Embedded Files
Online Profiles
Online Profiles
Inspirehep: inspirehep.net/authors/1037136
Google Scholar: scholar.google.com/citations?user=2f8dIKAAAAAJ
ORCID: 0000-0002-7107-0343
Researchgate: researchgate.net/profile/Subhadip-Mitra
Recent Papers
Constructing sensible baselines for Integrated Gradients
Jai Bardhan, Cyrin Neeraj, Mihir Rawat, Subhadip Mitra (Hyderabad, IIIT)
e-Print: arXiv:2412.13864 [cs.LG] | PDF
Machine learning methods have seen a meteoric rise in their applications in the scientific community. However, little effort has been put into understanding these "black box" models. We show how one can apply integrated gradients (IGs) to understand these models by designing different baselines, by taking an example case study in particle physics. We find that the zero-vector baseline does not provide good feature attributions and that an averaged baseline sampled from the background events provides consistently more reasonable attributions.
Loss function to optimise signal significance in particle physics
Jai Bardhan, Cyrin Neeraj, Subhadip Mitra (Hyderabad, IIIT), Tanumoy Mandal (IISER, Trivandrum)
e-Print: arXiv:2412.09500 [hep-ph] | PDF
We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a linear model and show that it produces decision boundaries that change according to the cross sections of the processes involved. We find that the models trained with the new loss have higher signal efficiency for similar values of estimated signal significance compared to ones trained with a cross-entropy loss, showing promise to improve the sensitivity of particle physics searches at colliders.
Machine learning tagged boosted dark photon: A signature of fermionic portal matter at the LHC
Shivam Verma, Sanjoy Biswas (RKMVU, West Bengal), Tanumoy Mandal (IISER, Trivandrum), Subhadip Mitra (Hyderabad, IIIT)
e-Print: arXiv:2410.06925 [hep-ph] | PDF
We use machine learning techniques to identify a boosted dark photon jet as a signature of a heavy vector-like fermionic portal matter (PM) connecting the visible and dark sectors. In this work, the fermionic PM, which mixes only with the Standard Model (SM) third-generation up-type quark, dominantly decays to a top quark and dark photon pair. The dark photon decays promptly to a pair of standard model fermions via gauge kinetic mixing. We have analyzed two different final states, namely, (i) exactly one tagged dark photon and exactly one tagged top-quark jet, and (ii) at least two tagged dark photons and at least one tagged top-quark jet at the 13 and 14 TeV LHC centre of mass energies. These final states receive significant contributions from the pair and single production processes of the top partner. The rich event topology of the signal processes, i.e., the presence of a boosted dark photon and top quark jet pair, along with the fact that the invariant mass of the system corresponds to the mass of the top partner, help us to suppress the potential SM backgrounds significantly. We have shown that one can set a 2σ exclusion limit of 3.0 TeV on the top partner mass with sinθL = 0.1 and assuming 100% branching ratio of the top partner in the exactly one tagged dark photon and exactly one tagged top quark jet final state at the 14 TeV LHC centre of mass energy assuming 300 fb^{-1} of integrated luminosity.
Teleportation fidelity of quantum repeater networks
Ganesh Mylavarapu, Subrata Ghosh, Chittaranjan Hens, Indranil Chakrabarty, Subhadip Mitra (Hyderabad, IIIT)
e-Print: arXiv:2409.20304 [quant-ph] | PDF
We show that the average of the maximum teleportation fidelities between all pairs of nodes in a large quantum repeater network is a measure of the resourcefulness of the network as a whole. We use simple Werner state-based models to characterise some fundamental (loopless) topologies (star, chain, and some trees) with respect to this measure in three (semi)realistic scenarios. Most of our results are analytic and are applicable for arbitrary network sizes. We identify the parameter ranges where these networks can achieve quantum advantages and show the large-N behaviours.
Unsupervised and lightly supervised learning in particle physics
Jai Bardhan (Hyderabad, IIIT), Tanumoy Mandal (IISER, Trivandrum, Sch. Phys.), Subhadip Mitra, Cyrin Neeraj, Monalisa Patra (Hyderabad, IIIT)
Published in Eur. Phys. J. Spec. Top. (2024)
e-Print: arXiv:2403.13676 [hep-ph] | PDF
We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data to identify deviations if we model the background data precisely. The learning can also be partially unsupervised when we can provide some information about the anomalies at the data level. Generative models are useful in speeding up detector simulations—they can mimic the computationally intensive task without large resources. They can also efficiently map detector-level data to parton-level data (i.e., data unfolding). In this review, we focus on interesting ideas and connections and briefly overview the underlying techniques wherever necessary
Fresh look at the LHC limits on scalar leptoquarks
Arvind Bhaskar (Bhubaneswar, Inst. Phys. and Hyderabad, IIIT), Arijit Das, Tanumoy Mandal (IISER, Trivandrum, Sch. Phys.), Subhadip Mitra (Hyderabad, IIIT), Rachit Sharma (IISER, Trivandrum, Sch. Phys.)
Published in Phys.Rev.D 109 (2024) 5, 055018
e-Print: arXiv:2312.09855 [hep-ph] | PDF
The scalar-leptoquark (sLQ) parameter space is well explored experimentally. The direct pair production searches at the LHC have excluded light sLQs almost model agnostically, and the high-pT dilepton tail data have put strong bounds on the leptoquark-quark-lepton Yukawa couplings for a wide range of sLQ masses. However, these do not show the complete picture. Previously, Mandal et al. [Single productions of coloured particles at the LHC: An example with scalar leptoquarks, J. High Energy Phys. 07 (2015) 028] showed how the dilepton-dijet data from the pair production searches could give strong limits on these couplings. This was possible by including the single-production contribution to the dilepton-dijet signal. In this paper, we take a fresh look at the LHC limits on all sLQs by following the same principle and combine all significant contributions—from pair and single productions, t-channel sLQ exchange and its interference with the Standard Model background—to the μμjj final state and recast the limits. We notice that the sLQ exchange and its interference with the background processes play significant roles in the limits. The μμjj-recast limits are comparable to or, in some cases, significantly better than the currently known limits (from high-pT dilepton data and direct searches), i.e., the LHC data rules out more parameter space than what is considered in the current literature. For the first time, we also show how including the QED processes can noticeably improve the sLQ mass exclusion limits from the QCD-only limits.
Emergent non-Markovianity and dynamical quantification of the quantum switch
Vishal Anand (Hyderabad, IIIT), Ananda G. Maity (Okinawa Inst. Sci. Tech.), Subhadip Mitra (Hyderabad, IIIT), Samyadeb Bhattacharya (Hyderabad, IIIT)
e-Print: arXiv:2307.01964 [quant-ph] | PDF
We investigate the dynamical aspects of the quantum switch and find a particular form of quantum memory emerging out of the switch action. We first analyse the loss of information in a general quantum evolution subjected to a quantum switch and propose a measure to quantify the switch-induced memory. We then derive an uncertainty relation between information loss and switch-induced memory. We explicitly consider the example of depolarizing dynamics and show how it is affected by the action of a quantum switch. For a more detailed analysis, we consider both the control qubit and the final measurement on the control qubit as noisy and investigate the said uncertainty relation. Further, while deriving the Lindblad-type dynamics for the reduced operation of the switch action, we identify that the switch-induced memory actually leads to the emergence of non-Markovianity. Interestingly, we demonstrate that the emergent non-Markovianity can be explicitly attributed to the switch operation by comparing it with other standard measures of non-Markovianity. Our investigation thus paves the way forward to understanding the quantum switch as an emerging non-Markovian quantum memory.
Pinning down the leptophobic Z' in leptonic final states with Deep Learning
Tanumoy Mandal (IISER, Trivandrum, Sch. Phys.), Aniket Masaye (IISER, Trivandrum, Sch. Phys.), Subhadip Mitra (Hyderabad, IIIT), Cyrin Neeraj (Hyderabad, IIIT), Naveen Reule (IISER, Trivandrum, Sch. Phys.), Kalp Shah (Hyderabad, IIIT)
Published in Phys.Lett.B 849 (2024) 138417
e-Print: arXiv:2307.01118 [hep-ph] | PDF
A leptophobic Z ′ that does not couple with the Standard Model leptons can evade the stringent bounds from the dilepton-resonance searches. In our earlier paper [T. Arun et al., Search for the Z ′ boson decaying to a right-handed neutrino pair in leptophobic U ( 1 ) models, Phys. Rev. D, 106 (2022) 095035], we presented two gauge anomaly-free U ( 1 ) models—one based on the Green-Schwarz (GS) anomaly cancellation mechanism, and the other on a grand unified theory (GUT) framework with gauge kinetic mixing—where a heavy leptophobic Z ′ is present along with right-handed neutrinos ( N_R ). We pointed out the interesting possibility of a correlated search for Z ′ and N_R at the LHC through the pp → Z ′ → N_RN_R channel. This channel can probe a part of the Z ′ parameter space beyond the reach of the standard dijet resonance searches. In this follow-up paper, we analyse the challenging monolepton final state arising from the decays of the N_R pair with Deep Learning. We present the high-luminosity LHC discovery reaches for six different GUT embeddings and a benchmark point in the GS setup. We also update our previous estimates in the dilepton channel with Deep Learning. We identify parameter regions that can be probed with the proposed channel but will remain inaccessible to dijet searches at the HL-LHC.
Page updated
Google Sites
Report abuse