Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph

Overview

NIRPS-ETC

Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph

February 2022 - Before NIRPS on sky

Original NIRPS ETC code by Bruno L. Canto Martins 2018-2019

Additional edits by Nolan Grieves (University of Geneva) 2020-2022

Overview

  • The NIRPS ETC uses spectra from the NASA Infrared Telescope Facility (IRTF) as SEDs to get estimated flux values for different spectral types: http://irtfweb.ifa.hawaii.edu/~spex/IRTF_Spectral_Library/
  • The ETC calculates efficiency at different wavelengths using seeing, atmospheric efficiency from TAPAS (http://cds-espri.ipsl.fr/tapas/), and the measured global efficiency of the instrument
  • The signal to noise ratio (SNR) at each pixel or bin is calculated from the fiber diameter, sampling, readout noise, resolution, efficiency, and flux in the pixel or bin from the IRTF template (flux=(10.**(0.4*(Ho-H)))*flux_st)
  • RV precisions are calculated using, dRV=c/(Q*sqrt(Ne-)), equation 12 of Bouchy et al. (2001: https://ui.adsabs.harvard.edu/abs/2001A%26A...374..733B/abstract). The quality factors Q for spectra are calculated with ENIRIC from Phoenix simulated spectra or from spectral templates from the Spirou spectrograph
    • -> see: NIRPS-ETC/intermediate_preparation/update_RV_estimates/README_update_RV_estimates

Use

$ python NIRPS_ETC.py

  • change observing options within the code at the top
    • Observation Mode (HA/HE)
    • Seeing, in arcsec (range 0.7-1.2)
    • Airmass (range 1.0-2.0)
    • Object magnitude (H band)
    • Exposure time (in sec)
    • Spectral type (F0V/F5V/G0V/G5V/G8V/K0V/K3V/K7V/M0V/M1V/M2V/M3V/M4V/M5V/M6V/M7V/M8V/M9V/L1V/L2V/L3V/L4V/L5V/L6V/L8V/T2V)
    • bandpass ('CFHT' or 'Eniric') #YJH bandpasses that will affect the range of the spectra used to calculate RV precision
  • outputs mean SNR, in YJH, and each order, and RV precisisons for certain spectral types

OR use script version:

$ python NIRPS_ETC_script.py

  • change inputs for each target in a space separated text file with columns:
    • target st obs_mode seeing airmass H t_exp bandpass
  • change input and output text files within code to desired option
  • outputs to file the mean SNR, YJH SNRs, and RV precisions

Contents

  • inputs/
    • NIRPS_STAR_templates.txt
      • SEDs from IRTF (update with intermediate_preparation/update_effs/update_effs.py)
    • NIRPS_effs.txt
      • global efficiency of instrument (update with intermediate_preparation/update_effs/update_effs.py)
    • NIRPS_tapas.txt
      • atmospheric efficiency from TAPAS (update with intermediate_preparation/update_effs/update_effs.py)
    • NIRPS_wave_range.txt
      • wavelength range of echelle orders (update with intermediate_preparation/update_effs/update_effs.py)
    • phoenix_Q_conversions_CFHT-bandpass.txt
      • Q factor conversions for different resolutions in CFHT defined YJH bandpasses (update with intermediate_preparation/update_RV_estimates/phoenix_qfactor_resolution_conversion.py)
    • phoenix_Q_conversions_eniric-bandpass.txt
      • Q factor conversions for different resolutions in Eniric defined YJH bandpasses (update with intermediate_preparation/update_RV_estimates/phoenix_qfactor_resolution_conversion.py)
    • phoenix_eniric_Qfactors_CFHT-bandpass.csv
      • Q factors from Eniric in CFHT defined YJH bandpasses (update with eniric using command in intermediate_preparation/update_RV_estimates/README_update_RV_estimates)
    • phoenix_eniric_Qfactors_eniric-bandpass.csv
      • Q factors from Eniric in Eniric defined YJH bandpasses (update with eniric using command in intermediate_preparation/update_RV_estimates/README_update_RV_estimates)
    • spirou_fit_Qvalues_CFHT-bandpass.txt
      • Q factors from Spirou templates in CFHT defined YJH bandpasses (update with intermediate_preparation/update_RV_estimates/fit_spirou_qfactors.py)
    • spirou_fit_Qvalues_eniric-bandpass.txt.
      • Q factors from Spirou templates in Eniric defined YJH bandpasses (update with intermediate_preparation/update_RV_estimates/fit_spirou_qfactors.py)
  • intermediate_preparation/
    • ETC_v3.0_CantoMartins/
      • original ETC by Bruno Canto Martins
    • add_stellar_templates/
      • add and update stellar templates
    • update_RV_estimates/
      • update RV estimates and Q values
    • update_effs/
      • update efficiency files and resample wavelength grid for tapas, effs, and star_templates
  • outputs/
    • outputs SNR for each order and wavelength vs SNR plot from NIRPS_ETC.py
  • NIRPS_ETC.py
    • main ETC code for a single star
  • NIRPS_ETC_script.py
    • script that runs ETC for stars in etc_targets_input.txt and outputs to etc_targets_output.txt
  • etc_targets_input.txt
    • example input file for NIRPS_ETC_script.py
  • etc_targets_output.txt
    • example ouput file for NIRPS_ETC_script.py
  • nirps_etc_lib.py
    • definitions for fucntions in ETC code
Owner
Nolan Grieves
Postdoctoral Research Scientist [email protected]
Nolan Grieves
Point Cloud Registration Network

PCRNet: Point Cloud Registration Network using PointNet Encoding Source Code Author: Vinit Sarode and Xueqian Li Paper | Website | Video | Pytorch Imp

ViNiT SaRoDe 59 Nov 19, 2022
[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning

Crafting Better Contrastive Views for Siamese Representation Learning (CVPR 2022 Oral) 2022-03-29: The paper was selected as a CVPR 2022 Oral paper! 2

249 Dec 28, 2022
Make differentially private training of transformers easy for everyone

private-transformers This codebase facilitates fast experimentation of differentially private training of Hugging Face transformers. What is this? Why

Xuechen Li 73 Dec 28, 2022
ScaleNet: A Shallow Architecture for Scale Estimation

ScaleNet: A Shallow Architecture for Scale Estimation Repository for the code of ScaleNet paper: "ScaleNet: A Shallow Architecture for Scale Estimatio

Axel Barroso 34 Nov 09, 2022
CMSC320 - Introduction to Data Science - Fall 2021

CMSC320 - Introduction to Data Science - Fall 2021 Instructors: Elias Jonatan Gonzalez and José Manuel Calderón Trilla Lectures: MW 3:30-4:45 & 5:00-6

Introduction to Data Science 6 Sep 12, 2022
PyTorch Implementation of AnimeGANv2

PyTorch implementation of AnimeGANv2

4k Jan 07, 2023
Offical implementation for "Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation".

Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation (NeurIPS 2021) by Qiming Hu, Xiaojie Guo. Dependencies P

Qiming Hu 31 Dec 20, 2022
gACSON software for visualization, processing and analysis of three-dimensional electron microscopy images

gACSON gACSON software is to visualize, segment, and analyze the morphology of neurons in three-dimensional electron microscopy images. If you use any

Andrea Behanova 2 May 31, 2022
Torchlight2 lan game server tool - A message forwarding tool for Torchlight 2 lan game

Torchlight 2 Lan Game Server Tool A message forwarding tool for Torchlight 2 lan

Huaijun Jiang 3 Nov 01, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022
SIEM Logstash parsing for more than hundred technologies

LogIndexer Pipeline Logstash Parsing Configurations for Elastisearch SIEM and OpenDistro for Elasticsearch SIEM Why this project exists The overhead o

146 Dec 29, 2022
Decorators for maximizing memory utilization with PyTorch & CUDA

torch-max-mem This package provides decorators for memory utilization maximization with PyTorch and CUDA by starting with a maximum parameter size and

Max Berrendorf 10 May 02, 2022
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

TensorFlowOnSpark TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from the T

Yahoo 3.8k Jan 04, 2023
U-2-Net: U Square Net - Modified for paired image training of style transfer

U2-Net: U Square Net Modified for paired image training of style transfer This is an unofficial repo making use of the code which was made available b

Doron Adler 43 Oct 03, 2022
Code accompanying the NeurIPS 2021 paper "Generating High-Quality Explanations for Navigation in Partially-Revealed Environments"

Generating High-Quality Explanations for Navigation in Partially-Revealed Environments This work presents an approach to explainable navigation under

RAIL Group @ George Mason University 1 Oct 28, 2022
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

Dataset Cartography Code for the paper Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics at EMNLP 2020. This repository cont

AI2 125 Dec 22, 2022
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023
PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning.

neural-combinatorial-rl-pytorch PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning. I have implemented the basic

Patrick E. 454 Jan 06, 2023
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pre-trained Feature

Industrial Image Anomaly Localization Based on Gaussian Clustering of Pre-trained Feature Q. Wan, L. Gao, X. Li and L. Wen, "Industrial Image Anomaly

smiler 6 Dec 25, 2022
Adaptive Attention Span for Reinforcement Learning

Adaptive Transformers in RL Official implementation of Adaptive Transformers in RL In this work we replicate several results from Stabilizing Transfor

100 Nov 15, 2022