Ultra-Stable Environment Control for the NEID Spectrometer: Design and Performance Demonstration

Two key areas of emphasis in contemporary experimental exoplanet science are the detailed characterization of transiting terrestrial planets, and the search for Earth analog planets to be targeted by future imaging missions. Both of these pursuits are dependent on an order-of-magnitude improvement in the measurement of stellar radial velocities (RV), setting a requirement on single-measurement instrumental uncertainty of order 10 cm/s. Achieving such extraordinary precision on a high-resolution spectrometer requires thermo-mechanically stabilizing the instrument to unprecedented levels. Here, we describe the Environment Control System (ECS) of the NEID Spectrometer, which will be commissioned on the 3.5 m WIYN Telescope at Kitt Peak National Observatory in 2019, and has a performance specification of on-sky RV precision < 50 cm/s. Because NEID's optical table and mounts are made from aluminum, which has a high coefficient of thermal expansion, sub-milliKelvin temperature control is especially critical. NEID inherits its ECS from that of the Habitable-zone Planet Finder (HPF), but with modifications for improved performance and operation near room temperature. Our full-system stability test shows the NEID system exceeds the already impressive performance of HPF, maintaining vacuum pressures below $10^{-6}$ Torr and an RMS temperature stability better than 0.4 mK over 30 days. Our ECS design is fully open-source; the design of our temperature-controlled vacuum chamber has already been made public, and here we release the electrical schematics for our custom Temperature Monitoring and Control (TMC) system.Comment: Accepted for publication in JATI

Using gold nanoparticles to improve performance of SERS-based immunoassay

Surface enhanced Raman scattering (SERS) can be used as an efficient readout strategy in immunoassays. SERS outperforms other conventional readout strategies such as fluorescence (as in ELISA) and radioactivity (as in RIA). It has been recently demonstrated that SERS-based immunoassays can discriminate samples of healthy individuals from samples of pancreatic cancer patients. Further improvements in sensitivity and reproducibility are expected to extend the practical applications of the SERS-based immunoassays. One critical component of the SERS-based assay is golden nanoparticles that carry Raman reporter molecules. The nanoparticles also provide enhancement of the Raman signal due to plasmon excitation upon laser illumination. Here we present several strategies designed to improve performance of the SERS-based immunoassay. We also make use of narrow Raman bands to design multiplexed detection platform. First, different protective strategies for golden nanoparticles are presented and discussed. The primary purpose of these strategies is to prevent heating of the particles due to intense laser light, thus enhancing properties of the gold nanoparticles can be longer sustained. Second, we also provide a recipe for creating a multiplexed detection platform. We varied which reporter molecules were added to the nanoparticles and measured the spectra from each. By combining nanoparticles labeled with reporter molecules having different well distinguishable wavelengths, it may be possible to measure levels of several biomarkers simultaneously in a single run

SNEWS2/snewpy: v1.3

What's Changed &lt;ul&gt; &lt;li&gt;&lt;strong&gt;Remove dependency on GLoBES.&lt;/strong&gt; SNEWPY now includes code to calculate event rates directly, making it much easier to install and improving performance of &lt;code&gt;snewpy.snowglobes.simulate()&lt;/code&gt; when using multiple time bins. SNOwGLoBES still needs to be downloaded separately, but it no longer needs to be compiled.&lt;/li&gt; &lt;li&gt;Added simplified interface to initialise models from physics parameters (e.g. progenitor mass, metallicity)&lt;ul&gt; &lt;li&gt;Added &lt;code&gt;param&lt;/code&gt; property and &lt;code&gt;get_param_combinations()&lt;/code&gt; function to each model class to explore available progenitors.&lt;/li&gt; &lt;li&gt;The first time a specific progenitor is initialised, SNEWPY automatically downloads the required input files to the &lt;a href="https://docs.astropy.org/en/stable/api/astropy.config.get_cache_dir.html?highlight=get_cache_dir"&gt;AstroPy cache directory&lt;/a&gt;, so users no longer need to manage files manually.&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt; &lt;li&gt;Added &lt;code&gt;get_flux()&lt;/code&gt; function to &lt;code&gt;SupernovaModel&lt;/code&gt; subclasses in &lt;code&gt;snewpy.models&lt;/code&gt;&lt;/li&gt; &lt;li&gt;Improved &lt;code&gt;get_initial_spectra(t, E)&lt;/code&gt; and &lt;code&gt;get_transformed_spectra(t, E)&lt;/code&gt; functions: all &lt;code&gt;SupernovaModel&lt;/code&gt; subclasses in &lt;code&gt;snewpy.models&lt;/code&gt; now support arrays of times as the argument &lt;code&gt;t&lt;/code&gt;&lt;/li&gt; &lt;li&gt;Fixed issue when using the &lt;code&gt;ar40kt_he&lt;/code&gt; and &lt;code&gt;wc100kt30prct_he&lt;/code&gt; detector configurations with &lt;code&gt;snewpy.snowglobes.simulate()&lt;/code&gt;&lt;/li&gt; &lt;li&gt;Various minor bugfixes, performance, documentation and other improvements&lt;/li&gt; &lt;/ul&gt; Compatibility and Deprecations &lt;ul&gt; &lt;li&gt;This version of SNEWPY supports Python 3.7 or higher.&lt;/li&gt; &lt;li&gt;Initialising a supernova model in &lt;code&gt;snewpy.models.ccsn&lt;/code&gt; from a file name is deprecated in favour of initialising from physics parameters. For details on parameters available for each model class, please see the &lt;code&gt;param&lt;/code&gt; property and &lt;code&gt;get_param_combinations()&lt;/code&gt; function or &lt;a href="https://snewpy.readthedocs.io/en/v1.3/models.html#module-snewpy.models.ccsn"&gt;read the documentation&lt;/a&gt;. (Under the hood, there are now separate classes in &lt;code&gt;snewpy.models.loaders&lt;/code&gt; that load models from a local file; however, these are not guaranteed to be stable and may change at any time without warning.)&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: &lt;a href="https://github.com/SNEWS2/snewpy/compare/v1.2.1...v1.3"&gt;https://github.com/SNEWS2/snewpy/compare/v1.2.1...v1.3&lt;/a&gt;&lt;/p&gt

SNEWS2/snewpy: v1.4.1

What's Changed Reverts name of an argument to MixingParameters to restore backwards compatibility Updates a few Jupyter notebooks to support new simulations added in SNEWPY v1.4 Full Changelog: https://github.com/SNEWS2/snewpy/compare/v1.4...v1.4.

SNEWS2/snewpy: v1.4

What's Changed Improved SNOwGLoBES integration. Data files for detectors are included when installing SNEWPY; SNOwGLoBES no longer needs to be downloaded separately. Users who want to use custom data files can still specify a SNOwGLoBES path as before. SNEWPY now requires SNOwGLoBES v1.3. Added several observer directions and progenitor masses for the Tamborra_2014, Walk_2018 and Walk_2019 models. Significant performance improvements for snewpy.snowglobes thanks to a new low-level interface for neutrino flux and event rate calculations. (Note: This low-level interface is currently not stable and should not be used directly.) Added a SNEWPY logo Various minor bugfixes, performance, documentation and other improvements Compatibility and Deprecations This version of SNEWPY supports Python 3.8 or higher. New Contributors @jakob2508 made their first contribution in https://github.com/SNEWS2/snewpy/pull/266 Full Changelog: https://github.com/SNEWS2/snewpy/compare/v1.3...v1.