CENTRE FOR THEORETICAL PHYSICS
St. Stephen’s College, Delhi
The Centre for Theoretical Physics at St. Stephen’s College, Delhi was established in January 2014 by two outstanding members of the Physics Department: Dr. Vikram Vyas and Dr. Abhinav Gupta.
The Objective
One of the serious lacunae of undergraduate science education in India is the artificial divide between teaching and research. This accounts for the paucity of original ideas in the sciences emerging from India and in the increasing reluctance on the part of young Indian scientists to take undergraduate teaching as a career. The objective behind starting the Centre for Theoretical Physics is to try and remedy this situation by creating a framework that facilitates collaborative research involving the students and the faculty members.
- Talks/Workshops/Seminars
- One Day RAD@home : Astronomy Workshop
Training workshop to join citizen science in astronomy via RAD@home
Speakers:
- Dr Ananda Hote (RAD@home)
- Dr Chiranjib Kumar (Amity University, Noida)
- Shruti Thakur (St. Stephen’s College)
- Dr Sampurnanand Jha (St. Stephen’s College)
Date
: 23 February, 2019
- Seminar organised by the centre in February 2014:
Speaker: Prof. Madhavan Varadarajan, Raman Research Institute, Bangalore.
Topic: A New Identity for the Dynamics of Gravity.
- Seminar organised by the centre in March 2014:
Speaker: Prof. Suvrat Raju, International Centre for Theoretical Sciences, Tata Institute of Fundamental Research, Bangalore.
Topic: Black Holes, Thermodynamics, and the Information Paradox in the AdS/CFT Correspondence.
- Undergraduate Research Projects
- Dark Energy and Accelerated Expansion
(A UG research project done in the
year 2017-18)
Authors: Shivi Baijal, Satvik Mishra, Chelsea Maria John, Dr. Sanil Unnikrishnan, Dr. Geetanjali Sethi
Abstract:
In this project, we explore the factors that drive the accelerated expansion of the universe. An impressive amount of different astrophysical data converges towards the picture of a spatially flat universe undergoing a phase of accelerated expansion. Using the Friedmann equations, we show that a flat universe cannot evolve to the present age if dark energy or cosmological constant is not included along with matter and radiation. In this work, we used various combinations of matter, radiation, and vacuum and found that only certain combinations between matter and vacuum are consistent with the current age observation. We then explored the possibility of the presence of some unknown parameter that, when combined with matter, would result in similar consistency. The nature of this dark energy dominating the energy content of the universe is still unknown. From our plots, we found that a range of values for the density parameter of this unknown parameter are viable candidates to explain cosmic acceleration.
- Statistical Techniques in Cosmology
(A UG research project done in the year 2017-18)
Authors: Chelsea Maria John, Shivi Baijal, Satvik Mishra, Dr.Geetanjali Sethi, Dr .Sanil Unnikrishnan
Abstract :
In this project, we constrain the parameters of three classes of dark energy cosmological models with Gemini Deep Field Survey data, based on time measurements. The cosmological models can be broadly divided into three broad categories. The first class models have dark energy as a new ingredient of the cosmic Hubble flow, the simplest case being the ɅCDM model scenario and the generalisation which we will refer to as QCDM model. This is in sharp contrast with the UDE models (second class), where there is a single fluid described by an equation of state comprehensive of all regimes of cosmic evolution, such as the parametric density model. Finally, according to the third class of models such as f(R)-gravity, accelerated expansion is the first evidence of a breakdown of the Einstein General Relativity (and thus the Friedmann equations) which has to be considered as a particular case of a more general theory of gravity. Most of the methods employed to test the cosmological models are essentially based on distance measurements to a particular class of objects .We use a method based on the lookback time of galaxy clusters and the age of the universe.
- Variable Chaplygin Gas: Constraints from Supernovae, BAO, Look
Back Time, and GRBs (A UG research project done in the year
2018-19) - Modelling COVID-19
- Probing the non-flat cosmological model using Hubble data and
Sne Ia - Examining Temporal Variation of the Fermi Coupling Constant using SNe Ia Light Curves
- Applications of Machine Learning to Modelling and Analysing Dynamical Systems
- Using Neuro- Evolutionary networks for Astronomically relevant classification problems
- Diagnosing Neurological/Psychiatric disorders from Electroencephalogram data using features of Non-Linear Analysis and Neural networks.
- Conducting a Radio Frequency Interference survey of the area and observing the 21 cm neutral-hydrogen line
- Study of Resonance Scattering using Transfer Matrix Approach
Authors: Bhuvan Agrawal, Geetanjali Sethi, and Shruti Thakur
Abstract:
We examine the variable Chaplygin gas (VCG) model with the aim of establishing tight constraints by using type-Ia supernovae, lookback time measurements, Baryon Acoustic Oscillations (BAO), and Gamma Ray Bursts. We report the parameter constraints obtained via statistical analysis of cosmic observables namely, luminosity distance, BAO distance and lookback time measurement.
(A UG research project done in the year 2020-21)
Authors: Aklanta, Varun, Reuel D’souza, Binayyak Bhusan Roy and Akshay Rana
Abstract:
The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created havoc all across the world. It is an infectious disease, and its fatality is very sensitive to the age group of the infected person. As it gets transported through human interaction, lockdowns have been imposed all across the countries in order to restrict the spread of this virus. In this paper, we try to model the pandemic COVID-19 on the large scale of a country, for which we use a SIR model and include additional complications at each stage. We include the system of lock-down (social distancing), the idea that the disease affects different age groups because of different probability of comorbid diseases being present, modelling the spread of infection in terms of different age groups because of different amounts of contacts and finally including transport in a very basic way by incorporation the idea of a fully-connected network
(A UG research project done in the year 2019-20)
Authors: Fiona Ann Jolly, Akshay Rana
Abstract:
The non-flat cosmological model proposes that the universe has a non-zero curvature, a deviation from the standard model of cosmology (flat-LCDM). To test this hypothesis, we use two key observations: the Hubble data obtained through the cosmic chronometers and type Ia supernovae observations obtained by the Pantheon survey. The Hubble constant provides insight into the current expansion rate of the universe, while Sne Ia are standard candles that measure the distance-redshift relationship and cosmic expansion history. Combining these two observations provides a very stringent constraint on the curvature of the universe. We probed the non-flat LCDM and non flat XCDM cosmological models with different parametric forms of equations of state parameters. Within a 95% confidence limit, our analysis supports the flat LCDM model.
(A UG research project done in the year 2022-23)
Authors: Akshay Rana, Vedanta Thapar, Hariprasad SV, Sandra Elsa Sanjai
Abstract:
In standard model, the Fermi coupling constant, , sets the strength of electroweak decay. We attempt an approach to constrain the temporal variation of the Fermi coupling constant . To probe it, Type Ia supernovae (SNe Ia) light curves are being used as a source of reliable primordial nucleosynthesis events across the redshifts. We utilised studies suggesting that in the initial phase after the SNe Ia explosion, the electroweak decay of is the key contributor to powering the SNe Ia light curve. We hence used the Pan-STARRS supernovae catalogue having 1169 supernovae light curves in , , , and spectral filters. The post-peak decrease in the apparent magnitude of light curves (in the rest frame of SNe) was related to the electroweak decay rate of primordial nucleosynthesis. Further, the decay rate relates to To keep the analysis independent of the cosmological model, we used the Hubble parameter measurement and a non-parametric statistical method, the Gaussian Process. Our study suggests a small yet finite temporal variation of and puts a strong upper bound on the present value of the fractional change in the Fermi coupling constant, using datasets spread over a redshift range .
(An ongoing UG research project in the year 2022-23)
Undergraduate Dissertation
Author: Vedanta Thapar, advised by Dr Abhinav Gupta
Abstract
We endeavour to explore a variety of neural network architectures, including Recurrent Neural Networks (RNNs) and their physics informed modifications such as Hamiltonian neural networks and Symplectic RNNs among others. These networks modify the architecture and/or training process to take into account physical knowledge of the system, such as knowledge of symmetries (conservation laws), as well as partial information about the dynamical laws of the system. We aim to apply these to dynamical systems such as the three-body problem, as well as to artificial dynamical models such as the Rossler/Lorentz system. The aim is to use ML to infer properties of the system from real world and simulated data. We endeavour to explore a variety of neural network architectures, including Recurrent Neural Networks (RNNs) and their physics informed modifications such as Hamiltonian neural networks and symplectic RNNs among others. These networks modify the architecture and/or training process to take into account physical knowledge of the system, such as knowledge of symmetries (conservation laws), as well as partial information about the dynamical laws of the system. We aim to apply these to dynamical systems such as the three-body problem as well as to artificial dynamical models such as the Rossler/Lorentz system. The aim is to use ML to infer properties of the system from real-world and simulated data.
(An ongoing UG research project in the year 2022-23)
Undergraduate Dissertation
Author: Hariprasad SV, advised by Dr Annu Malhotra
Abstract:
Fast radio bursts remain one of the enigmatic events that span milliseconds with flux densities ranging from 0.1 to 100 Jy. These events are classified from raw intensity data using various analysis methods. including de-dispersion of the data for trial DM values as well as performing RFI (Radio Frequency Interference) filtering. Since 2007, after the prototype source was discovered by Lorimer et al., the number of sources found has been growing. The amount of data obtained by various ground surveys, including the CHIME project, has been consistently increasing, along with the need for efficient data analysis pipelines. The work will be focused on the novelty of using genetic neural network algorithms for general data classification. FRB data classification, due to its complex nature, is suited for exploring the possibility of data classification in other raw data obtained from various astrophysical surveys. One major merit of using evolutionary algorithms is their parallelizability and faster runtimes. The novelty ensures the study of a creative approach to the problem.
(A UG research project done in the year 2022-23)
Authors : Hariprasad SV
Abstract
One notable method for recording brainwaves to identify neurological problems is electroencephalography (hereafter EEG). A trained neuro-physician can learn more about how the brain functions through the use of EEGs. However, conventionally, EEGs are only used to examine neurological problems (e.g., seizures). But abnormal links to neurological circuits can also exist in psychological illnesses like schizophrenia. Hence, EEGs can be an alternate source of data for the detection and classification of psychological disorders. A study on the classification of EEG data obtained from healthy individuals and individuals experiencing schizophrenia is conducted. The inherent nonlinear nature of brain waves is used for the dimensionality reduction of the data. Nonlinear parameters such as the Lyapunov exponent (LE) and Hurst exponent (HE) were selected as essential features. The EEG data was obtained from the publicly available EEG database of MV. Lomonosov Moscow State University. To perform noise reduction on the data, a more recently developed tunable Q factor based wavelet transform (TQWT) is used . Finally, for the classification, the 16 channel EEG time series is converted into spatial heatmaps using the aforementioned features. A convolutional neural network (CNN) is designed and trained with the modified data format for classification.
(An ongoing UG research project in the year 2022-23)
Authors: Dr. Geetanjali Sethi, Udish Sharma, Nadia Makhijani
Abstract
Radio-frequency interference refers to the nugatory signals from different frequency bands that get projected into a certain frequency band that is being worked in or signals in that very frequency band that may interfere with the intended purpose of the work being done. The presence of RFI leads to an increase in the detected power and antenna temperature, thereby decreasing the accuracy of the received data values. It often renders the whole setup useless as it’s impossible to make out the desired observation from the RFI. Neutral hydrogen radiates at a frequency of 1420.4 MHz. These emissions can be studied from various sources in the galaxy using a horn antenna of appropriate dimensions. Using the antenna and a circuit consisting of a few amplifiers, the H1 line emissions of the galactic centre have been observed.
(An ongoing UG research project in the year 2022-23)
Authors: Aarya Diwan, Tarush Tandon, Akshay Rana
Abstract:
The transfer-matrix method is a method generally used to analyse the propagation of waves (mechanical, electromagnetic, or matter) through a stratified medium. It allows us to computationally analyse the propagation of waves via multiple media that cause reflection, refraction, and transmission of the wave. We used this approach to solve for the transmission probabilities of the electrons across multiple potentials and to verify the Sturm-Liouville Eigenvalue Theorem. Further, we studied the quantum mechanical phenomenon of resonance scattering and used it to understand band formation in semiconductor devices.
- Competitions
- Guru Dhwani Antenna Design Competition – (2021-22)
A team of students and a faculty mentor designed a novel discone antenna to observe radio signals from Jupiter.
Achievement: Achieved First place in the national level competition organised by NRC-IUCAA
Team Members : Aishwarya David, Deepanshu Bisht, Hariprasad SV, Josiah Jose, Sandra Elsa Sanjai, Vedanta Thapar , guided by Dr Akshay Rana
- SWANTENNA 20 : Antenna Design Challenge (2020-21)
In a national-level competition organised by the Sky Watch Array Network (SWAN), Raman Research Institute, Bangalore, students designed a novel LPDA antenna that met given requirements.
Team Members : Rahul Mallikarjun, Binayyak Bhushan Roy, Prabhat, Rudra Kalra, Neel Lohit Dash, Mahak Sadhwani, Trisha Debnath, mentored by Dr Akshay Rana and Dr. Geetanjali Sethi
- Participation in University Physics Competition
The University Physics Competition is an international physics competition for undergraduate students. It is held annually on the first Saturday in November and lasts for 24 hours. The competition consists of solving challenging physics problems, with the goal of promoting interest in physics and recognizing outstanding undergraduate students.
Silver medal winning teams
Team 1. Kriti Baweja, Prabhat Kumar, Panya Jain mentored by Dr Sanjay Kumar (2020-21)
Team 2. Binayak Bhusan Roy, Chaitanya Varma, Rahul Mallikarjun mentored by Dr Abhinav Gupta (2020-21)
Accomplished competitors
Team 1: Reuel D’souza, Neil Poddar, Dhruv Tiwari mentored by Dr. Geetanjali Sethi (2019-20)
Team 2: Aleena Sibi, Neel Lohit Dash mentored by Dr Akshay Rana (2020-21)