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Articles 1 - 30 of 132
Full-Text Articles in Physical Sciences and Mathematics
Image-Based Malware Classification With Convolutional Neural Networks And Extreme Learning Machines, Mugdha Jain
Image-Based Malware Classification With Convolutional Neural Networks And Extreme Learning Machines, Mugdha Jain
Master's Projects
Research in the field of malware classification often relies on machine learning models that are trained on high level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this research, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or execution of code. Specifically, we visualize malware samples as images and employ image analysis techniques. In this context, we focus on two machine learning models, namely, Convolutional Neural Networks (CNN) and Extreme …
Hot Fusion Vs Cold Fusion For Malware Detection, Snehal Bichkar
Hot Fusion Vs Cold Fusion For Malware Detection, Snehal Bichkar
Master's Projects
A fundamental problem in malware research consists of malware detection, that is, dis- tinguishing malware samples from benign samples. This problem becomes more challeng- ing when we consider multiple malware families. A typical approach to this multi-family detection problem is to train a machine learning model for each malware family and score each sample against all models. The resulting scores are then used for classification. We refer to this approach as “cold fusion,” since we combine previously-trained models—no retraining of these base models is required when additional malware families are considered. An alternative approach is to train a single model …
Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur
Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur
Master's Projects
Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …
Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten
Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten
Master's Projects
Wildfire damage assessments are important information for first responders, govern- ment agencies, and insurance companies to estimate the cost of damages and to help provide relief to those affected by a wildfire. With the help of Earth Observation satellite technology, determining the burn area extent of a fire can be done with traditional remote sensing methods like Normalized Burn Ratio. Using Very High Resolution satellites can help give even more accurate damage assessments but will come with some tradeoffs; these satellites can provide higher spatial and temporal resolution at the expense of better spectral resolution. As a wildfire burn area …
Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg
Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg
Master's Projects
Inadequate drug experimental data and the use of unlicensed drugs may cause adverse drug reactions, especially in pediatric populations. Every year the U.S. Food and Drug Administration approves human prescription drugs for marketing. The labels associated with these drugs include information about clinical trials and drug response in pediatric population. In order for doctors to make an informed decision about the safety and effectiveness of these drugs for children, there is a need to analyze complex and often unstructured drug labels. In this work, first, an exploratory analysis of drug labels using a Natural Language Processing pipeline is performed. Second, …
The Distribution Of Ultra-Diffuse And Ultra-Compact Galaxies In The Frontier Fields, Steven Janssens, Roberto Abraham, Jean Brodie, Duncan Forbes, Aaron Romanowsky
The Distribution Of Ultra-Diffuse And Ultra-Compact Galaxies In The Frontier Fields, Steven Janssens, Roberto Abraham, Jean Brodie, Duncan Forbes, Aaron Romanowsky
Faculty Publications
Large low-surface-brightness galaxies have recently been found to be abundant in nearby galaxy clusters. In this paper, we investigate these ultra-diffuse galaxies (UDGs) in the six Hubble Frontier Fields galaxy clusters: A2744, MACS J0416.1−2403, MACS J0717.5+3745, MACS J1149.5+2223, AS1063, and A370. These are the most massive (1–3 × 1015 M ⊙) and distant (0.308 < z < 0.545) systems in which this class of galaxy has yet been discovered. We estimate that the clusters host of the order of ~200–1400 UDGs inside the virial radius (R 200), consistent with the UDG abundance–halo-mass relation found in the local universe, and suggest that UDGs may be formed in clusters. Within each cluster, however, we find that UDGs are not evenly distributed. Instead their projected spatial distributions are lopsided, and they are deficient in the regions of highest mass density as traced by gravitational lensing. While the deficiency of UDGs in central regions is not surprising, the lopsidedness is puzzling. The UDGs, and their lopsided spatial distributions, may be associated with known substructures late in their infall into the clusters, meaning that we find evidence both for formation of UDGs in clusters and for UDGs falling into clusters. We also investigate the ultra-compact dwarfs (UCDs) residing in the clusters, and find that the spatial distributions of UDGs and UCDs appear anticorrelated. Around 15% of UDGs exhibit either compact nuclei or nearby point sources. Taken together, these observations provide additional evidence for a picture in which at least some UDGs are destroyed in dense cluster environments and leave behind a residue of UCDs.
Toward Early Detection Of Pancreatic Cancer: An Evidence-Based Approach, Omid Sharagi
Toward Early Detection Of Pancreatic Cancer: An Evidence-Based Approach, Omid Sharagi
Master's Projects
This study observes how an evidential reasoning approach can be used as a diagnostic tool for early detection of pancreatic cancer. The evidential reasoning model combines the output of a linear Support Vector Classifier (SVC) with factors such as smoking history, health history, biopsy location, NGS technology used, and more to predict the likelihood of the disease. The SVC was trained using genomic data of pancreatic cancer patients derived from the National Cancer Institute (NIH) Genomic Data Commons (GDC). To test the evidential reasoning model, a variety of synthetic data was compiled to test the impact of combinations of different …
Image-Based Localization Of User-Interfaces, Riti Gupta
Image-Based Localization Of User-Interfaces, Riti Gupta
Master's Projects
Image localization corresponds to translating the text present in the images from one language to other language. The aim of the project is to develop a methodology to translate the text in image captions from English to Hindi by taking context of the images into account. A lot of work has been done in this field [22], but our aim was to explore if the accuracy can be further improved by consideration of the additional information imparted by the images apart from the text. We have explored Deep Learning using neural networks for this project. In particular, Recurrent Neural Networks …
A Hybrid Approach For Multi-Document Text Summarization, Rashmi Varma
A Hybrid Approach For Multi-Document Text Summarization, Rashmi Varma
Master's Projects
Text summarization has been a long studied topic in the field of natural language processing. There have been various approaches for both extractive text summarization as well as abstractive text summarization. Summarizing texts for a single document is a methodical task. But summarizing multiple documents poses as a greater challenge. This thesis explores the application of Latent Semantic Analysis, Text-Rank, Lex-Rank and Reduction algorithms for single document text summarization and compares it with the proposed approach of creating a hybrid system combining each of the above algorithms, individually, with Restricted Boltzmann Machines for multi-document text summarization and analyzing how all …
Music Retrieval System Using Query-By-Humming, Parth Patel
Music Retrieval System Using Query-By-Humming, Parth Patel
Master's Projects
Music Information Retrieval (MIR) is a particular research area of great interest because there are various strategies to retrieve music. To retrieve music, it is important to find a similarity between the input query and the matching music. Several solutions have been proposed that are currently being used in the application domain(s) such as Query- by-Example (QBE) which takes a sample of an audio recording playing in the background and retrieves the result. However, there is no efficient approach to solve this problem in a Query-by-Humming (QBH) application. In a Query-by-Humming application, the aim is to retrieve music that is …
3d Shape Prediction On Convolutional Deep Belief Networks, Gregory Y. Enriquez
3d Shape Prediction On Convolutional Deep Belief Networks, Gregory Y. Enriquez
Master's Projects
The field of image recognition software has grown immensely in recent years with the emergence of new deep learning techniques. Deep belief networks inspired by Hinton [11] were one of the earliest methodologies of deep learning in the late 2000s. More recently, convolutional neural networks have been used in deep learning techniques, architecture, and software to identify patterns in imagery in order to make predictions such as classification, image segmentation, etc. Traditional two-dimensional, or 2D, images stored as picture files, typically contain red, green, and blue color data for each individual pixel in the picture. However, more recent commercial 2.5D …
A Case Study Of Stratospheric Ozone Transport To The Northern San Francisco Bay Area And Sacramento Valley During Cabots 2016, Jodie Clark, Sen Chiao
A Case Study Of Stratospheric Ozone Transport To The Northern San Francisco Bay Area And Sacramento Valley During Cabots 2016, Jodie Clark, Sen Chiao
Faculty Research, Scholarly, and Creative Activity
The California Baseline Ozone Transport Study (CABOTS) was a major air quality study that collected ozone measurements aloft between mid-May and mid-August of 2016. Aircraft measurements, ground-based lidar measurements, and balloon-borne ozonesondes collected precise upper-air ozone measurements across the central and Southern California valley. Utilizing daily ozonesonde data from Bodega Bay, California, and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis data for 25 July to 14 August 2016, three stratospheric intrusion events are identified over Northern California influencing air masses above Bodega Bay and Sacramento simultaneously. Calculated percent daily changes in afternoon ozonesonde observations indicate increasing …
Blockchain In Libraries, Michael Meth
Blockchain In Libraries, Michael Meth
Faculty Research, Scholarly, and Creative Activity
This issue of Library Technology Reports (vol. 55, no. 8), “Blockchain in Libraries,” examines the application of blockchain in libraries. Blockchain technology has the ability to transform how libraries provide services and organize information. To date, most of these applications are still in the conceptual stage. However, sooner or later, development and implementation will follow. This report is intended to provide a primer on the technology and some thought starters. In chapter 2, the concept of blockchain is explained. Chapter 3 provides eight thought and conversation starters that look at how blockchain could be applied in libraries. Chapter 4 looks …
Transport Signatures Of Dirac States In Topological Insulator - Ferromagnet Heterostructures, Hilary M. Hurst
Transport Signatures Of Dirac States In Topological Insulator - Ferromagnet Heterostructures, Hilary M. Hurst
Faculty Research, Scholarly, and Creative Activity
No abstract provided.
Tidal Destruction In A Low-Mass Galaxy Environment: The Discovery Of Tidal Tails Around Ddo 44, Jeffrey Carlin, Christopher Garling, Annika Peter, Denija Crnojević, Duncan Forbes, Jonathan Hargis, Burçin Mutlu-Pakdil, Ragadeepika Pucha, Aaron Romanowsky, David Sand, Kristine Spekkens, Jay Strader, Beth Willman
Tidal Destruction In A Low-Mass Galaxy Environment: The Discovery Of Tidal Tails Around Ddo 44, Jeffrey Carlin, Christopher Garling, Annika Peter, Denija Crnojević, Duncan Forbes, Jonathan Hargis, Burçin Mutlu-Pakdil, Ragadeepika Pucha, Aaron Romanowsky, David Sand, Kristine Spekkens, Jay Strader, Beth Willman
Faculty Publications
We report the discovery of a 1° (~50 kpc) long stellar tidal stream emanating from the dwarf galaxy DDO 44, a likely satellite of Local Volume galaxy NGC 2403 located ~70 kpc in projection from its companion. NGC 2403 is a roughly Large Magellanic Cloud (LMC) stellar-mass galaxy 3 Mpc away, residing at the outer limits of the M81 group. We are mapping a large region around NGC 2403 as part of our Magellanic Analogs' Dwarf Companions and Stellar Halos survey, reaching point-source depths (90% completeness) of (g, i) = (26.5, 26.2). Density maps of old, metal-poor RGB stars reveal …
Spatially Resolved Stellar Kinematics Of The Ultra-Diffuse Galaxy Dragonfly 44. Ii. Constraints On Fuzzy Dark Matter, Asher Wasserman, Pieter Van Dokkum, Aaron Romanowsky, Jean Brodie, Shany Danieli, Duncan Forbes, Roberto Abraham, Christopher Martin, Matt Matuszewski, Alexa Villaume, John Tamanas, Stefano Profumo
Spatially Resolved Stellar Kinematics Of The Ultra-Diffuse Galaxy Dragonfly 44. Ii. Constraints On Fuzzy Dark Matter, Asher Wasserman, Pieter Van Dokkum, Aaron Romanowsky, Jean Brodie, Shany Danieli, Duncan Forbes, Roberto Abraham, Christopher Martin, Matt Matuszewski, Alexa Villaume, John Tamanas, Stefano Profumo
Faculty Publications
Given the absence of directly detected dark matter (DM) as weakly interacting massive particles, there is strong interest in the possibility that DM is an ultralight scalar field, here denoted as "fuzzy" DM. Ultra-diffuse galaxies, with the sizes of giant galaxies and the luminosities of dwarf galaxies, have a wide range of DM halo masses, thus providing new opportunities for exploring the connections between galaxies and their DM halos. Following up on new integral field unit spectroscopic observations and dynamics modeling of the DM-dominated ultra-diffuse galaxy Dragonfly 44 in the outskirts of the Coma Cluster, we present models of fuzzy …
Dark Matter And No Dark Matter: On The Halo Mass Of Ngc 1052, Duncan Forbes, Adebusola Alabi, Jean Brodie, Aaron Romanowsky
Dark Matter And No Dark Matter: On The Halo Mass Of Ngc 1052, Duncan Forbes, Adebusola Alabi, Jean Brodie, Aaron Romanowsky
Faculty Publications
The NGC 1052 group, and in particular the discovery of two ultra-diffuse galaxies with very low internal velocity dispersions, has been the subject of much attention recently. Here we present radial velocities for a sample of 77 globular clusters associated with NGC 1052 obtained on the Keck telescope. Their mean velocity and velocity dispersion are consistent with that of the host galaxy. Using a simple tracer mass estimator, we infer the enclosed dynamical mass and dark matter fraction of NGC 1052. Extrapolating our measurements with a Navarro–Frenk–White (NFW) mass profile we infer a total halo mass of 6.2(±0.2) × 1012 …
The Intensification Of Hurricane Maria 2017 In The Antilles, Mark Jury, Sen Chiao, Raphael Cécé
The Intensification Of Hurricane Maria 2017 In The Antilles, Mark Jury, Sen Chiao, Raphael Cécé
Faculty Publications, Meteorology and Climate Science
Environmental influences on Hurricane Maria in the Antilles Islands are analyzed at the large-scale (1–25 September) and at the meso-scale (17–20 September 2017). The storm intensified rapidly prior to landfall in Dominica, going from category 1 to 5 in 15 h. As the storm progressed toward Puerto Rico (PR), its NE flank entrained air from seas cooled by the earlier passage of two hurricanes, and strengthened on its SW flank. Operational model forecasts tended to delay intensification until west of the Antilles Islands, thus motivating two independent weather research and forecasting (WRF) simulations. These gave minimal track errors at 1- …
Modeling Energy Dynamics With The Energy-Interaction Diagram, Benedikt Harrer, Cassandra Paul
Modeling Energy Dynamics With The Energy-Interaction Diagram, Benedikt Harrer, Cassandra Paul
Faculty Publications
Energy is an important crosscutting concept in all science disciplines, and energy conservation is widely regarded as one of the most important principles in physics.1–3 Over the years, numerous graphical representations have been proposed that allow learners of physics to visualize energy states and dynamics in a particular situation.3–7 Each diagram highlights different aspects of energy and therefore may represent different conceptualizations of energy. Bar charts,8 for example, foreground the idea of multiple categories of energy to account for the distribution of energy in a system across those energy types. Similarly, pie charts5 highlight relative distribution …
Developing Spatially Accurate Rainfall Predictions For The San Francisco Bay Area Through Case Studies Of Atmospheric River And Other Synoptic Events, Alison Bridger, Dung Nguyen, Sen Chiao
Developing Spatially Accurate Rainfall Predictions For The San Francisco Bay Area Through Case Studies Of Atmospheric River And Other Synoptic Events, Alison Bridger, Dung Nguyen, Sen Chiao
Faculty Publications, Meteorology and Climate Science
Rainfall patterns in the San Francisco Bay Area (SFBA) are highly influenced by local topography. It has been a forecasting challenge for the main US forecast models. This study investigates the ability of the Weather Research and Forecasting (WRF) model to improve upon forecasts, with particular emphasis on the rain shadow common to the southern end of the SFBA. Three rain events were evaluated: a mid-season atmospheric river (AR) event with copious rains; a typical non-AR frontal passage rain event; and an area-wide rain event in which zero rain was recorded in the southern SFBA. The results show that, with …
Bootbandit: A Macos Bootloader Attack, Armen Boursalian, Mark Stamp
Bootbandit: A Macos Bootloader Attack, Armen Boursalian, Mark Stamp
Faculty Publications, Computer Science
Historically, the boot phase on personal computers left systems in a relatively vulnerable state. Because traditional antivirus software runs within the operating system, the boot environment is difficult to protect from malware. Examples of attacks against bootloaders include so‐called “evil maid” attacks, in which an intruder physically obtains a boot disk to install malicious software for obtaining the password used to encrypt a disk. The password then must be stored and retrieved again through physical access. In this paper, we discuss an attack that borrows concepts from the evil maid. We assume exploitation can be used to infect a bootloader …
Formation Of Ultra-Diffuse Galaxies In The Field And In Galaxy Groups, Fangzhou Jiang, Avishai Dekel, Jonathan Freundlich, Aaron Romanowsky, Aaron Dutton, Andrea Macciò, Arianna Di Cintio
Formation Of Ultra-Diffuse Galaxies In The Field And In Galaxy Groups, Fangzhou Jiang, Avishai Dekel, Jonathan Freundlich, Aaron Romanowsky, Aaron Dutton, Andrea Macciò, Arianna Di Cintio
Faculty Publications
We study ultra-diffuse galaxies (UDGs) in zoom in cosmological simulations, seeking the origin of UDGs in the field versus galaxy groups. We find that while field UDGs arise from dwarfs in a characteristic mass range by multiple episodes of supernova feedback (Di Cintio et al.), group UDGs may also form by tidal puffing up and they become quiescent by ram-pressure stripping. The field and group UDGs share similar properties, independent of distance from the group centre. Their dark-matter haloes have ordinary spin parameters and centrally dominant dark-matter cores. Their stellar components tend to have a prolate shape with a Sérsic …
Hyper Wide Field Imaging Of The Local Group Dwarf Irregular Galaxy Ic 1613: An Extended Component Of Metal-Poor Stars, Ragadeepika Pucha, Jeffrey Carlin, Beth Willman, Jay Strader, David Sand, Keith Bechtol, Jean Brodie, Denija Crnojević, Duncan Forbes, Christopher Garling, Jonathan Hargis, Annika Peter, Aaron Romanowsky
Hyper Wide Field Imaging Of The Local Group Dwarf Irregular Galaxy Ic 1613: An Extended Component Of Metal-Poor Stars, Ragadeepika Pucha, Jeffrey Carlin, Beth Willman, Jay Strader, David Sand, Keith Bechtol, Jean Brodie, Denija Crnojević, Duncan Forbes, Christopher Garling, Jonathan Hargis, Annika Peter, Aaron Romanowsky
Faculty Publications
Stellar halos offer fossil evidence for hierarchical structure formation. Since halo assembly is predicted to be scale-free, stellar halos around low-mass galaxies constrain properties such as star formation in the accreted subhalos and the formation of dwarf galaxies. However, few observational searches for stellar halos in dwarfs exist. Here we present gi photometry of resolved stars in isolated Local Group dwarf irregular galaxy IC 1613 (M sstarf ~ 108 M ⊙). These Subaru/Hyper Suprime-Cam observations are the widest and deepest of IC 1613 to date. We measure surface density profiles of young main-sequence, intermediate to old red giant branch, and …
Spatially Resolved Stellar Kinematics Of The Ultra-Diffuse Galaxy Dragonfly 44. I. Observations, Kinematics, And Cold Dark Matter Halo Fits, Pieter Van Dokkum, Asher Wasserman, Shany Danieli, Roberto Abraham, Jean Brodie, Charlie Conroy, Duncan Forbes, Christopher Martin, Matt Matuszewski, Aaron Romanowsky, Alexa Villaume
Spatially Resolved Stellar Kinematics Of The Ultra-Diffuse Galaxy Dragonfly 44. I. Observations, Kinematics, And Cold Dark Matter Halo Fits, Pieter Van Dokkum, Asher Wasserman, Shany Danieli, Roberto Abraham, Jean Brodie, Charlie Conroy, Duncan Forbes, Christopher Martin, Matt Matuszewski, Aaron Romanowsky, Alexa Villaume
Faculty Publications
We present spatially resolved stellar kinematics of the well-studied ultra-diffuse galaxy (UDG) Dragonfly 44, as determined from 25.3 hr of observations with the Keck Cosmic Web Imager. The luminosity-weighted dispersion within the half-light radius is ${\sigma }_{1/2}={33}_{-3}^{+3}$ km s−1, lower than what we had inferred before from a DEIMOS spectrum in the Hα region. There is no evidence for rotation, with ${V}_{\max }/\langle \sigma \rangle \lt 0.12$ (90% confidence) along the major axis, in possible conflict with models where UDGs are the high-spin tail of the normal dwarf galaxy distribution. The spatially averaged line profile is more peaked than a …
Predicting Switch-Like Behavior In Proteins Using Logistic Regression On Sequence-Based Descriptors, Benjamin Strauss
Predicting Switch-Like Behavior In Proteins Using Logistic Regression On Sequence-Based Descriptors, Benjamin Strauss
Master's Projects
Ligands can bind at specific protein locations, inducing conformational changes such as those involving secondary structure. Identifying these possible switches from sequence, including homology, is an important ongoing area of research. We attempt to predict possible secondary structure switches from sequence in proteins using machine learning, specifically a logistic regression approach with 48 N-acetyltransferases as our learning set and 5 sirtuins as our test set. Validated residue binary assignments of 0 (no change in secondary structure) and 1 (change in secondary structure) were determined (DSSP) from 3D X-ray structures for sets of virtually identical chains crystallized under different conditions. Our …
Accelerating Lattice Quantum Monte Carlo Simulations Using Artificial Neural Networks: Application To The Holstein Model, Shaozhi Li, Philip Dee, Ehsan Khatami, Steven Johnston
Accelerating Lattice Quantum Monte Carlo Simulations Using Artificial Neural Networks: Application To The Holstein Model, Shaozhi Li, Philip Dee, Ehsan Khatami, Steven Johnston
Faculty Publications
Monte Carlo (MC) simulations are essential computational approaches with widespread use throughout all areas of science. We present a method for accelerating lattice MC simulations using fully connected and convolutional artificial neural networks that are trained to perform local and global moves in configuration space, respectively. Both networks take local spacetime MC configurations as input features and can, therefore, be trained using samples generated by conventional MC runs on smaller lattices before being utilized for simulations on larger systems. This approach is benchmarked for the case of determinant quantum Monte Carlo (DQMC) studies of the two-dimensional Holstein model. We find …
Effect Of Strain On Charge Density Wave Order In The Holstein Model, Benjami Cohen-Stead, Natanael Costa, Ehsan Khatami, Richard Scalettar
Effect Of Strain On Charge Density Wave Order In The Holstein Model, Benjami Cohen-Stead, Natanael Costa, Ehsan Khatami, Richard Scalettar
Faculty Publications
We investigate charge ordering in the Holstein model in the presence of anisotropic hopping, tx,ty=1-δ,1+δ, as a model of the effect of strain on charge-density-wave (CDW) materials. Using quantum Monte Carlo simulations, we show that the CDW transition temperature is relatively insensitive to moderate anisotropy δ 0.3, but begins to decrease more rapidly at δ 0.4. However, the density correlations, as well as the kinetic energies parallel and perpendicular to the compressional axis, change significantly for moderate δ. Accompanying mean-field theory calculations show a similar qualitative structure, with the transition temperature relatively constant at small δ, and a more rapid …
Lanczos-Boosted Numerical Linked-Cluster Expansion For Quantum Lattice Models, Krishnakumar Bhattaram, Ehsan Khatami
Lanczos-Boosted Numerical Linked-Cluster Expansion For Quantum Lattice Models, Krishnakumar Bhattaram, Ehsan Khatami
Faculty Research, Scholarly, and Creative Activity
Numerical linked-cluster expansions allow one to calculate finite-temperature properties of quantum lattice models directly in the thermodynamic limit through exact solutions of small clusters. However, full diagonalization is often the limiting factor for these calculations. Here we show that a partial diagonalization of the largest clusters in the expansion using the Lanczos algorithm can be as useful as full diagonalization for the method while mitigating some of the time and memory issues. As test cases, we consider the frustrated Heisenberg model on the checkerboard lattice and the Fermi-Hubbard model on the square lattice. We find that our approach can surpass …
New Constraints On Early-Type Galaxy Assembly From Spectroscopic Metallicities Of Globular Clusters In M87, Alexa Villaume, Aaron Romanowsky, Jean Brodie, Jay Strader
New Constraints On Early-Type Galaxy Assembly From Spectroscopic Metallicities Of Globular Clusters In M87, Alexa Villaume, Aaron Romanowsky, Jean Brodie, Jay Strader
Faculty Publications
The observed characteristics of globular cluster (GC) systems, such as metallicity distributions, are commonly used to place constraints on galaxy formation models. However, obtaining reliable metallicity values is particularly difficult because of our limited means to obtain high quality spectroscopy of extragalactic GCs. Often, "color–metallicity relations" are invoked to convert easier-to-obtain photometric measurements into metallicities, but there is no consensus on what form these relations should take. In this paper we make use of multiple photometric data sets and iron metallicity values derived from applying full-spectrum stellar population synthesis models to deep Keck/LRIS spectra of 177 GCs centrally located around …
Spatially Resolved Stellar Populations And Kinematics With Kcwi: Probing The Assembly History Of The Massive Early-Type Galaxy Ngc 1407, Anna Ferré-Mateu, Duncan Forbes, Richard Mcdermid, Aaron Romanowsky, Jean Brodie
Spatially Resolved Stellar Populations And Kinematics With Kcwi: Probing The Assembly History Of The Massive Early-Type Galaxy Ngc 1407, Anna Ferré-Mateu, Duncan Forbes, Richard Mcdermid, Aaron Romanowsky, Jean Brodie
Faculty Publications
Using the newly commissioned Keck Cosmic Web Imager (KCWI) instrument on the Keck II telescope, we analyze the stellar kinematics and stellar populations of the well-studied massive early-type galaxy (ETG) NGC 1407. We obtained high signal-to-noise integral field spectra for a central and an outer (around one effective radius toward the southeast direction) pointing with integration times of just 600 s and 2400 s, respectively. We confirm the presence of a kinematically distinct core also revealed by VLT/MUSE data of the central regions. While NGC 1407 was previously found to have stellar populations characteristic of massive ETGs (with radially constant …