using the cosmic duality relation

We compare two different probes of the expansion history of the universe, namely, luminosity distances from type Ia supernovae and angular diameter distances from galaxy clusters, using the Bayesian interpretation of Crossing statistic [1, 2] in conjunction with the assumption of cosmic duality relation. Our analysis is conducted independently of any a-priori assumptions about the nature of dark energy. The model independent method which we invoke searches for inconsistencies between SNIa and galaxy cluster data sets. If detected such an inconsistency would imply the presence of systematics in either of the two data sets. Simulating observations based on expected WFIRST supernovae data and X-ray eROSITA + SZ Planck cluster data, we show that our method allows one to detect systematics with high precision and without advancing any hypothesis about the nature of dark energy.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Constraining Thawing Quintessence

We look at observational constraints on the thawing class of scalar field models proposed to explain the late time acceleration of the universe. Using the recently introduced `Statefinder Hierarchy', we compare these thawing class of models with other widely studied dark energy (and modified gravity) models to check the underlying parameter degeneracies. We put constraints on the deviations of these thawing models from the canonical ΛCDM model using a large class of observational data, e.g, the Supernova Type Ia data, the BAO data, the CMB data and data from the measurements of the Hubble parameter using red-envelope galaxies. We also forecast constraints using a simulated dataset for the future JDEM SNe survey. Our study shows that, although with current data it is difficult to distinguish different thawing models from ΛCDM, a future JDEM like mission would be able tell apart thawing models from ΛCDM for currently acceptable values of Ω_{m0}

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preprint2012arXiv

Energy Deposition Profiles and Entropy in Galaxy Clusters

We report the results of our study of fractional entropy enhancement in the intra-cluster medium (ICM) of the clusters from the representative XMM-Newton cluster structure survey (REXCESS). We compare the observed entropy profile of these clusters with that expected for the ICM without any feedback, as well as with the introduction of preheating and entropy change due to gas cooling. We make the first estimate of the total, as well as radial, non-gravitational energy deposition up to r500 for a large, nearly flux-limited, sample of clusters. We find that the total energy deposition corresponding to the entropy enhancement is proportional to the cluster temperature (and hence mass), and that the energy deposition per particle as a function of gas mass shows a similar profile in all clusters, with its being more pronounced in the central region than in the outer region. Our results support models of entropy enhancement through AGN feedback.

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preprint2011arXiv

Intracluster gas pressure, entropy injection and redshift evolution

We study the effect of entropy injection in the intracluster medium (ICM) in light of the recent observationally determined universal pressure profile of the ICM. Beginning with a power-law entropy profile that is expected in the absence of any feedback, we show that a simple universal prescription of entropy injection results in the final, observed universal pressure profile. This simple prescription has two components, one associated with an overall increase in entropy and another associated with injection in the central parts of the cluster. Importantly, both the components of entropy injection are needed to produce the final universal pressure profile. This is indicative of a need of both preheating the ICM as well {\it in situ} AGN/SNe heating. We demonstrate the usefulness of the method by extending the calculations to clusters at high redshift, and predict redshift evolution of cluster scaling relations that can be tested against data. We show that the self-similar evolution of the universal pressure profile is equivalent to a negative evolution of entropy injection with redshift, with a scaling $S_{inj} \propto (1+z)^{-0.8} S_{inj}(z=0)$. We also show the current observational data are indicative of the entropy injection decreasing with redshift.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Precision cosmology with a combination of wide and deep Sunyaev-Zeldovich cluster surveys

We show the advantages of a wedding cake design for Sunyaev-Zel'dovich cluster surveys. We show that by dividing up a cluster survey into a wide and a deep survey, one can essentially recover the cosmological information that would be diluted in a single survey of the same duration due to the uncertainties in our understanding of cluster physics. The parameter degeneracy directions of the deep and wide surveys are slightly different, and combining them breaks these degeneracies effectively. A variable depth survey with a few thousand clusters is as effective at constraining cosmological parameters as a single depth survey with a much larger cluster sample.

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preprint2010arXiv

Sunyaev-Zel'dovich scaling relations from a simple phenomenological model for galaxy clusters

We build a simple, {\it top-down} model for the gas density and temperature profiles for galaxy clusters. The gas is assumed to be in hydrostatic equilibrium along with a component of non-thermal pressure taken from simulations and the gas fraction approaches the cosmic mean value only at the virial radius or beyond. The free parameters of the model are the slope and normalisation of the concentration-mass relation, the gas polytropic index, and slope and normalisation of the mass-temperature relation. These parameters can be fixed from X-Ray and lensing observations. We compare our gas pressure profiles to the recently proposed `Universal' pressure profile by \cite{Arnaud09} and find very good agreement. We find that the Sunyaev-Zel'dovich Effect (SZE) scaling relations between the integrated SZE flux, $Y$, the cluster gas temperature, $T_{\rm sl}$, the cluster mass, $M_{\rm tot}$, and the gas mass, $M_{\rm gas}$ are in excellent agreement with the recently observed $r_{2500}$ SZE scaling relations by \cite{Bonamente08} and $r_{500}$ relation by \cite{Arnaud09}. The gas mass fraction increases with cluster mass and is given by $f_{\rm gas}(r_{500}) = 0.1324 + 0.0284 \,\rm{log}\, (\frac{M_{500}}{10^{15}h^{-1}M_\odot})$. This is within $10%$ of observed $f_{\rm gas}(r_{500})$. The consistency between the global properties of clusters detected in X-Rays and in SZE shows that we are looking at a common population of clusters as a whole, and there is no deficit of SZE flux relative to expectations from \xr scaling properties. Thus, it makes it easier to compare and cross-calibrate clusters from upcoming \xr and SZE surveys.

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preprint2010arXiv

Using clusters in SZE + x-ray surveys as an ensemble of rulers to constrain cosmology

Ongoing and upcoming surveys in x-rays and SZE are expected to jointly detect many clusters due to the large overlap in sky coverage. We show that, these clusters can be used as an ensemble of rulers to estimate the angular diameter distance, d_A(z). This comes at no extra observational cost, as these clusters form a subset of a much larger sample, assembled to build cluster number counts dn/dz. On using this d_A(z), the dark energy constraints can be improved by factors of 1.5 - 4, over those from just dn/dn. Even in the presence of a mass follow-up of 100 clusters (done for mass calibration), the dark energy constraints can be further tightened by factors of 2 - 3 . Adding d_A(z) from clusters is similar to adding d_L(z), from the SNe observations; for eg., dn/dn (from ACT/SPT) plus d_A(z) is comparable to dn/dz plus d_L(z) in constraining Omega_m and sigma_8.

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preprint2008arXiv

Spectroscopic Confirmation of a Massive Red-Sequence-Selected Galaxy Cluster at z = 1.34 in the SpARCS-South Cluster Survey

The Spitzer Adaptation of the Red-sequence Cluster Survey (SpARCS) is a z'-passband imaging survey, consisting of deep (z' ~ 24 AB) observations made from both hemispheres using the CFHT 3.6m and CTIO 4m telescopes. The survey was designed with the primary aim of detecting galaxy clusters at z >~ 1. In tandem with pre-existing 3.6um observations from the Spitzer Space Telescope SWIRE Legacy Survey, SpARCS detects clusters using an infrared adaptation of the two-filter red-sequence cluster technique. The total effective area of the SpARCS cluster survey is 41.9 deg^2. In this paper, we provide an overview of the 13.6 deg^2 Southern CTIO/MOSAICII observations. The 28.3 deg^2 Northern CFHT/MegaCam observations are summarized in a companion paper by Muzzin et al. (2008). In this paper, we also report spectroscopic confirmation of SpARCS J003550-431224, a very rich galaxy cluster at z = 1.335, discovered in the ELAIS-S1 field. To date, this is the highest spectroscopically confirmed redshift for a galaxy cluster discovered using the red-sequence technique. Based on nine confirmed members, SpARCS J003550-431224 has a preliminary velocity dispersion of 1050 +/- 230 km/s. With its proven capability for efficient cluster detection, SpARCS is a demonstration that we have entered an era of large, homogeneously-selected z > 1 cluster surveys.

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preprint2008arXiv

Spectroscopic Confirmation of Two Massive Red-Sequence-Selected Galaxy Clusters at z ~ 1.2 in the SpARCS-North Cluster Survey

The Spitzer Adaptation of the Red-sequence Cluster Survey (SpARCS) is a deep z'-band imaging survey covering the Spitzer SWIRE Legacy fields designed to create the first large homogeneously-selected sample of massive clusters at z > 1 using an infrared adaptation of the cluster red-sequence method. We present an overview of the northern component of the survey which has been observed with CFHT/MegaCam and covers 28.3 deg^2. The southern component of the survey was observed with CTIO/MOSAICII, covers 13.6 deg^2, and is summarized in a companion paper by Wilson et al. (2008). We also present spectroscopic confirmation of two rich cluster candidates at z ~ 1.2. Based on Nod-and-Shuffle spectroscopy from GMOS-N on Gemini there are 17 and 28 confirmed cluster members in SpARCS J163435+402151 and SpARCS J163852+403843 which have spectroscopic redshifts of 1.1798 and 1.1963, respectively. The clusters have velocity dispersions of 490 +/- 140 km/s and 650 +/- 160 km/s, respectively which imply masses (M200) of (1.0 +/- 0.9) x 10^{14} M_{solar} and (2.4 +/- 1.8) x 10^{14} M_{solar}. Confirmation of these candidates as bona fide massive clusters demonstrates that two-filter imaging is an effective, yet observationally efficient, method for selecting clusters at z > 1.

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preprint2001arXiv

Probing the Evolution of Gas Mass Fraction with Sunyaev-Zel'dovich Effect

Study of the primary anisotropies of the Cosmic Microwave Background (CMB) can be used to determine the cosmological parameters to a very high precision. The power spectrum of the secondary CMB anisotropies due to the thermal Sunyaev-Zel'dovich Effect (SZE) by clusters of galaxies, can then be studied, to constrain more cluster specific properties (like gas mass). We show the SZE power spectrum from clusters to be a sensitive probe of any possible evolution (or constancy) of the gas mass fraction. The position of the peak of the SZE power spectrum is a strong discriminatory signature of different gas mass fraction evolution models. For example, for a flat universe, there can be a difference in the $l$ values (of the peak) of {\it as much as 3000} between a constant gas mass fraction model and an evolutionary one. Moreover, observational determination of power spectrum, from blank sky surveys, is devoid of any selection effects that can possibly affect targeted X-ray or radio studies of gas mass fractions in galaxy clusters.

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