Hemant Sharma

Dr. Hemant Sharma


Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2017 in Acta Materialia
Verified
Journal Article
Closed access
DNDiwakar Naragani
Diwakar Naragani
MSMichael D. Sangid
Michael D. Sangid
PSPaul A. Shade
Paul A. Shade
JSJay C. Schuren
Jay C. Schuren
HSHemant Sharma
Hemant Sharma
IPIain Parr
Iain Parr

14 July 2012 in Journal of Applied Crystallography
Verified
Journal Article
Closed access
PDF file
HSHemant Sharma
Hemant Sharma
RHRichard M. Huizenga
Richard M. Huizenga
SOS. Erik Offerman
S. Erik Offerman

14 July 2012 in Journal of Applied Crystallography
Verified
Journal Article
Closed access
PDF file
HSHemant Sharma
Hemant Sharma
RHRichard M. Huizenga
Richard M. Huizenga
SOS. Erik Offerman
S. Erik Offerman

10 December 2021 in IUCrJ
Verified
Journal Article
Open access
PDF file
ZLZhengchun Liu
Zhengchun Liu
HSHemant Sharma
Hemant Sharma
JPJun-Sang Park
Jun-Sang Park
PKPeter Kenesei
Peter Kenesei
AMAntonino Miceli
Antonino Miceli
IFIan Foster
Ian Foster
X-ray diffraction based microscopy techniques such as high-energy diffraction microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak-shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in experiments. To this end, we propose BraggNN, a deep-learning based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75 and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance). When applied to a real experimental dataset and using grain positions from near-field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors compared with those calculated using pseudo-Voigt. Recent advances in deep-learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster on a consumer-class GPU card with out-of-the-box software.

1 April 2010 in Acta Materialia
Verified
Journal Article
Closed access
HSHemant Sharma
Hemant Sharma
SBStefan M.C. van Bohemen
Stefan M.C. van Bohemen
Roumen H. PetrovRoumen H. Petrov
Roumen H. Petrov
JSJilt Sietsma
Jilt Sietsma

6 November 2017 in Applied Physics Letters
Verified
Journal Article
Closed access
DYDaikang Yan
Daikang Yan
RDRalu Divan
Ralu Divan
LGLisa M. Gades
Lisa M. Gades
PKPeter Kenesei
Peter Kenesei
TMTimothy J. Madden
Timothy J. Madden
JUJoel N. Ullom
Joel N. Ullom
Transition-edge sensors (TESs) as microcalorimeters for high-energy-resolution X-ray spectroscopy are often fabricated with an absorber made of materials with high Z (for X-ray stopping power) and low heat capacity (for high resolving power). Bismuth represents one of the most compelling options. TESs with evaporated bismuth absorbers have shown spectra with undesirable and unexplained low-energy tails. We have developed TESs with electroplated bismuth absorbers over a gold layer that are not afflicted by this problem and that retain the other positive aspects of this material. To better understand these phenomena, we have studied a series of TESs with gold, gold/evaporated bismuth, and gold/electroplated bismuth absorbers, fabricated on the same die with identical thermal coupling. We show that the bismuth morphology is linked to the spectral response of X-ray TES microcalorimeters.